Cellular level models as tools for cytokine design.

PubMed

Radhakrishnan, Mala L; Tidor, Bruce

2010-01-01

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency. (c) 2010 American Institute of Chemical Engineers

Cosmic superstrings: Observable remnants of brane inflation

NASA Astrophysics Data System (ADS)

Wyman, Mark Charles

Brane inflation provides a natural dynamical model for the physics which underlie the inflationary paradigm. Besides their inflationary predictions, brane models imply another observable consequence: cosmic strings. In this dissertation I outline the background of how cosmic strings arise in brane inflationary models and how the properties of the strings and the models are mutually tied (Chapter 2). I then use cosmological observations to put limits on the properties of any actually-existing cosmic string network (Chapter 3). Next, I study the question of how cosmic superstrings, as the cosmic strings arising from string theory are known, could be distinct from classical gauge- theory cosmic strings. In particular, I propose an analytical model for the cosmological evolution of a network of binding cosmic strings (Chapter 4); I also describe the distinctive gravitational lensing phenomena that are caused by binding strings (Chapter 5). Finally, I lay out the background for the numerical study of a gauge theory model for the dynamics of cosmic superstring binding (Chapter 6).

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Competitive tuning: Competition's role in setting the frequency-dependence of Ca2+-dependent proteins

PubMed Central

Patel, Neal M.; Kinzer-Ursem, Tamara L.

2017-01-01

A number of neurological disorders arise from perturbations in biochemical signaling and protein complex formation within neurons. Normally, proteins form networks that when activated produce persistent changes in a synapse’s molecular composition. In hippocampal neurons, calcium ion (Ca2+) flux through N-methyl-D-aspartate (NMDA) receptors activates Ca2+/calmodulin signal transduction networks that either increase or decrease the strength of the neuronal synapse, phenomena known as long-term potentiation (LTP) or long-term depression (LTD), respectively. The calcium-sensor calmodulin (CaM) acts as a common activator of the networks responsible for both LTP and LTD. This is possible, in part, because CaM binding proteins are “tuned” to different Ca2+ flux signals by their unique binding and activation dynamics. Computational modeling is used to describe the binding and activation dynamics of Ca2+/CaM signal transduction and can be used to guide focused experimental studies. Although CaM binds over 100 proteins, practical limitations cause many models to include only one or two CaM-activated proteins. In this work, we view Ca2+/CaM as a limiting resource in the signal transduction pathway owing to its low abundance relative to its binding partners. With this view, we investigate the effect of competitive binding on the dynamics of CaM binding partner activation. Using an explicit model of Ca2+, CaM, and seven highly-expressed hippocampal CaM binding proteins, we find that competition for CaM binding serves as a tuning mechanism: the presence of competitors shifts and sharpens the Ca2+ frequency-dependence of CaM binding proteins. Notably, we find that simulated competition may be sufficient to recreate the in vivo frequency dependence of the CaM-dependent phosphatase calcineurin. Additionally, competition alone (without feedback mechanisms or spatial parameters) could replicate counter-intuitive experimental observations of decreased activation of Ca2+/CaM-dependent protein kinase II in knockout models of neurogranin. We conclude that competitive tuning could be an important dynamic process underlying synaptic plasticity. PMID:29107982

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state.

PubMed

Yang, Yan-Li; Deng, Hong-Xia; Xing, Gui-Yang; Xia, Xiao-Luan; Li, Hai-Fang

2015-02-01

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we investigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state. Z-values in the vision-related brain regions were calculated, confirming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental findings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

PubMed

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Convolutional neural network architectures for predicting DNA–protein binding

PubMed Central

Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

2016-01-01

Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

Approach to the unfolding and folding dynamics of add A-riboswitch upon adenine dissociation using a coarse-grained elastic network model

NASA Astrophysics Data System (ADS)

Li, Chunhua; Lv, Dashuai; Zhang, Lei; Yang, Feng; Wang, Cunxin; Su, Jiguo; Zhang, Yang

2016-07-01

Riboswitches are noncoding mRNA segments that can regulate the gene expression via altering their structures in response to specific metabolite binding. We proposed a coarse-grained Gaussian network model (GNM) to examine the unfolding and folding dynamics of adenosine deaminase (add) A-riboswitch upon the adenine dissociation, in which the RNA is modeled by a nucleotide chain with interaction networks formed by connecting adjoining atomic contacts. It was shown that the adenine binding is critical to the folding of the add A-riboswitch while the removal of the ligand can result in drastic increase of the thermodynamic fluctuations especially in the junction regions between helix domains. Under the assumption that the native contacts with the highest thermodynamic fluctuations break first, the iterative GNM simulations showed that the unfolding process of the adenine-free add A-riboswitch starts with the denature of the terminal helix stem, followed by the loops and junctions involving ligand binding pocket, and then the central helix domains. Despite the simplified coarse-grained modeling, the unfolding dynamics and pathways are shown in close agreement with the results from atomic-level MD simulations and the NMR and single-molecule force spectroscopy experiments. Overall, the study demonstrates a new avenue to investigate the binding and folding dynamics of add A-riboswitch molecule which can be readily extended for other RNA molecules.

Exact model reduction of combinatorial reaction networks

PubMed Central

Conzelmann, Holger; Fey, Dirk; Gilles, Ernst D

2008-01-01

Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs) to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks. PMID:18755034

Network Analysis Reveals the Recognition Mechanism for Mannose-binding Lectins

NASA Astrophysics Data System (ADS)

Zhao, Yunjie; Jian, Yiren; Zeng, Chen; Computational Biophysics Lab Team

The specific carbohydrate binding of mannose-binding lectin (MBL) protein in plants makes it a very useful molecular tool for cancer cell detection and other applications. The biological states of most MBL proteins are dimeric. Using dynamics network analysis on molecular dynamics (MD) simulations on the model protein of MBL, we elucidate the short- and long-range driving forces behind the dimer formation. The results are further supported by sequence coevolution analysis. We propose a general framework for deciphering the recognition mechanism underlying protein-protein interactions that may have potential applications in signaling pathways.

Probing Allosteric Inhibition Mechanisms of the Hsp70 Chaperone Proteins Using Molecular Dynamics Simulations and Analysis of the Residue Interaction Networks.

PubMed

Stetz, Gabrielle; Verkhivker, Gennady M

2016-08-22

Although molecular mechanisms of allosteric regulation in the Hsp70 chaperones have been extensively studied at both structural and functional levels, the current understanding of allosteric inhibition of chaperone activities by small molecules is still lacking. In the current study, using a battery of computational approaches, we probed allosteric inhibition mechanisms of E. coli Hsp70 (DnaK) and human Hsp70 proteins by small molecule inhibitors PET-16 and novolactone. Molecular dynamics simulations and binding free energy analysis were combined with network-based modeling of residue interactions and allosteric communications to systematically characterize and compare molecular signatures of the apo form, substrate-bound, and inhibitor-bound chaperone complexes. The results suggested a mechanism by which the allosteric inhibitors may leverage binding energy hotspots in the interaction networks to stabilize a specific conformational state and impair the interdomain allosteric control. Using the network-based centrality analysis and community detection, we demonstrated that substrate binding may strengthen the connectivity of local interaction communities, leading to a dense interaction network that can promote an efficient allosteric communication. In contrast, binding of PET-16 to DnaK may induce significant dynamic changes and lead to a fractured interaction network and impaired allosteric communications in the DnaK complex. By using a mechanistic-based analysis of distance fluctuation maps and allosteric propensities of protein residues, we determined that the allosteric network in the PET-16 complex may be small and localized due to the reduced communication and low cooperativity of the substrate binding loops, which may promote the higher rates of substrate dissociation and the decreased substrate affinity. In comparison with the significant effect of PET-16, binding of novolactone to HSPA1A may cause only moderate network changes and preserve allosteric coupling between the allosteric pocket and the substrate binding region. The impact of novolactone on the conformational dynamics and allosteric communications in the HSPA1A complex was comparable to the substrate effect, which is consistent with the experimental evidence that PET-16, but not novolactone binding, can significantly decrease substrate affinity. We argue that the unique dynamic and network signatures of PET-16 and novolactone may be linked with the experimentally observed functional effects of these inhibitors on allosteric regulation and substrate binding.

Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

NASA Astrophysics Data System (ADS)

Valencia, Daniel; Wilson, Evan; Jiang, Zhengping; Valencia-Zapata, Gustavo A.; Wang, Kuang-Chung; Klimeck, Gerhard; Povolotskyi, Michael

2018-04-01

Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green's functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

Ties That Bind: A Social Network Approach to Understanding Student Integration and Persistence. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Thomas, Scott L.

This study examined the social networks of college students and how such networks affect student commitment and persistence. The study's theoretical framework was based on application of the social network paradigm to Tinto's Student Integration Model, in which a student's initial commitment is modified over time as a result of the student's…

In-silico studies of neutral drift for functional protein interaction networks

NASA Astrophysics Data System (ADS)

Ali, Md Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

We have developed a minimal physically-motivated model of protein-protein interaction networks. Our system consists of two classes of enzymes, activators (e.g. kinases) and deactivators (e.g. phosphatases), and the enzyme-mediated activation/deactivation rates are determined by sequence-dependent binding strengths between enzymes and their targets. The network is evolved by introducing random point mutations in the binding sequences where we assume that each new mutation is either fixed or entirely lost. We apply this model to studies of neutral drift in networks that yield oscillatory dynamics, where we start, for example, with a relatively simple network and allow it to evolve by adding nodes and connections while requiring that dynamics be conserved. Our studies demonstrate both the importance of employing a sequence-based evolutionary scheme and the relative rapidity (in evolutionary time) for the redistribution of function over new nodes via neutral drift. Surprisingly, in addition to this redistribution time we discovered another much slower timescale for network evolution, reflecting hidden order in sequence space that we interpret in terms of sparsely connected domains.

Interaction between attentional systems and episodic memory encoding: the impact of conflict on binding of information.

PubMed

Sperduti, Marco; Armougum, Allan; Makowski, Dominique; Blondé, Philippe; Piolino, Pascale

2017-12-01

Episodic memory (EM) is defined as a long-term memory system that stores information that can be retrieved along with details of the context of the original events (binding). Several studies have shown that manipulation of attention during encoding can impact subsequent memory performance. An influential model of attention distinguishes between three partially independent attentional networks: the alerting, the orienting and the executive or conflict resolution component. To date, the impact of the engagement of these sub-systems during encoding on item and relational context binding has not been investigated. Here, we developed a new task combining the Attentional Network Test and an incidental episodic memory encoding task to study this issue. We reported that when the alerting network was not solicited, resolving conflict hindered item encoding. Moreover, resolving conflict, independently of the cueing condition, had a negative impact on context binding. These novel findings could have a potential impact in the understanding EM formation, and memory disorders in different populations, including healthy elderly people.

Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling.

PubMed

Bazeley, Peter S; Prithivi, Sridevi; Struble, Craig A; Povinelli, Richard J; Sem, Daniel S

2006-01-01

Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.

Fluctuation Dynamics Analysis of gp120 Envelope Protein Reveals a Topologically Based Communication Network

PubMed Central

Shrivastava, Indira; LaLonde, Judith M.

2012-01-01

HIV infection is initiated by binding of the viral glycoprotein gp120, to the cellular receptor CD4. Upon CD4 binding, gp120 undergoes conformational change, permitting binding to the chemokine receptor. Crystal structures of gp120 ternary complex reveal the CD4 bound conformation of gp120. We report here the application of Gaussian Network Model (GNM) to the crystal structures of gp120 bound to CD4 or CD4 mimic and 17b, to study the collective motions of the gp120 core and determine the communication propensities of the residue network. The GNM fluctuation profiles identify residues in the inner domain and outer domain that may facilitate conformational change or stability, respectively. Communication propensities delineate a residue network that is topologically suited for signal propagation from the Phe43 cavity throughout the gp120 outer domain. . These results provide a new context for interpreting gp120 core envelope structure-function relationships. PMID:20718047

The High-Resolution Structure of Activated Opsin Reveals a Conserved Solvent Network in the Transmembrane Region Essential for Activation.

PubMed

Blankenship, Elise; Vahedi-Faridi, Ardeschir; Lodowski, David T

2015-12-01

Rhodopsin, a light-activated G protein coupled receptor (GPCR), has been the subject of numerous biochemical and structural investigations, serving as a model receptor for GPCRs and their activation. We present the 2.3-Å resolution structure of native source rhodopsin stabilized in a conformation competent for G protein binding. An extensive water-mediated hydrogen bond network linking the chromophore binding site to the site of G protein binding is observed, providing connections to conserved motifs essential for GPCR activation. Comparison of this extensive solvent-mediated hydrogen-bonding network with the positions of ordered solvent in earlier crystallographic structures of rhodopsin photointermediates reveals both static structural and dynamic functional water-protein interactions present during the activation process. When considered along with observations that solvent occupies similar positions in the structures of other GPCRs, these analyses strongly support an integral role for this dynamic ordered water network in both rhodopsin and GPCR activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks.

PubMed

Rudling, Axel; Orro, Adolfo; Carlsson, Jens

2018-02-26

Water plays a major role in ligand binding and is attracting increasing attention in structure-based drug design. Water molecules can make large contributions to binding affinity by bridging protein-ligand interactions or by being displaced upon complex formation, but these phenomena are challenging to model at the molecular level. Herein, networks of ordered water molecules in protein binding sites were analyzed by clustering of molecular dynamics (MD) simulation trajectories. Locations of ordered waters (hydration sites) were first identified from simulations of high resolution crystal structures of 13 protein-ligand complexes. The MD-derived hydration sites reproduced 73% of the binding site water molecules observed in the crystal structures. If the simulations were repeated without the cocrystallized ligands, a majority (58%) of the crystal waters in the binding sites were still predicted. In addition, comparison of the hydration sites obtained from simulations carried out in the absence of ligands to those identified for the complexes revealed that the networks of ordered water molecules were preserved to a large extent, suggesting that the locations of waters in a protein-ligand interface are mainly dictated by the protein. Analysis of >1000 crystal structures showed that hydration sites bridged protein-ligand interactions in complexes with different ligands, and those with high MD-derived occupancies were more likely to correspond to experimentally observed ordered water molecules. The results demonstrate that ordered water molecules relevant for modeling of protein-ligand complexes can be identified from MD simulations. Our findings could contribute to development of improved methods for structure-based virtual screening and lead optimization.

Quantification of oil binding capacity of structuring fats: A novel method and its application.

PubMed

Omonov, Tolibjon S; Bouzidi, Laziz; Narine, Suresh S

2010-09-01

A robust, well-defined and reproducible method to accurately measure the oil binding capacity (OBC) of structuring fats was developed. The method was validated using two oil/fat model systems, i.e., fully hydrogenated canola oil (FHCO) in canola oil (CO) (FHCO/CO) and fully hydrogenated soybean oil (FHSO) in CO (FHSO/CO). The mixtures were crystallized from the melt down to three different temperatures (15, 25 and 35 degrees C) at constant rates of cooling and the OBC was measured after different periods of storage time. The critical concentration of hard fat at which the solid fat network is stable and effectively binds oil has been also measured for mixtures crystallized at temperatures close to room temperature, i.e., 25 degrees C. Crystal structure, melting behavior, microstructure, and solid fat content of these binary systems have been investigated in relation to the OBC of the solid fat network using X-ray diffraction (XRD), differential scanning calorimetry (DSC), polarized light microscopy (PLM), and wide-line pulsed nuclear magnetic resonance (pNMR) techniques. The two model systems exhibited similar trends in OBC over time, a behavior attributed to their similar TAG composition and polymorphism. However, relatively smaller OBC values were achieved by the CO/FHSO compared to CO/FHCO samples, largely due to differences in their solid network structure. Four successive decreasing linear segments, identifying successive mechanisms of oil migration/binding, were observed in the experimental OBC versus fat weight fraction curves. The critical concentration of hard fat, at which the solid fat network is effective in binding oil, was also determined and found to be approximately 6wt% for both systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Stochastic dynamics of genetic broadcasting networks

NASA Astrophysics Data System (ADS)

Potoyan, Davit A.; Wolynes, Peter G.

2017-11-01

The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a "time-scale crisis" for master genes that broadcast their signals to a large number of binding sites. We demonstrate that this time-scale crisis for clearance in a large broadcasting network can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying a model of the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκ B which broadcasts its signals to many downstream genes that regulate immune response, apoptosis, etc.

Control of actin-based motility through localized actin binding

PubMed Central

Banigan, Edward J.; Lee, Kun-Chun; Liu, Andrea J.

2014-01-01

A wide variety of cell biological and biomimetic systems use actin polymerization to drive motility. It has been suggested that an object such as a bacterium can propel itself by self-assembling a high concentration of actin behind it if it is repelled by actin. However, it is also known that it is essential for the moving object to bind actin. Therefore, a key question is how the actin tail can propel an object when it both binds and repels the object. We present a physically consistent Brownian dynamics model for actin-based motility that includes the minimal components of the dendritic nucleation model and allows for both attractive and repulsive interactions between actin and a moveable disk. We find that the concentration gradient of filamentous actin generated by polymerization is sufficient to propel the object, even with moderately strong binding interactions. Additionally, actin binding can act as a biophysical cap, and may directly control motility through modulation of network growth. Overall, this mechanism is robust in that it can drive motility against a load up to a stall pressure that depends on the Young’s modulus of the actin network and can explain several aspects of actin-based motility. PMID:24225232

Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease

PubMed Central

Nunez, Paul L.; Srinivasan, Ramesh

2013-01-01

The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide "entry points" to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits. PMID:24505628

Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.

PubMed

Fogelmark, Karl; Peterson, Carsten; Troein, Carl

2016-01-01

Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.

Introduction to focus issue: quantitative approaches to genetic networks.

PubMed

Albert, Réka; Collins, James J; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks using field-programmable gate arrays. Mathematical analyses will be essential for understanding naturally occurring genetic networks in diverse organisms and for providing a foundation for the improved development of synthetic genetic networks.

Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

NASA Astrophysics Data System (ADS)

Albert, Réka; Collins, James J.; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks using field-programmable gate arrays. Mathematical analyses will be essential for understanding naturally occurring genetic networks in diverse organisms and for providing a foundation for the improved development of synthetic genetic networks.

Stochastic flux analysis of chemical reaction networks

PubMed Central

2013-01-01

Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153

Stochastic flux analysis of chemical reaction networks.

PubMed

Kahramanoğulları, Ozan; Lynch, James F

2013-12-07

Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.

Network and Atomistic Simulations Unveil the Structural Determinants of Mutations Linked to Retinal Diseases

PubMed Central

Mariani, Simona; Dell'Orco, Daniele; Felline, Angelo; Raimondi, Francesco; Fanelli, Francesca

2013-01-01

A number of incurable retinal diseases causing vision impairments derive from alterations in visual phototransduction. Unraveling the structural determinants of even monogenic retinal diseases would require network-centered approaches combined with atomistic simulations. The transducin G38D mutant associated with the Nougaret Congenital Night Blindness (NCNB) was thoroughly investigated by both mathematical modeling of visual phototransduction and atomistic simulations on the major targets of the mutational effect. Mathematical modeling, in line with electrophysiological recordings, indicates reduction of phosphodiesterase 6 (PDE) recognition and activation as the main determinants of the pathological phenotype. Sub-microsecond molecular dynamics (MD) simulations coupled with Functional Mode Analysis improve the resolution of information, showing that such impairment is likely due to disruption of the PDEγ binding cavity in transducin. Protein Structure Network analyses additionally suggest that the observed slight reduction of theRGS9-catalyzed GTPase activity of transducin depends on perturbed communication between RGS9 and GTP binding site. These findings provide insights into the structural fundamentals of abnormal functioning of visual phototransduction caused by a missense mutation in one component of the signaling network. This combination of network-centered modeling with atomistic simulations represents a paradigm for future studies aimed at thoroughly deciphering the structural determinants of genetic retinal diseases. Analogous approaches are suitable to unveil the mechanism of information transfer in any signaling network either in physiological or pathological conditions. PMID:24009494

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks.

PubMed

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-10

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

NASA Astrophysics Data System (ADS)

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-01

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

An incoherent feedforward loop facilitates adaptive tuning of gene expression.

PubMed

Hong, Jungeui; Brandt, Nathan; Abdul-Rahman, Farah; Yang, Ally; Hughes, Tim; Gresham, David

2018-04-05

We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression. © 2018, Hong et al.

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

PubMed

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2017-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

PubMed Central

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2018-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them. PMID:27896980

The emergence of polychronization and feature binding in a spiking neural network model of the primate ventral visual system.

PubMed

Eguchi, Akihiro; Isbister, James B; Ahmad, Nasir; Stringer, Simon

2018-07-01

We present a hierarchical neural network model, in which subpopulations of neurons develop fixed and regularly repeating temporal chains of spikes (polychronization), which respond specifically to randomized Poisson spike trains representing the input training images. The performance is improved by including top-down and lateral synaptic connections, as well as introducing multiple synaptic contacts between each pair of pre- and postsynaptic neurons, with different synaptic contacts having different axonal delays. Spike-timing-dependent plasticity thus allows the model to select the most effective axonal transmission delay between neurons. Furthermore, neurons representing the binding relationship between low-level and high-level visual features emerge through visually guided learning. This begins to provide a way forward to solving the classic feature binding problem in visual neuroscience and leads to a new hypothesis concerning how information about visual features at every spatial scale may be projected upward through successive neuronal layers. We name this hypothetical upward projection of information the "holographic principle." (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A resource for characterizing genome-wide binding and putative target genes of transcription factors expressed during secondary growth and wood formation in Populus

Treesearch

Lijun Liu; Trevor Ramsay; Matthew S. Zinkgraf; David Sundell; Nathaniel Robert Street; Vladimir Filkov; Andrew Groover

2015-01-01

Identifying transcription factor target genes is essential for modeling the transcriptional networks underlying developmental processes. Here we report a chromatin immunoprecipitation sequencing (ChIP-seq) resource consisting of genome-wide binding regions and associated putative target genes for four Populus homeodomain transcription factors...

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

PubMed

Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

2015-03-07

Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p <10(-100)), suggesting that a high LR score is a reliable indicator for functional TF binding sites. Our network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP-CM transitions. We report a novel method to systematically integrate multi-dimensional -omics data and reconstruct the gene regulatory networks. This method will allow one to rapidly determine the cis-modules that regulate key genes during cardiac differentiation.

A Strategy Based on Protein-Protein Interface Motifs May Help in Identifying Drug Off-Targets

PubMed Central

Engin, H. Billur; Keskin, Ozlem; Nussinov, Ruth; Gursoy, Attila

2014-01-01

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can ‘attack’ nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, “The Interface Attack”, based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model which we call “Protein Interface and Interaction Network (P2IN)”, which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces, and which proteins may compete to bind the same surface region. We built the P2IN of p53 signaling network and performed network robustness analysis. We show that (1) ‘hitting’ frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes); (2) frequent interfaces are not always topologically critical elements in the network; and (3) interface attack may reveal functional changes in the system better than attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D. PMID:22817115

Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks

PubMed Central

Fogelmark, Karl; Peterson, Carsten; Troein, Carl

2016-01-01

Background Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated. Methodology To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved) networks, and predictions are validated against the transcriptional network of E. coli. Principal Findings We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks. PMID:26927540

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Chunhua; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 45108; Lv, Dashuai

Riboswitches are noncoding mRNA segments that can regulate the gene expression via altering their structures in response to specific metabolite binding. We proposed a coarse-grained Gaussian network model (GNM) to examine the unfolding and folding dynamics of adenosine deaminase (add) A-riboswitch upon the adenine dissociation, in which the RNA is modeled by a nucleotide chain with interaction networks formed by connecting adjoining atomic contacts. It was shown that the adenine binding is critical to the folding of the add A-riboswitch while the removal of the ligand can result in drastic increase of the thermodynamic fluctuations especially in the junction regionsmore » between helix domains. Under the assumption that the native contacts with the highest thermodynamic fluctuations break first, the iterative GNM simulations showed that the unfolding process of the adenine-free add A-riboswitch starts with the denature of the terminal helix stem, followed by the loops and junctions involving ligand binding pocket, and then the central helix domains. Despite the simplified coarse-grained modeling, the unfolding dynamics and pathways are shown in close agreement with the results from atomic-level MD simulations and the NMR and single-molecule force spectroscopy experiments. Overall, the study demonstrates a new avenue to investigate the binding and folding dynamics of add A-riboswitch molecule which can be readily extended for other RNA molecules.« less

Integrated Model of Chemical Perturbations of a Biological ...

EPA Pesticide Factsheets

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“”assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 52 (2.8%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in v

Enhancer Variants Synergistically Drive Dysfunction of a Gene Regulatory Network In Hirschsprung Disease

DOE PAGES

Chatterjee, Sumantra; Kapoor, Ashish; Akiyama, Jennifer A.; ...

2016-09-29

Common sequence variants in cis-regulatory elements (CREs) are suspected etiological causes of complex disorders. We previously identified an intronic enhancer variant in the RET gene disrupting SOX10 binding and increasing Hirschsprung disease (HSCR) risk 4-fold. We now show that two other functionally independent CRE variants, one binding Gata2 and the other binding Rarb, also reduce Ret expression and increase risk 2- and 1.7-fold. By studying human and mouse fetal gut tissues and cell lines, we demonstrate that reduced RET expression propagates throughout its gene regulatory network, exerting effects on both its positive and negative feedback components. We also provide evidencemore » that the presence of a combination of CRE variants synergistically reduces RET expression and its effects throughout the GRN. These studies show how the effects of functionally independent non-coding variants in a coordinated gene regulatory network amplify their individually small effects, providing a model for complex disorders.« less

Enhancer Variants Synergistically Drive Dysfunction of a Gene Regulatory Network In Hirschsprung Disease

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Sumantra; Kapoor, Ashish; Akiyama, Jennifer A.

Common sequence variants in cis-regulatory elements (CREs) are suspected etiological causes of complex disorders. We previously identified an intronic enhancer variant in the RET gene disrupting SOX10 binding and increasing Hirschsprung disease (HSCR) risk 4-fold. We now show that two other functionally independent CRE variants, one binding Gata2 and the other binding Rarb, also reduce Ret expression and increase risk 2- and 1.7-fold. By studying human and mouse fetal gut tissues and cell lines, we demonstrate that reduced RET expression propagates throughout its gene regulatory network, exerting effects on both its positive and negative feedback components. We also provide evidencemore » that the presence of a combination of CRE variants synergistically reduces RET expression and its effects throughout the GRN. These studies show how the effects of functionally independent non-coding variants in a coordinated gene regulatory network amplify their individually small effects, providing a model for complex disorders.« less

INTEGRATING GENETIC AND STRUCTURAL DATA ON HUMAN PROTEIN KINOME IN NETWORK-BASED MODELING OF KINASE SENSITIVITIES AND RESISTANCE TO TARGETED AND PERSONALIZED ANTICANCER DRUGS.

PubMed

Verkhivker, Gennady M

2016-01-01

The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling complex relationships between robustness of targeted kinase genes and binding specificity of targeted kinase drugs. We discuss how these approaches can exploit advances in chemical biology and network science to develop novel strategies for rationally tailored and robust personalized drug therapies.

Actin-induced dimerization of palladin promotes actin-bundling

PubMed Central

Vattepu, Ravi; Yadav, Rahul; Beck, Moriah R

2015-01-01

A subset of actin binding proteins is able to form crosslinks between two or more actin filaments, thus producing structures of parallel or networked bundles. These actin crosslinking proteins interact with actin through either bivalent binding or dimerization. We recently identified two binding sites within the actin binding domain of palladin, an actin crosslinking protein that plays an important role in normal cell adhesion and motility during wound healing and embryonic development. In this study, we show that actin induces dimerization of palladin. Furthermore, the extent of dimerization reflects earlier comparisons of actin binding and bundling between different domains of palladin. On the basis of these results we hypothesized that actin binding may promote a conformational change that results in dimerization of palladin, which in turn may drive the crosslinking of actin filaments. The proximal distance between two actin binding sites on crosslinking proteins determines the ultrastructural properties of the filament network, therefore we also explored interdomain interactions using a combination of chemical crosslinking experiments and actin cosedimentation assays. Limited proteolysis data reveals that palladin is less susceptible to enzyme digestion after actin binding. Our results suggest that domain movements in palladin are necessary for interactions with actin and are induced by interactions with actin filaments. Accordingly, we put forth a model linking the structural changes to functional dynamics. PMID:25307943

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

PubMed Central

Myint, Kyaw Z.; Xie, Xiang-Qun

2015-01-01

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

SH3 interactome conserves general function over specific form

PubMed Central

Xin, Xiaofeng; Gfeller, David; Cheng, Jackie; Tonikian, Raffi; Sun, Lin; Guo, Ailan; Lopez, Lianet; Pavlenco, Alevtina; Akintobi, Adenrele; Zhang, Yingnan; Rual, Jean-François; Currell, Bridget; Seshagiri, Somasekar; Hao, Tong; Yang, Xinping; Shen, Yun A; Salehi-Ashtiani, Kourosh; Li, Jingjing; Cheng, Aaron T; Bouamalay, Dryden; Lugari, Adrien; Hill, David E; Grimes, Mark L; Drubin, David G; Grant, Barth D; Vidal, Marc; Boone, Charles; Sidhu, Sachdev S; Bader, Gary D

2013-01-01

Src homology 3 (SH3) domains bind peptides to mediate protein–protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form. PMID:23549480

Global Maps of ProQ Binding In Vivo Reveal Target Recognition via RNA Structure and Stability Control at mRNA 3' Ends.

PubMed

Holmqvist, Erik; Li, Lei; Bischler, Thorsten; Barquist, Lars; Vogel, Jörg

2018-05-15

The conserved RNA-binding protein ProQ has emerged as the centerpiece of a previously unknown third large network of post-transcriptional control in enterobacteria. Here, we have used in vivo UV crosslinking and RNA sequencing (CLIP-seq) to map hundreds of ProQ binding sites in Salmonella enterica and Escherichia coli. Our analysis of these binding sites, many of which are conserved, suggests that ProQ recognizes its cellular targets through RNA structural motifs found in small RNAs (sRNAs) and at the 3' end of mRNAs. Using the cspE mRNA as a model for 3' end targeting, we reveal a function for ProQ in protecting mRNA against exoribonucleolytic activity. Taken together, our results underpin the notion that ProQ governs a post-transcriptional network distinct from those of the well-characterized sRNA-binding proteins, CsrA and Hfq, and suggest a previously unrecognized, sRNA-independent role of ProQ in stabilizing mRNAs. Copyright © 2018 Elsevier Inc. All rights reserved.

Reconstruction of the temporal signaling network in Salmonella-infected human cells.

PubMed

Budak, Gungor; Eren Ozsoy, Oyku; Aydin Son, Yesim; Can, Tolga; Tuncbag, Nurcan

2015-01-01

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models.

PubMed

Tian, Feifei; Tan, Rui; Guo, Tailin; Zhou, Peng; Yang, Li

2013-07-01

Domain-peptide recognition and interaction are fundamentally important for eukaryotic signaling and regulatory networks. It is thus essential to quantitatively infer the binding stability and specificity of such interaction based upon large-scale but low-accurate complex structure models which could be readily obtained from sophisticated molecular modeling procedure. In the present study, a new method is described for the fast and reliable prediction of domain-peptide binding affinity with coarse-grained structure models. This method is designed to tolerate strong random noises involved in domain-peptide complex structures and uses statistical modeling approach to eliminate systematic bias associated with a group of investigated samples. As a paradigm, this method was employed to model and predict the binding behavior of various peptides to four evolutionarily unrelated peptide-recognition domains (PRDs), i.e. human amph SH3, human nherf PDZ, yeast syh GYF and yeast bmh 14-3-3, and moreover, we explored the molecular mechanism and biological implication underlying the binding of cognate and noncognate peptide ligands to their domain receptors. It is expected that the newly proposed method could be further used to perform genome-wide inference of domain-peptide binding at three-dimensional structure level. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Computational modeling and molecular imprinting for the development of acrylic polymers with high affinity for bile salts.

PubMed

Yañez, Fernando; Chianella, Iva; Piletsky, Sergey A; Concheiro, Angel; Alvarez-Lorenzo, Carmen

2010-02-05

This work has focused on the rational development of polymers capable of acting as traps of bile salts. Computational modeling was combined with molecular imprinting technology to obtain networks with high affinity for cholate salts in aqueous medium. The screening of a virtual library of 18 monomers, which are commonly used for imprinted networks, identified N-(3-aminopropyl)-methacrylate hydrochloride (APMA.HCl), N,N-diethylamino ethyl methacrylate (DEAEM) and ethyleneglycol methacrylate phosphate (EGMP) as suitable functional monomers with medium-to-high affinity for cholic acid. The polymers were prepared with a fix cholic acid:functional monomer mole ratio of 1:4, but with various cross-linking densities. Compared to polymers prepared without functional monomer, both imprinted and non-imprinted microparticles showed a high capability to remove sodium cholate from aqueous medium. High affinity APMA-based particles even resembled the performance of commercially available cholesterol-lowering granules. The imprinting effect was evident in most of the networks prepared, showing that computational modeling and molecular imprinting can act synergistically to improve the performance of certain polymers. Nevertheless, both the imprinted and non-imprinted networks prepared with the best monomer (APMA.HCl) identified by the modeling demonstrated such high affinity for the template that the imprinting effect was less important. The fitting of adsorption isotherms to the Freundlich model indicated that, in general, imprinting increases the population of high affinity binding sites, except when the affinity of the functional monomer for the target molecule is already very high. The cross-linking density was confirmed as a key parameter that determines the accessibility of the binding points to sodium cholate. Materials prepared with 9% mol APMA and 91% mol cross-linker showed enough affinity to achieve binding levels of up to 0.4 mmol g(-1) (i.e., 170 mg g(-1)) under flow (1 mL min(-1)) of 0.2 mM sodium cholate solution. Copyright 2009 Elsevier B.V. All rights reserved.

Ionically Cross-Linked Polymer Networks for the Multiple-Month Release of Small Molecules

PubMed Central

2016-01-01

Long-term (multiple-week or -month) release of small, water-soluble molecules from hydrogels remains a significant pharmaceutical challenge, which is typically overcome at the expense of more-complicated drug carrier designs. Such approaches are payload-specific and include covalent conjugation of drugs to base materials or incorporation of micro- and nanoparticles. As a simpler alternative, here we report a mild and simple method for achieving multiple-month release of small molecules from gel-like polymer networks. Densely cross-linked matrices were prepared through ionotropic gelation of poly(allylamine hydrochloride) (PAH) with either pyrophosphate (PPi) or tripolyphosphate (TPP), all of which are commonly available commercial molecules. The loading of model small molecules (Fast Green FCF and Rhodamine B dyes) within these polymer networks increases with the payload/network binding strength and with the PAH and payload concentrations used during encapsulation. Once loaded into the PAH/PPi and PAH/TPP ionic networks, only a few percent of the payload is released over multiple months. This extended release is achieved regardless of the payload/network binding strength and likely reflects the small hydrodynamic mesh size within the gel-like matrices. Furthermore, the PAH/TPP networks show promising in vitro cytocompatibility with model cells (human dermal fibroblasts), though slight cytotoxic effects were exhibited by the PAH/PPi networks. Taken together, the above findings suggest that PAH/PPi and (especially) PAH/TPP networks might be attractive materials for the multiple-month delivery of drugs and other active molecules (e.g., fragrances or disinfectants). PMID:26811936

Architecture of the human regulatory network derived from ENCODE data.

PubMed

Gerstein, Mark B; Kundaje, Anshul; Hariharan, Manoj; Landt, Stephen G; Yan, Koon-Kiu; Cheng, Chao; Mu, Xinmeng Jasmine; Khurana, Ekta; Rozowsky, Joel; Alexander, Roger; Min, Renqiang; Alves, Pedro; Abyzov, Alexej; Addleman, Nick; Bhardwaj, Nitin; Boyle, Alan P; Cayting, Philip; Charos, Alexandra; Chen, David Z; Cheng, Yong; Clarke, Declan; Eastman, Catharine; Euskirchen, Ghia; Frietze, Seth; Fu, Yao; Gertz, Jason; Grubert, Fabian; Harmanci, Arif; Jain, Preti; Kasowski, Maya; Lacroute, Phil; Leng, Jing Jane; Lian, Jin; Monahan, Hannah; O'Geen, Henriette; Ouyang, Zhengqing; Partridge, E Christopher; Patacsil, Dorrelyn; Pauli, Florencia; Raha, Debasish; Ramirez, Lucia; Reddy, Timothy E; Reed, Brian; Shi, Minyi; Slifer, Teri; Wang, Jing; Wu, Linfeng; Yang, Xinqiong; Yip, Kevin Y; Zilberman-Schapira, Gili; Batzoglou, Serafim; Sidow, Arend; Farnham, Peggy J; Myers, Richard M; Weissman, Sherman M; Snyder, Michael

2012-09-06

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

PubMed Central

Avsec, Žiga; Cheng, Jun; Gagneur, Julien

2018-01-01

Abstract Motivation Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox. Availability and implementation Spline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017. Contact avsec@in.tum.de or gagneur@in.tum.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:29155928

Dynein's Network of Chemomechanical Motor Cycles

NASA Astrophysics Data System (ADS)

Shen, Weibo; Wang, Ziqing; Wang, Guodong

2012-07-01

An eight-state network model of dynein's chemomechanical motor cycles is developed, in which the states of an effective single dynein head are represented by the number of ATP binding at the primary site and the number of ATP binding at other three secondary sites. The binding and unbinding of ATP, as well as the hydrolysis of ATP and the reverse process, are characterized by transition rates between certain states. Our results show that the stall force of dynein increases fast with ATP up to 1 mM ATP, beyond which it increases slowly to a saturated value, and that load and ATP concentration can adjust the step size of dynein, i.e., dynein can shift gears according to conditions. These results are in agreement with experiments [R. Mallik, B. C. Carter, S. A. Lex, S. J. King and S. P. Gross, Nature 427, 649 (2004)].

Elastic Network Model of a Nuclear Transport Complex

NASA Astrophysics Data System (ADS)

Ryan, Patrick; Liu, Wing K.; Lee, Dockjin; Seo, Sangjae; Kim, Young-Jin; Kim, Moon K.

2010-05-01

The structure of Kap95p was obtained from the Protein Data Bank (www.pdb.org) and analyzed RanGTP plays an important role in both nuclear protein import and export cycles. In the nucleus, RanGTP releases macromolecular cargoes from importins and conversely facilitates cargo binding to exportins. Although the crystal structure of the nuclear import complex formed by importin Kap95p and RanGTP was recently identified, its molecular mechanism still remains unclear. To understand the relationship between structure and function of a nuclear transport complex, a structure-based mechanical model of Kap95p:RanGTP complex is introduced. In this model, a protein structure is simply modeled as an elastic network in which a set of coarse-grained point masses are connected by linear springs representing biochemical interactions at atomic level. Harmonic normal mode analysis (NMA) and anharmonic elastic network interpolation (ENI) are performed to predict the modes of vibrations and a feasible pathway between locked and unlocked conformations of Kap95p, respectively. Simulation results imply that the binding of RanGTP to Kap95p induces the release of the cargo in the nucleus as well as prevents any new cargo from attaching to the Kap95p:RanGTP complex.

Structure-Based Network Analysis of Activation Mechanisms in the ErbB Family of Receptor Tyrosine Kinases: The Regulatory Spine Residues Are Global Mediators of Structural Stability and Allosteric Interactions

PubMed Central

James, Kevin A.; Verkhivker, Gennady M.

2014-01-01

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues. PMID:25427151

Exact evaluation of the causal spectrum and localization properties of electronic states on a scale-free network

NASA Astrophysics Data System (ADS)

Xie, Pinchen; Yang, Bingjia; Zhang, Zhongzhi; Andrade, Roberto F. S.

2018-07-01

A deterministic network with tree structure is considered, for which the spectrum of its adjacency matrix can be exactly evaluated by a recursive renormalization approach. It amounts to successively increasing number of contributions at any finite step of construction of the tree, resulting in a causal chain. The resulting eigenvalues can be related the full energy spectrum of a nearest-neighbor tight-binding model defined on this structure. Given this association, it turns out that further properties of the eigenvectors can be evaluated, like the degree of quantum localization of the tight-binding eigenstates, expressed by the inverse participation ratio (IPR). It happens that, for the current model, the IPR's are also suitable to be analytically expressed in terms in corresponding eigenvalue chain. The resulting IPR scaling behavior is expressed by the tails of eigenvalue chains as well.

Architecture and dynamics of overlapped RNA regulatory networks.

PubMed

Lapointe, Christopher P; Preston, Melanie A; Wilinski, Daniel; Saunders, Harriet A J; Campbell, Zachary T; Wickens, Marvin

2017-11-01

A single protein can bind and regulate many mRNAs. Multiple proteins with similar specificities often bind and control overlapping sets of mRNAs. Yet little is known about the architecture or dynamics of overlapped networks. We focused on three proteins with similar structures and related RNA-binding specificities-Puf3p, Puf4p, and Puf5p of S. cerevisiae Using RNA Tagging, we identified a "super-network" comprised of four subnetworks: Puf3p, Puf4p, and Puf5p subnetworks, and one controlled by both Puf4p and Puf5p. The architecture of individual subnetworks, and thus the super-network, is determined by competition among particular PUF proteins to bind mRNAs, their affinities for binding elements, and the abundances of the proteins. The super-network responds dramatically: The remaining network can either expand or contract. These strikingly opposite outcomes are determined by an interplay between the relative abundance of the RNAs and proteins, and their affinities for one another. The diverse interplay between overlapping RNA-protein networks provides versatile opportunities for regulation and evolution. © 2017 Lapointe et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

PubMed

Gerstein, Mark B; Lu, Zhi John; Van Nostrand, Eric L; Cheng, Chao; Arshinoff, Bradley I; Liu, Tao; Yip, Kevin Y; Robilotto, Rebecca; Rechtsteiner, Andreas; Ikegami, Kohta; Alves, Pedro; Chateigner, Aurelien; Perry, Marc; Morris, Mitzi; Auerbach, Raymond K; Feng, Xin; Leng, Jing; Vielle, Anne; Niu, Wei; Rhrissorrakrai, Kahn; Agarwal, Ashish; Alexander, Roger P; Barber, Galt; Brdlik, Cathleen M; Brennan, Jennifer; Brouillet, Jeremy Jean; Carr, Adrian; Cheung, Ming-Sin; Clawson, Hiram; Contrino, Sergio; Dannenberg, Luke O; Dernburg, Abby F; Desai, Arshad; Dick, Lindsay; Dosé, Andréa C; Du, Jiang; Egelhofer, Thea; Ercan, Sevinc; Euskirchen, Ghia; Ewing, Brent; Feingold, Elise A; Gassmann, Reto; Good, Peter J; Green, Phil; Gullier, Francois; Gutwein, Michelle; Guyer, Mark S; Habegger, Lukas; Han, Ting; Henikoff, Jorja G; Henz, Stefan R; Hinrichs, Angie; Holster, Heather; Hyman, Tony; Iniguez, A Leo; Janette, Judith; Jensen, Morten; Kato, Masaomi; Kent, W James; Kephart, Ellen; Khivansara, Vishal; Khurana, Ekta; Kim, John K; Kolasinska-Zwierz, Paulina; Lai, Eric C; Latorre, Isabel; Leahey, Amber; Lewis, Suzanna; Lloyd, Paul; Lochovsky, Lucas; Lowdon, Rebecca F; Lubling, Yaniv; Lyne, Rachel; MacCoss, Michael; Mackowiak, Sebastian D; Mangone, Marco; McKay, Sheldon; Mecenas, Desirea; Merrihew, Gennifer; Miller, David M; Muroyama, Andrew; Murray, John I; Ooi, Siew-Loon; Pham, Hoang; Phippen, Taryn; Preston, Elicia A; Rajewsky, Nikolaus; Rätsch, Gunnar; Rosenbaum, Heidi; Rozowsky, Joel; Rutherford, Kim; Ruzanov, Peter; Sarov, Mihail; Sasidharan, Rajkumar; Sboner, Andrea; Scheid, Paul; Segal, Eran; Shin, Hyunjin; Shou, Chong; Slack, Frank J; Slightam, Cindie; Smith, Richard; Spencer, William C; Stinson, E O; Taing, Scott; Takasaki, Teruaki; Vafeados, Dionne; Voronina, Ksenia; Wang, Guilin; Washington, Nicole L; Whittle, Christina M; Wu, Beijing; Yan, Koon-Kiu; Zeller, Georg; Zha, Zheng; Zhong, Mei; Zhou, Xingliang; Ahringer, Julie; Strome, Susan; Gunsalus, Kristin C; Micklem, Gos; Liu, X Shirley; Reinke, Valerie; Kim, Stuart K; Hillier, LaDeana W; Henikoff, Steven; Piano, Fabio; Snyder, Michael; Stein, Lincoln; Lieb, Jason D; Waterston, Robert H

2010-12-24

We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

Stochastic dynamics of genetic broadcasting networks

NASA Astrophysics Data System (ADS)

Potoyan, Davit; Wolynes, Peter

The complex genetic programs of eukaryotic cells are often regulated by key transcription factors occupying or clearing out of a large number of genomic locations. Orchestrating the residence times of these factors is therefore important for the well organized functioning of a large network. The classic models of genetic switches sidestep this timing issue by assuming the binding of transcription factors to be governed entirely by thermodynamic protein-DNA affinities. Here we show that relying on passive thermodynamics and random release times can lead to a ''time-scale crisis'' of master genes that broadcast their signals to large number of binding sites. We demonstrate that this ''time-scale crisis'' can be resolved by actively regulating residence times through molecular stripping. We illustrate these ideas by studying the stochastic dynamics of the genetic network of the central eukaryotic master regulator NFκB which broadcasts its signals to many downstream genes that regulate immune response, apoptosis etc.

Mathematical model of highways network optimization

NASA Astrophysics Data System (ADS)

Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

2017-12-01

The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

Extending rule-based methods to model molecular geometry and 3D model resolution.

PubMed

Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia

2016-08-01

Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.

Analysis of the bacterial luciferase mobile loop by replica-exchange molecular dynamics.

PubMed

Campbell, Zachary T; Baldwin, Thomas O; Miyashita, Osamu

2010-12-15

Bacterial luciferase contains an extended 29-residue mobile loop. Movements of this loop are governed by binding of either flavin mononucleotide (FMNH2) or polyvalent anions. To understand this process, loop dynamics were investigated using replica-exchange molecular dynamics that yielded conformational ensembles in either the presence or absence of FMNH2. The resulting data were analyzed using clustering and network analysis. We observed the closed conformations that are visited only in the simulations with the ligand. Yet the mobile loop is intrinsically flexible, and FMNH2 binding modifies the relative populations of conformations. This model provides unique information regarding the function of a crystallographically disordered segment of the loop near the binding site. Structures at or near the fringe of this network were compatible with flavin binding or release. Finally, we demonstrate that the crystallographically observed conformation of the mobile loop bound to oxidized flavin was influenced by crystal packing. Thus, our study has revealed what we believe are novel conformations of the mobile loop and additional context for experimentally determined structures. Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Molecular modeling and residue interaction network studies on the mechanism of binding and resistance of the HCV NS5B polymerase mutants to VX-222 and ANA598.

PubMed

Xue, Weiwei; Jiao, Pingzu; Liu, Huanxiang; Yao, Xiaojun

2014-04-01

Hepatitis C virus (HCV) NS5B protein is an RNA-dependent RNA polymerase (RdRp) with essential functions in viral genome replication and represents a promising therapeutic target to develop direct-acting antivirals (DAAs). Multiple nonnucleoside inhibitors (NNIs) binding sites have been identified within the polymerase. VX-222 and ANA598 are two NNIs targeting thumb II site and palm I site of HCV NS5B polymerase, respectively. These two molecules have been shown to be very effective in phase II clinical trials. However, the emergence of resistant HCV replicon variants (L419M, M423T, I482L mutants to VX-222 and M414T, M414L, G554D mutants to ANA598) has significantly decreased their efficacy. To elucidate the molecular mechanism about how these mutations influenced the drug binding mode and decreased drug efficacy, we studied the binding modes of VX-222 and ANA598 to wild-type and mutant polymerase by molecular modeling approach. Molecular dynamics (MD) simulations results combined with binding free energy calculations indicated that the mutations significantly altered the binding free energy and the interaction for the drugs to polymerase. The further per-residue binding free energy decomposition analysis revealed that the mutations decreased the interactions with several key residues, such as L419, M423, L474, S476, I482, L497, for VX-222 and L384, N411, M414, Y415, Q446, S556, G557 for ANA598. These were the major origins for the resistance to these two drugs. In addition, by analyzing the residue interaction network (RIN) of the complexes between the drugs with wild-type and the mutant polymerase, we found that the mutation residues in the networks involved in the drug resistance possessed a relatively lower size of topology centralities. The shift of betweenness and closeness values of binding site residues in the mutant polymerase is relevant to the mechanism of drug resistance of VX-222 and ANA598. These results can provide an atomic-level understanding about the mechanisms of drug resistance conferred by the studied mutations and will be helpful to design more potent inhibitors which could effectively overcome drug resistance of antivirus agents. Copyright © 2014 Elsevier B.V. All rights reserved.

Elucidating the Functional Roles of Spatial Organization in Cross-Membrane Signal Transduction by a Hybrid Simulation Method.

PubMed

Chen, Jiawen; Xie, Zhong-Ru; Wu, Yinghao

2016-07-01

The ligand-binding of membrane receptors on cell surfaces initiates the dynamic process of cross-membrane signal transduction. It is an indispensable part of the signaling network for cells to communicate with external environments. Recent experiments revealed that molecular components in signal transduction are not randomly mixed, but spatially organized into distinctive patterns. These patterns, such as receptor clustering and ligand oligomerization, lead to very different gene expression profiles. However, little is understood about the molecular mechanisms and functional impacts of this spatial-temporal regulation in cross-membrane signal transduction. In order to tackle this problem, we developed a hybrid computational method that decomposes a model of signaling network into two simulation modules. The physical process of binding between receptors and ligands on cell surfaces are simulated by a diffusion-reaction algorithm, while the downstream biochemical reactions are modeled by stochastic simulation of Gillespie algorithm. These two processes are coupled together by a synchronization framework. Using this method, we tested the dynamics of a simple signaling network in which the ligand binding of cell surface receptors triggers the phosphorylation of protein kinases, and in turn regulates the expression of target genes. We found that spatial aggregation of membrane receptors at cellular interfaces is able to either amplify or inhibit downstream signaling outputs, depending on the details of clustering mechanism. Moreover, by providing higher binding avidity, the co-localization of ligands into multi-valence complex modulates signaling in very different ways that are closely related to the binding affinity between ligand and receptor. We also found that the temporal oscillation of the signaling pathway that is derived from genetic feedback loops can be modified by the spatial clustering of membrane receptors. In summary, our method demonstrates the functional importance of spatial organization in cross-membrane signal transduction. The method can be applied to any specific signaling pathway in cells.

Non-equilibrium repressor binding kinetics link DNA damage dose to transcriptional timing within the SOS gene network.

PubMed

Culyba, Matthew J; Kubiak, Jeffrey M; Mo, Charlie Y; Goulian, Mark; Kohli, Rahul M

2018-06-01

Biochemical pathways are often genetically encoded as simple transcription regulation networks, where one transcription factor regulates the expression of multiple genes in a pathway. The relative timing of each promoter's activation and shut-off within the network can impact physiology. In the DNA damage repair pathway (known as the SOS response) of Escherichia coli, approximately 40 genes are regulated by the LexA repressor. After a DNA damaging event, LexA degradation triggers SOS gene transcription, which is temporally separated into subsets of 'early', 'middle', and 'late' genes. Although this feature plays an important role in regulating the SOS response, both the range of this separation and its underlying mechanism are not experimentally defined. Here we show that, at low doses of DNA damage, the timing of promoter activities is not separated. Instead, timing differences only emerge at higher levels of DNA damage and increase as a function of DNA damage dose. To understand mechanism, we derived a series of synthetic SOS gene promoters which vary in LexA-operator binding kinetics, but are otherwise identical, and then studied their activity over a large dose-range of DNA damage. In distinction to established models based on rapid equilibrium assumptions, the data best fit a kinetic model of repressor occupancy at promoters, where the drop in cellular LexA levels associated with higher doses of DNA damage leads to non-equilibrium binding kinetics of LexA at operators. Operators with slow LexA binding kinetics achieve their minimal occupancy state at later times than operators with fast binding kinetics, resulting in a time separation of peak promoter activity between genes. These data provide insight into this remarkable feature of the SOS pathway by demonstrating how a single transcription factor can be employed to control the relative timing of each gene's transcription as a function of stimulus dose.

Probing the dynamic nature of water molecules and their influences on ligand binding in a model binding site.

PubMed

Cappel, Daniel; Wahlström, Rickard; Brenk, Ruth; Sotriffer, Christoph A

2011-10-24

The model binding site of the cytochrome c peroxidase (CCP) W191G mutant is used to investigate the structural and dynamic properties of the water network at the buried cavity using computational methods supported by crystallographic analysis. In particular, the differences of the hydration pattern between the uncomplexed state and various complexed forms are analyzed as well as the differences between five complexes of CCP W191G with structurally closely related ligands. The ability of docking programs to correctly handle the water molecules in these systems is studied in detail. It is found that fully automated prediction of water replacement or retention upon docking works well if some additional preselection is carried out but not necessarily if the entire water network in the cavity is used as input. On the other hand, molecular interaction fields for water calculated from static crystal structures and hydration density maps obtained from molecular dynamics simulations agree very well with crystallographically observed water positions. For one complex, the docking and MD results sensitively depend on the quality of the starting structure, and agreement is obtained only after redetermination of the crystal structure and refinement at higher resolution.

Topology and Dynamics of the Zebrafish Segmentation Clock Core Circuit

PubMed Central

Schröter, Christian; Isakova, Alina; Hens, Korneel; Soroldoni, Daniele; Gajewski, Martin; Jülicher, Frank; Maerkl, Sebastian J.; Deplancke, Bart; Oates, Andrew C.

2012-01-01

During vertebrate embryogenesis, the rhythmic and sequential segmentation of the body axis is regulated by an oscillating genetic network termed the segmentation clock. We describe a new dynamic model for the core pace-making circuit of the zebrafish segmentation clock based on a systematic biochemical investigation of the network's topology and precise measurements of somitogenesis dynamics in novel genetic mutants. We show that the core pace-making circuit consists of two distinct negative feedback loops, one with Her1 homodimers and the other with Her7:Hes6 heterodimers, operating in parallel. To explain the observed single and double mutant phenotypes of her1, her7, and hes6 mutant embryos in our dynamic model, we postulate that the availability and effective stability of the dimers with DNA binding activity is controlled in a “dimer cloud” that contains all possible dimeric combinations between the three factors. This feature of our model predicts that Hes6 protein levels should oscillate despite constant hes6 mRNA production, which we confirm experimentally using novel Hes6 antibodies. The control of the circuit's dynamics by a population of dimers with and without DNA binding activity is a new principle for the segmentation clock and may be relevant to other biological clocks and transcriptional regulatory networks. PMID:22911291

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Coupled biopolymer networks

NASA Astrophysics Data System (ADS)

Schwarz, J. M.; Zhang, Tao

2015-03-01

The actin cytoskeleton provides the cell with structural integrity and allows it to change shape to crawl along a surface, for example. The actin cytoskeleton can be modeled as a semiflexible biopolymer network that modifies its morphology in response to both external and internal stimuli. Just inside the inner nuclear membrane of a cell exists a network of filamentous lamin that presumably protects the heart of the cell nucleus--the DNA. Lamins are intermediate filaments that can also be modeled as semiflexible biopolymers. It turns out that the actin cytoskeletal biopolymer network and the lamin biopolymer network are coupled via a sequence of proteins that bridge the outer and inner nuclear membranes. We, therefore, probe the consequences of such a coupling via numerical simulations to understand the resulting deformations in the lamin network in response to perturbations in the cytoskeletal network. Such study could have implications for mechanical mechanisms of the regulation of transcription, since DNA--yet another semiflexible polymer--contains lamin-binding domains, and, thus, widen the field of epigenetics.

Binding of Thioflavin T and Related Probes to Polymorphic Models of Amyloid-β Fibrils.

PubMed

Peccati, Francesca; Pantaleone, Stefano; Riffet, Vanessa; Solans-Monfort, Xavier; Contreras-García, Julia; Guallar, Victor; Sodupe, Mariona

2017-09-28

Alzheimer's disease is a challenge of the utmost importance for contemporary society. An early diagnosis is essential for the development of treatments and for establishing a network of support for the patient. In this light, the deposition in the brain of amyloid-β fibrillar aggregates, which is a distinctive feature of Alzheimer, is key for an early detection of this disease. In this work we propose an atomistic study of the interaction of amyloid tracers with recently published polymorphic models of amyloid-β 1-40 and 1-42 fibrils, highlighting the relationship between marker architectures and binding affinity. This work uncovers the importance of quaternary structure, and in particular of junctions between amyloid-β protofilaments, as the key areas for marker binding.

Specificity and non-specificity in RNA–protein interactions

PubMed Central

Jankowsky, Eckhard; Harris, Michael E.

2016-01-01

Gene expression is regulated by complex networks of interactions between RNAs and proteins. Proteins that interact with RNA have been traditionally viewed as either specific or non-specific; specific proteins interact preferentially with defined RNA sequence or structure motifs, whereas non-specific proteins interact with RNA sites devoid of such characteristics. Recent studies indicate that the binary “specific vs. non-specific” classification is insufficient to describe the full spectrum of RNA–protein interactions. Here, we review new methods that enable quantitative measurements of protein binding to large numbers of RNA variants, and the concepts aimed as describing resulting binding spectra: affinity distributions, comprehensive binding models and free energy landscapes. We discuss how these new methodologies and associated concepts enable work towards inclusive, quantitative models for specific and non-specific RNA–protein interactions. PMID:26285679

The Role of Genome Accessibility in Transcription Factor Binding in Bacteria.

PubMed

Gomes, Antonio L C; Wang, Harris H

2016-04-01

ChIP-seq enables genome-scale identification of regulatory regions that govern gene expression. However, the biological insights generated from ChIP-seq analysis have been limited to predictions of binding sites and cooperative interactions. Furthermore, ChIP-seq data often poorly correlate with in vitro measurements or predicted motifs, highlighting that binding affinity alone is insufficient to explain transcription factor (TF)-binding in vivo. One possibility is that binding sites are not equally accessible across the genome. A more comprehensive biophysical representation of TF-binding is required to improve our ability to understand, predict, and alter gene expression. Here, we show that genome accessibility is a key parameter that impacts TF-binding in bacteria. We developed a thermodynamic model that parameterizes ChIP-seq coverage in terms of genome accessibility and binding affinity. The role of genome accessibility is validated using a large-scale ChIP-seq dataset of the M. tuberculosis regulatory network. We find that accounting for genome accessibility led to a model that explains 63% of the ChIP-seq profile variance, while a model based in motif score alone explains only 35% of the variance. Moreover, our framework enables de novo ChIP-seq peak prediction and is useful for inferring TF-binding peaks in new experimental conditions by reducing the need for additional experiments. We observe that the genome is more accessible in intergenic regions, and that increased accessibility is positively correlated with gene expression and anti-correlated with distance to the origin of replication. Our biophysically motivated model provides a more comprehensive description of TF-binding in vivo from first principles towards a better representation of gene regulation in silico, with promising applications in systems biology.

Structure of Serum Amyloid A Suggests a Mechanism for Selective Lipoprotein Binding and Functions: SAA as a Hub in Macromolecular Interaction Networks

PubMed Central

Frame, Nicholas M.; Gursky, Olga

2016-01-01

Serum amyloid A is a major acute-phase plasma protein that modulates innate immunity and cholesterol homeostasis. We combine sequence analysis with x-ray crystal structures to postulate that SAA acts as an intrinsically disordered hub mediating interactions among proteins, lipids and proteoglycans. A structural model of lipoprotein-bound SAA monomer is proposed wherein two α-helices from the N-domain form a concave hydrophobic surface that binds lipoproteins. A C-domain, connected to the N-domain via a flexible linker, binds polar/charged ligands including cell receptors, bridging them with lipoproteins and re-routing cholesterol transport. Our model is supported by the SAA cleavage in the inter-domain linker to generate the 1–76 fragment deposited in reactive amyloidosis. This model sheds new light on functions of this enigmatic protein. PMID:26918388

The Biomolecular Interaction Network Database and related tools 2005 update

PubMed Central

Alfarano, C.; Andrade, C. E.; Anthony, K.; Bahroos, N.; Bajec, M.; Bantoft, K.; Betel, D.; Bobechko, B.; Boutilier, K.; Burgess, E.; Buzadzija, K.; Cavero, R.; D'Abreo, C.; Donaldson, I.; Dorairajoo, D.; Dumontier, M. J.; Dumontier, M. R.; Earles, V.; Farrall, R.; Feldman, H.; Garderman, E.; Gong, Y.; Gonzaga, R.; Grytsan, V.; Gryz, E.; Gu, V.; Haldorsen, E.; Halupa, A.; Haw, R.; Hrvojic, A.; Hurrell, L.; Isserlin, R.; Jack, F.; Juma, F.; Khan, A.; Kon, T.; Konopinsky, S.; Le, V.; Lee, E.; Ling, S.; Magidin, M.; Moniakis, J.; Montojo, J.; Moore, S.; Muskat, B.; Ng, I.; Paraiso, J. P.; Parker, B.; Pintilie, G.; Pirone, R.; Salama, J. J.; Sgro, S.; Shan, T.; Shu, Y.; Siew, J.; Skinner, D.; Snyder, K.; Stasiuk, R.; Strumpf, D.; Tuekam, B.; Tao, S.; Wang, Z.; White, M.; Willis, R.; Wolting, C.; Wong, S.; Wrong, A.; Xin, C.; Yao, R.; Yates, B.; Zhang, S.; Zheng, K.; Pawson, T.; Ouellette, B. F. F.; Hogue, C. W. V.

2005-01-01

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. PMID:15608229

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor

PubMed Central

Judson, Richard S.; Magpantay, Felicia Maria; Chickarmane, Vijay; Haskell, Cymra; Tania, Nessy; Taylor, Jean; Xia, Menghang; Huang, Ruili; Rotroff, Daniel M.; Filer, Dayne L.; Houck, Keith A.; Martin, Matthew T.; Sipes, Nisha; Richard, Ann M.; Mansouri, Kamel; Setzer, R. Woodrow; Knudsen, Thomas B.; Crofton, Kevin M.; Thomas, Russell S.

2015-01-01

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available. PMID:26272952

A single charge in the actin binding domain of fascin can independently tune the linear and non-linear response of an actin bundle network.

PubMed

Maier, M; Müller, K W; Heussinger, C; Köhler, S; Wall, W A; Bausch, A R; Lieleg, O

2015-05-01

Actin binding proteins (ABPs) not only set the structure of actin filament assemblies but also mediate the frequency-dependent viscoelastic moduli of cross-linked and bundled actin networks. Point mutations in the actin binding domain of those ABPs can tune the association and dissociation dynamics of the actin/ABP bond and thus modulate the network mechanics both in the linear and non-linear response regime. We here demonstrate how the exchange of a single charged amino acid in the actin binding domain of the ABP fascin triggers such a modulation of the network rheology. Whereas the overall structure of the bundle networks is conserved, the transition point from strain-hardening to strain-weakening sensitively depends on the cross-linker off-rate and the applied shear rate. Our experimental results are consistent both with numerical simulations of a cross-linked bundle network and a theoretical description of the bundle network mechanics which is based on non-affine bending deformations and force-dependent cross-link dynamics.

The Verrucomicrobia LexA-Binding Motif: Insights into the Evolutionary Dynamics of the SOS Response.

PubMed

Erill, Ivan; Campoy, Susana; Kılıç, Sefa; Barbé, Jordi

2016-01-01

The SOS response is the primary bacterial mechanism to address DNA damage, coordinating multiple cellular processes that include DNA repair, cell division, and translesion synthesis. In contrast to other regulatory systems, the composition of the SOS genetic network and the binding motif of its transcriptional repressor, LexA, have been shown to vary greatly across bacterial clades, making it an ideal system to study the co-evolution of transcription factors and their regulons. Leveraging comparative genomics approaches and prior knowledge on the core SOS regulon, here we define the binding motif of the Verrucomicrobia, a recently described phylum of emerging interest due to its association with eukaryotic hosts. Site directed mutagenesis of the Verrucomicrobium spinosum recA promoter confirms that LexA binds a 14 bp palindromic motif with consensus sequence TGTTC-N4-GAACA. Computational analyses suggest that recognition of this novel motif is determined primarily by changes in base-contacting residues of the third alpha helix of the LexA helix-turn-helix DNA binding motif. In conjunction with comparative genomics analysis of the LexA regulon in the Verrucomicrobia phylum, electrophoretic shift assays reveal that LexA binds to operators in the promoter region of DNA repair genes and a mutagenesis cassette in this organism, and identify previously unreported components of the SOS response. The identification of tandem LexA-binding sites generating instances of other LexA-binding motifs in the lexA gene promoter of Verrucomicrobia species leads us to postulate a novel mechanism for LexA-binding motif evolution. This model, based on gene duplication, successfully addresses outstanding questions in the intricate co-evolution of the LexA protein, its binding motif and the regulatory network it controls.

Probing Molecular Mechanisms of the Hsp90 Chaperone: Biophysical Modeling Identifies Key Regulators of Functional Dynamics

PubMed Central

Dixit, Anshuman; Verkhivker, Gennady M.

2012-01-01

Deciphering functional mechanisms of the Hsp90 chaperone machinery is an important objective in cancer biology aiming to facilitate discovery of targeted anti-cancer therapies. Despite significant advances in understanding structure and function of molecular chaperones, organizing molecular principles that control the relationship between conformational diversity and functional mechanisms of the Hsp90 activity lack a sufficient quantitative characterization. We combined molecular dynamics simulations, principal component analysis, the energy landscape model and structure-functional analysis of Hsp90 regulatory interactions to systematically investigate functional dynamics of the molecular chaperone. This approach has identified a network of conserved regions common to the Hsp90 chaperones that could play a universal role in coordinating functional dynamics, principal collective motions and allosteric signaling of Hsp90. We have found that these functional motifs may be utilized by the molecular chaperone machinery to act collectively as central regulators of Hsp90 dynamics and activity, including the inter-domain communications, control of ATP hydrolysis, and protein client binding. These findings have provided support to a long-standing assertion that allosteric regulation and catalysis may have emerged via common evolutionary routes. The interaction networks regulating functional motions of Hsp90 may be determined by the inherent structural architecture of the molecular chaperone. At the same time, the thermodynamics-based “conformational selection” of functional states is likely to be activated based on the nature of the binding partner. This mechanistic model of Hsp90 dynamics and function is consistent with the notion that allosteric networks orchestrating cooperative protein motions can be formed by evolutionary conserved and sparsely connected residue clusters. Hence, allosteric signaling through a small network of distantly connected residue clusters may be a rather general functional requirement encoded across molecular chaperones. The obtained insights may be useful in guiding discovery of allosteric Hsp90 inhibitors targeting protein interfaces with co-chaperones and protein binding clients. PMID:22624053

Learning abstract visual concepts via probabilistic program induction in a Language of Thought.

PubMed

Overlan, Matthew C; Jacobs, Robert A; Piantadosi, Steven T

2017-11-01

The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization. Copyright © 2017 Elsevier B.V. All rights reserved.

Edible oil structures at low and intermediate concentrations. I. Modeling, computer simulation, and predictions for X ray scattering

NASA Astrophysics Data System (ADS)

Pink, David A.; Quinn, Bonnie; Peyronel, Fernanda; Marangoni, Alejandro G.

2013-12-01

Triacylglycerols (TAGs) are biologically important molecules which form the recently discovered highly anisotropic crystalline nanoplatelets (CNPs) and, ultimately, the large-scale fat crystal networks in edible oils. Identifying the hierarchies of these networks and how they spontaneously self-assemble is important to understanding their functionality and oil binding capacity. We have modelled CNPs and studied how they aggregate under the assumption that all CNPs are present before aggregation begins and that their solubility in the liquid oil is very low. We represented CNPs as rigid planar arrays of spheres with diameter ≈50 nm and defined the interaction between spheres in terms of a Hamaker coefficient, A, and a binding energy, VB. We studied three cases: weak binding, |VB|/kBT ≪ 1, physically realistic binding, VB = Vd(R, Δ), so that |VB|/kBT ≈ 1, and Strong binding with |VB|/kBT ≫ 1. We divided the concentration of CNPs, ϕ, with 0≤ϕ= 10-2 (solid fat content) ≤1, into two regions: Low and intermediate concentrations with 0<ϕ<0.25 and high concentrations with 0.25 < ϕ and considered only the first case. We employed Monte Carlo computer simulation to model CNP aggregation and analyzed them using static structure functions, S(q). We found that strong binding cases formed aggregates with fractal dimension, D, 1.7≤D ≤1.8, in accord with diffusion limited cluster-cluster aggregation (DLCA) and weak binding formed aggregates with D =3, indicating a random distribution of CNPs. We found that models with physically realistic intermediate binding energies formed linear multilayer stacks of CNPs (TAGwoods) with fractal dimension D =1 for ϕ =0.06,0.13, and 0.22. TAGwood lengths were greater at lower ϕ than at higher ϕ, where some of the aggregates appeared as thick CNPs. We increased the spatial scale and modelled the TAGwoods as rigid linear arrays of spheres of diameter ≈500 nm, interacting via the attractive van der Waals interaction. We found that TAGwoods aggregated via DLCA into clusters with fractal dimension D =1.7-1.8. As the simulations were run further, TAGwoods relaxed their positions in order to maximize the attractive interaction making the process look like reaction limited cluster-cluster aggregation with the fractal dimension increasing to D =2.0-2.1. For higher concentrations of CNPs, many TAGwood clusters were formed and, because of their weak interactions, were distributed randomly with D =3.0. We summarize the hierarchy of structures and make predictions for X-ray scattering.

Sequence-based model of gap gene regulatory network.

PubMed

Kozlov, Konstantin; Gursky, Vitaly; Kulakovskiy, Ivan; Samsonova, Maria

2014-01-01

The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded. The reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays.

Rapid molecular evolution across amniotes of the IIS/TOR network

PubMed Central

McGaugh, Suzanne E.; Bronikowski, Anne M.; Kuo, Chih-Horng; Reding, Dawn M.; Addis, Elizabeth A.; Flagel, Lex E.; Janzen, Fredric J.

2015-01-01

The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades. PMID:25991861

Rapid molecular evolution across amniotes of the IIS/TOR network.

PubMed

McGaugh, Suzanne E; Bronikowski, Anne M; Kuo, Chih-Horng; Reding, Dawn M; Addis, Elizabeth A; Flagel, Lex E; Janzen, Fredric J; Schwartz, Tonia S

2015-06-02

The insulin/insulin-like signaling and target of rapamycin (IIS/TOR) network regulates lifespan and reproduction, as well as metabolic diseases, cancer, and aging. Despite its vital role in health, comparative analyses of IIS/TOR have been limited to invertebrates and mammals. We conducted an extensive evolutionary analysis of the IIS/TOR network across 66 amniotes with 18 newly generated transcriptomes from nonavian reptiles and additional available genomes/transcriptomes. We uncovered rapid and extensive molecular evolution between reptiles (including birds) and mammals: (i) the IIS/TOR network, including the critical nodes insulin receptor substrate (IRS) and phosphatidylinositol 3-kinase (PI3K), exhibit divergent evolutionary rates between reptiles and mammals; (ii) compared with a proxy for the rest of the genome, genes of the IIS/TOR extracellular network exhibit exceptionally fast evolutionary rates; and (iii) signatures of positive selection and coevolution of the extracellular network suggest reptile- and mammal-specific interactions between members of the network. In reptiles, positively selected sites cluster on the binding surfaces of insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and insulin receptor (INSR); whereas in mammals, positively selected sites clustered on the IGF2 binding surface, suggesting that these hormone-receptor binding affinities are targets of positive selection. Further, contrary to reports that IGF2R binds IGF2 only in marsupial and placental mammals, we found positively selected sites clustered on the hormone binding surface of reptile IGF2R that suggest that IGF2R binds to IGF hormones in diverse taxa and may have evolved in reptiles. These data suggest that key IIS/TOR paralogs have sub- or neofunctionalized between mammals and reptiles and that this network may underlie fundamental life history and physiological differences between these amniote sister clades.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

RBind: computational network method to predict RNA binding sites.

PubMed

Wang, Kaili; Jian, Yiren; Wang, Huiwen; Zeng, Chen; Zhao, Yunjie

2018-04-26

Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction. The codes and datasets are available at https://zhaolab.com.cn/RBind. yjzhaowh@mail.ccnu.edu.cn. Supplementary data are available at Bioinformatics online.

BIND: the Biomolecular Interaction Network Database

PubMed Central

Bader, Gary D.; Betel, Doron; Hogue, Christopher W. V.

2003-01-01

The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD. PMID:12519993

Energetic studies on DNA-peptide interaction in relation to the enthalpy-entropy compensation paradox.

PubMed

Yang, Robin C K; Huang, Jonathan T B; Chien, Shih-Chuan; Huang, Roy; Jeng, Kee-Ching G; Chen, Yen-Chung; Liao, Mokai; Wu, Jia-Rong; Hung, Wei-Kang; Hung, Chia-Chun; Chen, Yu-Ling; Waring, Michael J; Sheh, Leung

2013-01-07

This study aims to interpret the energetic basis of complex DNA-peptide interactions according to a novel allosteric interaction network approach. In common with other designed peptides, five new conjugates incorporating the XPRK or XHypRK motif (Hyp = hydroxyproline) attached to a N-methylpyrrole (Py) tract with a basic tail have been found to display cooperative binding to DNA involving multiple monodentate as well as interstrand bidentate interactions. Using quantitative DNase I footprinting it appears that allosteric communication via cooperative binding to multiple sites on complementary DNA strands corresponds to two different types of DNA-peptide interaction network. Temperature variation experiments using a dodecapeptide RY-12 show that lower temperature (25 °C) favor a circuit type of allosteric interaction network, whereas higher temperatures (31 and 37 °C) afford only a partial-circuit type of network. Circular dichroism studies show that our five peptides induce significant local conformational changes in DNA via the minor groove, with apparently dimeric binding stoichiometry. Isothermal titration calorimetry reveals that these peptides, together with another seven for comparison, are strongly exothermic upon binding to a model 13-mer DNA duplex, characterized by ΔH ranging from -14.7 to -74.4 kcal mol(-1), and also high TΔS ranging from -6.5 to -65.9 kcal mol(-1). Multiple monodentate and bidentate interactions, as well as ionic forces that mediate positive cooperativity in sequence recognition, are consistent with a dramatic decrease in entropy and a 'tightening' effect of DNA conformation. Distinctive enthalpy-entropy compensation (EEC) relationships are demonstrated for the interaction of all twelve designed peptides with DNA, affording a straight line of slope close to unity when ΔH is plotted versus TΔS, with a y-axis intercept (average ΔG) corresponding to -8.5 kcal mol(-1), while the observed ΔG ranges from -8.2 to -9.1 kcal mol(-1) for the peptides. The EEC seen with peptide RY-12 binding to the model duplex persists throughout various incubation temperatures. The net compensation of energy between the favorable negative ΔH and unfavorable negative ΔS components thus constrains the value of net binding free energy ΔG within a remarkably constant range, as is clearly visible in a 3-dimensional energetic plot. We conclude that the preservation of a rather narrowly-defined ΔG value is central to the EEC in DNA-peptide interactions, illuminating the universal EEC paradox commonly found in diverse biochemical reactions.

PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

PubMed

Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

2017-02-03

The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simulation of large-scale rule-based models

PubMed Central

Colvin, Joshua; Monine, Michael I.; Faeder, James R.; Hlavacek, William S.; Von Hoff, Daniel D.; Posner, Richard G.

2009-01-01

Motivation: Interactions of molecules, such as signaling proteins, with multiple binding sites and/or multiple sites of post-translational covalent modification can be modeled using reaction rules. Rules comprehensively, but implicitly, define the individual chemical species and reactions that molecular interactions can potentially generate. Although rules can be automatically processed to define a biochemical reaction network, the network implied by a set of rules is often too large to generate completely or to simulate using conventional procedures. To address this problem, we present DYNSTOC, a general-purpose tool for simulating rule-based models. Results: DYNSTOC implements a null-event algorithm for simulating chemical reactions in a homogenous reaction compartment. The simulation method does not require that a reaction network be specified explicitly in advance, but rather takes advantage of the availability of the reaction rules in a rule-based specification of a network to determine if a randomly selected set of molecular components participates in a reaction during a time step. DYNSTOC reads reaction rules written in the BioNetGen language which is useful for modeling protein–protein interactions involved in signal transduction. The method of DYNSTOC is closely related to that of StochSim. DYNSTOC differs from StochSim by allowing for model specification in terms of BNGL, which extends the range of protein complexes that can be considered in a model. DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods. We demonstrate the ability of DYNSTOC to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions. Availability: DYNSTOC is free for non-commercial use. The C source code, supporting documentation and example input files are available at http://public.tgen.org/dynstoc/. Contact: dynstoc@tgen.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19213740

Polypentagonal ice-like water networks emerge solely in an activity-improved variant of ice-binding protein.

PubMed

Mahatabuddin, Sheikh; Fukami, Daichi; Arai, Tatsuya; Nishimiya, Yoshiyuki; Shimizu, Rumi; Shibazaki, Chie; Kondo, Hidemasa; Adachi, Motoyasu; Tsuda, Sakae

2018-05-22

Polypentagonal water networks were recently observed in a protein capable of binding to ice crystals, or ice-binding protein (IBP). To examine such water networks and clarify their role in ice-binding, we determined X-ray crystal structures of a 65-residue defective isoform of a Zoarcidae -derived IBP (wild type, WT) and its five single mutants (A20L, A20G, A20T, A20V, and A20I). Polypentagonal water networks composed of ∼50 semiclathrate waters were observed solely on the strongest A20I mutant, which appeared to include a tetrahedral water cluster exhibiting a perfect position match to the [Formula: see text] first prism plane of a single ice crystal. Inclusion of another symmetrical water cluster in the polypentagonal network showed a perfect complementarity to the waters constructing the [Formula: see text] pyramidal ice plane. The order of ice-binding strength was A20L < A20G < WT < A20T < A20V < A20I, where the top three mutants capable of binding to the first prism and the pyramidal ice planes commonly contained a bifurcated γ-CH 3 group. These results suggest that a fine-tuning of the surface of Zoarcidae -derived IBP assisted by a side-chain group regulates the holding property of its polypentagonal water network, the function of which is to freeze the host protein to specific ice planes. Copyright © 2018 the Author(s). Published by PNAS.

Network-specific mechanisms may explain the paradoxical effects of carbamazepine and phenytoin.

PubMed

Thomas, Evan A; Petrou, Steven

2013-07-01

A common notion of the mechanism by which the antiepileptic drugs (AEDs) carbamazepine and phenytoin act is that they block sodium channels by binding preferentially to the inactivated state, thereby allowing normal neuronal firing while blocking ictal activity. However, these drugs have unpredictable efficacy and, in some cases, may exacerbate seizures. Previous studies have suggested that reducing sodium channel availability in the dentate gyrus (DG) paradoxically increases excitability. We used a biophysically detailed computer model of the DG to test the hypothesis that AEDs increase excitability by disproportionately reducing negative feedback mechanisms. We built a Markov model of sodium channel gating that reproduces responses to voltage clamp experiments in the presence of carbamazepine and phenytoin. We incorporated this validated Markov model into a biophysically realistic computer model of DG neurons and networks. Simulated drug concentrations were similar to those measured in cerebral spinal fluid in medicated patients. Single neuron models were stimulated with current injections, and networks were stimulated with perforant path synaptic input. In the network model, environmental effects were studied by introducing mossy fiber sprouting. As expected, drugs reduced sodium channel availability, which in turn reduced action potential amplitude. This had only a small effect on action potential (AP) firing rate during brief (100 msec) current injections. Paradoxically, long current injections (2,500 msec) increased AP firing rates. This was caused by reduced calcium entry and consequently reduced activation of calcium activated potassium channels. It is important to note that the main determinant of drug effect was resting membrane potential (RMP) and not action potential firing rate. Binding of phenytoin and carbamazepine is slow and, thus drug effects are largely determined by the long term state of the RMP. This paradoxical AP firing increase was dependent on the unusually large calcium-activated potassium conductances expressed by DG granule cells. This predicts that drug efficacy in a given network will depend on the precise makeup of conductances in the network. RMP is expected to vary with the level of activity in the network. We simulated the effects of drugs on single shot stimulus responses in networks with mossy fiber sprouting and varied the RMP in all neurons as a model for network activity. For an RMP of -50 mV, representing an active network, drugs had no effect, or in some cases, increased excitability. Drugs had an increasingly larger inhibitory effect on network responses as RMP decreased. An important prediction is that drugs will be unable to block ictal activity invading an active network. Our key findings are that drug effects depend on both intrinsic properties of the network and its behavioral state. This may explain the paradoxical and unpredictable effects of some AEDs on seizure control in some patients. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

The ties that bind: a network approach to creating a programme in faculty development.

PubMed

Baker, Lindsay; Reeves, Scott; Egan-Lee, Eileen; Leslie, Karen; Silver, Ivan

2010-02-01

Current trends in medical education reflect the changing health care environment. An increasingly large and diverse student population, a move to more distributed models of education, greater community involvement and an emphasis on social accountability, interprofessional education and student-centred approaches to learning necessitate new approaches to faculty development to help faculty members respond effectively to this rapidly changing landscape. Drawing upon the tenets of network theory and the broader organisational literature, we propose a 'fishhook' model of faculty development programme formation. The model is based on seven key factors which supported the successful formation of a centralised programme for faculty development that addressed many of the contemporary issues in medical education. These factors include: environmental readiness; commitment and vision of a mobiliser; recruitment of key stakeholders and leaders to committees; formation of a collaborative network structure; accumulation of networking capital; legitimacy, and flexibility. Our aim in creating this model is to provide a guide for other medical schools to consider when developing similar programmes. The model can be adapted to reflect the local goals, settings and cultures of other medical education contexts.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

PubMed Central

Huber, Roland G.; Bond, Peter J.

2017-01-01

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners. PMID:29016650

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

PubMed

Ivanov, Stefan M; Cawley, Andrew; Huber, Roland G; Bond, Peter J; Warwicker, Jim

2017-01-01

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

PubMed Central

Hart, Thomas; Dider, Shihab; Han, Weiwei; Xu, Hua; Zhao, Zhongming; Xie, Lei

2016-01-01

Metformin, a drug prescribed to treat type-2 diabetes, exhibits anti-cancer effects in a portion of patients, but the direct molecular and genetic interactions leading to this pleiotropic effect have not yet been fully explored. To repurpose metformin as a precision anti-cancer therapy, we have developed a novel structural systems pharmacology approach to elucidate metformin’s molecular basis and genetic biomarkers of action. We integrated structural proteome-scale drug target identification with network biology analysis by combining structural genomic, functional genomic, and interactomic data. Through searching the human structural proteome, we identified twenty putative metformin binding targets and their interaction models. We experimentally verified the interactions between metformin and our top-ranked kinase targets. Notably, kinases, particularly SGK1 and EGFR were identified as key molecular targets of metformin. Subsequently, we linked these putative binding targets to genes that do not directly bind to metformin but whose expressions are altered by metformin through protein-protein interactions, and identified network biomarkers of phenotypic response of metformin. The molecular targets and the key nodes in genetic networks are largely consistent with the existing experimental evidence. Their interactions can be affected by the observed cancer mutations. This study will shed new light into repurposing metformin for safe, effective, personalized therapies. PMID:26841718

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

PubMed

Han, Youngmahn; Kim, Dongsup

2017-12-28

Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc . We developed a novel method for peptide-HLA-I binding predictions using DCNN trained on ILA data that encode peptide binding data and demonstrated the reliable performance of the DCNN in nonapeptide binding predictions through the independent evaluation on the latest IEDB benchmark datasets. Our approaches can be applied to characterize locally-clustered patterns in molecular interactions, such as protein/DNA, protein/RNA, and drug/protein interactions.

Structure-based optimization of salt-bridge network across the complex interface of PTPN4 PDZ domain with its peptide ligands in neuroglioma.

PubMed

Xiao, Xian; He, Qiang-Hua; Yu, Li-Yan; Wang, Song-Qing; Li, Yang; Yang, Hua; Zhang, Ai-Hua; Ma, Xiao-Hong; Peng, Yu-Jie; Chen, Bing

2017-02-01

The PTP non-receptor type 4 (PTPN4) is an important regulator protein in learning, spatial memory and cerebellar synaptic plasticity; targeting the PDZ domain of PTPN4 has become as attractive therapeutic strategy for human neuroglioma. Here, we systematically examined the complex crystal structures of PTPN4 PDZ domain with its known peptide ligands; a number of charged amino acid residues were identified in these ligands and in the peptide-binding pocket of PDZ domain, which can constitute a complicated salt-bridge network across the complex interface. Molecular dynamics (MD) simulations, binding free energy calculations and continuum model analysis revealed that the electrostatic effect plays a predominant role in domain-peptide binding, while other noncovalent interactions such as hydrogen bonds and hydrophobic forces are also responsible for the binding. The computational findings were then used to guide structure-based optimization of the interfacial salt-bridge network. Consequently, five peptides were rationally designed using the high-affinity binder Cyto8-RETEV (RETEV -COOH ) as template, including four single-point mutants (i.e. Cyto8-mtxe 0 : RETEE -COOH , Cyto8-mtxd -1 : RETDV -COOH , Cyto8-mtxd -3 : RDTEV -COOH and Cyto8-mtxk -4 : KETEV -COOH ) and one double-point mutant (i.e. Cyto8-mtxd -1 k -4 : KETDV -COOH ). Binding assays confirmed that three (Cyto8-mtxd -1 , Cyto8-mtxk -4 and Cyto8-mtxd -1 k -4 ) out of the five designed peptides exhibit moderately or considerably increased affinity as compared to the native peptide Cyto8-RETEV. Copyright © 2016 Elsevier Ltd. All rights reserved.

IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels.

PubMed

Kasahara, Kota; Kinoshita, Kengo

2016-01-01

Ion conduction mechanisms of ion channels are a long-standing conundrum. Although the molecular dynamics (MD) method has been extensively used to simulate ion conduction dynamics at the atomic level, analysis and interpretation of MD results are not straightforward due to complexity of the dynamics. In our previous reports, we proposed an analytical method called ion-binding state analysis to scrutinize and summarize ion conduction mechanisms by taking advantage of a variety of analytical protocols, e.g., the complex network analysis, sequence alignment, and hierarchical clustering. This approach effectively revealed the ion conduction mechanisms and their dependence on the conditions, i.e., ion concentration and membrane voltage. Here, we present an easy-to-use computational toolkit for ion-binding state analysis, called IBiSA_tools. This toolkit consists of a C++ program and a series of Python and R scripts. From the trajectory file of MD simulations and a structure file, users can generate several images and statistics of ion conduction processes. A complex network named ion-binding state graph is generated in a standard graph format (graph modeling language; GML), which can be visualized by standard network analyzers such as Cytoscape. As a tutorial, a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit. Users can trace the entire process of ion-binding state analysis step by step. The novel method for analysis of ion conduction mechanisms of ion channels can be easily used by means of IBiSA_tools. This software is distributed under an open source license at the following URL: http://www.ritsumei.ac.jp/~ktkshr/ibisa_tools/.

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor.

PubMed

Judson, Richard S; Magpantay, Felicia Maria; Chickarmane, Vijay; Haskell, Cymra; Tania, Nessy; Taylor, Jean; Xia, Menghang; Huang, Ruili; Rotroff, Daniel M; Filer, Dayne L; Houck, Keith A; Martin, Matthew T; Sipes, Nisha; Richard, Ann M; Mansouri, Kamel; Setzer, R Woodrow; Knudsen, Thomas B; Crofton, Kevin M; Thomas, Russell S

2015-11-01

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available. Published by Oxford University Press on behalf of the Society of Toxicology 2015. This work is written by US Government employees and is in the public domain in the US.

Identification of key residues for protein conformational transition using elastic network model.

PubMed

Su, Ji Guo; Xu, Xian Jin; Li, Chun Hua; Chen, Wei Zu; Wang, Cun Xin

2011-11-07

Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

PubMed

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

NASA Astrophysics Data System (ADS)

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Mass-action equilibrium and non-specific interactions in protein binding networks

NASA Astrophysics Data System (ADS)

Maslov, Sergei

2009-03-01

Large-scale protein binding networks serve as a paradigm of complex properties of living cells. These networks are naturally weighted with edges characterized by binding strength and protein-nodes -- by their concentrations. However, the state-of-the-art high-throughput experimental techniques generate just a binary (yes or no) information about individual interactions. As a result, most of the previous research concentrated just on topology of these networks. In a series of recent publications [1-4] my collaborators and I went beyond purely topological studies and calculated the mass-action equilibrium of a genome-wide binding network using experimentally determined protein concentrations, localizations, and reliable binding interactions in baker's yeast. We then studied how this equilibrium responds to large perturbations [1-2] and noise [3] in concentrations of proteins. We demonstrated that the change in the equilibrium concentration of a protein exponentially decays (and sign-alternates) with its network distance away from the perturbed node. This explains why, despite a globally connected topology, individual functional modules in such networks are able to operate fairly independently. In a separate study [4] we quantified the interplay between specific and non-specific binding interactions under crowded conditions inside living cells. We show how the need to limit the waste of resources constrains the number of types and concentrations of proteins that are present at the same time and at the same place in yeast cells. [1] S Maslov, I. Ispolatov, PNAS 104:13655 (2007). [2] S. Maslov, K. Sneppen, I. Ispolatov, New J. of Phys. 9: 273 (2007). [3] K-K. Yan, D. Walker, S. Maslov, PRL accepted (2008). [4] J. Zhang, S. Maslov, and E. I. Shakhnovich, Mol Syst Biol 4, 210 (2008).

Pyromellitamide aggregates and their response to anion stimuli.

PubMed

Webb, James E A; Crossley, Maxwell J; Turner, Peter; Thordarson, Pall

2007-06-06

The N,N',N'',N'''-1,2,4,5-tetra(ethylhexanoate) pyromellitamide is found to be capable of both intermolecular aggregation and binding to small anions. It is synthesized by aminolysis of pyromellitic anhydride with ethanolamine, followed by a reaction with hexanoyl chloride. The single-crystal X-ray structure of the pyromellitamide shows that it forms one-dimensional columnar stacks through an intermolecular hydrogen-bonding network. It also forms self-assembled gels in nonpolar solvents, presumably by a hydrogen-bonding network similar to the solid-state structure as shown by IR and XRD studies. Aggregation by intermolecular hydrogen bonding of the pyromellitamide is also observed by NMR and IR in solution. Fitting of NMR dilution data for pyromellitamide in d6-acetone to a cooperative aggregation model gave KE=232 M-1 and positive cooperativity of aggregation (rho=0.22). The pyromellitamide binds to a range of small anions with the binding strength decreasing in the order chloride>acetate>bromide>nitrate approximately iodide. The data indicate that the pyromellitamide binds two anions and that it displays negative cooperativity. The intermolecular aggregation of the pyromellitamide can also be altered using small anion stimuli; anion addition to preformed self-assembled pyromellitamide gels causes their collapse. The kinetics of anion-induced gel collapse are qualitatively correlated to the binding affinities of the same anions in solution. The cooperative anion binding properties and the sensitivity of the self-assembled gels formed by pyromellitamide toward anions could be useful in the development of sensors and switching/releasing devices.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

A dynamically coupled allosteric network underlies binding cooperativity in Src kinase

PubMed Central

Foda, Zachariah H.; Shan, Yibing; Kim, Eric T.; Shaw, David E.; Seeliger, Markus A.

2015-01-01

Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity. PMID:25600932

Structural Basis for the Inhibition of Gas Hydrates by α-Helical Antifreeze Proteins

PubMed Central

Sun, Tianjun; Davies, Peter L.; Walker, Virginia K.

2015-01-01

Kinetic hydrate inhibitors (KHIs) are used commercially to inhibit gas hydrate formation and growth in pipelines. However, improvement of these polymers has been constrained by the lack of verified molecular models. Since antifreeze proteins (AFPs) act as KHIs, we have used their solved x-ray crystallographic structures in molecular modeling to explore gas hydrate inhibition. The internal clathrate water network of the fish AFP Maxi, which extends to the protein’s outer surface, is remarkably similar to the {100} planes of structure type II (sII) gas hydrate. The crystal structure of this water web has facilitated the construction of in silico models for Maxi and type I AFP binding to sII hydrates. Here, we have substantiated our models with experimental evidence of Maxi binding to the tetrahydrofuran sII model hydrate. Both in silico and experimental evidence support the absorbance-inhibition mechanism proposed for KHI binding to gas hydrates. Based on the Maxi crystal structure we suggest that the inhibitor adsorbs to the gas hydrate lattice through the same anchored clathrate water mechanism used to bind ice. These results will facilitate the rational design of a next generation of effective green KHIs for the petroleum industry to ensure safe and efficient hydrocarbon flow. PMID:26488661

Locally excitatory, globally inhibitory oscillator networks: theory and application to scene segmentation

NASA Astrophysics Data System (ADS)

Wang, DeLiang; Terman, David

1995-01-01

A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated analytically and by computer simulation. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. We show analytically that with the selective gating mechanism the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate LEGION's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding, and may provide an effective computational framework for scene segmentation and figure/ground segregation.

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis

PubMed Central

2018-01-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that ‘leftover’ proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles. PMID:29518071

Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

PubMed

Holland, David O; Johnson, Margaret E

2018-03-01

Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module allows cells to tune where endocytosis occurs, providing sensitive control over cargo uptake via clathrin-coated vesicles.

HRD Motif as the Central Hub of the Signaling Network for Activation Loop Autophosphorylation in Abl Kinase.

PubMed

La Sala, Giuseppina; Riccardi, Laura; Gaspari, Roberto; Cavalli, Andrea; Hantschel, Oliver; De Vivo, Marco

2016-11-08

A number of structural factors modulate the activity of Abelson (Abl) tyrosine kinase, whose deregulation is often related to oncogenic processes. First, only the open conformation of the Abl kinase domain's activation loop (A-loop) favors ATP binding to the catalytic cleft. In this regard, the trans-autophosphorylation of the Y412 residue, which is located along the A-loop, favors the stability of the open conformation, in turn enhancing Abl activity. Another key factor for full Abl activity is the formation of active conformations of the catalytic DFG motif in the Abl kinase domain. Furthermore, binding of the SH2 domain to the N-lobe of the Abl kinase was recently demonstrated to have a long-range allosteric effect on the stabilization of the A-loop open state. Intriguingly, these distinct structural factors imply a complex signal transmission network for controlling the A-loop's flexibility and conformational preference for optimal Abl function. However, the exact dynamical features of this signal transmission network structure remain unclear. Here, we report on microsecond-long molecular dynamics coupled with enhanced sampling simulations of multiple Abl model systems, in the presence or absence of the SH2 domain and with the DFG motif flipped in two ways (in or out conformation). Through comparative analysis, our simulations augment the interpretation of the existing Abl experimental data, revealing a dynamical network of interactions that interconnect SH2 domain binding with A-loop plasticity and Y412 autophosphorylation in Abl. This signaling network engages the DFG motif and, importantly, other conserved structural elements of the kinase domain, namely, the EPK-ELK H-bond network and the HRD motif. Our results show that the signal propagation for modulating the A-loop spatial localization is highly dependent on the HRD motif conformation, which thus acts as the central hub of this (allosteric) signaling network controlling Abl activation and function.

An overview on the delivery of antitumor drug doxorubicin by carrier proteins.

PubMed

Agudelo, D; Bérubé, G; Tajmir-Riahi, H A

2016-07-01

Serum proteins play an increasing role as drug carriers in the clinical settings. In this review, we have compared the binding modalities of anticancer drug doxorubicin (DOX) to three model carrier proteins, human serum albumin (HSA), bovine serum albumin (BSA) and milk beta-lactoglobulin (β-LG) in order to determine the potential application of these model proteins in DOX delivery. Molecular modeling studies showed stronger binding of DOX with HSA than BSA and β-LG with the free binding energies of -10.75 (DOX-HSA), -9.31 (DOX-BSA) and -8.12kcal/mol (DOX-β-LG). Extensive H-boding network stabilizes DOX-protein conjugation and played a major role in drug-protein complex formation. DOX complexation induced major alterations of HSA and BSA conformations, while did not alter β-LG secondary structure. The literature review shows that these proteins can potentially be used for delivery of DOX in vitro and in vivo. Copyright © 2016 Elsevier B.V. All rights reserved.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

PubMed

Vivek-Ananth, R P; Samal, Areejit

2016-09-01

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Random Visitor: Defense against Identity Attacks in P2P Networks

NASA Astrophysics Data System (ADS)

Gu, Jabeom; Nah, Jaehoon; Kwon, Hyeokchan; Jang, Jonsoo; Park, Sehyun

Various advantages of cooperative peer-to-peer networks are strongly counterbalanced by the open nature of a distributed, serverless network. In such networks, it is relatively easy for an attacker to launch various attacks such as misrouting, corrupting, or dropping messages as a result of a successful identifier forgery. The impact of an identifier forgery is particularly severe because the whole network can be compromised by attacks such as Sybil or Eclipse. In this paper, we present an identifier authentication mechanism called random visitor, which uses one or more randomly selected peers as delegates of identity proof. Our scheme uses identity-based cryptography and identity ownership proof mechanisms collectively to create multiple, cryptographically protected indirect bindings between two peers, instantly when needed, through the delegates. Because of these bindings, an attacker cannot achieve an identifier forgery related attack against interacting peers without breaking the bindings. Therefore, our mechanism limits the possibility of identifier forgery attacks efficiently by disabling an attacker's ability to break the binding. The design rationale and framework details are presented. A security analysis shows that our scheme is strong enough against identifier related attacks and that the strength increases if there are many peers (more than several thousand) in the network.

Investigation of naphthofuran moiety as potential dual inhibitor against BACE-1 and GSK-3β: molecular dynamics simulations, binding energy, and network analysis to identify first-in-class dual inhibitors against Alzheimer's disease.

PubMed

Kumar, Akhil; Srivastava, Gaurava; Srivastava, Swati; Verma, Seema; Negi, Arvind S; Sharma, Ashok

2017-08-01

BACE-1 and GSK-3β are potential therapeutic drug targets for Alzheimer's disease. Recently, both the targets received attention for designing dual inhibitors for Alzheimer's disease. Until now, only two-scaffold triazinone and curcumin have been reported as BACE-1 and GSK-3β dual inhibitors. Docking, molecular dynamics, clustering, binding energy, and network analysis of triazinone derivatives with BACE-1 and GSK-3β was performed to get molecular insight into the first reported dual inhibitor. Further, we designed and evaluated a naphthofuran series for its ability to inhibit BACE-1 and GSK-3β with the computational approaches. Docking study of naphthofuran series showed a good binding affinity towards both the targets. Molecular dynamics, binding energy, and network analysis were performed to compare their binding with the targets and amino acids responsible for binding. Naphthofuran series derivatives showed good interaction within the active site residues of both of the targets. Hydrogen bond occupancy and binding energy suggested strong binding with the targets. Dual-inhibitor binding was mostly governed by the hydrophobic interactions for both of the targets. Per residue energy decomposition and network analysis identified the key residues involved in the binding and inhibiting BACE-1 and GSK-3β. The results indicated that naphthofuran series derivative 11 may be a promising first-in-class dual inhibitor against BACE-1 and GSK-3β. This naphthofuran series may be further explored to design better dual inhibitors. Graphical abstract Naphthofuran derivative as a dual inhibitor for BACE-1 and GSK-3β.

Dynamics of VEGF matrix-retention in vascular network patterning

NASA Astrophysics Data System (ADS)

Köhn-Luque, A.; de Back, W.; Yamaguchi, Y.; Yoshimura, K.; Herrero, M. A.; Miura, T.

2013-12-01

Vascular endothelial growth factor (VEGF) is a central regulator of blood vessel morphogenesis, although its role in patterning of endothelial cells into vascular networks is not fully understood. It has been suggested that binding of soluble VEGF to extracellular matrix components causes spatially restricted cues that guide endothelial cells into network patterns. Yet, current evidence for such a mechanism remains indirect. In this study, we quantitatively analyse the dynamics of VEGF retention in a controlled in vitro situation of human umbilical vascular endothelial cells (HUVECs) in Matrigel. We show that fluorescent VEGF accumulates in pericellular areas and colocalizes with VEGF binding molecules. Analysis of fluorescence recovery after photobleaching reveals that binding/unbinding to matrix molecules dominates VEGF dynamics in the pericellular region. Computational simulations using our experimental measurements of kinetic parameters show that matrix retention of chemotactic signals can lead to the formation of reticular cellular networks on a realistic timescale. Taken together, these results show that VEGF binds to matrix molecules in proximity of HUVECs in Matrigel, and suggest that bound VEGF drives vascular network patterning.

Distinct roles of a tyrosine-associated hydrogen-bond network in fine-tuning the structure and function of heme proteins: two cases designed for myoglobin.

PubMed

Liao, Fei; Yuan, Hong; Du, Ke-Jie; You, Yong; Gao, Shu-Qin; Wen, Ge-Bo; Lin, Ying-Wu; Tan, Xiangshi

2016-10-20

A hydrogen-bond (H-bond) network, specifically a Tyr-associated H-bond network, plays key roles in regulating the structure and function of proteins, as exemplified by abundant heme proteins in nature. To explore an approach for fine-tuning the structure and function of artificial heme proteins, we herein used myoglobin (Mb) as a model protein and introduced a Tyr residue in the secondary sphere of the heme active site at two different positions (107 and 138). We performed X-ray crystallography, UV-Vis spectroscopy, stopped-flow kinetics, and electron paramagnetic resonance (EPR) studies for the two single mutants, I107Y Mb and F138Y Mb, and compared to that of wild-type Mb under the same conditions. The results showed that both Tyr107 and Tyr138 form a distinct H-bond network involving water molecules and neighboring residues, which fine-tunes ligand binding to the heme iron and enhances the protein stability, respectively. Moreover, the Tyr107-associated H-bond network was shown to fine-tune both H2O2 binding and activation. With two cases demonstrated for Mb, this study suggests that the Tyr-associated H-bond network has distinct roles in regulating the protein structure, properties and functions, depending on its location in the protein scaffold. Therefore, it is possible to design a Tyr-associated H-bond network in general to create other artificial heme proteins with improved properties and functions.

MOCCS: Clarifying DNA-binding motif ambiguity using ChIP-Seq data.

PubMed

Ozaki, Haruka; Iwasaki, Wataru

2016-08-01

As a key mechanism of gene regulation, transcription factors (TFs) bind to DNA by recognizing specific short sequence patterns that are called DNA-binding motifs. A single TF can accept ambiguity within its DNA-binding motifs, which comprise both canonical (typical) and non-canonical motifs. Clarification of such DNA-binding motif ambiguity is crucial for revealing gene regulatory networks and evaluating mutations in cis-regulatory elements. Although chromatin immunoprecipitation sequencing (ChIP-seq) now provides abundant data on the genomic sequences to which a given TF binds, existing motif discovery methods are unable to directly answer whether a given TF can bind to a specific DNA-binding motif. Here, we report a method for clarifying the DNA-binding motif ambiguity, MOCCS. Given ChIP-Seq data of any TF, MOCCS comprehensively analyzes and describes every k-mer to which that TF binds. Analysis of simulated datasets revealed that MOCCS is applicable to various ChIP-Seq datasets, requiring only a few minutes per dataset. Application to the ENCODE ChIP-Seq datasets proved that MOCCS directly evaluates whether a given TF binds to each DNA-binding motif, even if known position weight matrix models do not provide sufficient information on DNA-binding motif ambiguity. Furthermore, users are not required to provide numerous parameters or background genomic sequence models that are typically unavailable. MOCCS is implemented in Perl and R and is freely available via https://github.com/yuifu/moccs. By complementing existing motif-discovery software, MOCCS will contribute to the basic understanding of how the genome controls diverse cellular processes via DNA-protein interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Network of Conformational Transitions in the Apo Form of NDM-1 Enzyme Revealed by MD Simulation and a Markov State Model.

PubMed

Gao, Kaifu; Zhao, Yunjie

2017-04-13

New Delhi metallo-β-lactamase-1 (NDM-1) is a novel β-lactamase enzyme that confers enteric bacteria with nearly complete resistance to all β-lactam antibiotics, so it raises a formidable and global threat to human health. However, the binding mechanism between apo-NDM-1 and antibiotics as well as related conformational changes remains poorly understood, which largely hinders the overcoming of its antibiotic resistance. In our study, long-time conventional molecular dynamics simulation and Markov state models were applied to reveal both the dynamical and conformational landscape of apo-NDM-1: the MD simulation demonstrates that loop L3, which is responsible for antibiotic binding, is the most flexible and undergoes dramatic conformational changes; moreover, the Markov state model built from the simulation maps four metastable states including open, semiopen, and closed conformations of loop L3 as well as frequent transitions between the states. Our findings propose a possible conformational selection model for the binding mechanism between apo-NDM-1 and antibiotics, which facilitates the design of novel inhibitors and antibiotics.

Failure tolerance of spike phase synchronization in coupled neural networks

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2011-09-01

Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

Modeling global changes induced by local perturbations to the HIV-1 capsid.

PubMed

Bergman, Shana; Lezon, Timothy R

2017-01-01

The HIV-1 capsid is a conical protein shell made up of hexamers and pentamers of the capsid protein. The capsid houses the viral genome and replication machinery, and its opening, or uncoating, within the host cell marks a critical step in the HIV-1 lifecycle. Binding of host factors such as TRIM5α and cyclophilin A (CypA) can alter the capsid's stability, accelerating or delaying the onset of uncoating and disrupting infectivity. We employ coarse-grained computational modeling to investigate the effects of point mutations and host factor binding on HIV-1 capsid stability. We find that the largest fluctuations occur in the low-curvature regions of the capsid, and that its structural dynamics are affected by perturbations at the inter-hexamer interfaces and near the CypA binding loop, suggesting roles for these features in capsid stability. Our models show that linking capsid proteins across hexamers attenuates vibration in the low-curvature regions of the capsid, but that linking within hexamers does not. These results indicate a possible mechanism through which CypA binding alters capsid stability and highlight the utility of coarse-grained network modeling for understanding capsid mechanics. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A flexible docking scheme to explore the binding selectivity of PDZ domains.

PubMed

Gerek, Z Nevin; Ozkan, S Banu

2010-05-01

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTALIGAND, we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density-95/Dlg/ZO-1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 A. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.

A flexible docking scheme to explore the binding selectivity of PDZ domains

PubMed Central

Gerek, Z Nevin; Ozkan, S Banu

2010-01-01

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using RosettaLigand, we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density-95/Dlg/ZO-1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately. PMID:20196074

Ties That Bind International Research Teams: A Network Multilevel Model of Interdisciplinary Collaboration

ERIC Educational Resources Information Center

Kollasch, Aurelia Wiktoria

2012-01-01

Today large research projects require substantial involvement of researchers from different organizations, disciplines, or cultures working in groups or teams to accomplish a common goal of producing, sharing, and disseminating scientific knowledge. This study focuses on the international research team that was launched in response to pressing…

A resource for characterizing genome-wide binding and putative target genes of transcription factors expressed during secondary growth and wood formation in Populus.

PubMed

Liu, Lijun; Ramsay, Trevor; Zinkgraf, Matthew; Sundell, David; Street, Nathaniel Robert; Filkov, Vladimir; Groover, Andrew

2015-06-01

Identifying transcription factor target genes is essential for modeling the transcriptional networks underlying developmental processes. Here we report a chromatin immunoprecipitation sequencing (ChIP-seq) resource consisting of genome-wide binding regions and associated putative target genes for four Populus homeodomain transcription factors expressed during secondary growth and wood formation. Software code (programs and scripts) for processing the Populus ChIP-seq data are provided within a publically available iPlant image, including tools for ChIP-seq data quality control and evaluation adapted from the human Encyclopedia of DNA Elements (ENCODE) project. Basic information for each transcription factor (including members of Class I KNOX, Class III HD ZIP, BEL1-like families) binding are summarized, including the number and location of binding regions, distribution of binding regions relative to gene features, associated putative target genes, and enriched functional categories of putative target genes. These ChIP-seq data have been integrated within the Populus Genome Integrative Explorer (PopGenIE) where they can be analyzed using a variety of web-based tools. We present an example analysis that shows preferential binding of transcription factor ARBORKNOX1 to the nearest neighbor genes in a pre-calculated co-expression network module, and enrichment for meristem-related genes within this module including multiple orthologs of Arabidopsis KNOTTED-like Arabidopsis 2/6. © 2015 Society for Experimental Biology and John Wiley & Sons Ltd This article has been contributed to by US Government employees and their work is in the public domain in the USA.

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.

PubMed

Nielsen, Morten; Andreatta, Massimo

2016-03-30

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .

Mechanically tunable actin networks using programmable DNA based cross-linkers

NASA Astrophysics Data System (ADS)

Schnauss, Joerg; Lorenz, Jessica; Schuldt, Carsten; Kaes, Josef; Smith, David

Cells employ multiple cross-linkers with very different properties. Studies of the entire phase space, however, were infeasible since they were restricted to naturally occurring cross-linkers. These components cannot be controllably varied and differ in many parameters. We resolve this limitation by forming artificial actin cross-linkers, which can be controllably varied. The basic building block is DNA enabling a well-defined length variation. DNA can be attached to actin binding peptides with known binding affinities. We used bulk rheology to investigate mechanical properties of these networks. We were able to reproduce mechanical features of actin networks cross-linked by fascin by using a short version of our artificial complex with a high binding affinity. Additionally, we were able to resemble findings for the cross-linker alpha-actinin by employing a long cross-linker with a low binding affinity. Between these natural limits we investigated three different cross-linker lengths each with two different binding affinities. With these controlled variations we are able to precisely screen the phase space of cross-linked actin networks by changing only one specific parameter and not the entire set of properties as in the case of naturally occurring cross-linking complexes.

Rationalizing Tight Ligand Binding through Cooperative Interaction Networks

PubMed Central

2011-01-01

Small modifications of the molecular structure of a ligand sometimes cause strong gains in binding affinity to a protein target, rendering a weakly active chemical series suddenly attractive for further optimization. Our goal in this study is to better rationalize and predict the occurrence of such interaction hot-spots in receptor binding sites. To this end, we introduce two new concepts into the computational description of molecular recognition. First, we take a broader view of noncovalent interactions and describe protein–ligand binding with a comprehensive set of favorable and unfavorable contact types, including for example halogen bonding and orthogonal multipolar interactions. Second, we go beyond the commonly used pairwise additive treatment of atomic interactions and use a small world network approach to describe how interactions are modulated by their environment. This approach allows us to capture local cooperativity effects and considerably improves the performance of a newly derived empirical scoring function, ScorpionScore. More importantly, however, we demonstrate how an intuitive visualization of key intermolecular interactions, interaction networks, and binding hot-spots supports the identification and rationalization of tight ligand binding. PMID:22087588

Locating the binding sites of folic acid with milk α- and β-caseins.

PubMed

Bourassa, P; Tajmir-Riahi, H A

2012-01-12

We located the binding sites of folic acid with milk α- and β-caseins at physiological conditions, using constant protein concentration and various folic acid contents. FTIR, UV-visible, and fluorescence spectroscopic methods as well as molecular modeling were used to analyze folic acid binding sites, the binding constant, and the effect of folic acid interaction on the stability and conformation of caseins. Structural analysis showed that folic acid binds caseins via both hydrophilic and hydrophobic contacts with overall binding constants of K(folic acid-α-caseins) = 4.8 (±0.6) × 10(4) M(-1) and K(folic acid-β-caseins) = 7.0 (±0.9) × 10(4) M(-1). The number of bound acid molecules per protein was 1.5 (±0.4) for α-casein and 1.4 (±0.3) for β-casein complexes. Molecular modeling showed different binding sites for folic acid on α- and β-caseins. The participation of several amino acids in folic acid-protein complexes was observed, which was stabilized by hydrogen bonding network and the free binding energy of -7.7 kcal/mol (acid-α-casein) and -8.1 kcal/mol (acid-β-casein). Folic acid complexation altered protein secondary structure by the reduction of α-helix from 35% (free α-casein) to 33% (acid-complex) and 32% (free β-casein) to 26% (acid-complex) indicating a partial protein destabilization. Caseins might act as carriers for transportation of folic acid to target molecules.

Understanding how social networking influences perceived satisfaction with conference experiences

USGS Publications Warehouse

van Riper, Carena J.; van Riper, Charles; Kyle, Gerard T.; Lee, Martha E.

2013-01-01

Social networking is a key benefit derived from participation in conferences that bind the ties of a professional community. Building social networks can lead to satisfactory experiences while furthering participants' long- and short-term career goals. Although investigations of social networking can lend insight into how to effectively engage individuals and groups within a professional cohort, this area has been largely overlooked in past research. The present study investigates the relationship between social networking and satisfaction with the 10th Biennial Conference of Research on the Colorado Plateau using structural equation modelling. Results partially support the hypothesis that three dimensions of social networking – interpersonal connections, social cohesion, and secondary associations – positively contribute to the performance of various conference attributes identified in two focus group sessions. The theoretical and applied contributions of this paper shed light on the social systems formed within professional communities and resource allocation among service providers.

Structural basis of carbohydrate recognition by lectin II from Ulex europaeus, a protein with a promiscuous carbohydrate-binding site.

PubMed

Loris, R; De Greve, H; Dao-Thi, M H; Messens, J; Imberty, A; Wyns, L

2000-08-25

Protein-carbohydrate interactions are the language of choice for inter- cellular communication. The legume lectins form a large family of homologous proteins that exhibit a wide variety of carbohydrate specificities. The legume lectin family is therefore highly suitable as a model system to study the structural principles of protein-carbohydrate recognition. Until now, structural data are only available for two specificity families: Man/Glc and Gal/GalNAc. No structural data are available for any of the fucose or chitobiose specific lectins. The crystal structure of Ulex europaeus (UEA-II) is the first of a legume lectin belonging to the chitobiose specificity group. The complexes with N-acetylglucosamine, galactose and fucosylgalactose show a promiscuous primary binding site capable of accommodating both N-acetylglucos amine or galactose in the primary binding site. The hydrogen bonding network in these complexes can be considered suboptimal, in agreement with the low affinities of these sugars. In the complexes with chitobiose, lactose and fucosyllactose this suboptimal hydrogen bonding network is compensated by extensive hydrophobic interactions in a Glc/GlcNAc binding subsite. UEA-II thus forms the first example of a legume lectin with a promiscuous binding site and illustrates the importance of hydrophobic interactions in protein-carbohydrate complexes. Together with other known legume lectin crystal structures, it shows how different specificities can be grafted upon a conserved structural framework. Copyright 2000 Academic Press.

Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

PubMed

Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

2016-10-24

Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

Global competition and local cooperation in a network of neural oscillators

NASA Astrophysics Data System (ADS)

Terman, David; Wang, DeLiang

An architecture of locally excitatory, globally inhibitory oscillator networks is proposed and investigated both analytically and by computer simulation. The model for each oscillator corresponds to a standard relaxation oscillator with two time scales. Oscillators are locally coupled by a scheme that resembles excitatory synaptic coupling, and each oscillator also inhibits other oscillators through a common inhibitor. Oscillators are driven to be oscillatory by external stimulation. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing the other oscillators from jumping up. We show analytically that with the selective gating mechanism, the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate the model's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ ground segregation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, Jerome M.; Kang, Kyungtae; Sastry, Madhavi

In this study we use mutants of human carbonic anhydrase (HCAII) to examine how changes in the organization of water within a binding pocket can alter the thermodynamics of protein–ligand association. Results from calorimetric, crystallographic, and theoretical analyses suggest that most mutations strengthen networks of water-mediated hydrogen bonds and reduce binding affinity by increasing the enthalpic cost and, to a lesser extent, the entropic benefit of rearranging those networks during binding. The organization of water within a binding pocket can thus determine whether the hydrophobic interactions in which it engages are enthalpy-driven or entropy-driven. Our findings highlight a possible asymmetrymore » in protein–ligand association by suggesting that, within the confines of the binding pocket of HCAII, binding events associated with enthalpically favorable rearrangements of water are stronger than those associated with entropically favorable ones.« less

Early stages of clathrin aggregation at a membrane in coarse-grained simulations

NASA Astrophysics Data System (ADS)

Giani, M.; den Otter, W. K.; Briels, W. J.

2017-04-01

The self-assembly process of clathrin coated pits during endocytosis has been simulated by combining and extending coarse grained models of the clathrin triskelion, the adaptor protein AP2, and a flexible network membrane. The AP2's core, upon binding to membrane and cargo, releases a motif that can bind clathrin. In conditions where the core-membrane-cargo binding is weak, the binding of this motif to clathrin can result in a stable complex. We characterize the conditions and mechanisms resulting in the formation of clathrin lattices that curve the membrane, i.e., clathrin coated pits. The mechanical properties of the AP2 β linker appear crucial to the orientation of the curved clathrin lattice relative to the membrane, with wild-type short linkers giving rise to the inward curving buds enabling endocytosis while long linkers produce upside-down cages and outward curving bulges.

Understanding large multiprotein complexes: applying a multiple allosteric networks model to explain the function of the Mediator transcription complex.

PubMed

Lewis, Brian A

2010-01-15

The regulation of transcription and of many other cellular processes involves large multi-subunit protein complexes. In the context of transcription, it is known that these complexes serve as regulatory platforms that connect activator DNA-binding proteins to a target promoter. However, there is still a lack of understanding regarding the function of these complexes. Why do multi-subunit complexes exist? What is the molecular basis of the function of their constituent subunits, and how are these subunits organized within a complex? What is the reason for physical connections between certain subunits and not others? In this article, I address these issues through a model of network allostery and its application to the eukaryotic RNA polymerase II Mediator transcription complex. The multiple allosteric networks model (MANM) suggests that protein complexes such as Mediator exist not only as physical but also as functional networks of interconnected proteins through which information is transferred from subunit to subunit by the propagation of an allosteric state known as conformational spread. Additionally, there are multiple distinct sub-networks within the Mediator complex that can be defined by their connections to different subunits; these sub-networks have discrete functions that are activated when specific subunits interact with other activator proteins.

Insights into the binding of GABA to the insect RDL receptor from atomistic simulations: a comparison of models

NASA Astrophysics Data System (ADS)

Comitani, Federico; Cohen, Netta; Ashby, Jamie; Botten, Dominic; Lummis, Sarah C. R.; Molteni, Carla

2014-01-01

The resistance to dieldrin (RDL) receptor is an insect pentameric ligand-gated ion channel (pLGIC). It is activated by the neurotransmitter γ-aminobutyric acid (GABA) binding to its extracellular domain; hence elucidating the atomistic details of this interaction is important for understanding how the RDL receptor functions. As no high resolution structures are currently available, we built homology models of the extracellular domain of the RDL receptor using different templates, including the widely used acetylcholine binding protein and two pLGICs, the Erwinia Chrysanthemi ligand-gated ion channel (ELIC) and the more recently resolved GluCl. We then docked GABA into the selected three dimensional structures, which we used as starting points for classical molecular dynamics simulations. This allowed us to analyze in detail the behavior of GABA in the binding sites, including the hydrogen bond and cation-π interaction networks it formed, the conformers it visited and the possible role of water molecules in mediating the interactions; we also estimated the binding free energies. The models were all stable and showed common features, including interactions consistent with experimental data and similar to other pLGICs; differences could be attributed to the quality of the models, which increases with increasing sequence identity, and the use of a pLGIC template. We supplemented the molecular dynamics information with metadynamics, a rare event method, by exploring the free energy landscape of GABA binding to the RDL receptor. Overall, we show that the GluCl template provided the best models. GABA forming direct salt-bridges with Arg211 and Glu204, and cation-π interactions with an aromatic cage including Tyr109, Phe206 and Tyr254, represents a favorable binding arrangement, and the interaction with Glu204 can also be mediated by a water molecule.

Water-Restructuring Mutations Can Reverse the Thermodynamic Signature of Ligand Binding to Human Carbonic Anhydrase

DOE PAGES

Fox, Jerome M.; Kang, Kyungtae; Sastry, Madhavi; ...

2017-03-02

In this study we use mutants of human carbonic anhydrase (HCAII) to examine how changes in the organization of water within a binding pocket can alter the thermodynamics of protein–ligand association. Results from calorimetric, crystallographic, and theoretical analyses suggest that most mutations strengthen networks of water-mediated hydrogen bonds and reduce binding affinity by increasing the enthalpic cost and, to a lesser extent, the entropic benefit of rearranging those networks during binding. The organization of water within a binding pocket can thus determine whether the hydrophobic interactions in which it engages are enthalpy-driven or entropy-driven. Our findings highlight a possible asymmetrymore » in protein–ligand association by suggesting that, within the confines of the binding pocket of HCAII, binding events associated with enthalpically favorable rearrangements of water are stronger than those associated with entropically favorable ones.« less

Controlling gene networks and cell fate with precision-targeted DNA-binding proteins and small-molecule-based genome readers

PubMed Central

Eguchi, Asuka; Lee, Garrett O.; Wan, Fang; Erwin, Graham S.; Ansari, Aseem Z.

2014-01-01

Transcription factors control the fate of a cell by regulating the expression of genes and regulatory networks. Recent successes in inducing pluripotency in terminally differentiated cells as well as directing differentiation with natural transcription factors has lent credence to the efforts that aim to direct cell fate with rationally designed transcription factors. Because DNA-binding factors are modular in design, they can be engineered to target specific genomic sequences and perform pre-programmed regulatory functions upon binding. Such precision-tailored factors can serve as molecular tools to reprogramme or differentiate cells in a targeted manner. Using different types of engineered DNA binders, both regulatory transcriptional controls of gene networks, as well as permanent alteration of genomic content, can be implemented to study cell fate decisions. In the present review, we describe the current state of the art in artificial transcription factor design and the exciting prospect of employing artificial DNA-binding factors to manipulate the transcriptional networks as well as epigenetic landscapes that govern cell fate. PMID:25145439

Modeling complex tone perception: grouping harmonics with combination-sensitive neurons.

PubMed

Medvedev, Andrei V; Chiao, Faye; Kanwal, Jagmeet S

2002-06-01

Perception of complex communication sounds is a major function of the auditory system. To create a coherent precept of these sounds the auditory system may instantaneously group or bind multiple harmonics within complex sounds. This perception strategy simplifies further processing of complex sounds and facilitates their meaningful integration with other sensory inputs. Based on experimental data and a realistic model, we propose that associative learning of combinations of harmonic frequencies and nonlinear facilitation of responses to those combinations, also referred to as "combination-sensitivity," are important for spectral grouping. For our model, we simulated combination sensitivity using Hebbian and associative types of synaptic plasticity in auditory neurons. We also provided a parallel tonotopic input that converges and diverges within the network. Neurons in higher-order layers of the network exhibited an emergent property of multifrequency tuning that is consistent with experimental findings. Furthermore, this network had the capacity to "recognize" the pitch or fundamental frequency of a harmonic tone complex even when the fundamental frequency itself was missing.

Linking disease-associated genes to regulatory networks via promoter organization

PubMed Central

Döhr, S.; Klingenhoff, A.; Maier, H.; de Angelis, M. Hrabé; Werner, T.; Schneider, R.

2005-01-01

Pathway- or disease-associated genes may participate in more than one transcriptional co-regulation network. Such gene groups can be readily obtained by literature analysis or by high-throughput techniques such as microarrays or protein-interaction mapping. We developed a strategy that defines regulatory networks by in silico promoter analysis, finding potentially co-regulated subgroups without a priori knowledge. Pairs of transcription factor binding sites conserved in orthologous genes (vertically) as well as in promoter sequences of co-regulated genes (horizontally) were used as seeds for the development of promoter models representing potential co-regulation. This approach was applied to a Maturity Onset Diabetes of the Young (MODY)-associated gene list, which yielded two models connecting functionally interacting genes within MODY-related insulin/glucose signaling pathways. Additional genes functionally connected to our initial gene list were identified by database searches with these promoter models. Thus, data-driven in silico promoter analysis allowed integrating molecular mechanisms with biological functions of the cell. PMID:15701758

A novel integrated framework and improved methodology of computer-aided drug design.

PubMed

Chen, Calvin Yu-Chian

2013-01-01

Computer-aided drug design (CADD) is a critical initiating step of drug development, but a single model capable of covering all designing aspects remains to be elucidated. Hence, we developed a drug design modeling framework that integrates multiple approaches, including machine learning based quantitative structure-activity relationship (QSAR) analysis, 3D-QSAR, Bayesian network, pharmacophore modeling, and structure-based docking algorithm. Restrictions for each model were defined for improved individual and overall accuracy. An integration method was applied to join the results from each model to minimize bias and errors. In addition, the integrated model adopts both static and dynamic analysis to validate the intermolecular stabilities of the receptor-ligand conformation. The proposed protocol was applied to identifying HER2 inhibitors from traditional Chinese medicine (TCM) as an example for validating our new protocol. Eight potent leads were identified from six TCM sources. A joint validation system comprised of comparative molecular field analysis, comparative molecular similarity indices analysis, and molecular dynamics simulation further characterized the candidates into three potential binding conformations and validated the binding stability of each protein-ligand complex. The ligand pathway was also performed to predict the ligand "in" and "exit" from the binding site. In summary, we propose a novel systematic CADD methodology for the identification, analysis, and characterization of drug-like candidates.

Resolving protein structure-function-binding site relationships from a binding site similarity network perspective.

PubMed

Mudgal, Richa; Srinivasan, Narayanaswamy; Chandra, Nagasuma

2017-07-01

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non-homologous protein families, leading to mis-annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold-function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold-function-binding site relationships has been systematically generated. A network-based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one-to-one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly-pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319-1335. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

PubMed Central

Tse, Amanda; Verkhivker, Gennady M.

2016-01-01

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment. PMID:27861609

Nanoparticle transport and delivery in a heterogeneous pulmonary vasculature.

PubMed

Sohrabi, Salman; Wang, Shunqiang; Tan, Jifu; Xu, Jiang; Yang, Jie; Liu, Yaling

2017-01-04

Quantitative understanding of nanoparticles delivery in a complex vascular networks is very challenging because it involves interplay of transport, hydrodynamic force, and multivalent interactions across different scales. Heterogeneous pulmonary network includes up to 16 generations of vessels in its arterial tree. Modeling the complete pulmonary vascular system in 3D is computationally unrealistic. To save computational cost, a model reconstructed from MRI scanned images is cut into an arbitrary pathway consisting of the upper 4-generations. The remaining generations are represented by an artificially rebuilt pathway. Physiological data such as branch information and connectivity matrix are used for geometry reconstruction. A lumped model is used to model the flow resistance of the branches that are cut off from the truncated pathway. Moreover, since the nanoparticle binding process is stochastic in nature, a binding probability function is used to simplify the carrier attachment and detachment processes. The stitched realistic and artificial geometries coupled with the lumped model at the unresolved outlets are used to resolve the flow field within the truncated arterial tree. Then, the biodistribution of 200nm, 700nm and 2µm particles at different vessel generations is studied. At the end, 0.2-0.5% nanocarrier deposition is predicted during one time passage of drug carriers through pulmonary vascular tree. Our truncated approach enabled us to efficiently model hemodynamics and accordingly particle distribution in a complex 3D vasculature providing a simple, yet efficient predictive tool to study drug delivery at organ level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stiffening of flexible SUMO1 protein upon peptide-binding: Analysis with anisotropic network model.

PubMed

Sarkar, Ranja

2018-01-01

SUMO (small ubiquitin-like modifier) proteins interact with a large number of target proteins via a key regulatory event called sumoylation that encompasses activation, conjugation and ligation of SUMO proteins through specific E1, E2, and E3-type enzymes respectively. Single-molecule atomic force microscopic (AFM) experiments performed to unravel bound SUMO1 along its NC termini direction reveal that E3-ligases (in the form of small peptides) increase mechanical stability (along the axis) of the flexible protein upon binding. The experimental results are expected to correlate with the intrinsic flexibility of bound SUMO1 protein in the native state i.e., the bound conformation of SUMO1 without the binding peptide. The native protein flexibility/stiffness can be measured as a spring constant by normal mode analysis. In the present study, protein normal modes are computed from the protein structural data (as input from protein databank) via a simple anisotropic network model (ANM). ANM is computationally inexpensive and hence, can be explored to investigate and compare the native conformational dynamics of unbound and bound (without the binding partner) structures, if the corresponding structural data (NMR/X-ray) are available. The paper illustrates that SUMO1 stiffens (native flexibility decreases) along the NC termini (end-to-end) direction of the protein upon binding to small peptides; however, the degree of stiffening is peptide sequence-specific. The theoretical results are demonstrated for NMR structures of unbound SUMO1 and that bound to two peptides having short amino acid motifs and of similar size, one being an M-IR2 peptide derived from RanBP2 protein and the other one derived from PIASX protein. The peptide derived from PIASX stiffens SUMO1 remarkably which is evident from an atomic-level normal mode analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

Concentration profiles of actin-binding molecules in lamellipodia

NASA Astrophysics Data System (ADS)

Falcke, Martin

2016-04-01

Motile cells form lamellipodia in the direction of motion, which are flat membrane protrusions containing an actin filament network. The network flows rearward relative to the leading edge of the lamellipodium due to actin polymerization at the front. Thus, actin binding molecules are subject to transport towards the rear of the cell in the bound state and diffuse freely in the unbound state. We analyze this reaction-diffusion-advection process with respect to the concentration profiles of these species and provide an analytic approximation for them. Network flow may cause a depletion zone of actin binding molecules close to the leading edge. The existence of such zone depends on the free molecule concentration in the cell body, on the ratio of the diffusion length to the distance bound molecules travel rearward with the flow before dissociating, and the ratio of the diffusion length to the width of the region with network flow and actin binding. Our calculations suggest the existence of depletion zones for the F-actin cross-linkers filamin and α-actinin in fish keratocytes (and other cell types), which is in line with the small elastic moduli of the F-actin network close to the leading edge found in measurements of the force motile cells are able to exert.

Magnetic resonance diffusion and relaxation characterization of water in the unfrozen vein network in polycrystalline ice and its response to microbial metabolic products

NASA Astrophysics Data System (ADS)

Brown, Jennifer R.; Brox, Timothy I.; Vogt, Sarah J.; Seymour, Joseph D.; Skidmore, Mark L.; Codd, Sarah L.

2012-12-01

Polycrystalline ice, as found in glaciers and the ice sheets of Antarctica, is a low porosity porous media consisting of a complicated and dynamic pore structure of liquid-filled intercrystalline veins within a solid ice matrix. In this work, Nuclear Magnetic Resonance measurements of relaxation rates and molecular diffusion, useful for probing pore structure and transport dynamics in porous systems, were used to physically characterize the unfrozen vein network structure in ice and its response to the presence of metabolic products produced by V3519-10, a cold tolerant microorganism isolated from the Vostok ice core. Recent research has found microorganisms that can remain viable and even metabolically active within icy environments at sub-zero temperatures. One potential mechanism of survival for V3519-10 is secretion of an extracellular ice binding protein that binds to the prism face of ice crystals and inhibits ice recrystallization, a coarsening process resulting in crystal growth with ice aging. Understanding the impact of ice binding activity on the bulk vein network structure in ice is important to modeling of frozen geophysical systems and in development of ice interacting proteins for biotechnology applications, such as cryopreservation of cell lines, and manufacturing processes in food sciences. Here, we present the first observations of recrystallization inhibition in low porosity ice containing V3519-10 extracellular protein extract as measured with Nuclear Magnetic Resonance and Magnetic Resonance Imaging.

PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

NASA Astrophysics Data System (ADS)

Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

2011-06-01

Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue we have assembled a representative set of papers written by experts with diverse scientific backgrounds, each offering a unique viewpoint on using computational and physics methods to study biological systems at different levels of organization. We start with studies that aim to decipher the mechanisms of molecular recognition using biophysics methods and then expand our scale, concluding the issue with studies of interaction networks at cellular and population levels. Biomolecules interact with each other in a highly specific manner and selectively recognize their partners among hundreds of thousands of other molecules. As the paper by Zhang et al points out, this recognition process should be fast and guided by long-range electrostatic forces that select and bring the interacting partners together. The authors show that the increase of salt concentration leads to destabilization of protein complexes, suggesting an optimization of the charge-charge interactions across the protein binding interfaces. The following paper by Berezovsky further explores the balance of different interactions in protein complexes and uses physical concepts to explain the entire spectrum of protein structural classes, from intrinsically disordered to hyperthermostable proteins. The author describes highly unstructured viral proteins at one end of the spectrum and discusses the balance of stabilizing interactions in protein complexes from thermophilic organisms at the other. Recently accumulated evidence has indicated that native proteins do not necessarily require a unique structure to be biologically active, and in some cases structural disorder or intrinsic flexibility can be a prerequisite for their function. From the physical point of view, these disordered/flexible proteins exist in dynamic equilibrium between different conformational states, some of which could be selected upon binding to another partner. Such a property allows disordered proteins to achieve specific binding and at the same time reversibility and diversity in their interactions. Interestingly, as is shown in the paper by Mészáros et al, even though some disordered regions and proteins have a tendency to fold upon binding, the structures of their complexes still reveal their inherent flexibility. Indeed, disordered proteins and their complexes have certain properties which distinguish them from proteins with well-defined structures. This is evident from the papers by Lobanov and Galzitskaya, and Mészáros et al, which show that such characteristic features of disordered proteins allow their successful computational prediction from the sequence alone. Computational prediction of protein disorder has been used in another study by Takeda et al where the authors investigate the role of disorder in the function of a specific actin capping protein. The paper presents normal mode analysis with the elastic network model to examine the mechanisms of intrinsic flexibility and its biological role in actin function. Analysis of the underlying mechanisms and key factors in protein recognition might be essential for the prediction of protein-protein interactions. The papers by Tuncbag et al and Hashimoto et al demonstrate how incorporating the physico-chemical properties of binding interfaces and their atomic details obtained from protein crystal structures might be used to increase the accuracy of predicted protein-protein interactions and provide data on relative orientations of interacting proteins and on the locations of binding sites. Moreover, analysis of protein-protein interactions might require further fine-tuning for different types of assemblies, like that shown in the example of homooligomers by Hashimoto et al. Studies of protein-protein interactions at the molecular level have contributed considerably to understanding the principles of large-scale organization of the cellular interactome. Using graph theory as a unifying language, many characteristic properties of bimolecular networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor—DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys2His2 zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary nor sufficient for transcription factor activity. Transcription regulation is a complex and still not fully understood process involving, in addition to protein-DNA binding, other factors such as epigenetic modifications and three-dimensional DNA organization. In this issue, Levens and Benham discuss another important mechanism which is likely to contribute to overall gene regulation—changes of DNA secondary structure in response to supercoiling-induced stress. Pointing out that DNA is "more than a cipher", they argue that the DNA structural transitions driven by negative supercoiling may have profound consequences for the cell and have to be accounted for in detailed models. There is considerable progress in physical modeling of DNA dynamics in response to stress. Such efforts, supported by experimental data, will bring us closer to an understanding of the role of supercoiling in gene regulation. Large-scale biomolecular interaction networks not only provide a system-level view of cellular processes, but are also increasingly used to model communications between molecules. The lack of sufficient biochemical data and the gigantic scale of the network prevented detailed modeling of network dynamics and have stimulated the development of simplified models such as the information flow approach described by Kim et al in this issue. Importantly, despite their simplicity, such models proved to be extremely useful for identifying network modules, essential nodes, and molecular pathways which are dysregulated in complex diseases such as cancer. Finally, moving from studies of single cells towards populations, one has to recognize the heterogeneity present within a population of cells. In the context of protein abundance, such cell-to-cell variation within clonal populations of cells, referred to as expression noise, has recently become a focus of intense cross-disciplinary research. Concerted efforts of experimentalists, physicists and mathematicians have brought us closer to understanding the source, potential drawbacks and benefits of noise for cell function. Differences in protein expression levels are even more pronounced in samples from mixed cell populations. How does such a mixture of cell populations affect the measurements of total gene expression? This question is addressed by Hebenstreit and Teichmann who show that decomposing a signal coming from a mixture of cellular populations requires insights from theoretical modeling. Recent technological advancements permitting genome-wide scale measurements of diverse molecular properties and consequently higher levels of quantitative reasoning are attracting physicists, mathematicians and computer scientists to the study of biological systems. Building on the synergy between these fields, we are entering an exciting era where physics methods are used in conjunction with these disciplines which, combined with statistical methods, provide quantitative descriptions of biology. Acknowledgments This project was funded with federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. This research was supported by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research and the National Library of Medicine at National Institutes of Health/DHHS. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Updated regulation curation model at the Saccharomyces Genome Database

PubMed Central

Engel, Stacia R; Skrzypek, Marek S; Hellerstedt, Sage T; Wong, Edith D; Nash, Robert S; Weng, Shuai; Binkley, Gail; Sheppard, Travis K; Karra, Kalpana; Cherry, J Michael

2018-01-01

Abstract The Saccharomyces Genome Database (SGD) provides comprehensive, integrated biological information for the budding yeast Saccharomyces cerevisiae, along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms. We have recently expanded our data model for regulation curation to address regulation at the protein level in addition to transcription, and are presenting the expanded data on the ‘Regulation’ pages at SGD. These pages include a summary describing the context under which the regulator acts, manually curated and high-throughput annotations showing the regulatory relationships for that gene and a graphical visualization of its regulatory network and connected networks. For genes whose products regulate other genes or proteins, the Regulation page includes Gene Ontology enrichment analysis of the biological processes in which those targets participate. For DNA-binding transcription factors, we also provide other information relevant to their regulatory function, such as DNA binding site motifs and protein domains. As with other data types at SGD, all regulatory relationships and accompanying data are available through YeastMine, SGD’s data warehouse based on InterMine. Database URL: http://www.yeastgenome.org PMID:29688362

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

PubMed

Strakova, Eva; Zikova, Alice; Vohradsky, Jiri

2014-01-01

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

Expression of human PQBP-1 in Drosophila impairs long-term memory and induces abnormal courtship.

PubMed

Yoshimura, Natsue; Horiuchi, Daisuke; Shibata, Masao; Saitoe, Minoru; Qi, Mei-Ling; Okazawa, Hitoshi

2006-04-17

Frame shift mutations of the polyglutamine binding protein-1 (PQBP1) gene lead to total or partial truncation of the C-terminal domain (CTD) and cause mental retardation in human patients. Interestingly, normal Drosophila homologue of PQBP-1 lacks CTD. As a model to analyze the molecular network of PQBP-1 affecting intelligence, we generated transgenic flies expressing human PQBP-1 with CTD. Pavlovian olfactory conditioning revealed that the transgenic flies showed disturbance of long-term memory. In addition, they showed abnormal courtship that male flies follow male flies. Abnormal functions of PQBP-1 or its binding partner might be linked to these symptoms.

An integrated workflow for analysis of ChIP-chip data.

PubMed

Weigelt, Karin; Moehle, Christoph; Stempfl, Thomas; Weber, Bernhard; Langmann, Thomas

2008-08-01

Although ChIP-chip is a powerful tool for genome-wide discovery of transcription factor target genes, the steps involving raw data analysis, identification of promoters, and correlation with binding sites are still laborious processes. Therefore, we report an integrated workflow for the analysis of promoter tiling arrays with the Genomatix ChipInspector system. We compare this tool with open-source software packages to identify PU.1 regulated genes in mouse macrophages. Our results suggest that ChipInspector data analysis, comparative genomics for binding site prediction, and pathway/network modeling significantly facilitate and enhance whole-genome promoter profiling to reveal in vivo sites of transcription factor-DNA interactions.

Shewregdb: Database and visualization environment for experimental and predicted regulatory information in Shewanella oneidensis mr-1

PubMed Central

Syed, Mustafa H; Karpinets, Tatiana V; Leuze, Michael R; Kora, Guruprasad H; Romine, Margaret R; Uberbacher, Edward C

2009-01-01

Shewanella oneidensis MR-1 is an important model organism for environmental research as it has an exceptional metabolic and respiratory versatility regulated by a complex regulatory network. We have developed a database to collect experimental and computational data relating to regulation of gene and protein expression, and, a visualization environment that enables integration of these data types. The regulatory information in the database includes predictions of DNA regulator binding sites, sigma factor binding sites, transcription units, operons, promoters, and RNA regulators including non-coding RNAs, riboswitches, and different types of terminators. Availability http://shewanella-knowledgebase.org:8080/Shewanella/gbrowserLanding.jsp PMID:20198195

Structural principles within the human-virus protein-protein interaction network

PubMed Central

Franzosa, Eric A.; Xia, Yu

2011-01-01

General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

1-3-A Resolution Structure of Human Glutathione S-Transferase With S-Hexyl Glutathione Bound Reveals Possible Extended Ligandin Binding Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trong, I.Le; Stenkamp, R.E.; Ibarra, C.

2005-08-22

Cytosolic glutathione S-transferases (GSTs) play a critical role in xenobiotic binding and metabolism, as well as in modulation of oxidative stress. Here, the high-resolution X-ray crystal structures of homodimeric human GSTA1-1 in the apo form and in complex with S-hexyl glutathione (two data sets) are reported at 1.8, 1.5, and 1.3A respectively. At this level of resolution, distinct conformations of the alkyl chain of S-hexyl glutathione are observed, reflecting the nonspecific nature of the hydrophobic substrate binding site (H-site). Also, an extensive network of ordered water, including 75 discrete solvent molecules, traverses the open subunit-subunit interface and connects the glutathionemore » binding sites in each subunit. In the highest-resolution structure, three glycerol moieties lie within this network and directly connect the amino termini of the glutathione molecules. A search for ligand binding sites with the docking program Molecular Operating Environment identified the ordered water network binding site, lined mainly with hydrophobic residues, suggesting an extended ligand binding surface for nonsubstrate ligands, the so-called ligandin site. Finally, detailed comparison of the structures reported here with previously published X-ray structures reveal a possible reaction coordinate for ligand-dependent conformational changes in the active site and the C-terminus.« less

Allosteric mechanism of quinoline inhibitors for HIV RT-associated RNase with MD simulation and dynamics fluctuation network.

PubMed

Cai, Yi; Liu, Hao; Chen, Haifeng

2018-03-01

The human immunodeficiency virus (HIV) is a retrovirus which infects T lymphocyte of human body and causes immunodeficiency. Reverse transcriptase inhibitors (RTIs) can inhibit some functions of RT, preventing virus synthesis (double-stranded DNA), so that HIV virus replication can be reduced. Experimental results indicate a series of benzimidazole-based inhibitors which target HIV RT-associated RNase to inhibit the reverse transcription of HIV virus. However, the allosteric mechanism is still unclear. Here, molecular dynamics simulations and dynamics fluctuation network analysis were used to reveal the binding mode between the inhibitors and RT-associated RNase. The most active molecule has more hydrophobic and electrostatic interactions than the less active inhibitor. Dynamics correlation network analysis indicates that the most active inhibitor perturbs the network of RT-associated RNase and decreases the correlation of nodes. 3D-QSAR model suggests that two robust and reliable models were constructed and validated by independent test set. 3D-QSAR model also shows that bulky negatively charged or hydrophilic substituent is favorable to bioactivity. These results reveal the allosteric mechanism of quinoline inhibitors and help to improve the bioactivity. © 2017 John Wiley & Sons A/S.

Networks of gold nanoparticles and bacteriophage as biological sensors and cell-targeting agents

PubMed Central

Souza, Glauco R.; Christianson, Dawn R.; Staquicini, Fernanda I.; Ozawa, Michael G.; Snyder, Evan Y.; Sidman, Richard L.; Miller, J. Houston; Arap, Wadih; Pasqualini, Renata

2006-01-01

Biological molecular assemblies are excellent models for the development of nanoengineered systems with desirable biomedical properties. Here we report an approach for fabrication of spontaneous, biologically active molecular networks consisting of bacteriophage (phage) directly assembled with gold (Au) nanoparticles (termed Au–phage). We show that when the phage are engineered so that each phage particle displays a peptide, such networks preserve the cell surface receptor binding and internalization attributes of the displayed peptide. The spontaneous organization of these targeted networks can be manipulated further by incorporation of imidazole (Au–phage–imid), which induces changes in fractal structure and near-infrared optical properties. The networks can be used as labels for enhanced fluorescence and dark-field microscopy, surface-enhanced Raman scattering detection, and near-infrared photon-to-heat conversion. Together, the physical and biological features within these targeted networks offer convenient multifunctional integration within a single entity with potential for nanotechnology-based biomedical applications. PMID:16434473

Characterizing protein domain associations by Small-molecule ligand binding

PubMed Central

Li, Qingliang; Cheng, Tiejun; Wang, Yanli; Bryant, Stephen H.

2012-01-01

Background Protein domains are evolutionarily conserved building blocks for protein structure and function, which are conventionally identified based on protein sequence or structure similarity. Small molecule binding domains are of great importance for the recognition of small molecules in biological systems and drug development. Many small molecules, including drugs, have been increasingly identified to bind to multiple targets, leading to promiscuous interactions with protein domains. Thus, a large scale characterization of the protein domains and their associations with respect to small-molecule binding is of particular interest to system biology research, drug target identification, as well as drug repurposing. Methods We compiled a collection of 13,822 physical interactions of small molecules and protein domains derived from the Protein Data Bank (PDB) structures. Based on the chemical similarity of these small molecules, we characterized pairwise associations of the protein domains and further investigated their global associations from a network point of view. Results We found that protein domains, despite lack of similarity in sequence and structure, were comprehensively associated through binding the same or similar small-molecule ligands. Moreover, we identified modules in the domain network that consisted of closely related protein domains by sharing similar biochemical mechanisms, being involved in relevant biological pathways, or being regulated by the same cognate cofactors. Conclusions A novel protein domain relationship was identified in the context of small-molecule binding, which is complementary to those identified by traditional sequence-based or structure-based approaches. The protein domain network constructed in the present study provides a novel perspective for chemogenomic study and network pharmacology, as well as target identification for drug repurposing. PMID:23745168

Unraveling the B. pseudomallei Heptokinase WcbL: From Structure to Drug Discovery

PubMed Central

Vivoli, Mirella; Isupov, Michail N.; Nicholas, Rebecca; Hill, Andrew; Scott, Andrew E.; Kosma, Paul; Prior, Joann L.; Harmer, Nicholas J.

2015-01-01

Summary Gram-negative bacteria utilize heptoses as part of their repertoire of extracellular polysaccharide virulence determinants. Disruption of heptose biosynthesis offers an attractive target for novel antimicrobials. A critical step in the synthesis of heptoses is their 1-O phosphorylation, mediated by kinases such as HldE or WcbL. Here, we present the structure of WcbL from Burkholderia pseudomallei. We report that WcbL operates through a sequential ordered Bi-Bi mechanism, loading the heptose first and then ATP. We show that dimeric WcbL binds ATP anti-cooperatively in the absence of heptose, and cooperatively in its presence. Modeling of WcbL suggests that heptose binding causes an elegant switch in the hydrogen-bonding network, facilitating the binding of a second ATP molecule. Finally, we screened a library of drug-like fragments, identifying hits that potently inhibit WcbL. Our results provide a novel mechanism for control of substrate binding and emphasize WcbL as an attractive anti-microbial target for Gram-negative bacteria. PMID:26687481

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

PubMed

Magnusson, Rasmus; Mariotti, Guido Pio; Köpsén, Mattias; Lövfors, William; Gawel, Danuta R; Jörnsten, Rebecka; Linde, Jörg; Nordling, Torbjörn E M; Nyman, Elin; Schulze, Sylvie; Nestor, Colm E; Zhang, Huan; Cedersund, Gunnar; Benson, Mikael; Tjärnberg, Andreas; Gustafsson, Mika

2017-06-01

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

DOE PAGES

Faria, Jose P.; Overbeek, Ross; Taylor, Ronald C.; ...

2016-03-18

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis. The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis. Finally, wemore » reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis. Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis, which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.« less

A Predictive Approach to Network Reverse-Engineering

NASA Astrophysics Data System (ADS)

Wiggins, Chris

2005-03-01

A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

Changes in flexibility upon binding: Application of the self-consistent pair contact probability method to protein-protein interactions

NASA Astrophysics Data System (ADS)

Canino, Lawrence S.; Shen, Tongye; McCammon, J. Andrew

2002-12-01

We extend the self-consistent pair contact probability method to the evaluation of the partition function for a protein complex at thermodynamic equilibrium. Specifically, we adapt the method for multichain models and introduce a parametrization for amino acid-specific pairwise interactions. This method is similar to the Gaussian network model but allows for the adjusting of the strengths of native state contacts. The method is first validated on a high resolution x-ray crystal structure of bovine Pancreatic Phospholipase A2 by comparing calculated B-factors with reported values. We then examine binding-induced changes in flexibility in protein-protein complexes, comparing computed results with those obtained from x-ray crystal structures and molecular dynamics simulations. In particular, we focus on the mouse acetylcholinesterase:fasciculin II and the human α-thrombin:thrombomodulin complexes.

Significance of the lipid phase in the dynamics and functions of the xanthophyll cycle as revealed by PsbS overexpression in tobacco and in-vitro de-epoxidation in monogalactosyldiacylglycerol micelles.

PubMed

Hieber, A David; Kawabata, Osamu; Yamamoto, Harry Y

2004-01-01

The dynamics of the xanthophyll cycle relative to non-photochemical quenching (NPQ) were examined in tobacco plants overexpressing violaxanthin de-epoxidase (VDE), PsbS and PsbS+VDE for effects on NPQ and violaxanthin (V) de-epoxidation over a range of light intensities. Induction of de-epoxidation and NPQ increased in overexpressed VDE and PsbS plants, respectively. Surprisingly, under low light, overexpressing PsbS enhanced de-epoxidation in addition to NPQ. The effect was hypothesized as due to PsbS binding zeaxanthin (Z) or inducing the binding of Z within the quenching complex, thus shifting the equilibrium toward higher de-epoxidation states. Studies in model systems show that Z can stereospecifically inhibit VDE activity against violaxanthin. This effect, observed under conditions of limiting lipid concentration, was interpreted as product feedback inhibition. These results support the hypothesis that the capacity of the thylakoid lipid phase for xanthophylls is limited and modulates xanthophyll-cycle activity, in conjunction with the release of V and binding of Z by pigment-binding proteins. These modulating factors are incorporated into a lipid-matrix model that has elements of a signal transduction system wherein the light-generated protons are the signal, VDE the signal receptor, Z the secondary messenger, the lipid phase the transduction network, and Z-binding proteins the targets.

Structural basis for spectrin recognition by ankyrin.

PubMed

Ipsaro, Jonathan J; Mondragón, Alfonso

2010-05-20

Maintenance of membrane integrity and organization in the metazoan cell is accomplished through intracellular tethering of membrane proteins to an extensive, flexible protein network. Spectrin, the principal component of this network, is anchored to membrane proteins through the adaptor protein ankyrin. To elucidate the atomic basis for this interaction, we determined a crystal structure of human betaI-spectrin repeats 13 to 15 in complex with the ZU5-ANK domain of human ankyrin R. The structure reveals the role of repeats 14 to 15 in binding, the electrostatic and hydrophobic contributions along the interface, and the necessity for a particular orientation of the spectrin repeats. Using structural and biochemical data as a guide, we characterized the individual proteins and their interactions by binding and thermal stability analyses. In addition to validating the structural model, these data provide insight into the nature of some mutations associated with cell morphology defects, including those found in human diseases such as hereditary spherocytosis and elliptocytosis. Finally, analysis of the ZU5 domain suggests it is a versatile protein-protein interaction module with distinct interaction surfaces. The structure represents not only the first of a spectrin fragment in complex with its binding partner, but also that of an intermolecular complex involving a ZU5 domain.

Programming biological models in Python using PySB.

PubMed

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Programming biological models in Python using PySB

PubMed Central

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis. PMID:23423320

Site-Specific Phosphorylation of VEGFR2 Is Mediated by Receptor Trafficking: Insights from a Computational Model

PubMed Central

Clegg, Lindsay Wendel; Mac Gabhann, Feilim

2015-01-01

Matrix-binding isoforms and non-matrix-binding isoforms of vascular endothelial growth factor (VEGF) are both capable of stimulating vascular remodeling, but the resulting blood vessel networks are structurally and functionally different. Here, we develop and validate a computational model of the binding of soluble and immobilized ligands to VEGF receptor 2 (VEGFR2), the endosomal trafficking of VEGFR2, and site-specific VEGFR2 tyrosine phosphorylation to study differences in induced signaling between these VEGF isoforms. In capturing essential features of VEGFR2 signaling and trafficking, our model suggests that VEGFR2 trafficking parameters are largely consistent across multiple endothelial cell lines. Simulations demonstrate distinct localization of VEGFR2 phosphorylated on Y1175 and Y1214. This is the first model to clearly show that differences in site-specific VEGFR2 activation when stimulated with immobilized VEGF compared to soluble VEGF can be accounted for by altered trafficking of VEGFR2 without an intrinsic difference in receptor activation. The model predicts that Neuropilin-1 can induce differences in the surface-to-internal distribution of VEGFR2. Simulations also show that ligated VEGFR2 and phosphorylated VEGFR2 levels diverge over time following stimulation. Using this model, we identify multiple key levers that alter how VEGF binding to VEGFR2 results in different coordinated patterns of multiple downstream signaling pathways. Specifically, simulations predict that VEGF immobilization, interactions with Neuropilin-1, perturbations of VEGFR2 trafficking, and changes in expression or activity of phosphatases acting on VEGFR2 all affect the magnitude, duration, and relative strength of VEGFR2 phosphorylation on tyrosines 1175 and 1214, and they do so predictably within our single consistent model framework. PMID:26067165

Two-Dimensional Wetting of a Stepped Copper Surface

NASA Astrophysics Data System (ADS)

Lin, C.; Avidor, N.; Corem, G.; Godsi, O.; Alexandrowicz, G.; Darling, G. R.; Hodgson, A.

2018-02-01

Highly corrugated, stepped surfaces present regular 1D arrays of binding sites, creating a complex, heterogeneous environment to water. Rather than decorating the hydrophilic step sites to form 1D chains, water on stepped Cu(511) forms an extended 2D network that binds strongly to the steps but bridges across the intervening hydrophobic Cu(100) terraces. The hydrogen-bonded network contains pentamer, hexamer, and octomer water rings that leave a third of the stable Cu step sites unoccupied in order to bind water H down close to the step dipole and complete three hydrogen bonds per molecule.

Signatures of Pleiotropy, Economy and Convergent Evolution in a Domain-Resolved Map of Human–Virus Protein–Protein Interaction Networks

PubMed Central

Garamszegi, Sara; Franzosa, Eric A.; Xia, Yu

2013-01-01

A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology. PMID:24339775

Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

PubMed

Garamszegi, Sara; Franzosa, Eric A; Xia, Yu

2013-01-01

A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology.

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

PubMed Central

Huang, Shao-shan Carol; Clarke, David C.; Gosline, Sara J. C.; Labadorf, Adam; Chouinard, Candace R.; Gordon, William; Lauffenburger, Douglas A.; Fraenkel, Ernest

2013-01-01

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets. PMID:23408876

Biophysical constraints on the computational capacity of biochemical signaling networks

NASA Astrophysics Data System (ADS)

Wang, Ching-Hao; Mehta, Pankaj

Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).

The transcriptional regulatory network mediated by banana (Musa acuminata) dehydration-responsive element binding (MaDREB) transcription factors in fruit ripening.

PubMed

Kuang, Jian-Fei; Chen, Jian-Ye; Liu, Xun-Cheng; Han, Yan-Chao; Xiao, Yun-Yi; Shan, Wei; Tang, Yang; Wu, Ke-Qiang; He, Jun-Xian; Lu, Wang-Jin

2017-04-01

Fruit ripening is a complex, genetically programmed process involving the action of critical transcription factors (TFs). Despite the established significance of dehydration-responsive element binding (DREB) TFs in plant abiotic stress responses, the involvement of DREBs in fruit ripening is yet to be determined. Here, we identified four genes encoding ripening-regulated DREB TFs in banana (Musa acuminata), MaDREB1, MaDREB2, MaDREB3, and MaDREB4, and demonstrated that they play regulatory roles in fruit ripening. We showed that MaDREB1-MaDREB4 are nucleus-localized, induced by ethylene and encompass transcriptional activation activities. We performed a genome-wide chromatin immunoprecipitation and high-throughput sequencing (ChIP-Seq) experiment for MaDREB2 and identified 697 genomic regions as potential targets of MaDREB2. MaDREB2 binds to hundreds of loci with diverse functions and its binding sites are distributed in the promoter regions proximal to the transcriptional start site (TSS). Most of the MaDREB2-binding targets contain the conserved (A/G)CC(G/C)AC motif and MaDREB2 appears to directly regulate the expression of a number of genes involved in fruit ripening. In combination with transcriptome profiling (RNA sequencing) data, our results indicate that MaDREB2 may serve as both transcriptional activator and repressor during banana fruit ripening. In conclusion, our study suggests a hierarchical regulatory model of fruit ripening in banana and that the MaDREB TFs may act as transcriptional regulators in the regulatory network. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Strand displacement by DNA polymerase III occurs through a tau-psi-chi link to single-stranded DNA-binding protein coating the lagging strand template.

PubMed

Yuan, Quan; McHenry, Charles S

2009-11-13

In addition to the well characterized processive replication reaction catalyzed by the DNA polymerase III holoenzyme on single-stranded DNA templates, the enzyme possesses an intrinsic strand displacement activity on flapped templates. The strand displacement activity is distinguished from the single-stranded DNA-templated reaction by a high dependence upon single-stranded DNA binding protein and an inability of gamma-complex to support the reaction in the absence of tau. However, if gamma-complex is present to load beta(2), a truncated tau protein containing only domains III-V will suffice. This truncated protein is sufficient to bind both the alpha subunit of DNA polymerase (Pol) III and chipsi. This is reminiscent of the minimal requirements for Pol III to replicate short single-stranded DNA-binding protein (SSB)-coated templates where tau is only required to serve as a scaffold to hold Pol III and chi in the same complex (Glover, B., and McHenry, C. (1998) J. Biol. Chem. 273, 23476-23484). We propose a model in which strand displacement by DNA polymerase III holoenzyme depends upon a Pol III-tau-psi-chi-SSB binding network, where SSB is bound to the displaced strand, stabilizing the Pol III-template interaction. The same interaction network is probably important for stabilizing the leading strand polymerase interactions with authentic replication forks. The specificity constant (k(cat)/K(m)) for the strand displacement reaction is approximately 300-fold less favorable than reactions on single-stranded templates and proceeds with a slower rate (150 nucleotides/s) and only moderate processivity (approximately 300 nucleotides). PriA, the initiator of replication restart on collapsed or misassembled replication forks, blocks the strand displacement reaction, even if added to an ongoing reaction.

Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

NASA Astrophysics Data System (ADS)

Yan, Koon-Kiu; Walker, Dylan; Maslov, Sergei

2008-12-01

We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker’s yeast, with empirical protein concentrations.

Self-assembly of thin, triangular prisms into open networks at a flat air-water interface

NASA Astrophysics Data System (ADS)

Solomon, Michael; Ferrar, Joseph; Bedi, Deshpreet; Zhou, Shangnan; Mao, Xiaoming

We observe capillary-driven binding between thin, equilateral triangle microprisms at a flat air-water interface. The triangles are fabricated from epoxy resin via SU-8 photolithography. For small thickness to length (T/L) ratios, two distinct pairwise particle-particle binding events occur with roughly equal frequency, and optical and environmental scanning electron microscopy (eSEM) demonstrate that these two distinct binding events are driven by the specific manner in which the interface is pinned to the particle surface. Additionally, particle bending is observed for the lowest T/L ratios, which leads to enhanced interface curvature and thus enhanced strength of capillary-driven attractions, and may also play a pivotal role in the dichotomy in particle-particle binding. Dichotomy in particle-particle binding is not observed at thicker T/L ratios, although capillary-driven binding still occurs. Ultimately, the particles self-assemble into space-spanning open networks, and the results suggest design parameters for the fabrication of building blocks of ordered open structures, such as the Kagome lattice.

Resemblance of actin-binding protein/actin gels to covalently crosslinked networks

NASA Astrophysics Data System (ADS)

Janmey, Paul A.; Hvidt, Søren; Lamb, Jennifer; Stossel, Thomas P.

1990-05-01

THE maintainance of the shape of cells is often due to their surface elasticity, which arises mainly from an actin-rich cytoplasmic cortex1,2. On locomotion, phagocytosis or fission, however, these cells become partially fluid-like. The finding of proteins that can bind to actin and control the assembly of, or crosslink, actin filaments, and of intracellular messages that regulate the activities of some of these actin-binding proteins, indicates that such 'gel sol' transformations result from the rearrangement of cortical actin-rich networks3. Alternatively, on the basis of a study of the mechanical properties of mixtures of actin filaments and an Acanthamoeba actin-binding protein, α-actinin, it has been proposed that these transformations can be accounted for by rapid exchange of crosslinks between actin filaments4: the cortical network would be solid when the deformation rate is greater than the rate of crosslink exchange, but would deform or 'creep' when deformation is slow enough to permit crosslinker molecules to rearrange. Here we report, however, that mixtures of actin filaments and actin-binding protein (ABP), an actin crosslinking protein of many higher eukaryotes, form gels Theologically equivalent to covalently crosslinked networks. These gels do not creep in response to applied stress on a time scale compatible with most cell-surface movements. These findings support a more complex and controlled mechanism underlying the dynamic mechanical properties of cortical cytoplasm, and can explain why cells do not collapse under the constant shear forces that often exist in tissues.

OncoPPi-informed discovery of mitogen-activated protein kinase kinase 3 as a novel binding partner of c-Myc | Office of Cancer Genomics

Cancer.gov

Mitogen-activated protein kinase kinase 3 (MKK3) is a dual threonine/tyrosine protein kinase that regulates inflammation, proliferation and apoptosis through specific phosphorylation and activation of the p38 mitogen-activated protein kinase. However, the role of MKK3 beyond p38-signaling remains elusive. Recently, we reported a protein-protein interaction (PPI) network of cancer-associated genes, termed OncoPPi, as a resource for the scientific community to generate new biological models. Analysis of the OncoPPi connectivity identified MKK3 as one of the major hub proteins in the network.

In silico evolution of the Drosophila gap gene regulatory sequence under elevated mutational pressure.

PubMed

Chertkova, Aleksandra A; Schiffman, Joshua S; Nuzhdin, Sergey V; Kozlov, Konstantin N; Samsonova, Maria G; Gursky, Vitaly V

2017-02-07

Cis-regulatory sequences are often composed of many low-affinity transcription factor binding sites (TFBSs). Determining the evolutionary and functional importance of regulatory sequence composition is impeded without a detailed knowledge of the genotype-phenotype map. We simulate the evolution of regulatory sequences involved in Drosophila melanogaster embryo segmentation during early development. Natural selection evaluates gene expression dynamics produced by a computational model of the developmental network. We observe a dramatic decrease in the total number of transcription factor binding sites through the course of evolution. Despite a decrease in average sequence binding energies through time, the regulatory sequences tend towards organisations containing increased high affinity transcription factor binding sites. Additionally, the binding energies of separate sequence segments demonstrate ubiquitous mutual correlations through time. Fewer than 10% of initial TFBSs are maintained throughout the entire simulation, deemed 'core' sites. These sites have increased functional importance as assessed under wild-type conditions and their binding energy distributions are highly conserved. Furthermore, TFBSs within close proximity of core sites exhibit increased longevity, reflecting functional regulatory interactions with core sites. In response to elevated mutational pressure, evolution tends to sample regulatory sequence organisations with fewer, albeit on average, stronger functional transcription factor binding sites. These organisations are also shaped by the regulatory interactions among core binding sites with sites in their local vicinity.

Both Intrinsic Substrate Preference and Network Context Contribute to Substrate Selection of Classical Tyrosine Phosphatases*

PubMed Central

Tinti, Michele; Paoluzi, Serena; Santonico, Elena; Masch, Antonia; Schutkowski, Mike

2017-01-01

Reversible tyrosine phosphorylation is a widespread post-translational modification mechanism underlying cell physiology. Thus, understanding the mechanisms responsible for substrate selection by kinases and phosphatases is central to our ability to model signal transduction at a system level. Classical protein-tyrosine phosphatases can exhibit substrate specificity in vivo by combining intrinsic enzymatic specificity with the network of protein-protein interactions, which positions the enzymes in close proximity to their substrates. Here we use a high throughput approach, based on high density phosphopeptide chips, to determine the in vitro substrate preference of 16 members of the protein-tyrosine phosphatase family. This approach helped identify one residue in the substrate binding pocket of the phosphatase domain that confers specificity for phosphopeptides in a specific sequence context. We also present a Bayesian model that combines intrinsic enzymatic specificity and interaction information in the context of the human protein interaction network to infer new phosphatase substrates at the proteome level. PMID:28159843

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

PubMed Central

Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

2007-01-01

Background Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org. PMID:17912365

A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, differential signaling, and relevance to oncogenic transformations.

PubMed

Liu, Yingting; Purvis, Jeremy; Shih, Andrew; Weinstein, Joshua; Agrawal, Neeraj; Radhakrishnan, Ravi

2007-06-01

We describe a hierarchical multiscale computational approach based on molecular dynamics simulations, free energy-based molecular docking simulations, deterministic network-based kinetic modeling, and hybrid discrete/continuum stochastic dynamics protocols to study the dimer-mediated receptor activation characteristics of the Erb family receptors, specifically the epidermal growth factor receptor (EGFR). Through these modeling approaches, we are able to extend the prior modeling of EGF-mediated signal transduction by considering specific EGFR tyrosine kinase (EGFRTK) docking interactions mediated by differential binding and phosphorylation of different C-terminal peptide tyrosines on the RTK tail. By modeling signal flows through branching pathways of the EGFRTK resolved on a molecular basis, we are able to transcribe the effects of molecular alterations in the receptor (e.g., mutant forms of the receptor) to differing kinetic behavior and downstream signaling response. Our molecular dynamics simulations show that the drug sensitizing mutation (L834R) of EGFR stabilizes the active conformation to make the system constitutively active. Docking simulations show preferential characteristics (for wildtype vs. mutant receptors) in inhibitor binding as well as preferential enhancement of phosphorylation of particular substrate tyrosines over others. We find that in comparison to the wildtype system, the L834R mutant RTK preferentially binds the inhibitor erlotinib, as well as preferentially phosphorylates the substrate tyrosine Y1068 but not Y1173. We predict that these molecular level changes result in preferential activation of the Akt signaling pathway in comparison to the Erk signaling pathway for cells with normal EGFR expression. For cells with EGFR over expression, the mutant over activates both Erk and Akt pathways, in comparison to wildtype. These results are consistent with qualitative experimental measurements reported in the literature. We discuss these consequences in light of how the network topology and signaling characteristics of altered (mutant) cell lines are shaped differently in relationship to native cell lines.

Binding of thrombin-activated platelets to a fibrin scaffold through α(IIb)β₃ evokes phosphatidylserine exposure on their cell surface.

PubMed

Brzoska, Tomasz; Suzuki, Yuko; Mogami, Hideo; Sano, Hideto; Urano, Tetsumei

2013-01-01

Recently, by employing intra-vital confocal microscopy, we demonstrated that platelets expose phosphatidylserine (PS) and fibrin accumulate only in the center of the thrombus but not in its periphery. To address the question how exposure of platelet anionic phospholipids is regulated within the thrombus, an in-vitro experiment using diluted platelet-rich plasma was employed, in which the fibrin network was formed in the presence of platelets, and PS exposure on the platelet surface was analyzed using Confocal Laser Scanning Microscopy. Almost all platelets exposed PS after treatment with tissue factor, thrombin or ionomycin. Argatroban abrogated fibrin network formation in all samples, however, platelet PS exposure was inhibited only in tissue factor- and thrombin-treated samples but not in ionomycin-treated samples. FK633, an α(IIb)β₃ antagonist, and cytochalasin B impaired platelet binding to the fibrin scaffold and significantly reduced PS exposure evoked by thrombin. Gly-Pro-Arg-Pro amide abrogated not only fibrin network formation, but also PS exposure on platelets without suppressing platelet binding to fibrin/fibrinogen. These results suggest that outside-in signals in platelets generated by their binding to the rigid fibrin network are essential for PS exposure after thrombin treatment.

Binding of Thrombin-Activated Platelets to a Fibrin Scaffold through αIIbβ3 Evokes Phosphatidylserine Exposure on Their Cell Surface

PubMed Central

Brzoska, Tomasz; Suzuki, Yuko; Mogami, Hideo; Sano, Hideto; Urano, Tetsumei

2013-01-01

Recently, by employing intra-vital confocal microscopy, we demonstrated that platelets expose phosphatidylserine (PS) and fibrin accumulate only in the center of the thrombus but not in its periphery. To address the question how exposure of platelet anionic phospholipids is regulated within the thrombus, an in-vitro experiment using diluted platelet-rich plasma was employed, in which the fibrin network was formed in the presence of platelets, and PS exposure on the platelet surface was analyzed using Confocal Laser Scanning Microscopy. Almost all platelets exposed PS after treatment with tissue factor, thrombin or ionomycin. Argatroban abrogated fibrin network formation in all samples, however, platelet PS exposure was inhibited only in tissue factor- and thrombin-treated samples but not in ionomycin-treated samples. FK633, an αIIbβ3 antagonist, and cytochalasin B impaired platelet binding to the fibrin scaffold and significantly reduced PS exposure evoked by thrombin. Gly-Pro-Arg-Pro amide abrogated not only fibrin network formation, but also PS exposure on platelets without suppressing platelet binding to fibrin/fibrinogen. These results suggest that outside-in signals in platelets generated by their binding to the rigid fibrin network are essential for PS exposure after thrombin treatment. PMID:23383331

Molecular Dynamics Simulations and Dynamic Network Analysis Reveal the Allosteric Unbinding of Monobody to H-Ras Triggered by R135K Mutation.

PubMed

Ni, Duan; Song, Kun; Zhang, Jian; Lu, Shaoyong

2017-10-26

Ras proteins, as small GTPases, mediate cell proliferation, survival and differentiation. Ras mutations have been associated with a broad spectrum of human cancers and thus targeting Ras represents a potential way forward for cancer therapy. A recently reported monobody NS1 allosterically disrupts the Ras-mediated signaling pathway, but its efficacy is reduced by R135K mutation in H-Ras. However, the detailed mechanism is unresolved. Here, using molecular dynamics (MD) simulations and dynamic network analysis, we explored the molecular mechanism for the unbinding of NS1 to H-Ras and shed light on the underlying allosteric network in H-Ras. MD simulations revealed that the overall structures of the two complexes did not change significantly, but the H-Ras-NS1 interface underwent significant conformational alteration in the mutant Binding free energy analysis showed that NS1 binding was unfavored after R135K mutation, which resulted in the unfavorable binding of NS1. Furthermore, the critical residues on H-Ras responsible for the loss of binding of NS1 were identified. Importantly, the allosteric networks for these important residues were revealed, which yielded a novel insight into the allosteric regulatory mechanism of H-Ras.

Molecular Dynamics Simulations and Dynamic Network Analysis Reveal the Allosteric Unbinding of Monobody to H-Ras Triggered by R135K Mutation

PubMed Central

Song, Kun; Zhang, Jian; Lu, Shaoyong

2017-01-01

Ras proteins, as small GTPases, mediate cell proliferation, survival and differentiation. Ras mutations have been associated with a broad spectrum of human cancers and thus targeting Ras represents a potential way forward for cancer therapy. A recently reported monobody NS1 allosterically disrupts the Ras-mediated signaling pathway, but its efficacy is reduced by R135K mutation in H-Ras. However, the detailed mechanism is unresolved. Here, using molecular dynamics (MD) simulations and dynamic network analysis, we explored the molecular mechanism for the unbinding of NS1 to H-Ras and shed light on the underlying allosteric network in H-Ras. MD simulations revealed that the overall structures of the two complexes did not change significantly, but the H-Ras–NS1 interface underwent significant conformational alteration in the mutant Binding free energy analysis showed that NS1 binding was unfavored after R135K mutation, which resulted in the unfavorable binding of NS1. Furthermore, the critical residues on H-Ras responsible for the loss of binding of NS1 were identified. Importantly, the allosteric networks for these important residues were revealed, which yielded a novel insight into the allosteric regulatory mechanism of H-Ras. PMID:29072601

Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype.

PubMed

Burghardt, Thomas P; Ajtai, Katalin

2018-04-11

The cardiac muscle sarcomere contains multiple proteins contributing to contraction energy transduction and its regulation during a heartbeat. Inheritable heart disease mutants affect most of them but none more frequently than the ventricular myosin motor and cardiac myosin binding protein c (mybpc3). These co-localizing proteins have mybpc3 playing a regulatory role to the energy transducing motor. Residue substitution and functional domain assignment of each mutation in the protein sequence decides, under the direction of a sensible disease model, phenotype and pathogenicity. The unknown model mechanism is decided here using a method combing neural and Bayes networks. Missense single nucleotide polymorphisms (SNPs) are clues for the disease mechanism summarized in an extensive database collecting mutant sequence location and residue substitution as independent variables that imply the dependent disease phenotype and pathogenicity characteristics in 4 dimensional data points (4ddps). The SNP database contains entries with the majority having one or both dependent data entries unfulfilled. A neural network relating causes (mutant residue location and substitution) and effects (phenotype and pathogenicity) is trained, validated, and optimized using fulfilled 4ddps. It then predicts unfulfilled 4ddps providing the implicit disease model. A discrete Bayes network interprets fulfilled and predicted 4ddps with conditional probabilities for phenotype and pathogenicity given mutation location and residue substitution thus relating the neural network implicit model to explicit features of the motor and mybpc3 sequence and structural domains. Neural/Bayes network forecasting automates disease mechanism modeling by leveraging the world wide human missense SNP database that is in place and expanding. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Application of counterpropagation artificial neural network for modelling properties of fish antibiotics.

PubMed

Maran, E; Novic, M; Barbieri, P; Zupan, J

2004-01-01

The present study focuses on fish antibiotics which are an important group of pharmaceuticals used in fish farming to treat infections and, until recently, most of them have been exposed to the environment with very little attention. Information about the environmental behaviour and the description of the environmental fate of medical substances are difficult or expensive to obtain. The experimental information in terms of properties is reported when available, in other cases, it is estimated by standard tools as those provided by the United States Environmental Protection Agency EPISuite software and by custom quantitative structure-activity relationship (QSAR) applications. In this study, a QSAR screening of 15 fish antibiotics and 132 xenobiotic molecules was performed with two aims: (i) to develop a model for the estimation of octanol--water partition coefficient (logP) and (ii) to estimate the relative binding affinity to oestrogen receptor (log RBA) using a model constructed on the activities of 132 xenobiotic compounds. The custom models are based on constitutional, topological, electrostatic and quantum chemical descriptors computed by the CODESSA software. Kohonen neural networks (self organising maps) were used to study similarity between the considered chemicals while counter-propagation artificial neural networks were used to estimate the properties.

The ribonucleoprotein Csr network.

PubMed

Seyll, Ethel; Van Melderen, Laurence

2013-11-08

Ribonucleoprotein complexes are essential regulatory components in bacteria. In this review, we focus on the carbon storage regulator (Csr) network, which is well conserved in the bacterial world. This regulatory network is composed of the CsrA master regulator, its targets and regulators. CsrA binds to mRNA targets and regulates translation either negatively or positively. Binding to small non-coding RNAs controls activity of this protein. Expression of these regulators is tightly regulated at the level of transcription and stability by various global regulators (RNAses, two-component systems, alarmone). We discuss the implications of these complex regulations in bacterial adaptation.

KLK14 interactions with HAI-1 and HAI-2 serine protease inhibitors: A molecular dynamics and relative free-energy calculations study.

PubMed

Solís-Calero, Christian; Carvalho, Hernandes F

2017-11-01

Kallikrein 14 (KLK14) is a serine protease linked to several pathologies including prostate cancer and positively correlates with Gleason score. Though KLK14 functioning in cancer is poorly understood, it has been implicated in HGF/Met signaling, given that KLK14 proteolytically inhibits HGF activator-inhibitor 1 (HAI-1), which strongly inhibits pro-HGF activators, thereby contributing to tumor progression. In this work, KLK14 binding to either hepatocyte growth factor activator inhibitor type-1 (HAI-1) or type-2 (HAI-2) was essayed using homology modeling, molecular dynamic simulations and free-energy calculations through MM/PBSA and MM/GBSA. KLK14 was successfully modeled. Calculated free energies suggested higher binding affinity for the KLK14/HAI-1 interaction than for KLK14/HAI-2. This difference in binding affinity is largely explained by the higher stability of the hydrogen-bond networks in KLK14/HAI-1 along the simulation trajectory. A key arginine residue in both HAI-1 and HAI-2 is responsible for their interaction with the S1 pocket in KLK14. Additionally, MM/GBSA free-energy decomposition postulates that KLK14 Asp174 and Trp196 are hotspots for binding HAI-1 and HAI-2. © 2017 International Federation for Cell Biology.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants.

PubMed

Chow, Chi-Nga; Zheng, Han-Qin; Wu, Nai-Yun; Chien, Chia-Hung; Huang, Hsien-Da; Lee, Tzong-Yi; Chiang-Hsieh, Yi-Fan; Hou, Ping-Fu; Yang, Tien-Yi; Chang, Wen-Chi

2016-01-04

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Bacterial periplasmic sialic acid-binding proteins exhibit a conserved binding site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gangi Setty, Thanuja; Cho, Christine; Govindappa, Sowmya

2014-07-01

Structure–function studies of sialic acid-binding proteins from F. nucleatum, P. multocida, V. cholerae and H. influenzae reveal a conserved network of hydrogen bonds involved in conformational change on ligand binding. Sialic acids are a family of related nine-carbon sugar acids that play important roles in both eukaryotes and prokaryotes. These sialic acids are incorporated/decorated onto lipooligosaccharides as terminal sugars in multiple bacteria to evade the host immune system. Many pathogenic bacteria scavenge sialic acids from their host and use them for molecular mimicry. The first step of this process is the transport of sialic acid to the cytoplasm, which oftenmore » takes place using a tripartite ATP-independent transport system consisting of a periplasmic binding protein and a membrane transporter. In this paper, the structural characterization of periplasmic binding proteins from the pathogenic bacteria Fusobacterium nucleatum, Pasteurella multocida and Vibrio cholerae and their thermodynamic characterization are reported. The binding affinities of several mutations in the Neu5Ac binding site of the Haemophilus influenzae protein are also reported. The structure and the thermodynamics of the binding of sugars suggest that all of these proteins have a very well conserved binding pocket and similar binding affinities. A significant conformational change occurs when these proteins bind the sugar. While the C1 carboxylate has been identified as the primary binding site, a second conserved hydrogen-bonding network is involved in the initiation and stabilization of the conformational states.« less

A Network of Hydrophobic Residues Impeding Helix αC Rotation Maintains Latency of Kinase Gcn2, Which Phosphorylates the α Subunit of Translation Initiation Factor 2▿

PubMed Central

Gárriz, Andrés; Qiu, Hongfang; Dey, Madhusudan; Seo, Eun-Joo; Dever, Thomas E.; Hinnebusch, Alan G.

2009-01-01

Kinase Gcn2 is activated by amino acid starvation and downregulates translation initiation by phosphorylating the α subunit of translation initiation factor 2 (eIF2α). The Gcn2 kinase domain (KD) is inert and must be activated by tRNA binding to the adjacent regulatory domain. Previous work indicated that Saccharomyces cerevisiae Gcn2 latency results from inflexibility of the hinge connecting the N and C lobes and a partially obstructed ATP-binding site in the KD. Here, we provide strong evidence that a network of hydrophobic interactions centered on Leu-856 also promotes latency by constraining helix αC rotation in the KD in a manner relieved during amino acid starvation by tRNA binding and autophosphorylation of Thr-882 in the activation loop. Thus, we show that mutationally disrupting the hydrophobic network in various ways constitutively activates eIF2α phosphorylation in vivo and bypasses the requirement for a key tRNA binding motif (m2) and Thr-882 in Gcn2. In particular, replacing Leu-856 with any nonhydrophobic residue activates Gcn2, while substitutions with various hydrophobic residues maintain kinase latency. We further provide strong evidence that parallel, back-to-back dimerization of the KD is a step on the Gcn2 activation pathway promoted by tRNA binding and autophosphorylation. Remarkably, mutations that disrupt the L856 hydrophobic network or enhance hinge flexibility eliminate the need for the conserved salt bridge at the parallel dimer interface, implying that KD dimerization facilitates the reorientation of αC and remodeling of the active site for enhanced ATP binding and catalysis. We propose that hinge remodeling, parallel dimerization, and reorientation of αC are mutually reinforcing conformational transitions stimulated by tRNA binding and secured by the ensuing autophosphorylation of T882 for stable kinase activation. PMID:19114556

A network of hydrophobic residues impeding helix alphaC rotation maintains latency of kinase Gcn2, which phosphorylates the alpha subunit of translation initiation factor 2.

PubMed

Gárriz, Andrés; Qiu, Hongfang; Dey, Madhusudan; Seo, Eun-Joo; Dever, Thomas E; Hinnebusch, Alan G

2009-03-01

Kinase Gcn2 is activated by amino acid starvation and downregulates translation initiation by phosphorylating the alpha subunit of translation initiation factor 2 (eIF2alpha). The Gcn2 kinase domain (KD) is inert and must be activated by tRNA binding to the adjacent regulatory domain. Previous work indicated that Saccharomyces cerevisiae Gcn2 latency results from inflexibility of the hinge connecting the N and C lobes and a partially obstructed ATP-binding site in the KD. Here, we provide strong evidence that a network of hydrophobic interactions centered on Leu-856 also promotes latency by constraining helix alphaC rotation in the KD in a manner relieved during amino acid starvation by tRNA binding and autophosphorylation of Thr-882 in the activation loop. Thus, we show that mutationally disrupting the hydrophobic network in various ways constitutively activates eIF2alpha phosphorylation in vivo and bypasses the requirement for a key tRNA binding motif (m2) and Thr-882 in Gcn2. In particular, replacing Leu-856 with any nonhydrophobic residue activates Gcn2, while substitutions with various hydrophobic residues maintain kinase latency. We further provide strong evidence that parallel, back-to-back dimerization of the KD is a step on the Gcn2 activation pathway promoted by tRNA binding and autophosphorylation. Remarkably, mutations that disrupt the L856 hydrophobic network or enhance hinge flexibility eliminate the need for the conserved salt bridge at the parallel dimer interface, implying that KD dimerization facilitates the reorientation of alphaC and remodeling of the active site for enhanced ATP binding and catalysis. We propose that hinge remodeling, parallel dimerization, and reorientation of alphaC are mutually reinforcing conformational transitions stimulated by tRNA binding and secured by the ensuing autophosphorylation of T882 for stable kinase activation.

Multimodal Imaging of Alzheimer Pathophysiology in the Brain's Default Mode Network

DOE PAGES

Shin, Jonghan; Kepe, Vladimir; Small, Gary W.; ...

2011-01-01

The spatial correlations between the brain's default mode network (DMN) and the brain regions known to develop pathophysiology in Alzheimer's disease (AD) have recently attracted much attention. In this paper, we compare results of different functional and structural imaging modalities, including MRI and PET, and highlight different patterns of anomalies observed within the DMN. Multitracer PET imaging in subjects with and without dementia has demonstrated that [C-11]PIB- and [F-18]FDDNP-binding patterns in patients with AD overlap within nodes of the brain's default network including the prefrontal, lateral parietal, lateral temporal, and posterior cingulate cortices, with the exception of the medial temporalmore » cortex (especially, the hippocampus) where significant discrepancy between increased [F-18]FDDNP binding and negligible [C-11]PIB-binding was observed. [F-18]FDDNP binding in the medial temporal cortex—a key constituent of the DMN—coincides with both the presence of amyloid and tau pathology, and also with cortical areas with maximal atrophy as demonstrated by T1-weighted MR imaging of AD patients.« less

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

An approach to adjustment of relativistic mean field model parameters

NASA Astrophysics Data System (ADS)

Bayram, Tuncay; Akkoyun, Serkan

2017-09-01

The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN) method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs) of 58Ni and 208Pb have been found in agreement with the literature values.

The social brain: scale-invariant layering of Erdős-Rényi networks in small-scale human societies.

PubMed

Harré, Michael S; Prokopenko, Mikhail

2016-05-01

The cognitive ability to form social links that can bind individuals together into large cooperative groups for safety and resource sharing was a key development in human evolutionary and social history. The 'social brain hypothesis' argues that the size of these social groups is based on a neurologically constrained capacity for maintaining long-term stable relationships. No model to date has been able to combine a specific socio-cognitive mechanism with the discrete scale invariance observed in ethnographic studies. We show that these properties result in nested layers of self-organizing Erdős-Rényi networks formed by each individual's ability to maintain only a small number of social links. Each set of links plays a specific role in the formation of different social groups. The scale invariance in our model is distinct from previous 'scale-free networks' studied using much larger social groups; here, the scale invariance is in the relationship between group sizes, rather than in the link degree distribution. We also compare our model with a dominance-based hierarchy and conclude that humans were probably egalitarian in hunter-gatherer-like societies, maintaining an average maximum of four or five social links connecting all members in a largest social network of around 132 people. © 2016 The Author(s).

Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling

PubMed Central

Wittmann, Dominik M; Krumsiek, Jan; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Klamt, Steffen; Theis, Fabian J

2009-01-01

Background The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems. PMID:19785753

Age and sex differences in oxytocin and vasopressin V1a receptor binding densities in the rat brain: focus on the social decision-making network.

PubMed

Smith, Caroline J W; Poehlmann, Max L; Li, Sara; Ratnaseelan, Aarane M; Bredewold, Remco; Veenema, Alexa H

2017-03-01

Oxytocin (OT) and vasopressin (AVP) regulate various social behaviors via activation of the OT receptor (OTR) and the AVP V1a receptor (V1aR) in the brain. Social behavior often differs across development and between the sexes, yet our understanding of age and sex differences in brain OTR and V1aR binding remains incomplete. Here, we provide an extensive analysis of OTR and V1aR binding density throughout the brain in juvenile and adult male and female rats, with a focus on regions within the social decision-making network. OTR and V1aR binding density were higher in juveniles than in adults in regions associated with reward and socio-spatial memory and higher in adults than in juveniles in key regions of the social decision-making network and in cortical regions. We discuss possible implications of these shifts in OTR and V1aR binding density for the age-specific regulation of social behavior. Furthermore, sex differences in OTR and V1aR binding density were less numerous than age differences. The direction of these sex differences was region-specific for OTR but consistently higher in females than in males for V1aR. Finally, almost all sex differences in OTR and V1aR binding density were already present in juveniles and occurred in regions with denser binding in adults compared to juveniles. Possible implications of these sex differences for the sex-specific regulation of behavior, as well potential underlying mechanisms, are discussed. Overall, these findings provide an important framework for testing age- and sex-specific roles of OTR and V1aR in the regulation of social behavior.

A Small World of Neuronal Synchrony

PubMed Central

Yu, Shan; Huang, Debin; Singer, Wolf

2008-01-01

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding. PMID:18400792

Recrystallization inhibition in ice due to ice binding protein activity detected by nuclear magnetic resonance.

PubMed

Brown, Jennifer R; Seymour, Joseph D; Brox, Timothy I; Skidmore, Mark L; Wang, Chen; Christner, Brent C; Luo, Bing-Hao; Codd, Sarah L

2014-09-01

Liquid water present in polycrystalline ice at the interstices between ice crystals results in a network of liquid-filled veins and nodes within a solid ice matrix, making ice a low porosity porous media. Here we used nuclear magnetic resonance (NMR) relaxation and time dependent self-diffusion measurements developed for porous media applications to monitor three dimensional changes to the vein network in ices with and without a bacterial ice binding protein (IBP). Shorter effective diffusion distances were detected as a function of increased irreversible ice binding activity, indicating inhibition of ice recrystallization and persistent small crystal structure. The modification of ice structure by the IBP demonstrates a potential mechanism for the microorganism to enhance survivability in ice. These results highlight the potential of NMR techniques in evaluation of the impact of IBPs on vein network structure and recrystallization processes; information useful for continued development of ice-interacting proteins for biotechnology applications.

microRNA as a Potential Vector for the Propagation of Robustness in Protein Expression and Oscillatory Dynamics within a ceRNA Network

PubMed Central

Gérard, Claude; Novák, Béla

2013-01-01

microRNAs (miRNAs) are small noncoding RNAs that are important post-transcriptional regulators of gene expression. miRNAs can induce thresholds in protein synthesis. Such thresholds in protein output can be also achieved by oligomerization of transcription factors (TF) for the control of gene expression. First, we propose a minimal model for protein expression regulated by miRNA and by oligomerization of TF. We show that miRNA and oligomerization of TF generate a buffer, which increases the robustness of protein output towards molecular noise as well as towards random variation of kinetics parameters. Next, we extend the model by considering that the same miRNA can bind to multiple messenger RNAs, which accounts for the dynamics of a minimal competing endogenous RNAs (ceRNAs) network. The model shows that, through common miRNA regulation, TF can control the expression of all proteins formed by the ceRNA network, even if it drives the expression of only one gene in the network. The model further suggests that the threshold in protein synthesis mediated by the oligomerization of TF can be propagated to the other genes, which can increase the robustness of the expression of all genes in such ceRNA network. Furthermore, we show that a miRNA could increase the time delay of a “Goodwin-like” oscillator model, which may favor the occurrence of oscillations of large amplitude. This result predicts important roles of miRNAs in the control of the molecular mechanisms leading to the emergence of biological rhythms. Moreover, a model for the latter oscillator embedded in a ceRNA network indicates that the oscillatory behavior can be propagated, via the shared miRNA, to all proteins formed by such ceRNA network. Thus, by means of computational models, we show that miRNAs could act as vectors allowing the propagation of robustness in protein synthesis as well as oscillatory behaviors within ceRNA networks. PMID:24376695

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Binding sites of resveratrol, genistein, and curcumin with milk α- and β-caseins.

PubMed

Bourassa, P; Bariyanga, J; Tajmir-Riahi, H A

2013-02-07

The binding sites of antioxidant polyphenols resveratrol, genistein, and curcumin are located with milk α- and β-caseins in aqueous solution. FTIR, CD, and fluorescence spectroscopic methods and molecular modeling were used to analyze polyphenol binding sites, the binding constant, and the effects of complexation on casein stability and conformation. Structural analysis showed that polyphenols bind casein via hydrophilic and hydrophobic interactions with the number of bound polyphenol molecules (n) 1.20 for resveratrol, 1.42 for genistein, and 1.43 for curcumin with α-casein and 1.14 for resveratrol, 1.27 for genistein, and 1.27 for curcumin with β-casein. The overall binding constants of the complexes formed are K(res-α-casein) = 1.9 (±0.6) × 10(4) M(-1), K(gen-α-casein) = 1.8 (±0.4) × 10(4) M(-1), and K(cur-α-casein) = 2.8 (±0.8) × 10(4) M(-1) with α-casein and K(res-β-casein) = 2.3 (±0.3) × 10(4) M(-1), K(gen-β-casein) = 3.0 (±0.5) × 10(4) M(-1), and K(cur-β-casein) = 3.1 (±0.5) × 10(4) M(-1) for β-casein. Molecular modeling showed the participation of several amino acids in polyphenol-protein complexes, which were stabilized by the hydrogen bonding network with the free binding energy of -11.56 (resveratrol-α-casein), -12.35 (resveratrol-β-casein), -9.68 (genistein-α-casein), -9.97 (genistein-β-casein), -8.89 (curcumin-α-casein), and -10.70 kcal/mol (curcumin-β-casein). The binding sites of polyphenols are different with α- and β-caseins. Polyphenol binding altered casein conformation with reduction of α-helix, indicating a partial protein destabilization. Caseins might act as carriers to transport polyphenol in vitro.

Glucagon-Like Peptide-1 Receptor Ligand Interactions: Structural Cross Talk between Ligands and the Extracellular Domain

PubMed Central

West, Graham M.; Willard, Francis S.; Sloop, Kyle W.; Showalter, Aaron D.; Pascal, Bruce D.; Griffin, Patrick R.

2014-01-01

Activation of the glucagon-like peptide-1 receptor (GLP-1R) in pancreatic β-cells potentiates insulin production and is a current therapeutic target for the treatment of type 2 diabetes mellitus (T2DM). Like other class B G protein-coupled receptors (GPCRs), the GLP-1R contains an N-terminal extracellular ligand binding domain. N-terminal truncations on the peptide agonist generate antagonists capable of binding to the extracellular domain, but not capable of activating full length receptor. The main objective of this study was to use Hydrogen/deuterium exchange (HDX) to identify how the amide hydrogen bonding network of peptide ligands and the extracellular domain of GLP-1R (nGLP-1R) were altered by binding interactions and to then use this platform to validate direct binding events for putative GLP-1R small molecule ligands. The HDX studies presented here for two glucagon-like peptide-1 receptor (GLP-1R) peptide ligands indicates that the antagonist exendin-4[9-39] is significantly destabilized in the presence of nonionic detergents as compared to the agonist exendin-4. Furthermore, HDX can detect stabilization of exendin-4 and exendin-4[9-39] hydrogen bonding networks at the N-terminal helix [Val19 to Lys27] upon binding to the N-terminal extracellular domain of GLP-1R (nGLP-1R). In addition we show hydrogen bonding network stabilization on nGLP-1R in response to ligand binding, and validate direct binding events with the extracellular domain of the receptor for putative GLP-1R small molecule ligands. PMID:25180755

Mutation-Induced Population Shift in the MexR Conformational Ensemble Disengages DNA Binding: A Novel Mechanism for MarR Family Derepression.

PubMed

Anandapadamanaban, Madhanagopal; Pilstål, Robert; Andresen, Cecilia; Trewhella, Jill; Moche, Martin; Wallner, Björn; Sunnerhagen, Maria

2016-08-02

MexR is a repressor of the MexAB-OprM multidrug efflux pump operon of Pseudomonas aeruginosa, where DNA-binding impairing mutations lead to multidrug resistance (MDR). Surprisingly, the crystal structure of an MDR-conferring MexR mutant R21W (2.19 Å) presented here is closely similar to wild-type MexR. However, our extended analysis, by molecular dynamics and small-angle X-ray scattering, reveals that the mutation stabilizes a ground state that is deficient of DNA binding and is shared by both mutant and wild-type MexR, whereas the DNA-binding state is only transiently reached by the more flexible wild-type MexR. This population shift in the conformational ensemble is effected by mutation-induced allosteric coupling of contact networks that are independent in the wild-type protein. We propose that the MexR-R21W mutant mimics derepression by small-molecule binding to MarR proteins, and that the described allosteric model based on population shifts may also apply to other MarR family members. Copyright © 2016 Elsevier Ltd. All rights reserved.

PI(3,5)P2 controls endosomal branched actin dynamics by regulating cortactin–actin interactions

PubMed Central

Hong, Nan Hyung; Qi, Aidong

2015-01-01

Branched actin critically contributes to membrane trafficking by regulating membrane curvature, dynamics, fission, and transport. However, how actin dynamics are controlled at membranes is poorly understood. Here, we identify the branched actin regulator cortactin as a direct binding partner of phosphatidylinositol 3,5-bisphosphate (PI(3,5)P2) and demonstrate that their interaction promotes turnover of late endosomal actin. In vitro biochemical studies indicated that cortactin binds PI(3,5)P2 via its actin filament-binding region. Furthermore, PI(3,5)P2 competed with actin filaments for binding to cortactin, thereby antagonizing cortactin activity. These findings suggest that PI(3,5)P2 formation on endosomes may remove cortactin from endosome-associated branched actin. Indeed, inhibition of PI(3,5)P2 production led to cortactin accumulation and actin stabilization on Rab7+ endosomes. Conversely, inhibition of Arp2/3 complex activity greatly reduced cortactin localization to late endosomes. Knockdown of cortactin reversed PI(3,5)P2-inhibitor–induced actin accumulation and stabilization on endosomes. These data suggest a model in which PI(3,5)P2 binding removes cortactin from late endosomal branched actin networks and thereby promotes net actin turnover. PMID:26323691

RNA binding protein RNPC1 inhibits breast cancer cells metastasis via activating STARD13-correlated ceRNA network.

PubMed

Zhang, Zhiting; Guo, Qianqian; Zhang, Shufang; Xiang, Chenxi; Guo, Xinwei; Zhang, Feng; Gao, Lanlan; Ni, Haiwei; Xi, Tao; Zheng, Lufeng

2018-05-07

RNA binding proteins (RBPs) are pivotal post-transcriptional regulators. RNPC1, an RBP, acts as a tumor suppressor through binding and regulating the expression of target genes in cancer cells. This study disclosed that RNPC1 expression was positively correlated with breast cancer patients' relapse free and overall survival, and RNPC1suppressed breast cancer cells metastasis. Mechanistically, RNPC1 promoting a competing endogenous network (ceRNA) crosstalk between STARD13, CDH5, HOXD10, and HOXD1 (STARD13-correlated ceRNA network) that we previously confirmed in breast cancer cells through stabilizing the transcripts and thus facilitating the expression of these four genes in breast cancer cells. Furthermore, RNPC1 overexpression restrained the promotion of STARD13, CDH5, HOXD10, and HOXD1 knockdown on cell metastasis. Notably, RNPC1 expression was positively correlated with CDH5, HOXD1 and HOXD10 expression in breast cancer tissues, and attenuated adriamycin resistance. Taken together, these results identified that RNPC1 could inhibit breast cancer cells metastasis via promoting STARD13-correlated ceRNA network.

Chiral direction and interconnection of helical three-connected networks in metal-organic frameworks.

PubMed

Prior, T J; Rosseinsky, M J

2003-03-10

The control of the interpenetration and chirality of a family of metal-organic frameworks is discussed. These systems contain two- (A) and four-fold (B) interpenetration of helical three-connected networks generated by binding the 1,3,5-benzenetricarboxylate (btc) ligand to a metal center. These frameworks have the general formula Ni(3)(btc)(2)X(m)Y(n).solvent (where X = pyridine or 4-picoline, Y = ethylene glycol, 1,2-propanediol, 1,4-butanediol, meso-2,3-butanediol, 1,2,6-hexanetriol, glycerol). The structural and chemical effects of modifying the alcohol and aromatic amine ligands bound to the metal center include controlling the thermal stability and the degree of interpenetration. Covalent linking of the four interpenetrating networks in the A family and the switching of diol binding from mono- to bidentate are demonstrated. Recognition of chiral diols by the hand of the network helices is investigated by binding an alcohol ligand with two chiral centers of opposite sense to the same helix. This reveals the subtle nature of the helix-ligand interaction.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

PubMed Central

Kozlov, Konstantin N.; Kulakovskiy, Ivan V.; Zubair, Asif; Marjoram, Paul; Lawrie, David S.; Nuzhdin, Sergey V.; Samsonova, Maria G.

2017-01-01

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects. PMID:28898266

Affinity, Avidity, and Kinetics of Target Sequence Binding to LC8 Dynein Light Chain Isoforms*

PubMed Central

Radnai, László; Rapali, Péter; Hódi, Zsuzsa; Süveges, Dániel; Molnár, Tamás; Kiss, Bence; Bécsi, Bálint; Erdödi, Ferenc; Buday, László; Kardos, József; Kovács, Mihály; Nyitray, László

2010-01-01

LC8 dynein light chain (DYNLL) is a highly conserved eukaryotic hub protein with dozens of binding partners and various functions beyond being a subunit of dynein and myosin Va motor proteins. Here, we compared the kinetic and thermodynamic parameters of binding of both mammalian isoforms, DYNLL1 and DYNLL2, to two putative consensus binding motifs (KXTQTX and XG(I/V)QVD) and report only subtle differences. Peptides containing either of the above motifs bind to DYNLL2 with micromolar affinity, whereas a myosin Va peptide (lacking the conserved Gln) and the noncanonical Pak1 peptide bind with Kd values of 9 and 40 μm, respectively. Binding of the KXTQTX motif is enthalpy-driven, although that of all other peptides is both enthalpy- and entropy-driven. Moreover, the KXTQTX motif shows strikingly slower off-rate constant than the other motifs. As most DYNLL partners are homodimeric, we also assessed the binding of bivalent ligands to DYNLL2. Compared with monovalent ligands, a significant avidity effect was found as follows: Kd values of 37 and 3.5 nm for a dimeric myosin Va fragment and a Leu zipper dimerized KXTQTX motif, respectively. Ligand binding kinetics of DYNLL can best be described by a conformational selection model consisting of a slow isomerization and a rapid binding step. We also studied the binding of the phosphomimetic S88E mutant of DYNLL2 to the dimeric myosin Va fragment, and we found a significantly lower apparent Kd value (3 μm). We conclude that the thermodynamic and kinetic fine-tuning of binding of various ligands to DYNLL could have physiological relevance in its interaction network. PMID:20889982

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations

NASA Astrophysics Data System (ADS)

Yakovenko, Oleksandr; Jones, Steven J. M.

2018-01-01

We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (https://drugdesigndata.org/). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.

Cost-effective handoff scheme based on mobility-aware dual pointer forwarding in proxy mobile IPv6 networks.

PubMed

Son, Seungsik; Jeong, Jongpil

2014-01-01

In this paper, a mobility-aware Dual Pointer Forwarding scheme (mDPF) is applied in Proxy Mobile IPv6 (PMIPv6) networks. The movement of a Mobile Node (MN) is classified as intra-domain and inter-domain handoff. When the MN moves, this scheme can reduce the high signaling overhead for intra-handoff/inter-handoff, because the Local Mobility Anchor (LMA) and Mobile Access Gateway (MAG) are connected by pointer chains. In other words, a handoff is aware of low mobility between the previously attached MAG (pMAG) and newly attached MAG (nMAG), and another handoff between the previously attached LMA (pLMA) and newly attached LMA (nLMA) is aware of high mobility. Based on these mobility-aware binding updates, the overhead of the packet delivery can be reduced. Also, we analyse the binding update cost and packet delivery cost for route optimization, based on the mathematical analytic model. Analytical results show that our mDPF outperforms the PMIPv6 and the other pointer forwarding schemes, in terms of reducing the total cost of signaling.

Core and region-enriched networks of behaviorally regulated genes and the singing genome

PubMed Central

Whitney, Osceola; Pfenning, Andreas R.; Howard, Jason T.; Blatti, Charles A; Liu, Fang; Ward, James M.; Wang, Rui; Audet, Jean-Nicolas; Kellis, Manolis; Mukherjee, Sayan; Sinha, Saurabh; Hartemink, Alexander J.; West, Anne E.; Jarvis, Erich D.

2015-01-01

Songbirds represent an important model organism for elucidating molecular mechanisms that link genes with complex behaviors, in part because they have discrete vocal learning circuits that have parallels with those that mediate human speech. We found that ~10% of the genes in the avian genome were regulated by singing, and we found a striking regional diversity of both basal and singing-induced programs in the four key song nuclei of the zebra finch, a vocal learning songbird. The region-enriched patterns were a result of distinct combinations of region-enriched transcription factors (TFs), their binding motifs, and presinging acetylation of histone 3 at lysine 27 (H3K27ac) enhancer activity in the regulatory regions of the associated genes. RNA interference manipulations validated the role of the calcium-response transcription factor (CaRF) in regulating genes preferentially expressed in specific song nuclei in response to singing. Thus, differential combinatorial binding of a small group of activity-regulated TFs and predefined epigenetic enhancer activity influences the anatomical diversity of behaviorally regulated gene networks. PMID:25504732

Intrathecal administration of AYX2 DNA decoy produces a long-term pain treatment in rat models of chronic pain by inhibiting the KLF6, KLF9, and KLF15 transcription factors

PubMed Central

Klukinov, Michael; Harris, Scott; Manning, Donald C; Xie, Simon; Pascual, Conrado; Taylor, Bradley K; Donahue, Renee R; Yeomans, David C

2017-01-01

Background Nociception is maintained by genome-wide regulation of transcription in the dorsal root ganglia—spinal cord network. Hence, transcription factors constitute a promising class of targets for breakthrough pharmacological interventions to treat chronic pain. DNA decoys are oligonucleotides and specific inhibitors of transcription factor activities. A methodological series of in vivo–in vitro screening cycles was performed with decoy/transcription factor couples to identify targets capable of producing a robust and long-lasting inhibition of established chronic pain. Decoys were injected intrathecally and their efficacy was tested in the spared nerve injury and chronic constriction injury models of chronic pain in rats using repetitive von Frey testing. Results Results demonstrated that a one-time administration of decoys binding to the Kruppel-like transcription factors (KLFs) 6, 9, and 15 produces a significant and weeks–month long reduction in mechanical hypersensitivity compared to controls. In the spared nerve injury model, decoy efficacy was correlated to its capacity to bind KLF15 and KLF9 at a specific ratio, while in the chronic constriction injury model, efficacy was correlated to the combined binding capacity to KLF6 and KLF9. AYX2, an 18-bp DNA decoy binding KLF6, KLF9, and KLF15, was optimized for clinical development, and it demonstrated significant efficacy in these models. Conclusions These data highlight KLF6, KLF9, and KLF15 as transcription factors required for the maintenance of chronic pain and illustrate the potential therapeutic benefits of AYX2 for the treatment of chronic pain. PMID:28814144

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1.

PubMed

Gonzalez, J; Marchand-Geneste, N; Giraudel, J L; Shimada, T

2012-01-01

To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

Functional classification of skeletal muscle networks. I. Normal physiology

PubMed Central

Wang, Yu; Winters, Jack

2012-01-01

Extensive measurements of the parts list of human skeletal muscle through transcriptomics and other phenotypic assays offer the opportunity to reconstruct detailed functional models. Through integration of vast amounts of data present in databases and extant knowledge of muscle function combined with robust analyses that include a clustering approach, we present both a protein parts list and network models for skeletal muscle function. The model comprises the four key functional family networks that coexist within a functional space; namely, excitation-activation family (forward pathways that transmit a motoneuronal command signal into the spatial volume of the cell and then use Ca2+ fluxes to bind Ca2+ to troponin C sites on F-actin filaments, plus transmembrane pumps that maintain transmission capacity); mechanical transmission family (a sophisticated three-dimensional mechanical apparatus that bidirectionally couples the millions of actin-myosin nanomotors with external axial tensile forces at insertion sites); metabolic and bioenergetics family (pathways that supply energy for the skeletal muscle function under widely varying demands and provide for other cellular processes); and signaling-production family (which represents various sensing, signal transduction, and nuclear infrastructure that controls the turn over and structural integrity and regulates the maintenance, regeneration, and remodeling of the muscle). Within each family, we identify subfamilies that function as a unit through analysis of large-scale transcription profiles of muscle and other tissues. This comprehensive network model provides a framework for exploring functional mechanisms of the skeletal muscle in normal and pathophysiology, as well as for quantitative modeling. PMID:23085959

On the intrinsic flexibility of the opioid receptor through multiscale modeling approaches

NASA Astrophysics Data System (ADS)

Vercauteren, Daniel; FosséPré, Mathieu; Leherte, Laurence; Laaksonen, Aatto

Numerous releases of G protein-coupled receptors crystalline structures created the opportunity for computational methods to widely explore their dynamics. Here, we study the biological implication of the intrinsic flexibility properties of opioid receptor OR. First, one performed classical all-atom (AA) Molecular Dynamics (MD) simulations of OR in its apo-form. We highlighted that the various degrees of bendability of the α-helices present important consequences on the plasticity of the binding site. Hence, this latter adopts a wide diversity of shape and volume, explaining why OR interacts with very diverse ligands. Then, one introduces a new strategy for parameterizing purely mechanical but precise coarse-grained (CG) elastic network models (ENMs). The CG ENMs reproduced in a high accurate way the flexibility properties of OR versus the AA simulations. At last, one uses network modularization to design multi-grained (MG) models. They represent a novel type of low resolution models, different in nature versus CG models as being true multi-resolution models, i . e ., each MG grouping a different number of residues. The three parts constitute hierarchical and multiscale approach for tackling the flexibility of OR.

New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

PubMed

Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

2012-01-21

Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

PubMed

Metwally, Abdelkader A; Hathout, Rania M

2015-08-03

We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

Architecture of the Saccharomyces cerevisiae SAGA transcription coactivator complex

PubMed Central

Han, Yan; Luo, Jie; Ranish, Jeffrey; Hahn, Steven

2014-01-01

The conserved transcription coactivator SAGA is comprised of several modules that are involved in activator binding, TBP binding, histone acetylation (HAT) and deubiquitination (DUB). Crosslinking and mass spectrometry, together with genetic and biochemical analyses, were used to determine the molecular architecture of the SAGA-TBP complex. We find that the SAGA Taf and Taf-like subunits form a TFIID-like core complex at the center of SAGA that makes extensive interactions with all other SAGA modules. SAGA-TBP binding involves a network of interactions between subunits Spt3, Spt8, Spt20, and Spt7. The HAT and DUB modules are in close proximity, and the DUB module modestly stimulates HAT function. The large activator-binding subunit Tra1 primarily connects to the TFIID-like core via its FAT domain. These combined results were used to derive a model for the arrangement of the SAGA subunits and its interactions with TBP. Our results provide new insight into SAGA function in gene regulation, its structural similarity with TFIID, and functional interactions between the SAGA modules. PMID:25216679

Delay and Disruption Tolerant Networking MACHETE Model

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.; Gao, Jay L.

2011-01-01

To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity, the ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses.

Simulation Modeling and Performance Evaluation of Space Networks

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John

2006-01-01

In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions

Modeling the Binding of Neurotransmitter Transporter Inhibitors with Molecular Dynamics and Free Energy Calculations

NASA Astrophysics Data System (ADS)

Jean, Bernandie

The monoamine transporter (MAT) proteins responsible for the reuptake of the neurotransmitter substrates, dopamine, serotonin, and norepinephrine, are drug targets for the treatment of psychiatric disorders including depression, anxiety, and attention deficit hyperactivity disorder. Small molecules that inhibit these proteins can serve as useful therapeutic agents. However, some dopamine transporter (DAT) inhibitors, such as cocaine and methamphetamine, are highly addictive and abusable. Efforts have been made to develop small molecules that will inhibit the transporters and elucidate specific binding site interactions. This work provides knowledge of molecular interactions associated with MAT inhibitors by offering an atomistic perspective that can guide designs of new pharmacotherapeutics with enhanced activity. The work described herein evaluates intermolecular interactions using computational methods to reveal the mechanistic detail of inhibitors binding in the DAT. Because cocaine recognizes the extracellular-facing or outward-facing (OF) DAT conformation and benztropine recognizes the intracellular-facing or inward-facing (IF) conformation, it was postulated that behaviorally "typical" (abusable, locomotor psychostimulant) inhibitors stabilize the OF DAT and "atypical" (little or no abuse potential) inhibitors favor IF DAT. Indeed, behaviorally-atypical cocaine analogs have now been shown to prefer the OF DAT conformation. Specifically, the binding interactions of two cocaine analogs, LX10 and LX11, were studied in the OF DAT using molecular dynamics simulations. LX11 was able to interact with residues of transmembrane helix 8 and bind in a fashion that allowed for hydration of the primary binding site (S1) from the intracellular space, thus impacting the intracellular interaction network capable of regulating conformational transitions in DAT. Additionally, a novel serotonin transporter (SERT) inhibitor previously discovered through virtual screening at the SERT secondary binding site (S2) was studied. Intermolecular interactions between SM11 and SERT have been assessed using binding free energy calculations to predict the ligand-binding site and optimize ligand-binding interactions. Results indicate the addition of atoms to the 4-chlorobenzyl moiety were most energetically favorable. The simulations carried out in DAT and SERT were supported by experimental results. Furthermore, the co-crystal structures of DAT and SERT share similar ligand-binding interactions with the homology models used in this study.

Enterprise systems security management: a framework for breakthrough protection

NASA Astrophysics Data System (ADS)

Farroha, Bassam S.; Farroha, Deborah L.

2010-04-01

Securing the DoD information network is a tremendous task due to its size, access locations and the amount of network intrusion attempts on a daily basis. This analysis investigates methods/architecture options to deliver capabilities for secure information sharing environment. Crypto-binding and intelligent access controls are basic requirements for secure information sharing in a net-centric environment. We introduce many of the new technology components to secure the enterprise. The cooperative mission requirements lead to developing automatic data discovery and data stewards granting access to Cross Domain (CD) data repositories or live streaming data. Multiple architecture models are investigated to determine best-of-breed approaches including SOA and Private/Public Clouds.

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry.

PubMed

Pflieger, Delphine; Gonnet, Florence; de la Fuente van Bentem, Sergio; Hirt, Heribert; de la Fuente, Alberto

2011-01-01

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks. Copyright © 2010 Wiley Periodicals, Inc.

Bio-inspired network optimization in soft materials — Insights from the plant cell wall

NASA Astrophysics Data System (ADS)

Vincent, R. R.; Cucheval, A.; Hemar, Y.; Williams, M. A. K.

2009-01-01

The dynamic-mechanical responses of ionotropic gels made from the biopolymer pectin have recently been investigated by microrheological experiments and found to exhibit behaviour indicative of semi-flexible polymer networks. In this work we investigate the gelling behaviour of pectin systems in which an enzyme (pectinmethylesterase, PME) is used to liberate ion-binding sites on initially inert polymers, while in the presence of ions. This is in contrast to the previous work, where it was the release of ions (rather than ion-binding groups) that was controlled and the polymers had pre-existing cross-linkable moieties. In stark contrast to the semi-flexible network paradigm of biological gels and the previous work on pectin, the gels studied herein exhibit the properties of chemically cross-linked networks of flexible polymers.

On the binding determinants of the glutamate agonist with the glutamate receptor ligand binding domain.

PubMed

Speranskiy, Kirill; Kurnikova, Maria

2005-08-30

Ionotropic glutamate receptors (GluRs) are ligand-gated membrane channel proteins found in the central neural system that mediate a fast excitatory response of neurons. In this paper, we report theoretical analysis of the ligand-protein interactions in the binding pocket of the S1S2 (ligand binding) domain of the GluR2 receptor in the closed conformation. By utilizing several theoretical methods ranging from continuum electrostatics to all-atom molecular dynamics simulations and quantum chemical calculations, we were able to characterize in detail glutamate agonist binding to the wild-type and E705D mutant proteins. A theoretical model of the protein-ligand interactions is validated via direct comparison of theoretical and Fourier transform infrared spectroscopy (FTIR) measured frequency shifts of the ligand's carboxylate group vibrations [Jayaraman et al. (2000) Biochemistry 39, 8693-8697; Cheng et al. (2002) Biochemistry 41, 1602-1608]. A detailed picture of the interactions in the binding site is inferred by analyzing contributions to vibrational frequencies produced by protein residues forming the ligand-binding pocket. The role of mobility and hydrogen-bonding network of water in the ligand-binding pocket and the contribution of protein residues exposed in the binding pocket to the binding and selectivity of the ligand are discussed. It is demonstrated that the molecular surface of the protein in the ligand-free state has mainly positive electrostatic potential attractive to the negatively charged ligand, and the potential produced by the protein in the ligand-binding pocket in the closed state is complementary to the distribution of the electrostatic potential produced by the ligand itself. Such charge complementarity ensures specificity to the unique charge distribution of the ligand.

Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation.

PubMed

Verkhivker, G M

2016-10-20

Protein kinases are central to proper functioning of cellular networks and are an integral part of many signal transduction pathways. The family of protein kinases represents by far the largest and most important class of therapeutic targets in oncology. Dimerization-induced activation has emerged as a common mechanism of allosteric regulation in BRAF kinases, which play an important role in growth factor signalling and human diseases. Recent studies have revealed that most of the BRAF inhibitors can induce dimerization and paradoxically stimulate enzyme transactivation by conferring an active conformation in the second monomer of the kinase dimer. The emerging connections between inhibitor binding and BRAF kinase domain dimerization have suggested a molecular basis of the activation mechanism in which BRAF inhibitors may allosterically modulate the stability of the dimerization interface and affect the organization of residue interaction networks in BRAF kinase dimers. In this work, we integrated structural bioinformatics analysis, molecular dynamics and binding free energy simulations with the protein structure network analysis of the BRAF crystal structures to determine dynamic signatures of BRAF conformations in complexes with different types of inhibitors and probe the mechanisms of the inhibitor-induced dimerization and paradoxical activation. The results of this study highlight previously unexplored relationships between types of BRAF inhibitors, inhibitor-induced changes in the residue interaction networks and allosteric modulation of the kinase activity. This study suggests a mechanism by which BRAF inhibitors could promote or interfere with the paradoxical activation of BRAF kinases, which may be useful in informing discovery efforts to minimize the unanticipated adverse biological consequences of these therapeutic agents.

Recurrent rewiring and emergence of RNA regulatory networks.

PubMed

Wilinski, Daniel; Buter, Natascha; Klocko, Andrew D; Lapointe, Christopher P; Selker, Eric U; Gasch, Audrey P; Wickens, Marvin

2017-04-04

Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

Functional connectivity supporting the selective maintenance of feature-location binding in visual working memory

PubMed Central

Takahama, Sachiko; Saiki, Jun

2014-01-01

Information on an object's features bound to its location is very important for maintaining object representations in visual working memory. Interactions with dynamic multi-dimensional objects in an external environment require complex cognitive control, including the selective maintenance of feature-location binding. Here, we used event-related functional magnetic resonance imaging to investigate brain activity and functional connectivity related to the maintenance of complex feature-location binding. Participants were required to detect task-relevant changes in feature-location binding between objects defined by color, orientation, and location. We compared a complex binding task requiring complex feature-location binding (color-orientation-location) with a simple binding task in which simple feature-location binding, such as color-location, was task-relevant and the other feature was task-irrelevant. Univariate analyses showed that the dorsolateral prefrontal cortex (DLPFC), hippocampus, and frontoparietal network were activated during the maintenance of complex feature-location binding. Functional connectivity analyses indicated cooperation between the inferior precentral sulcus (infPreCS), DLPFC, and hippocampus during the maintenance of complex feature-location binding. In contrast, the connectivity for the spatial updating of simple feature-location binding determined by reanalyzing the data from Takahama et al. (2010) demonstrated that the superior parietal lobule (SPL) cooperated with the DLPFC and hippocampus. These results suggest that the connectivity for complex feature-location binding does not simply reflect general memory load and that the DLPFC and hippocampus flexibly modulate the dorsal frontoparietal network, depending on the task requirements, with the infPreCS involved in the maintenance of complex feature-location binding and the SPL involved in the spatial updating of simple feature-location binding. PMID:24917833

Functional connectivity supporting the selective maintenance of feature-location binding in visual working memory.

PubMed

Takahama, Sachiko; Saiki, Jun

2014-01-01

Information on an object's features bound to its location is very important for maintaining object representations in visual working memory. Interactions with dynamic multi-dimensional objects in an external environment require complex cognitive control, including the selective maintenance of feature-location binding. Here, we used event-related functional magnetic resonance imaging to investigate brain activity and functional connectivity related to the maintenance of complex feature-location binding. Participants were required to detect task-relevant changes in feature-location binding between objects defined by color, orientation, and location. We compared a complex binding task requiring complex feature-location binding (color-orientation-location) with a simple binding task in which simple feature-location binding, such as color-location, was task-relevant and the other feature was task-irrelevant. Univariate analyses showed that the dorsolateral prefrontal cortex (DLPFC), hippocampus, and frontoparietal network were activated during the maintenance of complex feature-location binding. Functional connectivity analyses indicated cooperation between the inferior precentral sulcus (infPreCS), DLPFC, and hippocampus during the maintenance of complex feature-location binding. In contrast, the connectivity for the spatial updating of simple feature-location binding determined by reanalyzing the data from Takahama et al. (2010) demonstrated that the superior parietal lobule (SPL) cooperated with the DLPFC and hippocampus. These results suggest that the connectivity for complex feature-location binding does not simply reflect general memory load and that the DLPFC and hippocampus flexibly modulate the dorsal frontoparietal network, depending on the task requirements, with the infPreCS involved in the maintenance of complex feature-location binding and the SPL involved in the spatial updating of simple feature-location binding.

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain.

PubMed

Hou, Tingjun; Zhang, Wei; Case, David A; Wang, Wei

2008-02-29

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.

Biologically Plausible, Human-Scale Knowledge Representation.

PubMed

Crawford, Eric; Gingerich, Matthew; Eliasmith, Chris

2016-05-01

Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony (Shastri & Ajjanagadde, ), "mesh" binding (van der Velde & de Kamps, ), and conjunctive binding (Smolensky, ). Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture (SPA) approach to modeling cognition (Eliasmith, ) do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well-suited to providing a biologically plausible account of the structured representations that underwrite human cognition. Copyright © 2015 Cognitive Science Society, Inc.

Inference of Expanded Lrp-Like Feast/Famine Transcription Factor Targets in a Non-Model Organism Using Protein Structure-Based Prediction

PubMed Central

Ashworth, Justin; Plaisier, Christopher L.; Lo, Fang Yin; Reiss, David J.; Baliga, Nitin S.

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer. PMID:25255272

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

PubMed

Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Differential regulation of NMDA receptors by d-serine and glycine in mammalian spinal locomotor networks

PubMed Central

Acton, David

2017-01-01

Activation of N-methyl-d-aspartate receptors (NMDARs) requires the binding of a coagonist, either d-serine or glycine, in addition to glutamate. Changes in occupancy of the coagonist binding site are proposed to modulate neural networks including those controlling swimming in frog tadpoles. Here, we characterize regulation of the NMDAR coagonist binding site in mammalian spinal locomotor networks. Blockade of NMDARs by d(−)-2-amino-5-phosphonopentanoic acid (d-APV) or 5,7-dichlorokynurenic acid reduced the frequency and amplitude of pharmacologically induced locomotor-related activity recorded from the ventral roots of spinal-cord preparations from neonatal mice. Furthermore, d-APV abolished synchronous activity induced by blockade of inhibitory transmission. These results demonstrate an important role for NMDARs in murine locomotor networks. Bath-applied d-serine enhanced the frequency of locomotor-related but not disinhibited bursting, indicating that coagonist binding sites are saturated during the latter but not the former mode of activity. Depletion of endogenous d-serine by d-amino acid oxidase or the serine-racemase inhibitor erythro-β-hydroxy-l-aspartic acid (HOAsp) increased the frequency of locomotor-related activity, whereas application of l-serine to enhance endogenous d-serine synthesis reduced burst frequency, suggesting a requirement for d-serine at a subset of synapses onto inhibitory interneurons. Consistent with this, HOAsp was ineffective during disinhibited activity. Bath-applied glycine (1–100 µM) failed to alter locomotor-related activity, whereas ALX 5407, a selective inhibitor of glycine transporter-1 (GlyT1), enhanced burst frequency, supporting a role for GlyT1 in NMDAR regulation. Together these findings indicate activity-dependent and synapse-specific regulation of the coagonist binding site within spinal locomotor networks, illustrating the importance of NMDAR regulation in shaping motor output. NEW & NOTEWORTHY We provide evidence that NMDARs within murine spinal locomotor networks determine the frequency and amplitude of ongoing locomotor-related activity in vitro and that NMDARs are regulated by d-serine and glycine in a synapse-specific and activity-dependent manner. In addition, glycine transporter-1 is shown to be an important regulator of NMDARs during locomotor-related activity. These results show how excitatory transmission can be tuned to diversify the output repertoire of spinal locomotor networks in mammals. PMID:28202572

Out of Place, Out of Mind: Schema-Driven False Memory Effects for Object-Location Bindings

ERIC Educational Resources Information Center

Lew, Adina R.; Howe, Mark L.

2017-01-01

Events consist of diverse elements, each processed in specialized neocortical networks, with temporal lobe memory systems binding these elements to form coherent event memories. We provide a novel theoretical analysis of an unexplored consequence of the independence of memory systems for elements and their bindings, 1 that raises the paradoxical…

Mechanism of conformational coupling in SecA: Key role of hydrogen-bonding networks and water interactions.

PubMed

Milenkovic, Stefan; Bondar, Ana-Nicoleta

2016-02-01

SecA uses the energy yielded by the binding and hydrolysis of adenosine triphosphate (ATP) to push secretory pre-proteins across the plasma membrane in bacteria. Hydrolysis of ATP occurs at the nucleotide-binding site, which contains the conserved carboxylate groups of the DEAD-box helicases. Although crystal structures provide valuable snapshots of SecA along its reaction cycle, the mechanism that ensures conformational coupling between the nucleotide-binding site and the other domains of SecA remains unclear. The observation that SecA contains numerous hydrogen-bonding groups raises important questions about the role of hydrogen-bonding networks and hydrogen-bond dynamics in long-distance conformational couplings. To address these questions, we explored the molecular dynamics of SecA from three different organisms, with and without bound nucleotide, in water. By computing two-dimensional hydrogen-bonding maps we identify networks of hydrogen bonds that connect the nucleotide-binding site to remote regions of the protein, and sites in the protein that respond to specific perturbations. We find that the nucleotide-binding site of ADP-bound SecA has a preferred geometry whereby the first two carboxylates of the DEAD motif bridge via hydrogen-bonding water. Simulations of a mutant with perturbed ATP hydrolysis highlight the water-bridged geometry as a key structural element of the reaction path. Copyright © 2015. Published by Elsevier B.V.

Programming of a Mn-coordinated 4-4‧-biphenyl dicarboxylic acid nanosystem on Au(1 1 1) and investigation of the non-covalent binding of C60 molecules

NASA Astrophysics Data System (ADS)

Zhang, Yan-Feng; Zhu, Na; Komeda, T.

The fabrication of Mn-based coordination networks on a Au(1 1 1) substrate with 4-4 '-biphenyl dicarboxylic acid (BDA) as the linker molecule was investigated by scanning tunneling microscopy. Intriguing structures of ladder and rectangular-shaped networks were obtained by controlling the ratios of deposited amount of BDA molecules and Mn atoms. These structures are well explained by models in which BDA molecules occupy the perimeter of the rectangles and a pair of two Mn atoms are placed at the lattice points. For the rectangular structure, further two phases of a rectangular and a square networks were identified in which the paired Mn atoms were directing an identical direction and 90° rotated in an alternate manner, respectively. In addition, it was revealed that the open space surrounded by rectangle BDA molecules could capture a dimer of C60 molecules which were deposited on the Mn-based BDA networks.

Receptor binding mode and pharmacological characterization of a potent and selective dual CXCR1/CXCR2 non-competitive allosteric inhibitor

PubMed Central

Bertini, R; Barcelos, LS; Beccari, AR; Cavalieri, B; Moriconi, A; Bizzarri, C; Di Benedetto, P; Di Giacinto, C; Gloaguen, I; Galliera, E; Corsi, MM; Russo, RC; Andrade, SP; Cesta, MC; Nano, G; Aramini, A; Cutrin, JC; Locati, M; Allegretti, M; Teixeira, MM

2012-01-01

BACKGROUND AND PURPOSE DF 2156A is a new dual inhibitor of IL-8 receptors CXCR1 and CXCR2 with an optimal pharmacokinetic profile. We characterized its binding mode, molecular mechanism of action and selectivity, and evaluated its therapeutic potential. EXPERIMENTAL APPROACH The binding mode, molecular mechanism of action and selectivity were investigated using chemotaxis of L1.2 transfectants and human leucocytes, in addition to radioligand and [35S]-GTPγS binding approaches. The therapeutic potential of DF 2156A was evaluated in acute (liver ischaemia and reperfusion) and chronic (sponge-induced angiogenesis) experimental models of inflammation. KEY RESULTS A network of polar interactions stabilized by a direct ionic bond between DF 2156A and Lys99 on CXCR1 and the non-conserved residue Asp293 on CXCR2 are the key determinants of DF 2156A binding. DF 2156A acted as a non-competitive allosteric inhibitor blocking the signal transduction leading to chemotaxis without altering the binding affinity of natural ligands. DF 2156A effectively and selectively inhibited CXCR1/CXCR2-mediated chemotaxis of L1.2 transfectants and leucocytes. In a murine model of sponge-induced angiogenesis, DF 2156A reduced leucocyte influx, TNF-α production and neovessel formation. In vitro, DF 2156A prevented proliferation, migration and capillary-like organization of HUVECs in response to human IL-8. In a rat model of liver ischaemia and reperfusion (I/R) injury, DF 2156A decreased PMN and monocyte-macrophage infiltration and associated hepatocellular injury. CONCLUSION AND IMPLICATIONS DF 2156A is a non-competitive allosteric inhibitor of both IL-8 receptors CXCR1 and CXCR2. It prevented experimental angiogenesis and hepatic I/R injury in vivo and, therefore, has therapeutic potential for acute and chronic inflammatory diseases. PMID:21718305

Specific minor groove solvation is a crucial determinant of DNA binding site recognition

PubMed Central

Harris, Lydia-Ann; Williams, Loren Dean; Koudelka, Gerald B.

2014-01-01

The DNA sequence preferences of nearly all sequence specific DNA binding proteins are influenced by the identities of bases that are not directly contacted by protein. Discrimination between non-contacted base sequences is commonly based on the differential abilities of DNA sequences to allow narrowing of the DNA minor groove. However, the factors that govern the propensity of minor groove narrowing are not completely understood. Here we show that the differential abilities of various DNA sequences to support formation of a highly ordered and stable minor groove solvation network are a key determinant of non-contacted base recognition by a sequence-specific binding protein. In addition, disrupting the solvent network in the non-contacted region of the binding site alters the protein's ability to recognize contacted base sequences at positions 5–6 bases away. This observation suggests that DNA solvent interactions link contacted and non-contacted base recognition by the protein. PMID:25429976

Identification of a regulation network in response to cadmium toxicity using blood clam Tegillarca granosa as model

PubMed Central

Bao, Yongbo; Liu, Xiao; Zhang, Weiwei; Cao, Jianping; Li, Wei; Li, Chenghua; Lin, Zhihua

2016-01-01

Clam, a filter-feeding lamellibranch mollusk, is capable to accumulate high levels of trace metals and has therefore become a model for investigation the mechanism of heavy metal toxification. In this study, the effects of cadmium were characterized in the gills of Tegillarca granosa during a 96-hour exposure course using integrated metabolomic and proteomic approaches. Neurotoxicity and disturbances in energy metabolism were implicated according to the metabolic responses after Cd exposure, and eventually affected the osmotic function of gill tissue. Proteomic analysis showed that oxidative stress, calcium-binding and sulfur-compound metabolism proteins were key factors responding to Cd challenge. A knowledge-based network regulation model was constructed with both metabolic and proteomic data. The model suggests that Cd stimulation mainly inhibits a core regulation network that is associated with histone function, ribosome processing and tight junctions, with the hub proteins actin, gamma 1 and Calmodulin 1. Moreover, myosin complex inhibition causes abnormal tight junctions and is linked to the irregular synthesis of amino acids. For the first time, this study provides insight into the proteomic and metabolomic changes caused by Cd in the blood clam T. granosa and suggests a potential toxicological pathway for Cd. PMID:27760991

Modeling Analyte Transport and Capture in Porous Bead Sensors

PubMed Central

Chou, Jie; Lennart, Alexis; Wong, Jorge; Ali, Mehnaaz F.; Floriano, Pierre N.; Christodoulides, Nicolaos; Camp, James; McDevitt, John T.

2013-01-01

Porous agarose microbeads, with high surface to volume ratios and high binding densities, are attracting attention as highly sensitive, affordable sensor elements for a variety of high performance bioassays. While such polymer microspheres have been extensively studied and reported on previously and are now moving into real-world clinical practice, very little work has been completed to date to model the convection, diffusion, and binding kinetics of soluble reagents captured within such fibrous networks. Here, we report the development of a three-dimensional computational model and provide the initial evidence for its agreement with experimental outcomes derived from the capture and detection of representative protein and genetic biomolecules in 290μm porous beads. We compare this model to antibody-mediated capture of C-reactive protein and bovine serum albumin, along with hybridization of oligonucleotide sequences to DNA probes. These results suggest that due to the porous interior of the agarose bead, internal analyte transport is both diffusion- and convection-based, and regardless of the nature of analyte, the bead interiors reveal an interesting trickle of convection-driven internal flow. Based on this model, the internal to external flow rate ratio is found to be in the range of 1:3100 to 1:170 for beads with agarose concentration ranging from 0.5% to 8% for the sensor ensembles here studied. Further, both model and experimental evidence suggest that binding kinetics strongly affect analyte distribution of captured reagents within the beads. These findings reveal that high association constants create a steep moving boundary in which unbound analytes are held back at the periphery of the bead sensor. Low association constants create a more shallow moving boundary in which unbound analytes diffuse further into the bead before binding. These models agree with experimental evidence and thus serve as a new tool set for the study of bio-agent transport processes within a new class of medical microdevices. PMID:22250703

In-vivo detection of binary PKA network interactions upon activation of endogenous GPCRs

PubMed Central

Röck, Ruth; Bachmann, Verena; Bhang, Hyo-eun C; Malleshaiah, Mohan; Raffeiner, Philipp; Mayrhofer, Johanna E; Tschaikner, Philipp M; Bister, Klaus; Aanstad, Pia; Pomper, Martin G; Michnick, Stephen W; Stefan, Eduard

2015-01-01

Membrane receptor-sensed input signals affect and modulate intracellular protein-protein interactions (PPIs). Consequent changes occur to the compositions of protein complexes, protein localization and intermolecular binding affinities. Alterations of compartmentalized PPIs emanating from certain deregulated kinases are implicated in the manifestation of diseases such as cancer. Here we describe the application of a genetically encoded Protein-fragment Complementation Assay (PCA) based on the Renilla Luciferase (Rluc) enzyme to compare binary PPIs of the spatially and temporally controlled protein kinase A (PKA) network in diverse eukaryotic model systems. The simplicity and sensitivity of this cell-based reporter allows for real-time recordings of mutually exclusive PPIs of PKA upon activation of selected endogenous G protein-coupled receptors (GPCRs) in cancer cells, xenografts of mice, budding yeast, and zebrafish embryos. This extends the application spectrum of Rluc PCA for the quantification of PPI-based receptor-effector relationships in physiological and pathological model systems. PMID:26099953

Mechano-genetic DNA hydrogels as a simple, reconstituted model to probe the effect of active fluctuations on gene transcription

NASA Astrophysics Data System (ADS)

Nguyen, Dan; Saleh, Omar

Active fluctuations - non-directed fluctuations attributable, not to thermal energy, but to non-equilibrium processes - are thought to influence biology by increasing the diffusive motion of biomolecules. Dense DNA regions within cells (i.e. chromatin) are expected to exhibit such phenomena, as they are cross-linked networks that continually experience propagating forces arising from dynamic cellular activity. Additional agitation within these gene-encoding DNA networks could have potential genetic consequences. By changing the local mobility of transcriptional machinery and regulatory proteins towards/from their binding sites, and thereby influencing transcription rates, active fluctuations could prove to be a physical means of modulating gene expression. To begin probing this effect, we construct genetic DNA hydrogels, as a simple, reconstituted model of chromatin, and quantify transcriptional output from these hydrogels in the presence/absence of active fluctuations.

Is it the shape of the cavity, or the shape of the water in the cavity?

NASA Astrophysics Data System (ADS)

Snyder, Phillip W.; Lockett, Matthew R.; Moustakas, Demetri T.; Whitesides, George M.

2014-04-01

Historical interpretations of the thermodynamics characterizing biomolecular recognition have marginalized the role of water. An important (even, perhaps, dominant) contribution to molecular recognition in water comes from the "hydrophobic effect," in which non-polar portions of a ligand interact preferentially with non-polar regions of a protein. Water surrounds the ligand, and water fills the binding pocket of the protein: when the protein-ligand complex forms, and hydrophobic surfaces of the binding pocket and the ligand approach one another, the molecules (and hydrogen-bonded networks of molecules) of water associated with both surfaces rearrange and, in part, entirely escape into the bulk solution. It is now clear that neither of the two most commonly cited rationalizations for the hydrophobic effect-an entropy-dominated hydrophobic effect, in which ordered waters at the surface of the ligand, and water at the surface of the protein, are released to the bulk upon binding, and a "lock-and-key" model, in which the surface of a ligand interacts directly with a surface of a protein having a complementary shape-can account for water-mediated interactions between the ligand and the protein, and neither is sufficient to account for the experimental observation of both entropy- andenthalpy-dominated hydrophobic effects. What is now clear is that there is no single hydrophobic effect, with a universally applicable, common, thermodynamic description: different processes (i.e., partitioning between phases of different hydrophobicity, aggregation in water, and binding) with different thermodynamics, depend on the molecular-level details of the structures of the molecules involved, and of the aggregates that form. A "water-centric" description of the hydrophobic effect in biomolecular recognition focuses on the structures of water surrounding the ligand, and of water filling the binding pocket of the protein, both before and after binding. This view attributes the hydrophobic effect to changes in the free energy of the networks of hydrogen bonds that are formed, broken, or re-arranged when two hydrophobic surfaces approach (but do not necessarily contact) one another. The details of the molecular topography (and the polar character) of the mole- cular surfaces play an important role in determining the structure of these networks of hydrogen-bonded waters, and in the thermodynamic description of the hydrophobic effect(s). Theorists have led the formulation of this "water-centric view", although experiments are now supplying support for it. It poses complex problems for would-be "designers" of protein-ligand interactions, and for so-called "rational drug design".

Open Source Software Projects Needing Security Investments

DTIC Science & Technology

2015-06-19

modtls, BouncyCastle, gpg, otr, axolotl. 7. Static analyzers: Clang, Frama-C. 8. Nginx. 9. OpenVPN . It was noted that the funding model may be similar...to OpenSSL, where consulting funds the company. It was also noted that OpenVPN needs to correctly use OpenSSL in order to be secure, so focusing on...Dovecot 4. Other high-impact network services: OpenSSH, OpenVPN , BIND, ISC DHCP, University of Delaware NTPD 5. Core infrastructure data parsers

Energy transport pathway in proteins: Insights from non-equilibrium molecular dynamics with elastic network model.

PubMed

Wang, Wei Bu; Liang, Yu; Zhang, Jing; Wu, Yi Dong; Du, Jian Jun; Li, Qi Ming; Zhu, Jian Zhuo; Su, Ji Guo

2018-06-22

Intra-molecular energy transport between distant functional sites plays important roles in allosterically regulating the biochemical activity of proteins. How to identify the specific intra-molecular signaling pathway from protein tertiary structure remains a challenging problem. In the present work, a non-equilibrium dynamics method based on the elastic network model (ENM) was proposed to simulate the energy propagation process and identify the specific signaling pathways within proteins. In this method, a given residue was perturbed and the propagation of energy was simulated by non-equilibrium dynamics in the normal modes space of ENM. After that, the simulation results were transformed from the normal modes space to the Cartesian coordinate space to identify the intra-protein energy transduction pathways. The proposed method was applied to myosin and the third PDZ domain (PDZ3) of PSD-95 as case studies. For myosin, two signaling pathways were identified, which mediate the energy transductions form the nucleotide binding site to the 50 kDa cleft and the converter subdomain, respectively. For PDZ3, one specific signaling pathway was identified, through which the intra-protein energy was transduced from ligand binding site to the distant opposite side of the protein. It is also found that comparing with the commonly used cross-correlation analysis method, the proposed method can identify the anisotropic energy transduction pathways more effectively.

Ties that Bind: A Social Network Approach To Understanding Student Integration and Persistence.

ERIC Educational Resources Information Center

Thomas, Scott L.

2000-01-01

This study used a social network paradigm to examine college student integration of 329 college freshmen at a private liberal arts college. Analysis of the structural aspects of students' on-campus associations found differential effects of various social network characteristics on student commitment and persistence. (DB)

CBL-CIPK network for calcium signaling in higher plants

NASA Astrophysics Data System (ADS)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

Protein Aggregates Are Recruited to Aggresome by Histone Deacetylase 6 via Unanchored Ubiquitin C Termini*

PubMed Central

Ouyang, Hui; Ali, Yousuf O.; Ravichandran, Mani; Dong, Aiping; Qiu, Wei; MacKenzie, Farrell; Dhe-Paganon, Sirano; Arrowsmith, Cheryl H.; Zhai, R. Grace

2012-01-01

The aggresome pathway is activated when proteasomal clearance of misfolded proteins is hindered. Misfolded polyubiquitinated protein aggregates are recruited and transported to the aggresome via the microtubule network by a protein complex consisting of histone deacetylase 6 (HDAC6) and the dynein motor complex. The current model suggests that HDAC6 recognizes protein aggregates by binding directly to polyubiquitinated proteins. Here, we show that there are substantial amounts of unanchored ubiquitin in protein aggregates with solvent-accessible C termini. The ubiquitin-binding domain (ZnF-UBP) of HDAC6 binds exclusively to the unanchored C-terminal diglycine motif of ubiquitin instead of conjugated polyubiquitin. The unanchored ubiquitin C termini in the aggregates are generated in situ by aggregate-associated deubiquitinase ataxin-3. These results provide structural and mechanistic bases for the role of HDAC6 in aggresome formation and further suggest a novel ubiquitin-mediated signaling pathway, where the exposure of ubiquitin C termini within protein aggregates enables HDAC6 recognition and transport to the aggresome. PMID:22069321

Investigation of electronic transport through a ladder-like graphene nanoribbon including random distributed impurities

NASA Astrophysics Data System (ADS)

Esmaili, Esmat; Mardaani, Mohammad; Rabani, Hassan

2018-01-01

The electronic transport of a ladder-like graphene nanoribbon which the on-site or hopping energies of a small part of it can be random is modeled by using the Green's function technique within the nearest neighbor tight-binding approach. We employ a unitary transformation in order to convert the Hamiltonian of the nanoribbon to the Hamiltonian of a tight-binding ladder-like network. In this case, the disturbed part of the system includes the second neighbor hopping interactions. While, the converted Hamiltonian of each ideal part is equivalent to the Hamiltonian of two periodic on-site chains. Therefore, we can insert the self-energies of the alternative on-site tight-binding chains to the inverse of the Green's function matrix of the ladder-like part. In this viewpoint, the conductance is constructed from two trans and cis contributions. The results show that increasing the disorder strength causes the increase and decrease of the conductance of the trans and cis contributions, respectively.

The synthetic diazonamide DZ-2384 has distinct effects on microtubule curvature and dynamics without neurotoxicity

PubMed Central

Wieczorek, Michal; Tcherkezian, Joseph; Bernier, Cynthia; Prota, Andrea E.; Chaaban, Sami; Rolland, Yannève; Godbout, Claude; Hancock, Mark A.; Arezzo, Joseph C.; Ocal, Ozhan; Rocha, Cecilia; Olieric, Natacha; Hall, Anita; Ding, Hui; Bramoullé, Alexandre; Annis, Matthew G.; Zogopoulos, George; Harran, Patrick G.; Wilkie, Thomas M.; Brekken, Rolf A.; Siegel, Peter M.; Steinmetz, Michel O.; Shore, Gordon C.; Brouhard, Gary J.; Roulston, Anne

2017-01-01

Microtubule-targeting agents (MTAs) are widely used anticancer agents, but toxicities such as neuropathy limit their clinical use. MTAs bind to and alter the stability of microtubules, causing cell death in mitosis. We describe DZ-2384, a preclinical compound that exhibits potent antitumor activity in models of multiple cancer types. It has an unusually high safety margin and lacks neurotoxicity in rats at effective plasma concentrations. DZ-2384 binds the vinca domain of tubulin in a distinct way, imparting structurally and functionally different effects on microtubule dynamics compared to other vinca-binding compounds. X-ray crystallography and electron microscopy studies demonstrate that DZ-2384 causes straightening of curved protofilaments, an effect proposed to favor polymerization of tubulin. Both DZ-2384 and the vinca alkaloid vinorelbine inhibit microtubule growth rate; however, DZ-2384 increases the rescue frequency and preserves the microtubule network in nonmitotic cells and in primary neurons. This differential modulation of tubulin results in a potent MTA therapeutic with enhanced safety. PMID:27856798

Stable Binding of the Conserved Transcription Factor Grainy Head to its Target Genes Throughout Drosophila melanogaster Development

PubMed Central

Nevil, Markus; Bondra, Eliana R.; Schulz, Katharine N.; Kaplan, Tommy; Harrison, Melissa M.

2017-01-01

It has been suggested that transcription factor binding is temporally dynamic, and that changes in binding determine transcriptional output. Nonetheless, this model is based on relatively few examples in which transcription factor binding has been assayed at multiple developmental stages. The essential transcription factor Grainy head (Grh) is conserved from fungi to humans, and controls epithelial development and barrier formation in numerous tissues. Drosophila melanogaster, which possess a single grainy head (grh) gene, provide an excellent system to study this conserved factor. To determine whether temporally distinct binding events allow Grh to control cell fate specification in different tissue types, we used a combination of ChIP-seq and RNA-seq to elucidate the gene regulatory network controlled by Grh during four stages of embryonic development (spanning stages 5–17) and in larval tissue. Contrary to expectations, we discovered that Grh remains bound to at least 1146 genomic loci over days of development. In contrast to this stable DNA occupancy, the subset of genes whose expression is regulated by Grh varies. Grh transitions from functioning primarily as a transcriptional repressor early in development to functioning predominantly as an activator later. Our data reveal that Grh binds to target genes well before the Grh-dependent transcriptional program commences, suggesting it sets the stage for subsequent recruitment of additional factors that execute stage-specific Grh functions. PMID:28007888

Construction of regulatory networks using expression time-series data of a genotyped population.

PubMed

Yeung, Ka Yee; Dombek, Kenneth M; Lo, Kenneth; Mittler, John E; Zhu, Jun; Schadt, Eric E; Bumgarner, Roger E; Raftery, Adrian E

2011-11-29

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Biochemical and Structural Characterization of Lysophosphatidic Acid Binding by a Humanized Monoclonal Antibody

DOE Office of Scientific and Technical Information (OSTI.GOV)

J Fleming; J Wojciak; M Campbell

Lysophosphatidic acid (LPA) is a common product of glycerophospholipid metabolism and an important mediator of signal transduction. Aberrantly high LPA concentrations accompany multiple disease states. One potential approach for treatment of these diseases, therefore, is the therapeutic application of antibodies that recognize and bind LPA as their antigen. We have determined the X-ray crystal structure of an anti-LPA antibody (LT3015) Fab fragment in its antigen-free form to 2.15 {angstrom} resolution and in complex with two LPA isotypes (14:0 and 18:2) to resolutions of 1.98 and 2.51 {angstrom}, respectively. The variable CDR (complementarity-determining region) loops at the antigen binding site adoptmore » nearly identical conformations in the free and antigen-bound crystal structures. The crystallographic models reveal that the LT3015 antibody employs both heavy- and light-chain CDR loops to create a network of eight hydrogen bonds with the glycerophosphate head group of its LPA antigen. The head group is almost completely excluded from contact with solvent, while the hydrocarbon tail is partially solvent-exposed. In general, mutation of amino acid residues at the antigen binding site disrupts LPA binding. However, the introduction of particular mutations chosen strategically on the basis of the structures can positively influence LPA binding affinity. Finally, these structures elucidate the exquisite specificity demonstrated by an anti-lipid antibody for binding a structurally simple and seemingly unconstrained target molecule.« less

Functional diversification of ROK-family transcriptional regulators of sugar catabolism in the Thermotogae phylum

PubMed Central

Kazanov, Marat D.; Li, Xiaoqing; Gelfand, Mikhail S.; Osterman, Andrei L.; Rodionov, Dmitry A.

2013-01-01

Large and functionally heterogeneous families of transcription factors have complex evolutionary histories. What shapes specificities toward effectors and DNA sites in paralogous regulators is a fundamental question in biology. Bacteria from the deep-branching lineage Thermotogae possess multiple paralogs of the repressor, open reading frame, kinase (ROK) family regulators that are characterized by carbohydrate-sensing domains shared with sugar kinases. We applied an integrated genomic approach to study functions and specificities of regulators from this family. A comparative analysis of 11 Thermotogae genomes revealed novel mechanisms of transcriptional regulation of the sugar utilization networks, DNA-binding motifs and specific functions. Reconstructed regulons for seven groups of ROK regulators were validated by DNA-binding assays using purified recombinant proteins from the model bacterium Thermotoga maritima. All tested regulators demonstrated specific binding to their predicted cognate DNA sites, and this binding was inhibited by specific effectors, mono- or disaccharides from their respective sugar catabolic pathways. By comparing ligand-binding domains of regulators with structurally characterized kinases from the ROK family, we elucidated signature amino acid residues determining sugar-ligand regulator specificity. Observed correlations between signature residues and the sugar-ligand specificities provide the framework for structure functional classification of the entire ROK family. PMID:23209028

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states

PubMed Central

2014-01-01

Background At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a σ-factor to recognize the genomic location at which the process initiates. Although the crucial role of σ-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function. Results Using multiple genome-scale measurements, we elucidated the network of σ-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 σ-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative σ-factors (the σ70 and σ38 regulons), confirming the competition model of σ substitution and negative regulation by alternative σ-factors. Comparison with σ-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent. Conclusions The reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks. PMID:24461193

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

PubMed Central

Weeding, Emma; Houle, Jason

2010-01-01

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications. PMID:20639523

EHD proteins: Key conductors of endocytic transport

PubMed Central

Naslavsky, Naava; Caplan, Steve

2010-01-01

Regulation of endocytic transport is controlled by an elaborate network of proteins. Rab GTP-binding proteins and their effectors have well-defined roles in mediating specific endocytic transport steps, but until recently, less was known about the four mammalian dynamin-like C-terminal Eps15 Homology Domain (EHD) proteins that also regulate endocytic events. In recent years, however, great strides have been made in understanding the structure and function of these unique proteins. Indeed, a growing body of literature addresses EHD protein structure, interactions with binding partners, functions in mammalian cells, and the generation of various new model systems. Accordingly, this is now an opportune time to pause and review the function and mechanisms of action of EHD proteins, and to highlight some of the challenges and future directions for the field. PMID:21067929

Steroid ligands bind human sex hormone-binding globulin in specific orientations and produce distinct changes in protein conformation.

PubMed

Grishkovskaya, Irina; Avvakumov, George V; Hammond, Geoffrey L; Catalano, Maria G; Muller, Yves A

2002-08-30

The amino-terminal laminin G-like domain of human sex hormone-binding globulin (SHBG) contains a single high affinity steroid-binding site. Crystal structures of this domain in complex with several different steroid ligands have revealed that estradiol occupies the SHBG steroid-binding site in an opposite orientation when compared with 5 alpha-dihydrotestosterone or C19 androgen metabolites (5 alpha-androstan-3 beta,17 beta-diol and 5 alpha-androstan-3 beta,17 alpha-diol) or the synthetic progestin levonorgestrel. Substitution of specific residues within the SHBG steroid-binding site confirmed that Ser(42) plays a key role in determining high affinity interactions by hydrogen bonding to functional groups at C3 of the androstanediols and levonorgestrel and the hydroxyl at C17 of estradiol. Among residues participating in the hydrogen bond network with hydroxy groups at C17 of C19 steroids or C3 of estradiol, Asp(65) appears to be the most important. The different binding mode of estradiol is associated with a difference in the position/orientation of residues (Leu(131) and Lys(134)) in the loop segment (Leu(131)-His(136)) that covers the steroid-binding site as well as others (Leu(171)-Lys(173) and Trp(84)) on the surface of human SHBG and may provide a basis for ligand-dependent interactions between SHBG and other macromolecules. These new crystal structures have also enabled us to construct a simple space-filling model that can be used to predict the characteristics of novel SHBG ligands.

NF-{kappa}B p65 represses {beta}-catenin-activated transcription of cyclin D1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hwang, Injoo; Choi, Yong Seok; Jeon, Mi-Ya

2010-12-03

Research highlights: {yields} Cyclin D1 transcription is directly activated by {beta}-catenin; however, {beta}-catenin-induced cyclin D1 transcription is reduced by NF-{kappa}B p65. {yields} Protein-protein interaction between NF-{kappa}B p65 and {beta}-catenin might be responsible for p65-mediated repression of cyclin D1. {yields} One of five putative binding sites, located further upstream of other sites, is the major {beta}-catenin binding site in the cyclin D1 promoter. {yields} NF-{kappa}B binding site in cyclin D1 is occupied not only by p65 but also by {beta}-catenin, which is dynamically regulated by the signal. -- Abstract: Signaling crosstalk between the {beta}-catenin and NF-{kappa}B pathways represents a functional network.more » To test whether the crosstalk also occurs on their common target genes, the cyclin D1 promoter was used as a model because it contains binding sites for both proteins. {beta}-catenin activated transcription from the cyclin D1 promoter, while co-expression of NF-{kappa}B p65 reduced {beta}-catenin-induced transcription. Chromatin immunoprecipitation revealed lithium chloride-induced binding of {beta}-catenin on one of the T-cell activating factor binding sites. More interestingly, {beta}-catenin binding was greatly reduced by NF-{kappa}B p65, possibly by the protein-protein interaction between the two proteins. Such a dynamic and complex binding of {beta}-catenin and NF-{kappa}B on promoters might contribute to the regulated expression of their target genes.« less

A Salmonella typhimurium-translocated Glycerophospholipid:Cholesterol Acyltransferase Promotes Virulence by Binding to the RhoA Protein Switch Regions

DOE Office of Scientific and Technical Information (OSTI.GOV)

LaRock, Doris L.; Brzovic, Peter S.; Levin, Itay

Salmonella enterica serovar typhimurium translocates a glycerophospholipid: cholesterol acyltransferase (SseJ) into the host cytosol after its entry into mammalian cells. SseJ is recruited to the cytoplasmic face of the host cell phagosome membrane where it is activated upon binding the small GTPase, RhoA. SseJ is regulated similarly to cognate eukaryotic effectors, as only the GTP-bound form of RhoA family members stimulates enzymatic activity. Using NMR and biochemistry, this work demonstrates that SseJ competes effectively with Rhotekin, ROCK, and PKN1 in binding to a similar RhoA surface. The RhoA surface that binds SseJ includes the regulatory switch regions that control activationmore » of mammalian effectors. These data were used to create RhoA mutants with altered SseJ binding and activation. This structure-function analysis supports a model in which SseJ activation occurs predominantly through binding to residues within switch region II. We further defined the nature of the interaction between SseJ and RhoA by constructing SseJ mutants in the RhoA binding surface. These data indicate that SseJ binding to RhoA is required for recruitment of SseJ to the endosomal network and for full Salmonella virulence for inbred susceptible mice, indicating that regulation of SseJ by small GTPases is an important virulence strategy of this bacterial pathogen. The dependence of a bacterial effector on regulation by a mammalian GTPase defines further how intimately host pathogen interactions have coevolved through similar and divergent evolutionary strategies.« less

Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks.

PubMed

Rogers, Julia M; Bulyk, Martha L

2018-04-25

Sequence-specific transcription factors (TFs) bind short DNA sequences in the genome to regulate the expression of target genes. In the last decade, numerous technical advances have enabled the determination of the DNA-binding specificities of many of these factors. Large-scale screens of many TFs enabled the creation of databases of TF DNA-binding specificities, typically represented as position weight matrices (PWMs). Although great progress has been made in determining and predicting binding specificities systematically, there are still many surprises to be found when studying a particular TF's interactions with DNA in detail. Paralogous TFs' binding specificities can differ in subtle ways, in a manner that is not immediately apparent from looking at their PWMs. These differences affect gene regulatory outputs and enable TFs to rewire transcriptional networks over evolutionary time. This review discusses recent observations made in the study of TF-DNA interactions that highlight the importance of continued in-depth analysis of TF-DNA interactions and their inherent complexity. This article is categorized under: Biological Mechanisms > Regulatory Biology. © 2018 Wiley Periodicals, Inc.

DeepSite: protein-binding site predictor using 3D-convolutional neural networks.

PubMed

Jiménez, J; Doerr, S; Martínez-Rosell, G; Rose, A S; De Fabritiis, G

2017-10-01

An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. gianni.defabritiis@upf.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

PubMed Central

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D.H.; Jo, Brian; Gao, Chuan; McDowell, Ian C.; Engelhardt, Barbara E.

2017-01-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. PMID:29021288

Structural organization of G-protein-coupled receptors

NASA Astrophysics Data System (ADS)

Lomize, Andrei L.; Pogozheva, Irina D.; Mosberg, Henry I.

1999-07-01

Atomic-resolution structures of the transmembrane 7-α-helical domains of 26 G-protein-coupled receptors (GPCRs) (including opsins, cationic amine, melatonin, purine, chemokine, opioid, and glycoprotein hormone receptors and two related proteins, retinochrome and Duffy erythrocyte antigen) were calculated by distance geometry using interhelical hydrogen bonds formed by various proteins from the family and collectively applied as distance constraints, as described previously [Pogozheva et al., Biophys. J., 70 (1997) 1963]. The main structural features of the calculated GPCR models are described and illustrated by examples. Some of the features reflect physical interactions that are responsible for the structural stability of the transmembrane α-bundle: the formation of extensive networks of interhelical H-bonds and sulfur-aromatic clusters that are spatially organized as 'polarity gradients' the close packing of side-chains throughout the transmembrane domain; and the formation of interhelical disulfide bonds in some receptors and a plausible Zn2+ binding center in retinochrome. Other features of the models are related to biological function and evolution of GPCRs: the formation of a common 'minicore' of 43 evolutionarily conserved residues; a multitude of correlated replacements throughout the transmembrane domain; an Na+-binding site in some receptors, and excellent complementarity of receptor binding pockets to many structurally dissimilar, conformationally constrained ligands, such as retinal, cyclic opioid peptides, and cationic amine ligands. The calculated models are in good agreement with numerous experimental data.

TDP-43 functions within a network of hnRNP proteins to inhibit the production of a truncated human SORT1 receptor.

PubMed

Mohagheghi, Fatemeh; Prudencio, Mercedes; Stuani, Cristiana; Cook, Casey; Jansen-West, Karen; Dickson, Dennis W; Petrucelli, Leonard; Buratti, Emanuele

2016-02-01

The aggregation and mislocalization of RNA-binding proteins leads to the aberrant regulation of RNA metabolism and is a key feature of many neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia. However, the pathological consequences of abnormal deposition of TDP-43 and other RNA-binding proteins remain unclear, as the specific molecular events that drive neurodegeneration have been difficult to identify and continue to be elusive. Here, we provide novel insight into the complexity of the RNA-binding protein network by demonstrating that the inclusion of exon 17b in the SORT1 mRNA, a pathologically relevant splicing event known to be regulated by TDP-43, is also considerably affected by additional RNA-binding proteins, such as hnRNP L, PTB/nPTB and hnRNP A1/A2. Most importantly, the expression of hnRNP A1/A2 and PTB/nPTB is significantly altered in patients with frontotemporal dementia with TDP-43-positive inclusions (FTLD-TDP), indicating that perturbations in RNA metabolism and processing in FTLD-TDP are not exclusively driven by a loss of TDP-43 function. These results also suggest that a comprehensive assessment of the RNA-binding protein network will dramatically advance our current understanding of the role of TDP-43 in disease pathogenesis, as well as enhance both diagnostic and therapeutic capabilities. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Out of place, out of mind: Schema-driven false memory effects for object-location bindings.

PubMed

Lew, Adina R; Howe, Mark L

2017-03-01

Events consist of diverse elements, each processed in specialized neocortical networks, with temporal lobe memory systems binding these elements to form coherent event memories. We provide a novel theoretical analysis of an unexplored consequence of the independence of memory systems for elements and their bindings, 1 that raises the paradoxical prediction that schema-driven false memories can act solely on the binding of event elements despite the superior retrieval of individual elements. This is because if 2, or more, schema-relevant elements are bound together in unexpected conjunctions, the unexpected conjunction will increase attention during encoding to both the elements and their bindings, but only the bindings will receive competition with evoked schema-expected bindings. We test our model by examining memory for object-location bindings in recognition (Study 1) and recall (Studies 2 and 3) tasks. After studying schema-relevant objects in unexpected locations (e.g., pan on a stool in a kitchen scene), participants who then viewed these objects in expected locations (e.g., pan on stove) at test were more likely to falsely remember this object-location pairing as correct, compared with participants that viewed a different unexpected object-location pairing (e.g., pan on floor). In recall, participants were more likely to correctly remember individual schema-relevant objects originally viewed in unexpected, as opposed to expected locations, but were then more likely to misplace these items in the original room scene to expected places, relative to control schema-irrelevant objects. Our theoretical analysis and novel paradigm provide a tool for investigating memory distortions acting on binding processes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles

PubMed Central

Stojnic, Robert; Fu, Audrey Qiuyan; Adryan, Boris

2012-01-01

Inferring the combinatorial regulatory code of transcription factors (TFs) from genome-wide TF binding profiles is challenging. A major reason is that TF binding profiles significantly overlap and are therefore highly correlated. Clustered occurrence of multiple TFs at genomic sites may arise from chromatin accessibility and local cooperation between TFs, or binding sites may simply appear clustered if the profiles are generated from diverse cell populations. Overlaps in TF binding profiles may also result from measurements taken at closely related time intervals. It is thus of great interest to distinguish TFs that directly regulate gene expression from those that are indirectly associated with gene expression. Graphical models, in particular Bayesian networks, provide a powerful mathematical framework to infer different types of dependencies. However, existing methods do not perform well when the features (here: TF binding profiles) are highly correlated, when their association with the biological outcome is weak, and when the sample size is small. Here, we develop a novel computational method, the Neighbourhood Consistent PC (NCPC) algorithms, which deal with these scenarios much more effectively than existing methods do. We further present a novel graphical representation, the Direct Dependence Graph (DDGraph), to better display the complex interactions among variables. NCPC and DDGraph can also be applied to other problems involving highly correlated biological features. Both methods are implemented in the R package ddgraph, available as part of Bioconductor (http://bioconductor.org/packages/2.11/bioc/html/ddgraph.html). Applied to real data, our method identified TFs that specify different classes of cis-regulatory modules (CRMs) in Drosophila mesoderm differentiation. Our analysis also found depletion of the early transcription factor Twist binding at the CRMs regulating expression in visceral and somatic muscle cells at later stages, which suggests a CRM-specific repression mechanism that so far has not been characterised for this class of mesodermal CRMs. PMID:23144600

Molecular modeling of fibronectin adsorption on topographically nanostructured rutile (110) surfaces

NASA Astrophysics Data System (ADS)

Guo, Chuangqiang; Wu, Chunya; Chen, Mingjun; Zheng, Ting; Chen, Ni; Cummings, Peter T.

2016-10-01

To investigate the topographical dependency of protein adsorption, molecular dynamics simulations were employed to describe the adsorption behavior of the tenth type-III module of fibronectin (FN-III10) on nanostructured rutile (110) surfaces. The results indicated that the residence time of adsorbed FN-III10 largely relied on its binding mode (direct or indirect) with the substrate and the region for protein migration on the periphery (protrusion) or in the interior (cavity or groove) of nanostructures. In the direct binding mode, FN-III10 molecules were found to be 'trapped' at the anchoring sites of rutile surface, or even penetrate deep into the interior of nanostructures, regardless of the presented geometrical features. In the indirect binding mode, FN-III10 molecules were indirectly connected to the substrate via a hydrogen-bond network (linking FN-III10 and interfacial hydrations). The facets created by nanostructures, which exerted restraints on protein migration, were suggested to play an important role in the stability of indirect FN-III10-rutile binding. However, a doubly unfavorable situation - indirect FN-III10-rutile connections bridged by a handful of mediating waters and few constraints on movement of protein provided by nanostructures - would result in an early desorption of protein.

Rce1, a novel transcriptional repressor, regulates cellulase gene expression by antagonizing the transactivator Xyr1 in Trichoderma reesei.

PubMed

Cao, Yanli; Zheng, Fanglin; Wang, Lei; Zhao, Guolei; Chen, Guanjun; Zhang, Weixin; Liu, Weifeng

2017-07-01

Cellulase gene expression in the model cellulolytic fungus Trichoderma reesei is supposed to be controlled by an intricate regulatory network involving multiple transcription factors. Here, we identified a novel transcriptional repressor of cellulase gene expression, Rce1. Disruption of the rce1 gene not only facilitated the induced expression of cellulase genes but also led to a significant delay in terminating the induction process. However, Rce1 did not participate in Cre1-mediated catabolite repression. Electrophoretic mobility shift (EMSA) and DNase I footprinting assays in combination with chromatin immunoprecipitation (ChIP) demonstrated that Rce1 could bind directly to a cbh1 (cellobiohydrolase 1-encoding) gene promoter region containing a cluster of Xyr1 binding sites. Furthermore, competitive binding assays revealed that Rce1 antagonized Xyr1 from binding to the cbh1 promoter. These results indicate that intricate interactions exist between a variety of transcription factors to ensure tight and energy-efficient regulation of cellulase gene expression in T. reesei. This study also provides important clues regarding increased cellulase production in T. reesei. © 2017 John Wiley & Sons Ltd.

ATP-Binding Cassette Proteins: Towards a Computational View of Mechanism

NASA Astrophysics Data System (ADS)

Liao, Jielou

2004-03-01

Many large machine proteins can generate mechanical force and undergo large-scale conformational changes (LSCC) to perform varying biological tasks in living cells by utilizing ATP. Important examples include ATP-binding cassette (ABC) transporters. They are membrane proteins that couple ATP binding and hydrolysis to the translocation of substrates across membranes [1]. To interpret how the mechanical force generated by ATP binding and hydrolysis is propagated, a coarse-grained ATP-dependent harmonic network model (HNM) [2,3] is applied to the ABC protein, BtuCD. This protein machine transports vitamin B12 across membranes. The analysis shows that subunits of the protein move against each other in a concerted manner. The lowest-frequency modes of the BtuCD protein are found to link the functionally critical domains, and are suggested to be responsible for large-scale ATP-coupled conformational changes. [1] K. P. Locher, A. T. Lee and D. C. Rees. Science 296, 1091-1098 (2002). [2] Atilgan, A. R., S. R. Durell, R. L. Jernigan, M. C. Demirel, O. Keskin, and I. Bahar. Biophys. J. 80, 505-515(2002); M. M Tirion, Phys. Rev. Lett. 77, 1905-1908 (1996). [3] J. -L. Liao and D. N. Beratan, 2003, to be published.

Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model

PubMed Central

Kim, Min Hyeok; Kim, Young Jin; Kim, Hee Ryung; Jeon, Tae-Joon; Choi, Jae Boong; Chung, Ka Young; Kim, Moon Ki

2016-01-01

Agonist-activated G protein-coupled receptors (GPCRs) interact with GDP-bound G protein heterotrimers (Gαβγ) promoting GDP/GTP exchange, which results in dissociation of Gα from the receptor and Gβγ. The GTPase activity of Gα hydrolyzes GTP to GDP, and the GDP-bound Gα interacts with Gβγ, forming a GDP-bound G protein heterotrimer. The G protein cycle is allosterically modulated by conformational changes of the Gα subunit. Although biochemical and biophysical methods have elucidated the structure and dynamics of Gα, the precise conformational mechanisms underlying the G protein cycle are not fully understood yet. Simulation methods could help to provide additional details to gain further insight into G protein signal transduction mechanisms. In this study, using the available X-ray crystal structures of Gα, we simulated the entire G protein cycle and described not only the steric features of the Gα structure, but also conformational changes at each step. Each reference structure in the G protein cycle was modeled as an elastic network model and subjected to normal mode analysis. Our simulation data suggests that activated receptors trigger conformational changes of the Gα subunit that are thermodynamically favorable for opening of the nucleotide-binding pocket and GDP release. Furthermore, the effects of GTP binding and hydrolysis on mobility changes of the C and N termini and switch regions are elucidated. In summary, our simulation results enabled us to provide detailed descriptions of the structural and dynamic features of the G protein cycle. PMID:27483005

Network analysis reveals the recognition mechanism for complex formation of mannose-binding lectins

NASA Astrophysics Data System (ADS)

Jian, Yiren; Zhao, Yunjie; Zeng, Chen

The specific carbohydrate binding of lectin makes the protein a powerful molecular tool for various applications including cancer cell detection due to its glycoprotein profile on the cell surface. Most biologically active lectins are dimeric. To understand the structure-function relation of lectin complex, it is essential to elucidate the short- and long-range driving forces behind the dimer formation. Here we report our molecular dynamics simulations and associated dynamical network analysis on a particular lectin, i.e., the mannose-binding lectin from garlic. Our results, further supported by sequence coevolution analysis, shed light on how different parts of the complex communicate with each other. We propose a general framework for deciphering the recognition mechanism underlying protein-protein interactions that may have potential applications in signaling pathways.

Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

PubMed

Cona, Filippo; Zavaglia, Melissa; Ursino, Mauro

2012-04-01

Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

Mechanics of composite actin networks: in vitro and cellular perspectives

NASA Astrophysics Data System (ADS)

Upadhyaya, Arpita

2014-03-01

Actin filaments and associated actin binding proteins play an essential role in governing the mechanical properties of eukaryotic cells. Even though cells have multiple actin binding proteins (ABPs) that exist simultaneously to maintain the structural and mechanical integrity of the cellular cytoskeleton, how these proteins work together to determine the properties of actin networks is not well understood. The ABP, palladin, is essential for the integrity of cell morphology and movement during development. Palladin coexists with alpha-actinin in stress fibers and focal adhesions and binds to both actin and alpha-actinin. To obtain insight into how mutually interacting actin crosslinking proteins modulate the properties of actin networks, we have characterized the micro-structure and mechanics of actin networks crosslinked with palladin and alpha-actinin. Our studies on composite networks of alpha-actinin/palladin/actin show that palladin and alpha-actinin synergistically determine network viscoelasticity. We have further examined the role of palladin in cellular force generation and mechanosensing. Traction force microscopy revealed that TAFs are sensitive to substrate stiffness as they generate larger forces on substrates of increased stiffness. Contrary to expectations, knocking down palladin increased the forces generated by cells, and also inhibited the ability to sense substrate stiffness for very stiff gels. This was accompanied by significant differences in the actin organization and adhesion dynamics of palladin knock down cells. Perturbation experiments also suggest altered myosin activity in palladin KD cells. Our results suggest that the actin crosslinkers such as palladin and myosin motors coordinate for optimal cell function and to prevent aberrant behavior as in cancer metastasis.

Numerical modeling of fluid and oxygen exchanges through microcirculation for the assessment of microcirculation alterations caused by type 2 diabetes.

PubMed

Tang, Yuanliang; He, Ying

2018-05-01

Type 2 diabetes mellitus (DM2) is frequently accompanied by microcirculation complications, including structural and functional alterations, which may have serious effects on substance exchanges between blood and interstitial tissue and the health of organs. In this paper, we aim to study the influence of microcirculation alterations in DM2 patients on fluid and oxygen exchanges through a model analysis. A fluid flow and oxygen transport model were developed by considering the interplay between blood in capillary network and interstitial tissue. The two regions were separately represented by 1D network model and 3D volume model, and the immersed boundary method (IBM) was adopted to solve fluid and mass transfer between these two regions. By using the model, the steady flow field and the distributions of oxygen in capillary network and surrounding tissue were firstly simulated. In the interstitial volume, fluid pressure and oxygen tension decreased with the increase of distance from the network; in the network, oxygen tension in blood plasma dropped from 100 mm Hg at the entrance to about 40 mm Hg at the exit. We further tested several structural and functional disorders related to diabetic pathological conditions. Simulated results show that the impaired connectivity of the network could result in poor robustness in maintaining blood flow and perfused surface; under high fluid permeability conditions of capillary walls, the pressure gradient was much larger around the capillary bed, and this alteration led to a saturation level of the interstitial pressure when lymphatic flow drainage can't work effectively; the variations in network connectivity and permeability of capillary wall also had unfavorable influence on oxygen distributions in interstitial tissue. In addition, when the oxygen releasing capacity of hemoglobin was confined by glycosylated hemoglobin (HbA1) in the case of diabetes, the plasma could not be complemented with adequate oxygen and thus the hypoxic tissue range will be extended. This study illustrates that when microcirculation disturbances, including the structure of capillary network, the wall osmosis property and the capacity of blood binding oxygen occur in DM2, some negative impacts are raised on microvascular hemodynamics and metabolism circumstance of interstitial tissue. Copyright © 2018 Elsevier Inc. All rights reserved.

Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring.

PubMed

Gong, Xinqi; Wang, Panwen; Yang, Feng; Chang, Shan; Liu, Bin; He, Hongqiu; Cao, Libin; Xu, Xianjin; Li, Chunhua; Chen, Weizu; Wang, Cunxin

2010-11-15

Protein-protein docking has made much progress in recent years, but challenges still exist. Here we present the application of our docking approach HoDock in CAPRI. In this approach, a binding site prediction is implemented to reduce docking sampling space and filter out unreasonable docked structures, and a network-based enhanced combinatorial scoring function HPNCscore is used to evaluate the decoys. The experimental information was combined with the predicted binding site to pick out the most likely key binding site residues. We applied the HoDock method in the recent rounds of the CAPRI experiments, and got good results as predictors on targets 39, 40, and 41. We also got good results as scorers on targets 35, 37, 40, and 41. This indicates that our docking approach can contribute to the progress of protein-protein docking methods and to the understanding of the mechanism of protein-protein interactions. © 2010 Wiley-Liss, Inc.

Combinatorial influence of environmental parameters on transcription factor activity.

PubMed

Knijnenburg, T A; Wessels, L F A; Reinders, M J T

2008-07-01

Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. The Matlab code is available from the authors upon request. Supplementary data are available at Bioinformatics online.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis

PubMed Central

Peterson, Eliza J.R.; Reiss, David J.; Turkarslan, Serdar; Minch, Kyle J.; Rustad, Tige; Plaisier, Christopher L.; Longabaugh, William J.R.; Sherman, David R.; Baliga, Nitin S.

2014-01-01

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection. PMID:25232098

The Free Energy Landscape of Small Molecule Unbinding

PubMed Central

Huang, Danzhi; Caflisch, Amedeo

2011-01-01

The spontaneous dissociation of six small ligands from the active site of FKBP (the FK506 binding protein) is investigated by explicit water molecular dynamics simulations and network analysis. The ligands have between four (dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms, and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each ligand) are grouped according to a set of intermolecular distances into nodes of a network, and the direct transitions between them are the links. The network analysis reveals that the bound state consists of several subbasins, i.e., binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts. The dissociation kinetics show a simple (i.e., single-exponential) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state. The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site, which correspond to multiple pathways of dissociation. For the six small ligands of FKBP, the weaker the binding affinity the closer to the bound state (along the intermolecular distance) are the transition state structures, which is a new manifestation of Hammond behavior. Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution. Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape (both thermodynamics and kinetics) of ligand unbinding. PMID:21390201

The free energy landscape of small molecule unbinding.

PubMed

Huang, Danzhi; Caflisch, Amedeo

2011-02-01

The spontaneous dissociation of six small ligands from the active site of FKBP (the FK506 binding protein) is investigated by explicit water molecular dynamics simulations and network analysis. The ligands have between four (dimethylsulphoxide) and eleven (5-diethylamino-2-pentanone) non-hydrogen atoms, and an affinity for FKBP ranging from 20 to 0.2 mM. The conformations of the FKBP/ligand complex saved along multiple trajectories (50 runs at 310 K for each ligand) are grouped according to a set of intermolecular distances into nodes of a network, and the direct transitions between them are the links. The network analysis reveals that the bound state consists of several subbasins, i.e., binding modes characterized by distinct intermolecular hydrogen bonds and hydrophobic contacts. The dissociation kinetics show a simple (i.e., single-exponential) time dependence because the unbinding barrier is much higher than the barriers between subbasins in the bound state. The unbinding transition state is made up of heterogeneous positions and orientations of the ligand in the FKBP active site, which correspond to multiple pathways of dissociation. For the six small ligands of FKBP, the weaker the binding affinity the closer to the bound state (along the intermolecular distance) are the transition state structures, which is a new manifestation of Hammond behavior. Experimental approaches to the study of fragment binding to proteins have limitations in temporal and spatial resolution. Our network analysis of the unbinding simulations of small inhibitors from an enzyme paints a clear picture of the free energy landscape (both thermodynamics and kinetics) of ligand unbinding.

Avidin as a Model for Charge Driven Transport into Cartilage and Drug Delivery for treating Early Stage Post-traumatic Osteoarthritis

PubMed Central

Bajpayee, Ambika G.; Wong, Cliff R.; Bawendi, Moungi G.; Frank, Eliot H.; Grodzinsky, Alan J.

2013-01-01

Local drug delivery into cartilage remains a challenge due to its dense extracellular matrix of negatively charged proteoglycans enmeshed within a collagen fibril network. The high negative fixed charge density of cartilage offers the unique opportunity to utilize electrostatic interactions to augment transport, binding and retention of drug carriers. With the goal of developing particle-based drug delivery mechanisms for treating post-traumatic osteoarthritis, our objectives were, first, to determine the size range of a variety of solutes that could penetrate and diffuse through normal cartilage and enzymatically treated cartilage to mimic early stages of OA, and second, to investigate the effects of electrostatic interactions on particle partitioning, uptake and binding within cartilage using the highly positively charged protein, Avidin, as a model. Results showed that solutes having a hydrodynamic diameter ≤ 10 nm can penetrate into the full thickness of cartilage explants while larger sized solutes were trapped in the tissue’s superficial zone. Avidin had a 400-fold higher uptake than its neutral same-sized counterpart, NeutrAvidin, and >90% of the absorbed Avidin remained within cartilage explants for at least 15 days. We report reversible, weak binding (KD ~150 μM) of Avidin to intratissue sites in cartilage. The large effective binding site density (NT ~ 2920 μM) within cartilage matrix facilitates Avidin’s retention, making its structure suitable for particle based drug delivery into cartilage. PMID:24120044

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

PubMed

Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

2016-11-29

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

Aβ delays fibrin clot lysis by altering fibrin structure and attenuating plasminogen binding to fibrin

PubMed Central

Zamolodchikov, Daria

2012-01-01

Alzheimer disease is characterized by the presence of increased levels of the β-amyloid peptide (Aβ) in the brain parenchyma and cerebral blood vessels. This accumulated Aβ can bind to fibrin(ogen) and render fibrin clots more resistant to degradation. Here, we demonstrate that Aβ42 specifically binds to fibrin and induces a tighter fibrin network characterized by thinner fibers and increased resistance to lysis. However, Aβ42-induced structural changes cannot be the sole mechanism of delayed lysis because Aβ overlaid on normal preformed clots also binds to fibrin and delays lysis without altering clot structure. In this regard, we show that Aβ interferes with the binding of plasminogen to fibrin, which could impair plasmin generation and fibrin degradation. Indeed, plasmin generation by tissue plasminogen activator (tPA), but not streptokinase, is slowed in fibrin clots containing Aβ42, and clot lysis by plasmin, but not trypsin, is delayed. Notably, plasmin and tPA activities, as well as tPA-dependent generation of plasmin in solution, are not decreased in the presence of Aβ42. Our results indicate the existence of 2 mechanisms of Aβ42 involvement in delayed fibrinolysis: (1) through the induction of a tighter fibrin network composed of thinner fibers, and (2) through inhibition of plasmin(ogen)–fibrin binding. PMID:22238323

Transcriptional Network Analysis in Muscle Reveals AP-1 as a Partner of PGC-1α in the Regulation of the Hypoxic Gene Program

PubMed Central

Baresic, Mario; Salatino, Silvia; Kupr, Barbara

2014-01-01

Skeletal muscle tissue shows an extraordinary cellular plasticity, but the underlying molecular mechanisms are still poorly understood. Here, we use a combination of experimental and computational approaches to unravel the complex transcriptional network of muscle cell plasticity centered on the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a regulatory nexus in endurance training adaptation. By integrating data on genome-wide binding of PGC-1α and gene expression upon PGC-1α overexpression with comprehensive computational prediction of transcription factor binding sites (TFBSs), we uncover a hitherto-underestimated number of transcription factor partners involved in mediating PGC-1α action. In particular, principal component analysis of TFBSs at PGC-1α binding regions predicts that, besides the well-known role of the estrogen-related receptor α (ERRα), the activator protein 1 complex (AP-1) plays a major role in regulating the PGC-1α-controlled gene program of the hypoxia response. Our findings thus reveal the complex transcriptional network of muscle cell plasticity controlled by PGC-1α. PMID:24912679

Small-molecule RORγt antagonists inhibit T helper 17 cell transcriptional network by divergent mechanisms.

PubMed

Xiao, Sheng; Yosef, Nir; Yang, Jianfei; Wang, Yonghui; Zhou, Ling; Zhu, Chen; Wu, Chuan; Baloglu, Erkan; Schmidt, Darby; Ramesh, Radha; Lobera, Mercedes; Sundrud, Mark S; Tsai, Pei-Yun; Xiang, Zhijun; Wang, Jinsong; Xu, Yan; Lin, Xichen; Kretschmer, Karsten; Rahl, Peter B; Young, Richard A; Zhong, Zhong; Hafler, David A; Regev, Aviv; Ghosh, Shomir; Marson, Alexander; Kuchroo, Vijay K

2014-04-17

We identified three retinoid-related orphan receptor gamma t (RORγt)-specific inhibitors that suppress T helper 17 (Th17) cell responses, including Th17-cell-mediated autoimmune disease. We systemically characterized RORγt binding in the presence and absence of drugs with corresponding whole-genome transcriptome sequencing. RORγt acts as a direct activator of Th17 cell signature genes and a direct repressor of signature genes from other T cell lineages; its strongest transcriptional effects are on cis-regulatory sites containing the RORα binding motif. RORγt is central in a densely interconnected regulatory network that shapes the balance of T cell differentiation. Here, the three inhibitors modulated the RORγt-dependent transcriptional network to varying extents and through distinct mechanisms. Whereas one inhibitor displaced RORγt from its target loci, the other two inhibitors affected transcription predominantly without removing DNA binding. Our work illustrates the power of a system-scale analysis of transcriptional regulation to characterize potential therapeutic compounds that inhibit pathogenic Th17 cells and suppress autoimmunity. Copyright © 2014 Elsevier Inc. All rights reserved.

Using peptide array to identify binding motifs and interaction networks for modular domains.

PubMed

Li, Shawn S-C; Wu, Chenggang

2009-01-01

Specific protein-protein interactions underlie all essential biological processes and form the basis of cellular signal transduction. The recognition of a short, linear peptide sequence in one protein by a modular domain in another represents a common theme of macromolecular recognition in cells, and the importance of this mode of protein-protein interaction is highlighted by the large number of peptide-binding domains encoded by the human genome. This phenomenon also provides a unique opportunity to identify protein-protein binding events using peptide arrays and complementary biochemical assays. Accordingly, high-density peptide array has emerged as a useful tool by which to map domain-mediated protein-protein interaction networks at the proteome level. Using the Src-homology 2 (SH2) and 3 (SH3) domains as examples, we describe the application of oriented peptide array libraries in uncovering specific motifs recognized by an SH2 domain and the use of high-density peptide arrays in identifying interaction networks mediated by the SH3 domain. Methods reviewed here could also be applied to other modular domains, including catalytic domains, that recognize linear peptide sequences.

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

PubMed Central

Hamey, Fiona K.; Nestorowa, Sonia; Kinston, Sarah J.; Kent, David G.; Wilson, Nicola K.

2017-01-01

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships. PMID:28584094

Cooperative Allosteric Ligand Binding in Calmodulin

NASA Astrophysics Data System (ADS)

Nandigrami, Prithviraj

Conformational dynamics is often essential for a protein's function. For example, proteins are able to communicate the effect of binding at one site to a distal region of the molecule through changes in its conformational dynamics. This so called allosteric coupling fine tunes the sensitivity of ligand binding to changes in concentration. A conformational change between a "closed" (apo) and an "open" (holo) conformation upon ligation often produces this coupling between binding sites. Enhanced sensitivity between the unbound and bound ensembles leads to a sharper binding curve. There are two basic conceptual frameworks that guide our visualization about ligand binding mechanisms. First, a ligand can stabilize the unstable "open" state from a dynamic ensemble of conformations within the unbound basin. This binding mechanism is called conformational selection. Second, a ligand can weakly bind to the low-affinity "closed" state followed by a conformational transition to the "open" state. In this dissertation, I focus on molecular dynamics simulations to understand microscopic origins of ligand binding cooperativity. A minimal model of allosteric binding transitions must include ligand binding/unbinding events, while capturing the transition mechanism between two distinct meta-stable free energy basins. Due in part to computational timescales limitations, work in this dissertation describes large-scale conformational transitions through a simplified, coarse-grained model based on the energy basins defined by the open and closed conformations of the protein Calmodulin (CaM). CaM is a ubiquitous calcium-binding protein consisting of two structurally similar globular domains connected by a flexible linker. The two domains of CaM, N-terminal domain (nCaM) and C-terminal domain (cCaM) consists of two helix-loop-helix motifs (the EF-hands) connected by a flexible linker. Each domain of CaM consists of two binding loops and binds 2 calcium ions each. The intact domain binds up to 4 calcium ions. The simulations use a coupled molecular dynamics/monte carlo scheme where the protein dynamics is simulated explicitly, while ligand binding/unbinding are treated implicitly. In the model, ligand binding/unbinding events coupled with a conformational change of the protein within the grand canonical ensemble. Here, ligand concentration is controlled through the chemical potential (micro). This allows us to use a simple thermodynamic model to analyze the simulated data and quantify binding cooperativity. Simulated binding titration curves are calculated through equilibrium simulations at different values of micro. First, I study domain opening transitions of isolated nCaM and cCaM in the absence of calcium. This work is motivated by results from a recent analytic variational model that predicts distinct domain opening transition mechanism for the domains of CaM. This is a surprising result because the domains have the same folded state topology. In the simulations, I find the two domains of CaM have distinct transition mechanism over a broad range of temperature, in harmony with the analytic predictions. In particular, the simulated transition mechanism of nCaM follows a two-state behavior, while domain opening in cCaM involves global unfolding and refolding of the tertiary structure. The unfolded intermediate also appears in the landscape of nCaM, but at a higher temperature than it appears in cCaM's energy landscape. This is consistent with nCaM's higher thermal stability. Under approximate physiological conditions, majority of the sampled transitions in cCaM involves unfolding and refolding during conformational change. Kinetically, the transient unfolding and refolding in cCaM significantly slows the domain opening and closing rates in cCaM. Second, I investigate the structural origins of binding affinity and allosteric cooperativity of binding 2 calcium-ions to each domain of CaM. In my work, I predict the order of binding strength of CaM's loops. I analyze simulated binding curves within the framework of the classic Monod-Wyman-Changeux (MWC) model of allostery to extract the binding free energies to the closed and open ensembles. The simulations predict that cCaM binds calcium with higher affinity and greater cooperativity than nCaM. Where it is possible to compare, these predictions are in good agreement with experimental results. The analysis of the simulations offers a rationale for why the two domains differ in cooperativity: the higher cooperativity of cCaM is due to larger difference in affinity of its binding loops. Third, I extend the work to investigate structural origins of binding cooperativity of 4 calcium-ions to intact CaM. I characterize the microscopic cooperativities of each ligation state and provide a kinetic description of the binding mechanism. Due to the heterogeneous nature of CaM's loops, as predicted in our simulations of isolated domains, I focus on investigating the influence of this heterogeneity on the kinetic flux of binding pathways as a function of concentration. The formalism developed for Network Models of protein folding kinetics, is used to evaluate the directed flux of all possible pathways between unligated and fully loaded CaM. (Abstract shortened by ProQuest.).

ENES the European Network for Earth System modelling and its infrastructure projects IS-ENES

NASA Astrophysics Data System (ADS)

Guglielmo, Francesca; Joussaume, Sylvie; Parinet, Marie

2016-04-01

The scientific community working on climate modelling is organized within the European Network for Earth System modelling (ENES). In the past decade, several European university departments, research centres, meteorological services, computer centres, and industrial partners engaged in the creation of ENES with the purpose of working together and cooperating towards the further development of the network, by signing a Memorandum of Understanding. As of 2015, the consortium counts 47 partners. The climate modelling community, and thus ENES, faces challenges which are both science-driven, i.e. analysing of the full complexity of the Earth System to improve our understanding and prediction of climate changes, and have multi-faceted societal implications, as a better representation of climate change on regional scales leads to improved understanding and prediction of impacts and to the development and provision of climate services. ENES, promoting and endorsing projects and initiatives, helps in developing and evaluating of state-of-the-art climate and Earth system models, facilitates model inter-comparison studies, encourages exchanges of software and model results, and fosters the use of high performance computing facilities dedicated to high-resolution multi-model experiments. ENES brings together public and private partners, integrates countries underrepresented in climate modelling studies, and reaches out to different user communities, thus enhancing European expertise and competitiveness. In this need of sophisticated models, world-class, high-performance computers, and state-of-the-art software solutions to make efficient use of models, data and hardware, a key role is played by the constitution and maintenance of a solid infrastructure, developing and providing services to the different user communities. ENES has investigated the infrastructural needs and has received funding from the EU FP7 program for the IS-ENES (InfraStructure for ENES) phase I and II projects. We present here the case study of an existing network of institutions brought together toward common goals by a non-binding agreement, ENES, and of its two IS-ENES projects. These latter will be discussed in their double role as a means to provide and/or maintain the actual infrastructure (hardware, software, skilled human resources, services) to achieve ENES scientific goals -fulfilling the aims set in a strategy document-, but also to inform and provide to the network a structured way of working and of interacting with the extended community. The genesis and evolution of the network and the interaction network/projects will also be analysed in terms of long-term sustainability.

Inferring genome-wide functional modulatory network: a case study on NF-κB/RelA transcription factor.

PubMed

Li, Xueling; Zhu, Min; Brasier, Allan R; Kudlicki, Andrzej S

2015-04-01

How different pathways lead to the activation of a specific transcription factor (TF) with specific effects is not fully understood. We model context-specific transcriptional regulation as a modulatory network: triplets composed of a TF, target gene, and modulator. Modulators usually affect the activity of a specific TF at the posttranscriptional level in a target gene-specific action mode. This action may be classified as enhancement, attenuation, or inversion of either activation or inhibition. As a case study, we inferred, from a large collection of expression profiles, all potential modulations of NF-κB/RelA. The predicted modulators include many proteins previously not reported as physically binding to RelA but with relevant functions, such as RNA processing, cell cycle, mitochondrion, ubiquitin-dependent proteolysis, and chromatin modification. Modulators from different processes exert specific prevalent action modes on distinct pathways. Modulators from noncoding RNA, RNA-binding proteins, TFs, and kinases modulate the NF-κB/RelA activity with specific action modes consistent with their molecular functions and modulation level. The modulatory networks of NF-κB/RelA in the context epithelial-mesenchymal transition (EMT) and burn injury have different modulators, including those involved in extracellular matrix (FBN1), cytoskeletal regulation (ACTN1), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long intergenic nonprotein coding RNA, and tumor suppression (FOXP1) for EMT, and TXNIP, GAPDH, PKM2, IFIT5, LDHA, NID1, and TPP1 for burn injury.

The Crystal Structures of Apo and cAMP-Bound GlxR from Corynebacterium glutamicum Reveal Structural and Dynamic Changes upon cAMP Binding in CRP/FNR Family Transcription Factors

PubMed Central

Townsend, Philip D.; Jungwirth, Britta; Pojer, Florence; Bußmann, Michael; Money, Victoria A.; Cole, Stewart T.; Pühler, Alfred; Tauch, Andreas; Bott, Michael; Cann, Martin J.; Pohl, Ehmke

2014-01-01

The cyclic AMP-dependent transcriptional regulator GlxR from Corynebacterium glutamicum is a member of the super-family of CRP/FNR (cyclic AMP receptor protein/fumarate and nitrate reduction regulator) transcriptional regulators that play central roles in bacterial metabolic regulatory networks. In C. glutamicum, which is widely used for the industrial production of amino acids and serves as a non-pathogenic model organism for members of the Corynebacteriales including Mycobacterium tuberculosis, the GlxR homodimer controls the transcription of a large number of genes involved in carbon metabolism. GlxR therefore represents a key target for understanding the regulation and coordination of C. glutamicum metabolism. Here we investigate cylic AMP and DNA binding of GlxR from C. glutamicum and describe the crystal structures of apo GlxR determined at a resolution of 2.5 Å, and two crystal forms of holo GlxR at resolutions of 2.38 and 1.82 Å, respectively. The detailed structural analysis and comparison of GlxR with CRP reveals that the protein undergoes a distinctive conformational change upon cyclic AMP binding leading to a dimer structure more compatible to DNA-binding. As the two binding sites in the GlxR homodimer are structurally identical dynamic changes upon binding of the first ligand are responsible for the allosteric behavior. The results presented here show how dynamic and structural changes in GlxR lead to optimization of orientation and distance of its two DNA-binding helices for optimal DNA recognition. PMID:25469635

The fibrinogen-binding M1 protein reduces pharyngeal cell adherence and colonization phenotypes of M1T1 group A Streptococcus.

PubMed

Anderson, Ericka L; Cole, Jason N; Olson, Joshua; Ryba, Bryan; Ghosh, Partho; Nizet, Victor

2014-02-07

Group A Streptococcus (GAS) is a leading human pathogen producing a diverse array of infections from simple pharyngitis ("strep throat") to invasive conditions, including necrotizing fasciitis and toxic shock syndrome. The surface-anchored GAS M1 protein is a classical virulence factor that promotes phagocyte resistance and exaggerated inflammation by binding host fibrinogen (Fg) to form supramolecular networks. In this study, we used a virulent WT M1T1 GAS strain and its isogenic M1-deficient mutant to examine the role of M1-Fg binding in a proximal step in GAS infection-interaction with the pharyngeal epithelium. Expression of the M1 protein reduced GAS adherence to human pharyngeal keratinocytes by 2-fold, and this difference was increased to 4-fold in the presence of Fg. In stationary phase, surface M1 protein cleavage by the GAS cysteine protease SpeB eliminated Fg binding and relieved its inhibitory effect on GAS pharyngeal cell adherence. In a mouse model of GAS colonization of nasal-associated lymphoid tissue, M1 protein expression was associated with an average 6-fold decreased GAS recovery in isogenic strain competition assays. Thus, GAS M1 protein-Fg binding reduces GAS pharyngeal cell adherence and colonization in a fashion that is counterbalanced by SpeB. Inactivation of SpeB during the shift to invasive GAS disease allows M1-Fg binding, increasing pathogen phagocyte resistance and proinflammatory activities.

A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo

NASA Technical Reports Server (NTRS)

Davidson, Eric H.; Rast, Jonathan P.; Oliveri, Paola; Ransick, Andrew; Calestani, Cristina; Yuh, Chiou-Hwa; Minokawa, Takuya; Amore, Gabriele; Hinman, Veronica; Arenas-Mena, Cesar;

Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology.

PubMed

Gu, Jiangyong; Li, Li; Wang, Dongmei; Zhu, Wei; Han, Ling; Zhao, Ruizhi; Xu, Xiaojie; Lu, Chuanjian

2018-06-01

Psoriasis vulgaris is a chronic inflammatory and immune-mediated skin disease. 44 metabonomics biomarkers were identified by high-throughput liquid chromatography coupled to mass spectrometry in our previous work, but the roles of metabonomics biomarkers in the pathogenesis of psoriasis is unclear. The metabonomics biomarker-enzyme network was constructed. The key metabonomics biomarkers and enzymes were screened out by network analysis. The binding affinity between each metabonomics biomarker and target was calculated by molecular docking. A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways. Long-chain fatty acids, phospholipids, Estradiol and NADH were the most important metabonomics biomarkers. Most key enzymes belonged hydrolase, thioesterase and acyltransferase. Six proteins (TNF-alpha, MAPK3, iNOS, eNOS, COX2 and mTOR) were extensively involved in inflammatory reaction, immune response and cell proliferation, and might be drug targets for psoriasis. PI3K-Akt signaling pathway and five other pathways had close correlation with the pathogenesis of psoriasis and could deserve further research. The inflammatory reaction, immune response and cell proliferation are mainly involved in psoriasis. Network pharmacology provide a new insight into the relationships between metabonomics biomarkers and the pathogenesis of psoriasis. KEY MESSAGES   • Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes.   • Six proteins were screened out as important drug targets for psoriasis.   • A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

Allele-Specific Chromatin Recruitment and Therapeutic Vulnerabilities of ESR1 Activating Mutations.

PubMed

Jeselsohn, Rinath; Bergholz, Johann S; Pun, Matthew; Cornwell, MacIntosh; Liu, Weihan; Nardone, Agostina; Xiao, Tengfei; Li, Wei; Qiu, Xintao; Buchwalter, Gilles; Feiglin, Ariel; Abell-Hart, Kayley; Fei, Teng; Rao, Prakash; Long, Henry; Kwiatkowski, Nicholas; Zhang, Tinghu; Gray, Nathanael; Melchers, Diane; Houtman, Rene; Liu, X Shirley; Cohen, Ofir; Wagle, Nikhil; Winer, Eric P; Zhao, Jean; Brown, Myles

2018-02-12

Estrogen receptor α (ER) ligand-binding domain (LBD) mutations are found in a substantial number of endocrine treatment-resistant metastatic ER-positive (ER + ) breast cancers. We investigated the chromatin recruitment, transcriptional network, and genetic vulnerabilities in breast cancer models harboring the clinically relevant ER mutations. These mutants exhibit both ligand-independent functions that mimic estradiol-bound wild-type ER as well as allele-specific neomorphic properties that promote a pro-metastatic phenotype. Analysis of the genome-wide ER binding sites identified mutant ER unique recruitment mediating the allele-specific transcriptional program. Genetic screens identified genes that are essential for the ligand-independent growth driven by the mutants. These studies provide insights into the mechanism of endocrine therapy resistance engendered by ER mutations and potential therapeutic targets. Copyright © 2018 Elsevier Inc. All rights reserved.

Structural Basis for Binding and Selectivity of Antimalarial and Anticancer Ethylenediamine Inhibitors to Protein Farnesyltransferase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hast, Michael A.; Fletcher, Steven; Cummings, Christopher G.

Protein farnesyltransferase (FTase) catalyzes an essential posttranslational lipid modification of more than 60 proteins involved in intracellular signal transduction networks. FTase inhibitors have emerged as a significant target for development of anticancer therapeutics and, more recently, for the treatment of parasitic diseases caused by protozoan pathogens, including malaria (Plasmodium falciparum). We present the X-ray crystallographic structures of complexes of mammalian FTase with five inhibitors based on an ethylenediamine scaffold, two of which exhibit over 1000-fold selective inhibition of P. falciparum FTase. These structures reveal the dominant determinants in both the inhibitor and enzyme that control binding and selectivity. Comparison tomore » a homology model constructed for the P. falciparum FTase suggests opportunities for further improving selectivity of a new generation of antimalarial inhibitors.« less

Interlocked positive and negative feedback network motifs regulate β-catenin activity in the adherens junction pathway

PubMed Central

Klinke, David J.; Horvath, Nicholas; Cuppett, Vanessa; Wu, Yueting; Deng, Wentao; Kanj, Rania

2015-01-01

The integrity of epithelial tissue architecture is maintained through adherens junctions that are created through extracellular homotypic protein–protein interactions between cadherin molecules. Cadherins also provide an intracellular scaffold for the formation of a multiprotein complex that contains signaling proteins, including β-catenin. Environmental factors and controlled tissue reorganization disrupt adherens junctions by cleaving the extracellular binding domain and initiating a series of transcriptional events that aim to restore tissue homeostasis. However, it remains unclear how alterations in cell adhesion coordinate transcriptional events, including those mediated by β-catenin in this pathway. Here were used quantitative single-cell and population-level in vitro assays to quantify the endogenous pathway dynamics after the proteolytic disruption of the adherens junctions. Using prior knowledge of isolated elements of the overall network, we interpreted these data using in silico model-based inference to identify the topology of the regulatory network. Collectively the data suggest that the regulatory network contains interlocked network motifs consisting of a positive feedback loop, which is used to restore the integrity of adherens junctions, and a negative feedback loop, which is used to limit β-catenin–induced gene expression. PMID:26224311

Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

PubMed

Mlinsek, G; Novic, M; Hodoscek, M; Solmajer, T

2001-01-01

Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

Modeling of the U1 snRNP assembly pathway in alternative splicing in human cells using Petri nets.

PubMed

Kielbassa, J; Bortfeldt, R; Schuster, S; Koch, I

2009-02-01

The investigation of spliceosomal processes is currently a topic of intense research in molecular biology. In the molecular mechanism of alternative splicing, a multi-protein-RNA complex - the spliceosome - plays a crucial role. To understand the biological processes of alternative splicing, it is essential to comprehend the biogenesis of the spliceosome. In this paper, we propose the first abstract model of the regulatory assembly pathway of the human spliceosomal subunit U1. Using Petri nets, we describe its highly ordered assembly that takes place in a stepwise manner. Petri net theory represents a mathematical formalism to model and analyze systems with concurrent processes at different abstraction levels with the possibility to combine them into a uniform description language. There exist many approaches to determine static and dynamic properties of Petri nets, which can be applied to analyze biochemical systems. In addition, Petri net tools usually provide intuitively understandable graphical network representations, which facilitate the dialog between experimentalists and theoreticians. Our Petri net model covers binding, transport, signaling, and covalent modification processes. Through the computation of structural and behavioral Petri net properties and their interpretation in biological terms, we validate our model and use it to get a better understanding of the complex processes of the assembly pathway. We can explain the basic network behavior, using minimal T-invariants which represent special pathways through the network. We find linear as well as cyclic pathways. We determine the P-invariants that represent conserved moieties in a network. The simulation of the net demonstrates the importance of the stability of complexes during the maturation pathway. We can show that complexes that dissociate too fast, hinder the formation of the complete U1 snRNP.

Roles of glutamates and metal ions in a rationally designed nitric oxide reductase based on myoglobin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Y.W.; Robinson, H.; Yeung, N.

2010-05-11

A structural and functional model of bacterial nitric oxide reductase (NOR) has been designed by introducing two glutamates (Glu) and three histidines (His) in sperm whale myoglobin. X-ray structural data indicate that the three His and one Glu (V68E) residues bind iron, mimicking the putative FeB site in NOR, while the second Glu (I107E) interacts with a water molecule and forms a hydrogen bonding network in the designed protein. Unlike the first Glu (V68E), which lowered the heme reduction potential by {approx}110 mV, the second Glu has little effect on the heme potential, suggesting that the negatively charged Glu hasmore » a different role in redox tuning. More importantly, introducing the second Glu resulted in a {approx}100% increase in NOR activity, suggesting the importance of a hydrogen bonding network in facilitating proton delivery during NOR reactivity. In addition, EPR and X-ray structural studies indicate that the designed protein binds iron, copper, or zinc in the FeB site, each with different effects on the structures and NOR activities, suggesting that both redox activity and an intermediate five-coordinate heme-NO species are important for high NOR activity. The designed protein offers an excellent model for NOR and demonstrates the power of using designed proteins as a simpler and more well-defined system to address important chemical and biological issues.« less

Roles of Glutamates and Metal ions in a Rationally Designed Nitric Oxide Reductase Based on Myoglobin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Y Lin; N Yeung; Y Gao

2011-12-31

A structural and functional model of bacterial nitric oxide reductase (NOR) has been designed by introducing two glutamates (Glu) and three histidines (His) in sperm whale myoglobin. X-ray structural data indicate that the three His and one Glu (V68E) residues bind iron, mimicking the putative FeB site in NOR, while the second Glu (I107E) interacts with a water molecule and forms a hydrogen bonding network in the designed protein. Unlike the first Glu (V68E), which lowered the heme reduction potential by {approx}110 mV, the second Glu has little effect on the heme potential, suggesting that the negatively charged Glu hasmore » a different role in redox tuning. More importantly, introducing the second Glu resulted in a {approx}100% increase in NOR activity, suggesting the importance of a hydrogen bonding network in facilitating proton delivery during NOR reactivity. In addition, EPR and X-ray structural studies indicate that the designed protein binds iron, copper, or zinc in the FeB site, each with different effects on the structures and NOR activities, suggesting that both redox activity and an intermediate five-coordinate heme-NO species are important for high NOR activity. The designed protein offers an excellent model for NOR and demonstrates the power of using designed proteins as a simpler and more well-defined system to address important chemical and biological issues.« less

Overexpression of lncRNA H19 enhances carcinogenesis and metastasis of gastric cancer

PubMed Central

Li, Hao; Yu, Beiqin; Li, Jianfang; Su, Liping; Yan, Min; Zhu, Zhenggang; Liu, Bingya

2014-01-01

Long non-coding RNAs (lncRNAs) play key roles in the progression and metastasis of some carcinomas. We previously showed that the expression of lncRNA H19 (H19) was higher in gastric cancer (GC) tissues than that in paired noncanerous tissues. However, the underlying mechanisms remain unclear. In this study, H19/miR-675 knockdown models in the MKN45 cell line and ectopic expression models in the SGC7901 cell line were established, and a co-expression network of H19 was generated to identify target genes by RIP and DLR. The results showed that overexpression of H19 promoted the features of GC including proliferation, migration, invasion and metastasis. An H19 co-expression network identified ISM1 as a binding protein of H19, and its expression was positively correlated with that of H19. CALN1 was identified as a target gene of miR-675 and its expression was negatively correlated with that of miR-675. H19 and MiR-675 function in a similar manner. However, H19 RNA actively binds to ISM1 and miR-675 targets CALN1. These differences suggest that H19 plays other roles besides encoding miR-675 in GC. Our results suggest that the effect of H19 in GC is mediated by the direct upregulation of ISM1 and the indirect suppression of CALN1 expression via miR-675. PMID:24810858

Overexpression of lncRNA H19 enhances carcinogenesis and metastasis of gastric cancer.

PubMed

Li, Hao; Yu, Beiqin; Li, Jianfang; Su, Liping; Yan, Min; Zhu, Zhenggang; Liu, Bingya

2014-04-30

Long non-coding RNAs (lncRNAs) play key roles in the progression and metastasis of some carcinomas. We previously showed that the expression of lncRNA H19 (H19) was higher in gastric cancer (GC) tissues than that in paired noncanerous tissues. However, the underlying mechanisms remain unclear. In this study, H19/miR-675 knockdown models in the MKN45 cell line and ectopic expression models in the SGC7901 cell line were established, and a co-expression network of H19 was generated to identify target genes by RIP and DLR. The results showed that overexpression of H19 promoted the features of GC including proliferation, migration, invasion and metastasis. An H19 co-expression network identified ISM1 as a binding protein of H19, and its expression was positively correlated with that of H19. CALN1 was identified as a target gene of miR-675 and its expression was negatively correlated with that of miR-675. H19 and MiR-675 function in a similar manner. However, H19 RNA actively binds to ISM1 and miR-675 targets CALN1. These differences suggest that H19 plays other roles besides encoding miR-675 in GC. Our results suggest that the effect of H19 in GC is mediated by the direct upregulation of ISM1 and the indirect suppression of CALN1 expression via miR-675.

A relay network of extracellular heme-binding proteins drives C. albicans iron acquisition from hemoglobin.

PubMed

Kuznets, Galit; Vigonsky, Elena; Weissman, Ziva; Lalli, Daniela; Gildor, Tsvia; Kauffman, Sarah J; Turano, Paola; Becker, Jeffrey; Lewinson, Oded; Kornitzer, Daniel

2014-10-01

Iron scavenging constitutes a crucial challenge for survival of pathogenic microorganisms in the iron-poor host environment. Candida albicans, like many microbial pathogens, is able to utilize iron from hemoglobin, the largest iron pool in the host's body. Rbt5 is an extracellular glycosylphosphatidylinositol (GPI)-anchored heme-binding protein of the CFEM family that facilitates heme-iron uptake by an unknown mechanism. Here, we characterize an additional C. albicans CFEM protein gene, PGA7, deletion of which elicits a more severe heme-iron utilization phenotype than deletion of RBT5. The virulence of the pga7-/- mutant is reduced in a mouse model of systemic infection, consistent with a requirement for heme-iron utilization for C. albicans pathogenicity. The Pga7 and Rbt5 proteins exhibit distinct cell wall attachment, and discrete localization within the cell envelope, with Rbt5 being more exposed than Pga7. Both proteins are shown here to efficiently extract heme from hemoglobin. Surprisingly, while Pga7 has a higher affinity for heme in vitro, we find that heme transfer can occur bi-directionally between Pga7 and Rbt5, supporting a model in which they cooperate in a heme-acquisition relay. Together, our data delineate the roles of Pga7 and Rbt5 in a cell surface protein network that transfers heme from extracellular hemoglobin to the endocytic pathway, and provide a paradigm for how receptors embedded in the cell wall matrix can mediate nutrient uptake across the fungal cell envelope.

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Linking network topology to function. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin, A. Krzywicki and M. Zagorski

NASA Astrophysics Data System (ADS)

di Bernardo, Diego

2016-07-01

The review by Martin et al. deals with a long standing problem at the interface of complex systems and molecular biology, that is the relationship between the topology of a complex network and its function. In biological terms the problem translates to relating the topology of gene regulatory networks (GRNs) to specific cellular functions. GRNs control the spatial and temporal activity of the genes encoded in the cell's genome by means of specialised proteins called Transcription Factors (TFs). A TF is able to recognise and bind specifically to a sequence (TF biding site) of variable length (order of magnitude of 10) found upstream of the sequence encoding one or more genes (at least in prokaryotes) and thus activating or repressing their transcription. TFs can thus be distinguished in activator and repressor. The picture can become more complex since some classes of TFs can form hetero-dimers consisting of a protein complex whose subunits are the individual TFs. Heterodimers can have completely different binding sites and activity compared to their individual parts. In this review the authors limit their attention to prokaryotes where the complexity of GRNs is somewhat reduced. Moreover they exploit a unique feature of living systems, i.e. evolution, to understand whether function can shape network topology. Indeed, prokaryotes such as bacteria are among the oldest living systems that have become perfectly adapted to their environment over geological scales and thus have reached an evolutionary steady-state where the fitness of the population has reached a plateau. By integrating in silico analysis and comparative evolution, the authors show that indeed function does tend to shape the structure of a GRN, however this trend is not always present and depends on the properties of the network being examined. Interestingly, the trend is more apparent for sparse networks, i.e. where the density of edges is very low. Sparsity is indeed one of the most prominent features of natural occurring GRNs, and more specifically GRNs have been found to approximate a power-law ;scale-free; degree distribution by Barabasi and Albert [2]. Why sparsity arises is still under debate, but Price in 1976 proposed a model [1], later renamed ;preferential attachment; by Barabasi and Albert [2], able to give rise to sparse scale-free networks. In this model, a network grows over time (such as GRN during evolution) by sequential addition of new nodes (caused by genome duplications) that attach with higher probability to nodes with higher degree. In this review, Martin et al. propose that sparsity could also be caused phenotypic constrains even in the absence of genome duplications, in order for the network to be robust against random mutations in the genome sequence, which in turn affect the specificity of TF binding sites. The authors also found that network motifs, i.e. subnetworks consisting of 3 or 4 nodes with a specific topology that are over-represented in the network, are also shaped by phenotypic constrains. Theoretical and computational approaches to understand the forces that shape network topology are of extreme interest in biology, although at this stage their impact has been limited. Neverteless, these approaches may soon have important practical applications. The era of synthetic biology is upon us, novel organisms with ;minimal genomes; are being built with the dual aim of simplifying engineering of new functions useful to humans and to understand which is the minimal set of genes needed to support life [3]. The first minimal organism has just been created [3] by randomly deleting genes and genomic regions until a minimal set supporting cell growth and replication was found. The GRN of this minimal organism has not been investigated yet, but it will be of limited complexity. What is the GRN structure in this organism? Will the cell phenotypes be robust to mutations? Is it possible to re-engineer the GRN in order to find an optimal structure that confers phenotypic robustness to the cell? All of these questions can be tackled only by understanding the guiding principles linking network topology to network function.

Microsolvation of Fluoromethane.

PubMed

Rosenberg, Robert E

2016-09-29

Fluorinated organic compounds are ubiquitous in the pharmaceutical and agricultural industries. To better discern the mode of action of these compounds, it is critical to understand the potential for and strength of hydrogen bonds involving fluorine. It is known that CH3F forms a hydrogen bond with H2O in the gas phase but does not dissolve in bulk water. This paper examines CH3F surrounded by one to six water molecules. For systems of similar topologies, CH3F formed hydrogen bonds of nearly the same strength as water. Although CH3F can bind to a second water cluster with only a modest loss in binding energy, it must bind to these clusters as a double hydrogen bond acceptor. This means that CH3F cannot form a low-energy cyclic 2D hydrogen bonding network with water molecules, which limits its solubility in bulk water. However, CH3F should be able to bind to the periphery of small hydrogen bonding networks. These conclusions were not appreciably altered by SMD calculations. A more complete consideration of solvation, especially entropic effects, was not undertaken. Data for geometries, population changes, and vibrational frequency shifts were also analyzed and compared to binding energies.

MHC2NNZ: A novel peptide binding prediction approach for HLA DQ molecules

NASA Astrophysics Data System (ADS)

Xie, Jiang; Zeng, Xu; Lu, Dongfang; Liu, Zhixiang; Wang, Jiao

2017-07-01

The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

NMR and molecular modeling of wine tannins binding to saliva proteins: revisiting astringency from molecular and colloidal prospects.

PubMed

Cala, Olivier; Pinaud, Noël; Simon, Cécile; Fouquet, Eric; Laguerre, Michel; Dufourc, Erick J; Pianet, Isabelle

2010-11-01

In organoleptic science, the association of tannins to saliva proteins leads to the poorly understood phenomenon of astringency. To decipher this interaction at molecular and colloidal levels, the binding of 4 procyanidin dimers (B1-4) and 1 trimer (C2) to a human saliva proline-rich peptide, IB7(14), was studied. Interactions have been characterized by measuring dissociation constants, sizes of complexes, number, and nature of binding sites using NMR (chemical shift variations, diffusion-ordered spectroscopy, and saturation transfer diffusion). The binding sites were identified using molecular mechanics, and the hydrophilic/hydrophobic nature of the interactions was resolved by calculating the molecular lipophilicity potential within the complexes. The following comprehensive scheme can be proposed: 1) below the tannin critical micelle concentration (CMC), interaction is specific, and the procyanidin anchorage always occurs on the same three IB7(14) sites. The tannin 3-dimensional structure plays a key role in the binding force and in the tannin's ability to act as a bidentate ligand: tannins adopting an extended conformation exhibit higher affinity toward protein and initiate the formation of a network. 2) Above the CMC, after the first specific hydrophilic interaction has taken place, a random hydrophobic stacking occurs between tannins and proteins. The whole process is discussed in the general frame of wine tannins eliciting astringency.

Bifurcation of synchronous oscillations into torus in a system of two reciprocally inhibitory silicon neurons: experimental observation and modeling.

PubMed

Bondarenko, Vladimir E; Cymbalyuk, Gennady S; Patel, Girish; Deweerth, Stephen P; Calabrese, Ronald L

2004-12-01

Oscillatory activity in the central nervous system is associated with various functions, like motor control, memory formation, binding, and attention. Quasiperiodic oscillations are rarely discussed in the neurophysiological literature yet they may play a role in the nervous system both during normal function and disease. Here we use a physical system and a model to explore scenarios for how quasiperiodic oscillations might arise in neuronal networks. An oscillatory system of two mutually inhibitory neuronal units is a ubiquitous network module found in nervous systems and is called a half-center oscillator. Previously we created a half-center oscillator of two identical oscillatory silicon (analog Very Large Scale Integration) neurons and developed a mathematical model describing its dynamics. In the mathematical model, we have shown that an in-phase limit cycle becomes unstable through a subcritical torus bifurcation. However, the existence of this torus bifurcation in experimental silicon two-neuron system was not rigorously demonstrated or investigated. Here we demonstrate the torus predicted by the model for the silicon implementation of a half-center oscillator using complex time series analysis, including bifurcation diagrams, mapping techniques, correlation functions, amplitude spectra, and correlation dimensions, and we investigate how the properties of the quasiperiodic oscillations depend on the strengths of coupling between the silicon neurons. The potential advantages and disadvantages of quasiperiodic oscillations (torus) for biological neural systems and artificial neural networks are discussed.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

PubMed

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis

2017-11-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.

Cross-Linking Molecules Modify Composite Actin Networks Independently

NASA Astrophysics Data System (ADS)

Schmoller, K. M.; Lieleg, O.; Bausch, A. R.

2008-09-01

While cells make use of many actin binding proteins (ABPs) simultaneously to tailor the mechanical properties of the cytoskeleton, the detailed interplay of different ABPs is not understood. By a combination of macrorheological measurements and confocal microscopy, we show that the ABPs fascin and filamin modify the structural and viscoelastic properties of composite in vitro actin networks independently. The outnumbering ABP dictates the local network structure and therefore also dominates the macromechanical network response.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

PubMed

Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

Ligand Binding: Molecular Mechanics Calculation of the Streptavidin-Biotin Rupture Force

NASA Astrophysics Data System (ADS)

Grubmuller, Helmut; Heymann, Berthold; Tavan, Paul

1996-02-01

The force required to rupture the streptavidin-biotin complex was calculated here by computer simulations. The computed force agrees well with that obtained by recent single molecule atomic force microscope experiments. These simulations suggest a detailed multiple-pathway rupture mechanism involving five major unbinding steps. Binding forces and specificity are attributed to a hydrogen bond network between the biotin ligand and residues within the binding pocket of streptavidin. During rupture, additional water bridges substantially enhance the stability of the complex and even dominate the binding inter-actions. In contrast, steric restraints do not appear to contribute to the binding forces, although conformational motions were observed.

The impact of the metabotropic glutamate receptor and other gene family interaction networks on autism

PubMed Central

Hadley, Dexter; Wu, Zhi-liang; Kao, Charlly; Kini, Akshata; Mohamed-Hadley, Alisha; Thomas, Kelly; Vazquez, Lyam; Qiu, Haijun; Mentch, Frank; Pellegrino, Renata; Kim, Cecilia; Connolly, John; Pinto, Dalila; Merikangas, Alison; Klei, Lambertus; Vorstman, Jacob A.S.; Thompson, Ann; Regan, Regina; Pagnamenta, Alistair T.; Oliveira, Bárbara; Magalhaes, Tiago R.; Gilbert, John; Duketis, Eftichia; De Jonge, Maretha V.; Cuccaro, Michael; Correia, Catarina T.; Conroy, Judith; Conceição, Inês C.; Chiocchetti, Andreas G.; Casey, Jillian P.; Bolshakova, Nadia; Bacchelli, Elena; Anney, Richard; Zwaigenbaum, Lonnie; Wittemeyer, Kerstin; Wallace, Simon; Engeland, Herman van; Soorya, Latha; Rogé, Bernadette; Roberts, Wendy; Poustka, Fritz; Mouga, Susana; Minshew, Nancy; McGrew, Susan G.; Lord, Catherine; Leboyer, Marion; Le Couteur, Ann S.; Kolevzon, Alexander; Jacob, Suma; Guter, Stephen; Green, Jonathan; Green, Andrew; Gillberg, Christopher; Fernandez, Bridget A.; Duque, Frederico; Delorme, Richard; Dawson, Geraldine; Café, Cátia; Brennan, Sean; Bourgeron, Thomas; Bolton, Patrick F.; Bölte, Sven; Bernier, Raphael; Baird, Gillian; Bailey, Anthony J.; Anagnostou, Evdokia; Almeida, Joana; Wijsman, Ellen M.; Vieland, Veronica J.; Vicente, Astrid M.; Schellenberg, Gerard D.; Pericak-Vance, Margaret; Paterson, Andrew D.; Parr, Jeremy R.; Oliveira, Guiomar; Almeida, Joana; Café, Cátia; Mouga, Susana; Correia, Catarina; Nurnberger, John I.; Monaco, Anthony P.; Maestrini, Elena; Klauck, Sabine M.; Hakonarson, Hakon; Haines, Jonathan L.; Geschwind, Daniel H.; Freitag, Christine M.; Folstein, Susan E.; Ennis, Sean; Coon, Hilary; Battaglia, Agatino; Szatmari, Peter; Sutcliffe, James S.; Hallmayer, Joachim; Gill, Michael; Cook, Edwin H.; Buxbaum, Joseph D.; Devlin, Bernie; Gallagher, Louise; Betancur, Catalina; Scherer, Stephen W.; Glessner, Joseph; Hakonarson, Hakon

2014-01-01

Although multiple reports show that defective genetic networks underlie the aetiology of autism, few have translated into pharmacotherapeutic opportunities. Since drugs compete with endogenous small molecules for protein binding, many successful drugs target large gene families with multiple drug binding sites. Here we search for defective gene family interaction networks (GFINs) in 6,742 patients with the ASDs relative to 12,544 neurologically normal controls, to find potentially druggable genetic targets. We find significant enrichment of structural defects (P≤2.40E−09, 1.8-fold enrichment) in the metabotropic glutamate receptor (GRM) GFIN, previously observed to impact attention deficit hyperactivity disorder (ADHD) and schizophrenia. Also, the MXD-MYC-MAX network of genes, previously implicated in cancer, is significantly enriched (P≤3.83E−23, 2.5-fold enrichment), as is the calmodulin 1 (CALM1) gene interaction network (P≤4.16E−04, 14.4-fold enrichment), which regulates voltage-independent calcium-activated action potentials at the neuronal synapse. We find that multiple defective gene family interactions underlie autism, presenting new translational opportunities to explore for therapeutic interventions. PMID:24927284

Covalent modifications of the amyloid beta peptide by hydroxynonenal: Effects on metal ion binding by monomers and insights into the fibril topology.

PubMed

Grasso, G; Komatsu, H; Axelsen, P H

2017-09-01

Amyloid β peptides (Aβ) and metal ions are associated with oxidative stress in Alzheimer's disease (AD). Oxidative stress, acting on ω-6 polyunsaturated fatty acyl chains, produces diverse products, including 4-hydroxy-2-nonenal (HNE), which can covalently modify the Aβ that helped to produce it. To examine possible feedback mechanisms involving Aβ, metal ions and HNE production, the effects of HNE modification and fibril formation on metal ion binding was investigated. Results indicate that copper(II) generally inhibits the modification of His side chains in Aβ by HNE, but that once modified, copper(II) still binds to Aβ with high affinity. Fibril formation protects only one of the three His residues in Aβ from HNE modification, and this protection is consistent with proposed models of fibril structure. These results provide insight into a network of biochemical reactions that may be operating as a consequence of oxidative stress in AD, or as part of the pathogenic process. Copyright © 2016. Published by Elsevier Inc.

Protein Aggregates Are Recruited to Aggresome by Histone Deacetylase 6 via Unanchored Ubiquitin C Termini

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ouyang, Hui; Ali, Yousuf O.; Ravichandran, Mani

2012-07-11

The aggresome pathway is activated when proteasomal clearance of misfolded proteins is hindered. Misfolded polyubiquitinated protein aggregates are recruited and transported to the aggresome via the microtubule network by a protein complex consisting of histone deacetylase 6 (HDAC6) and the dynein motor complex. The current model suggests that HDAC6 recognizes protein aggregates by binding directly to polyubiquitinated proteins. Here, we show that there are substantial amounts of unanchored ubiquitin in protein aggregates with solvent-accessible C termini. The ubiquitin-binding domain (ZnF-UBP) of HDAC6 binds exclusively to the unanchored C-terminal diglycine motif of ubiquitin instead of conjugated polyubiquitin. The unanchored ubiquitin Cmore » termini in the aggregates are generated in situ by aggregate-associated deubiquitinase ataxin-3. These results provide structural and mechanistic bases for the role of HDAC6 in aggresome formation and further suggest a novel ubiquitin-mediated signaling pathway, where the exposure of ubiquitin C termini within protein aggregates enables HDAC6 recognition and transport to the aggresome.« less

Refining mass formulas for astrophysical applications: A Bayesian neural network approach

NASA Astrophysics Data System (ADS)

Utama, R.; Piekarewicz, J.

2017-10-01

Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

A bioinformatic survey of RNA-binding proteins in Plasmodium.

PubMed

Reddy, B P Niranjan; Shrestha, Sony; Hart, Kevin J; Liang, Xiaoying; Kemirembe, Karen; Cui, Liwang; Lindner, Scott E

2015-11-02

The malaria parasites in the genus Plasmodium have a very complicated life cycle involving an invertebrate vector and a vertebrate host. RNA-binding proteins (RBPs) are critical factors involved in every aspect of the development of these parasites. However, very few RBPs have been functionally characterized to date in the human parasite Plasmodium falciparum. Using different bioinformatic methods and tools we searched P. falciparum genome to list and annotate RBPs. A representative 3D models for each of the RBD domain identified in P. falciparum was created using I-TESSAR and SWISS-MODEL. Microarray and RNAseq data analysis pertaining PfRBPs was performed using MeV software. Finally, Cytoscape was used to create protein-protein interaction network for CITH-Dozi and Caf1-CCR4-Not complexes. We report the identification of 189 putative RBP genes belonging to 13 different families in Plasmodium, which comprise 3.5% of all annotated genes. Almost 90% (169/189) of these genes belong to six prominent RBP classes, namely RNA recognition motifs, DEAD/H-box RNA helicases, K homology, Zinc finger, Puf and Alba gene families. Interestingly, almost all of the identified RNA-binding helicases and KH genes have cognate homologs in model species, suggesting their evolutionary conservation. Exploration of the existing P. falciparum blood-stage transcriptomes revealed that most RBPs have peak mRNA expression levels early during the intraerythrocytic development cycle, which taper off in later stages. Nearly 27% of RBPs have elevated expression in gametocytes, while 47 and 24% have elevated mRNA expression in ookinete and asexual stages. Comparative interactome analyses using human and Plasmodium protein-protein interaction datasets suggest extensive conservation of the PfCITH/PfDOZI and PfCaf1-CCR4-NOT complexes. The Plasmodium parasites possess a large number of putative RBPs belonging to most of RBP families identified so far, suggesting the presence of extensive post-transcriptional regulation in these parasites. Taken together, in silico identification of these putative RBPs provides a foundation for future functional studies aimed at defining a unique network of post-transcriptional regulation in P. falciparum.

Gel-sol transition of the cytoplasm and its regulation

NASA Astrophysics Data System (ADS)

Janmey, Paul A.

1991-05-01

The cytoplasm of motile cells contains a dynamic system of filamentous protein polymers that endow the cell with elasticity permitting it to maintain its shape in the presence of mechanical forces encountered in vivo. Part of this cytoskeleton is composed of filaments of polymerized actin. Remodeling of this network is required for cell motility and cytoplasmic restructuring, and the reversible polymerization of actin per se has been suggested to cause morphologic changes such as cell ruffling and pseudopd extension. Changes in the degree of polymerization of acting and in the association of actin filaments into supramolecular structures are often associated with cell activation. Such activation is initiated by extracellular signals that bind to receptors which are often coupled by G-proteins to the production of intracellular second messangers. Cytoplasmic gel-sol transitions therefore can occur by formation and dissolution of actin networks, mediated by a variety of actin-binding proteins which are regulated by intracellular signalling molecules such as Ca2+ and polyphosphoinositides. The effects of three actin binding proteins: profilin, gelsolin and ABP (Tilamin) on the polymerization of actin and the viscoelasticity of the resulting networks measured in vitro suggest possible roles of these proteins in vivo. In particular, gelsolin, which activated by Ca2+ to sever and cap actin filaments, and released from filament ends by PIP2, appears to be a likely candidate for regulation of gel-sol transitions in response to cell activation. Recent results demonstrate that the hydrolysis of ATP that occurs following actin polymerization also influences the structure of the resulting filament. In addition being regulated by acting-binding proteins, the viscoelasticity of actin networks is also affected by the presence of the other two classes of cytoplasmic protein polymers, microtubules and intermediate filaments.

Functional Specialization and Flexibility in Human Association Cortex

PubMed Central

Yeo, B. T. Thomas; Krienen, Fenna M.; Eickhoff, Simon B.; Yaakub, Siti N.; Fox, Peter T.; Buckner, Randy L.; Asplund, Christopher L.; Chee, Michael W.L.

2015-01-01

The association cortex supports cognitive functions enabling flexible behavior. Here, we explored the organization of human association cortex by mathematically formalizing the notion that a behavioral task engages multiple cognitive components, which are in turn supported by multiple overlapping brain regions. Application of the model to a large data set of neuroimaging experiments (N = 10 449) identified complex zones of frontal and parietal regions that ranged from being highly specialized to highly flexible. The network organization of the specialized and flexible regions was explored with an independent resting-state fMRI data set (N = 1000). Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participated in multiple components to different degrees. This heterogeneous selectivity was predicted by the connectivity between flexible and specialized regions. Functionally flexible regions might support binding or integrating specialized brain networks that, in turn, contribute to the ability to execute multiple and varied tasks. PMID:25249407

Modular Evolution of DNA-Binding Preference of a Tbrain Transcription Factor Provides a Mechanism for Modifying Gene Regulatory Networks

PubMed Central

Cheatle Jarvela, Alys M.; Brubaker, Lisa; Vedenko, Anastasia; Gupta, Anisha; Armitage, Bruce A.; Bulyk, Martha L.; Hinman, Veronica F.

2014-01-01

Gene regulatory networks (GRNs) describe the progression of transcriptional states that take a single-celled zygote to a multicellular organism. It is well documented that GRNs can evolve extensively through mutations to cis-regulatory modules (CRMs). Transcription factor proteins that bind these CRMs may also evolve to produce novelty. Coding changes are considered to be rarer, however, because transcription factors are multifunctional and hence are more constrained to evolve in ways that will not produce widespread detrimental effects. Recent technological advances have unearthed a surprising variation in DNA-binding abilities, such that individual transcription factors may recognize both a preferred primary motif and an additional secondary motif. This provides a source of modularity in function. Here, we demonstrate that orthologous transcription factors can also evolve a changed preference for a secondary binding motif, thereby offering an unexplored mechanism for GRN evolution. Using protein-binding microarray, surface plasmon resonance, and in vivo reporter assays, we demonstrate an important difference in DNA-binding preference between Tbrain protein orthologs in two species of echinoderms, the sea star, Patiria miniata, and the sea urchin, Strongylocentrotus purpuratus. Although both orthologs recognize the same primary motif, only the sea star Tbr also has a secondary binding motif. Our in vivo assays demonstrate that this difference may allow for greater evolutionary change in timing of regulatory control. This uncovers a layer of transcription factor binding divergence that could exist for many pairs of orthologs. We hypothesize that this divergence provides modularity that allows orthologous transcription factors to evolve novel roles in GRNs through modification of binding to secondary sites. PMID:25016582

System Design for Nano-Network Communications

NASA Astrophysics Data System (ADS)

ShahMohammadian, Hoda

The potential applications of nanotechnology in a wide range of areas necessities nano-networking research. Nano-networking is a new type of networking which has emerged by applying nanotechnology to communication theory. Therefore, this dissertation presents a framework for physical layer communications in a nano-network and addresses some of the pressing unsolved challenges in designing a molecular communication system. The contribution of this dissertation is proposing well-justified models for signal propagation, noise sources, optimum receiver design and synchronization in molecular communication channels. The design of any communication system is primarily based on the signal propagation channel and noise models. Using the Brownian motion and advection molecular statistics, separate signal propagation and noise models are presented for diffusion-based and flow-based molecular communication channels. It is shown that the corrupting noise of molecular channels is uncorrelated and non-stationary with a signal dependent magnitude. The next key component of any communication system is the reception and detection process. This dissertation provides a detailed analysis of the effect of the ligand-receptor binding mechanism on the received signal, and develops the first optimal receiver design for molecular communications. The bit error rate performance of the proposed receiver is evaluated and the impact of medium motion on the receiver performance is investigated. Another important feature of any communication system is synchronization. In this dissertation, the first blind synchronization algorithm is presented for the molecular communication channels. The proposed algorithm uses a non-decision directed maximum likelihood criterion for estimating the channel delay. The Cramer-Rao lower bound is also derived and the performance of the proposed synchronization algorithm is evaluated by investigating its mean square error.

Reprogramming cell fate with a genome-scale library of artificial transcription factors.

PubMed

Eguchi, Asuka; Wleklinski, Matthew J; Spurgat, Mackenzie C; Heiderscheit, Evan A; Kropornicka, Anna S; Vu, Catherine K; Bhimsaria, Devesh; Swanson, Scott A; Stewart, Ron; Ramanathan, Parameswaran; Kamp, Timothy J; Slukvin, Igor; Thomson, James A; Dutton, James R; Ansari, Aseem Z

2016-12-20

Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression of Oct4 (POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly target Oct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices.

Reprogramming cell fate with a genome-scale library of artificial transcription factors

PubMed Central

Eguchi, Asuka; Wleklinski, Matthew J.; Spurgat, Mackenzie C.; Heiderscheit, Evan A.; Kropornicka, Anna S.; Vu, Catherine K.; Bhimsaria, Devesh; Swanson, Scott A.; Stewart, Ron; Ramanathan, Parameswaran; Kamp, Timothy J.; Slukvin, Igor; Thomson, James A.; Dutton, James R.; Ansari, Aseem Z.

2016-01-01

Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression of Oct4 (POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly target Oct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices. PMID:27930301

The functional landscape bound to the transcription factors of Escherichia coli K-12.

PubMed

Pérez-Rueda, Ernesto; Tenorio-Salgado, Silvia; Huerta-Saquero, Alejandro; Balderas-Martínez, Yalbi I; Moreno-Hagelsieb, Gabriel

2015-10-01

Motivated by the experimental evidences accumulated in the last ten years and based on information deposited in RegulonDB, literature look up, and sequence analysis, we analyze the repertoire of 304 DNA-binding Transcription factors (TFs) in Escherichia coli K-12. These regulators were grouped in 78 evolutionary families and are regulating almost half of the total genes in this bacterium. In structural terms, 60% of TFs are composed by two-domains, 30% are monodomain, and 10% three- and four-structural domains. As previously noticed, the most abundant DNA-binding domain corresponds to the winged helix-turn-helix, with few alternative DNA-binding structures, resembling the hypothesis of successful protein structures with the emergence of new ones at low scales. In summary, we identified and described the characteristics associated to the DNA-binding TF in E. coli K-12. We also identified twelve functional modules based on a co-regulated gene matrix. Finally, diverse regulons were predicted based on direct associations between the TFs and potential regulated genes. This analysis should increase our knowledge about the gene regulation in the bacterium E. coli K-12, and provide more additional clues for comprehensive modelling of transcriptional regulatory networks in other bacteria. Copyright © 2015 Elsevier Ltd. All rights reserved.

Krüppel-like factors are effectors of nuclear receptor signaling

PubMed Central

Knoedler, Joseph R.; Denver, Robert J.

2015-01-01

Binding of steroid and thyroid hormones to their cognate nuclear receptors (NRs) impacts virtually every aspect of postembryonic development, physiology and behavior, and inappropriate signaling by NRs may contribute to disease. While NRs regulate genes by direct binding to hormone response elements in the genome, their actions may depend on the activity of other transcription factors (TFs) that may or may not bind DNA. The Krüppel-like family of transcription factors (KLF) is an evolutionarily conserved class of DNA-binding proteins that influence many aspects of development and physiology. Several members of this family have been shown to play diverse roles in NR signaling. For example, KLFs 1) act as accessory transcription factors for NR actions, 2) regulate expression of NR genes, and 3) as gene products of primary NR response genes function as key players in NR-dependent transcriptional networks. In mouse models, deletion of different KLFs leads to aberrant transcriptional and physiological responses to hormones, underscoring the importance of these proteins in the regulation of hormonal signaling. Understanding the functional relationships between NRs and KLFs will yield important insights into mechanisms of NR signaling. In this review we present a conceptual framework for understanding how KLFs participate in NR signaling, and we provide examples of how these proteins function to effect hormone action. PMID:24642391

β-Catenin recognizes a specific RNA motif in the cyclooxygenase-2 mRNA 3′-UTR and interacts with HuR in colon cancer cells

PubMed Central

Kim, Inae; Kwak, Hoyun; Lee, Hee Kyu; Hyun, Soonsil; Jeong, Sunjoo

2012-01-01

RNA-binding proteins regulate multiple steps of RNA metabolism through both dynamic and combined binding. In addition to its crucial roles in cell adhesion and Wnt-activated transcription in cancer cells, β-catenin regulates RNA alternative splicing and stability possibly by binding to target RNA in cells. An RNA aptamer was selected for specific binding to β-catenin to address RNA recognition by β-catenin more specifically. Here, we characterized the structural properties of the RNA aptamer as a model and identified a β-catenin RNA motif. Similar RNA motif was found in cellular RNA, Cyclooxygenase-2 (COX-2) mRNA 3′-untranslated region (3′-UTR). More significantly, the C-terminal domain of β-catenin interacted with HuR and the Armadillo repeat domain associated with RNA to form the RNA–β-catenin–HuR complex in vitro and in cells. Furthermore, the tertiary RNA–protein complex was predominantly found in the cytoplasm of colon cancer cells; thus, it might be related to COX-2 protein level and cancer progression. Taken together, the β-catenin RNA aptamer was valuable for deducing the cellular RNA aptamer and identifying novel and oncogenic RNA–protein networks in colon cancer cells. PMID:22544606

Proteins with similar architecture exhibit similar large-scale dynamic behavior.

PubMed Central

Keskin, O; Jernigan, R L; Bahar, I

2000-01-01

We have investigated the similarities and differences in the computed dynamic fluctuations exhibited by six members of a protein fold family with a coarse-grained Gaussian network model. Specifically, we consider the cofactor binding fragment of CysB; the lysine/arginine/ornithine-binding protein (LAO); the enzyme porphobilinogen deaminase (PBGD); the ribose-binding protein (RBP); the N-terminal lobe of ovotransferrin in apo-form (apo-OVOT); and the leucine/isoleucine/valine-binding protein (LIVBP). All have domains that resemble a Rossmann fold, but there are also some significant differences. Results indicate that similar global dynamic behavior is preserved for the members of a fold family, and that differences usually occur in regions only where specific function is localized. The present work is a computational demonstration that the scaffold of a protein fold may be utilized for diverse purposes. LAO requires a bound ligand before it conforms to the large-scale fluctuation behavior of the three other members of the family, CysB, PBGD, and RBP, all of which contain a substrate (cofactor) at the active site cleft. The dynamics of the ligand-free enzymes LIVBP and apo-OVOT, on the other hand, concur with that of unliganded LAO. The present results suggest that it is possible to construct structure alignments based on dynamic fluctuation behavior. PMID:10733987

Crystal molecular dynamics simulations to speed up MM/PB(GB)SA evaluation of binding free energies of di-mannose deoxy analogs with P51G-m4-Cyanovirin-N.

PubMed

Vorontsov, Ivan I; Miyashita, Osamu

2011-04-30

Complexes of two Cyanovirin-N (CVN) mutants, m4-CVN and P51G-m4-CVN, with deoxy di-mannose analogs were employed as models to generate conformational ensembles using explicit water Molecular Dynamics (MD) simulations in solution and in crystal environment. The results were utilized for evaluation of binding free energies with the molecular mechanics Poisson-Boltzmann (or Generalized Born) surface area, MM/PB(GB)SA, methods. The calculations provided the ranking of deoxy di-mannose ligands affinity in agreement with available qualitative experimental evidences. This confirms the importance of the hydrogen-bond network between di-mannose 3'- and 4'-hydroxyl groups and the protein binding site B(M) as a basis of the CVN activity as an effective HIV fusion inhibitor. Comparison of binding free energies averaged over snapshots from the solution and crystal simulations showed high promises in the use of the crystal matrix for acceleration of the conformational ensemble generation, the most time consuming step in MM/PB(GB)SA approach. Correlation between energy values based on solution versus crystal ensembles is 0.95 for both MM/PBSA and MM/GBSA methods. Copyright © 2010 Wiley Periodicals, Inc.

Mutations in extracellular matrix genes NID1 and LAMC1 cause autosomal dominant Dandy-Walker malformation and occipital cephaloceles

PubMed Central

Darbro, Benjamin W.; Mahajan, Vinit B.; Gakhar, Lokesh; Skeie, Jessica M.; Campbell, Elizabeth; Wu, Shu; Bing, Xinyu; Millen, Kathleen J.; Dobyns, William B.; Kessler, John A.; Jalali, Ali; Cremer, James; Segre, Alberto; Manak, J. Robert; Aldinger, Kimerbly A.; Suzuki, Satoshi; Natsume, Nagato; Ono, Maya; Hai, Huynh Dai; Viet, Le Thi; Loddo, Sara; Valente, Enza M.; Bernardini, Laura; Ghonge, Nitin; Ferguson, Polly J.; Bassuk, Alexander G.

2013-01-01

We performed whole-exome sequencing of a family with autosomal dominant Dandy-Walker malformation and occipital cephaloceles (ADDWOC) and detected a mutation in the extracellular matrix protein encoding gene NID1. In a second family, protein interaction network analysis identified a mutation in LAMC1, which encodes a NID1 binding partner. Structural modeling the NID1-LAMC1 complex demonstrated that each mutation disrupts the interaction. These findings implicate the extracellular matrix in the pathogenesis of Dandy-Walker spectrum disorders. PMID:23674478

Ties That Bind: The Practice of Social Networks. Number Two in a Series of Reports on Social Networks from The Annie E. Casey Foundation

ERIC Educational Resources Information Center

Bailey, Terri J.

2006-01-01

This report documents the Annie E. Casey Foundation's effort to learn from families, communities and organizations around the country about their experiences with social network strategies and approaches. Using the voices and experiences of the families and organizations visited, the report summarizes findings from these visits and helps lay the…

Identification of cation-binding sites on actin that drive polymerization and modulate bending stiffness

PubMed Central

Kang, Hyeran; Bradley, Michael J.; McCullough, Brannon R.; Pierre, Anaëlle; Grintsevich, Elena E.; Reisler, Emil; De La Cruz, Enrique M.

2012-01-01

The assembly of actin monomers into filaments and networks plays vital roles throughout eukaryotic biology, including intracellular transport, cell motility, cell division, determining cellular shape, and providing cells with mechanical strength. The regulation of actin assembly and modulation of filament mechanical properties are critical for proper actin function. It is well established that physiological salt concentrations promote actin assembly and alter the overall bending mechanics of assembled filaments and networks. However, the molecular origins of these salt-dependent effects, particularly if they involve nonspecific ionic strength effects or specific ion-binding interactions, are unknown. Here, we demonstrate that specific cation binding at two discrete sites situated between adjacent subunits along the long-pitch helix drive actin polymerization and determine the filament bending rigidity. We classify the two sites as “polymerization” and “stiffness” sites based on the effects that mutations at the sites have on salt-dependent filament assembly and bending mechanics, respectively. These results establish the existence and location of the cation-binding sites that confer salt dependence to the assembly and mechanics of actin filaments. PMID:23027950

A regulatory network to segregate the identity of neuronal subtypes.

PubMed

Lee, Seunghee; Lee, Bora; Joshi, Kaumudi; Pfaff, Samuel L; Lee, Jae W; Lee, Soo-Kyung

2008-06-01

Spinal motor neurons (MNs) and V2 interneurons (V2-INs) are specified by two related LIM-complexes, MN-hexamer and V2-tetramer, respectively. Here we show how multiple parallel and complementary feedback loops are integrated to assign these two cell fates accurately. While MN-hexamer response elements (REs) are specific to MN-hexamer, V2-tetramer-REs can bind both LIM-complexes. In embryonic MNs, however, two factors cooperatively suppress the aberrant activation of V2-tetramer-REs. First, LMO4 blocks V2-tetramer assembly. Second, MN-hexamer induces a repressor, Hb9, which binds V2-tetramer-REs and suppresses their activation. V2-INs use a similar approach; V2-tetramer induces a repressor, Chx10, which binds MN-hexamer-REs and blocks their activation. Thus, our study uncovers a regulatory network to segregate related cell fates, which involves reciprocal feedforward gene regulatory loops.

Exploring QSARs of the interaction of flavonoids with GABA (A) receptor using MLR, ANN and SVM techniques.

PubMed

Deeb, Omar; Shaik, Basheerulla; Agrawal, Vijay K

2014-10-01

Quantitative Structure-Activity Relationship (QSAR) models for binding affinity constants (log Ki) of 78 flavonoid ligands towards the benzodiazepine site of GABA (A) receptor complex were calculated using the machine learning methods: artificial neural network (ANN) and support vector machine (SVM) techniques. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. The descriptor selection and model building were performed with 10-fold cross-validation using the training data set. The SVM and MLR coefficient of determination values are 0.944 and 0.879, respectively, for the training set and are higher than those of ANN models. Though the SVM model shows improvement of training set fitting, the ANN model was superior to SVM and MLR in predicting the test set. Randomization test is employed to check the suitability of the models.

Identifying gene coexpression networks underlying the dynamic regulation of wood-forming tissues in Populus under diverse environmental conditions.

PubMed

Zinkgraf, Matthew; Liu, Lijun; Groover, Andrew; Filkov, Vladimir

2017-06-01

Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments associated individual conserved modules with distinct biological processes underlying wood formation, such as cell-wall biosynthesis, meristem development and epigenetic pathways. Module genes were also enriched for DNase I hypersensitivity footprints and binding from four transcription factors associated with wood formation. The conserved modules are excellent candidates for modeling core developmental pathways common to wood formation in diverse environments and genotypes, and serve as testbeds for hypothesis generation and testing for future studies. No claim to original US government works. New Phytologist © 2017 New Phytologist Trust.

Structural and Functional Evidence for Testosterone Activation of GPRC6A in Peripheral Tissues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pi, Min; Kapoor, Karan; Wu, Yunpeng

G protein-coupled receptor (GPCR) family C group 6 member A (GPRC6A) is a multiligand GPCR that is activated by cations, L-amino acids, and osteocalcin. GPRC6A plays an important role in the regulation of testosterone (T) production and energy metabolism in mice. T has rapid, transcription-independent (nongenomic) effects that are mediated by a putative GPCR. We previously found that T can activate GPRC6A in vitro, but the possibility that T is a ligand for GPRC6A remains controversial. Here, we demonstrate direct T binding to GPRC6A and construct computational structural models of GPRC6A that are used to identify potential binding poses ofmore » T. Mutations of the predicted binding site residues were experimentally found to block T activation of GPRC6A, in agreement with the modeling. Using Gpr6ca(-/-) mice, we confirmed that loss of GPRC6A resulted in loss of T rapid signaling responses and elucidated several biological functions regulated by GPRC6A-dependent T rapid signaling, including T stimulation of insulin secretion in pancreatic islets and enzyme expression involved in the biosynthesis of T in Leydig cells. Finally, we identified a stereo-specific effect of an R-isomer of a selective androgen receptor modulator that is predicted to bind to and shown to activate GPRC6A but not androgen receptor. Together, our data show that GPRC6A directly mediates the rapid signaling response to T and uncovers previously unrecognized endocrine networks.« less

Structural and Functional Evidence for Testosterone Activation of GPRC6A in Peripheral Tissues

DOE PAGES

Pi, Min; Kapoor, Karan; Wu, Yunpeng; ...

2015-01-01

G protein-coupled receptor (GPCR) family C group 6 member A (GPRC6A) is a multiligand GPCR that is activated by cations, L-amino acids, and osteocalcin. GPRC6A plays an important role in the regulation of testosterone (T) production and energy metabolism in mice. T has rapid, transcription-independent (nongenomic) effects that are mediated by a putative GPCR. We previously found that T can activate GPRC6A in vitro, but the possibility that T is a ligand for GPRC6A remains controversial. Here, we demonstrate direct T binding to GPRC6A and construct computational structural models of GPRC6A that are used to identify potential binding poses ofmore » T. Mutations of the predicted binding site residues were experimentally found to block T activation of GPRC6A, in agreement with the modeling. Using Gpr6ca(-/-) mice, we confirmed that loss of GPRC6A resulted in loss of T rapid signaling responses and elucidated several biological functions regulated by GPRC6A-dependent T rapid signaling, including T stimulation of insulin secretion in pancreatic islets and enzyme expression involved in the biosynthesis of T in Leydig cells. Finally, we identified a stereo-specific effect of an R-isomer of a selective androgen receptor modulator that is predicted to bind to and shown to activate GPRC6A but not androgen receptor. Together, our data show that GPRC6A directly mediates the rapid signaling response to T and uncovers previously unrecognized endocrine networks.« less

Switching bonds in a DNA gel: an all-DNA vitrimer.

PubMed

Romano, Flavio; Sciortino, Francesco

2015-02-20

We design an all-DNA system that behaves like vitrimers, innovative plastics with self-healing and stress-releasing properties. The DNA sequences are engineered to self-assemble first into tetra- and bifunctional units which, upon further cooling, bind to each other forming a fully bonded network gel. An innovative design of the binding regions of the DNA sequences, exploiting a double toehold-mediated strand displacement, generates a network gel which is able to reshuffle its bonds, retaining at all times full bonding. As in vitrimers, the rate of bond switching can be controlled via a thermally activated catalyst, which in the present design is very short DNA strands.

Atomistic simulations and network-based modeling of the Hsp90-Cdc37 chaperone binding with Cdk4 client protein: A mechanism of chaperoning kinase clients by exploiting weak spots of intrinsically dynamic kinase domains

PubMed Central

Czemeres, Josh; Buse, Kurt

2017-01-01

A fundamental role of the Hsp90 and Cdc37 chaperones in mediating conformational development and activation of diverse protein kinase clients is essential in signal transduction. There has been increasing evidence that the Hsp90-Cdc37 system executes its chaperoning duties by recognizing conformational instability of kinase clients and modulating their folding landscapes. The recent cryo-electron microscopy structure of the Hsp90-Cdc37-Cdk4 kinase complex has provided a framework for dissecting regulatory principles underlying differentiation and recruitment of protein kinase clients to the chaperone machinery. In this work, we have combined atomistic simulations with protein stability and network-based rigidity decomposition analyses to characterize dynamic factors underlying allosteric mechanism of the chaperone-kinase cycle and identify regulatory hotspots that control client recognition. Through comprehensive characterization of conformational dynamics and systematic identification of stabilization centers in the unbound and client- bound Hsp90 forms, we have simulated key stages of the allosteric mechanism, in which Hsp90 binding can induce instability and partial unfolding of Cdk4 client. Conformational landscapes of the Hsp90 and Cdk4 structures suggested that client binding can trigger coordinated dynamic changes and induce global rigidification of the Hsp90 inter-domain regions that is coupled with a concomitant increase in conformational flexibility of the kinase client. This process is allosteric in nature and can involve reciprocal dynamic exchanges that exert global effect on stability of the Hsp90 dimer, while promoting client instability. The network-based rigidity analysis and emulation of thermal unfolding of the Cdk4-cyclin D complex and Hsp90-Cdc37-Cdk4 complex revealed weak spots of kinase instability that are present in the native Cdk4 structure and are targeted by the chaperone during client recruitment. Our findings suggested that this mechanism may be exploited by the Hsp90-Cdc37 chaperone to recruit and protect intrinsically dynamic kinase clients from degradation. The results of this investigation are discussed and interpreted in the context of diverse experimental data, offering new insights into mechanisms of chaperone regulation and binding. PMID:29267381

Transient Binding and Viscous Dissipation in Semi-flexible Polymer Networks

NASA Astrophysics Data System (ADS)

Lieleg, Oliver; Claessens, Mireille; Bausch, Andreas

2008-03-01

Nature specifically chooses from a myriad of actin binding proteins (ABPs) to tailor the cytoskeletal microstructure. Herein, cells rely on the dynamics of the cytoskeleton as its structural and mechanical adaptability is crucial to allow for dynamic processes. A molecular understanding of such biological complexity calls for an in vitro system with well-defined structural rearrangements and cross-linker dynamics to elucidate the physical origin of the unique viscoelastic properties of cells. As we present here, the frequency-dependent viscoelastic response of cross-linked in vitro actin networks is determined by the binding kinetics of cross-linking molecules. Independent from the particular network structure, the viscous dissipation (loss modulus) exhibits a pronounced minimum in an intermediate frequency which is dominated by elasticity. We show that in this frequency regime the molecular origin of the viscoelastic response is given by the non-static nature of actin/ABP bonds as they are subjugated to chemical on/off kinetics. The time scale of the resulting stress release is set by the lifetime distribution of the cross-linking molecule and therefore can be tuned independently from other relaxation mechanisms. We speculate that unbinding of distinct cross-links might be the molecular mechanism employed by cells for mechanosensing.

The synthesis and characterization of environmentally-responsive water-swellable and water-soluble polymers for wastewater remediation

NASA Astrophysics Data System (ADS)

Armentrout, Rodney Scott

The primary research goal is the development of new polymeric materials that demonstrate the environmentally-responsive sequestration of common water foulants, including surfactants and oils. Water-swellable and water-soluble polymers have been synthesized, structurally characterized, and their physical properties have been determined. In addition, the ability of the materials to sequester model water foulants has been evaluated. Anionic crosslinked polymer networks of 2-acrylamido-2-methyl-1-propanesulfonic acid, acrylamide, and methylene bisacrylamide have been synthesized and characterized by determining the equilibrium water contents as a function of ionic content of the polymer network. The molar ratio of bound surfactant to ionic group was determined to be less than one for all hydrogels studied, indicating an ion-exchange binding mechanism with minimal hydrophobic interactions between bound and unbound surfactant molecules is responsible for surfactant binding. Cationic crosslinked cyclopolymer networks of N,N-diallyl- N-methyl amine (DAMA) and N,N,N,N-tetraallyl ammonium chloride (TAAC) have been synthesized and characterized by determining the equilibrium water content as a function of pH. A maximum in the equilibrium water content is observed for pH-6 when the polymer is fully ionized. The solubilization of a model water foulant, p-cresol, by the polymeric surfactant, Pluronic F127, has been studied via equilibrium dialysis, dynamic light scattering and ultrafiltration experiments. It has been shown that at 25°C p-cresol is readily solubilized by F127 since the polymeric surfactant exists in a multimer conformation. Ultrafiltration experiments have demonstrated that the polymer-foulant binding interactions are largely unaffected by shear in a hollow fiber membrane. Copolymers of the zwitterionic monomer, 3-(N,N-diallyl- N-methyl ammonio) propane sulfonate (DAMAPS) and N,N-diallyl- N,N-dimethylammonium chloride (DADMAC) (the DADS series) or the pH-responsive hydrophobic monomer, N,N-diallyl-N-methyl amine (DAMA) (the DAMS series) have been prepared in a 0.5 M NaCl aqueous solution using 2-hydroxy-1-[4-(hydroxy-ethoxy)phenyl]-2-methyl-1-propanone (Irgacure 2959) as the free-radical photoinitiator. 13C NMR data indicate that the resulting polymers maintain the five-membered ring structure in the cis conformation common to diallylammonium salts. Equilibrium dialysis experiments demonstrate that pH-responsive hydrophobic microdomain formation may be utilized to control the solubilization of the organic solute, p-cresol. Ultrafiltration experiments have demonstrated that the polymer-foulant binding interactions are largely unaffected by shear in a hollow fiber membrane. Macromolecular aggregates of the poly( N,N-diallyl-N-methyl amine)/p-cresol complexes lead to fouling of the ultrafiltration membrane. However, incorporation of the sulfobetaine moiety hinders the formation of the macroscopic structures and higher permeate flux rates are achieved. (Abstract shortened by UMI.)

Molecular dynamics simulations on networks of heparin and collagen.

PubMed

Kulke, Martin; Geist, Norman; Friedrichs, Wenke; Langel, Walter

2017-06-01

Synthetic scaffolds containing collagen (Type I) are of increasing interest for bone tissue engineering, especially for highly porous biomaterials in combination with glycosaminoglycans. In experiments the integration of heparin during the fibrillogenesis resulted in different types of collagen fibrils, but models for this aggregation on a molecular scale were only tentative. We conducted molecular dynamic simulations investigating the binding of heparin to collagen and the influence of the telopeptides during collagen aggregation. This aims at explaining experimental findings on a molecular level. Novel structures for N- and C-telopeptides were developed with the TIGER2 replica exchange algorithm and dihedral principle component analysis. We present an extended statistical analysis of the mainly electrostatic interaction between heparin and collagen and identify several binding sites. Finally, we propose a molecular mechanism for the influence of glycosaminoglycans on the morphology of collagen fibrils. Proteins 2017; 85:1119-1130. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Ice cream structure modification by ice-binding proteins.

PubMed

Kaleda, Aleksei; Tsanev, Robert; Klesment, Tiina; Vilu, Raivo; Laos, Katrin

2018-04-25

Ice-binding proteins (IBPs), also known as antifreeze proteins, were added to ice cream to investigate their effect on structure and texture. Ice recrystallization inhibition was assessed in the ice cream mixes using a novel accelerated microscope assay and the ice cream microstructure was studied using an ice crystal dispersion method. It was found that adding recombinantly produced fish type III IBPs at a concentration 3 mg·L -1 made ice cream hard and crystalline with improved shape preservation during melting. Ice creams made with IBPs (both from winter rye, and type III IBP) had aggregates of ice crystals that entrapped pockets of the ice cream mixture in a rigid network. Larger individual ice crystals and no entrapment in control ice creams was observed. Based on these results a model of ice crystals aggregates formation in the presence of IBPs was proposed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Immobilization of concanavalin A receptors during differentiation of neuroblastoma cells.

PubMed

Fishman, M C; Dragsten, P R; Spector, I

1981-04-30

Neuroblastoma cells serve as a useful model of neuronal development because compounds such as dimethyl sulphoxide (DMSO) and dibutyryl cyclic AMP cause them to undergo a process of controlled differentiation in tissue culture, during which they can extend long processes, develop characteristic excitability mechanisms, synthesize neurotransmitters and form synapses. We have used the technique of fluorescence photobleaching recovery to study the lateral mobility of cell-surface constituents during the differentiation of neuroblastoma clone N1E-115 cells. The concanavalin A (Con A) binding sites appear as discrete patches distributed over the entire cell surface and exhibit lateral mobility in undifferentiated cells comparable with that of surface glycoproteins of other cells. After induction of differentiation, however, the vast majority of Con A binding sites become immobilized, and we present data which suggest that the mechanism of this immobilization may involve linkage to the internal actin network.

Regulation of alternative splicing in Drosophila by 56 RNA binding proteins

DOE PAGES

Brooks, Angela N.; Duff, Michael O.; May, Gemma; ...

2015-08-20

Alternative splicing is regulated by RNA binding proteins (RBPs) that recognize pre-mRNA sequence elements and activate or repress adjacent exons. Here, we used RNA interference and RNA-seq to identify splicing events regulated by 56 Drosophila proteins, some previously unknown to regulate splicing. Nearly all proteins affected alternative first exons, suggesting that RBPs play important roles in first exon choice. Half of the splicing events were regulated by multiple proteins, demonstrating extensive combinatorial regulation. We observed that SR and hnRNP proteins tend to act coordinately with each other, not antagonistically. We also identified a cross-regulatory network where splicing regulators affected themore » splicing of pre-mRNAs encoding other splicing regulators. In conclusion, this large-scale study substantially enhances our understanding of recent models of splicing regulation and provides a resource of thousands of exons that are regulated by 56 diverse RBPs.« less

Regulation of transcriptional activators by DNA-binding domain ubiquitination

PubMed Central

Landré, Vivien; Revi, Bhindu; Mir, Maria Gil; Verma, Chandra; Hupp, Ted R; Gilbert, Nick; Ball, Kathryn L

2017-01-01

Ubiquitin is a key component of the regulatory network that maintains gene expression in eukaryotes, yet the molecular mechanism(s) by which non-degradative ubiquitination modulates transcriptional activator (TA) function is unknown. Here endogenous p53, a stress-activated transcription factor required to maintain health, is stably monoubiquitinated, following pathway activation by IR or Nutlin-3 and localized to the nucleus where it becomes tightly associated with chromatin. Comparative structure–function analysis and in silico modelling demonstrate a direct role for DNA-binding domain (DBD) monoubiquitination in TA activation. When attached to the DBD of either p53, or a second TA IRF-1, ubiquitin is orientated towards, and makes contact with, the DNA. The contact is made between a predominantly cationic surface on ubiquitin and the anionic DNA. Our data demonstrate an unexpected role for ubiquitin in the mechanism of TA-activity enhancement and provides insight into a new level of transcriptional regulation. PMID:28362432

Yeast ribonuclease III uses a network of multiple hydrogen bonds for RNA binding and cleavage.

PubMed

Lavoie, Mathieu; Abou Elela, Sherif

2008-08-19

Members of the bacterial RNase III family recognize a variety of short structured RNAs with few common features. It is not clear how this group of enzymes supports high cleavage fidelity while maintaining a broad base of substrates. Here we show that the yeast orthologue of RNase III (Rnt1p) uses a network of 2'-OH-dependent interactions to recognize substrates with different structures. We designed a series of bipartite substrates permitting the distinction between binding and cleavage defects. Each substrate was engineered to carry a single or multiple 2'- O-methyl or 2'-fluoro ribonucleotide substitutions to prevent the formation of hydrogen bonds with a specific nucleotide or group of nucleotides. Interestingly, introduction of 2'- O-methyl ribonucleotides near the cleavage site increased the rate of catalysis, indicating that 2'-OH are not required for cleavage. Substitution of nucleotides in known Rnt1p binding site with 2'- O-methyl ribonucleotides inhibited cleavage while single 2'-fluoro ribonucleotide substitutions did not. This indicates that while no single 2'-OH is essential for Rnt1p cleavage, small changes in the substrate structure are not tolerated. Strikingly, several nucleotide substitutions greatly increased the substrate dissociation constant with little or no effect on the Michaelis-Menten constant or rate of catalysis. Together, the results indicate that Rnt1p uses a network of nucleotide interactions to identify its substrate and support two distinct modes of binding. One mode is primarily mediated by the dsRNA binding domain and leads to the formation of stable RNA/protein complex, while the other requires the presence of the nuclease and N-terminal domains and leads to RNA cleavage.

Examining the neural correlates of active and passive forms of verbal-spatial binding in working memory.

PubMed

Grot, Stéphanie; Leclerc, Marie-Eve; Luck, David

2018-05-23

We designed an fMRI study to pinpoint the neural correlates of active and passive binding in working memory. Participants were instructed to memorize three words and three spatial locations. In the passive binding condition, words and spatial locations were directly presented as bound. Conversely, in the active binding condition, words and spatial locations were presented as separated, and participants were directed to intentionally create associations between them. Our results showed that participants performed better on passive binding relative to active binding. FMRI analysis revealed that both binding conditions induced greater activity within the hippocampus. Additionally, our analyses divulged regions specifically engaged in passive and active binding. Altogether, these data allow us to propose the hippocampus as a central candidate for working memory binding. When needed, a frontal-parietal network can contribute to the rearrangement of information. These findings may inform theories of working memory binding. Copyright © 2018. Published by Elsevier B.V.

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2017-02-28

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep.

Combinatorial influence of environmental parameters on transcription factor activity

PubMed Central

Knijnenburg, T.A.; Wessels, L.F.A.; Reinders, M.J.T.

2008-01-01

Motivation: Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. Results: We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. Availability: The Matlab code is available from the authors upon request. Contact: t.a.knijnenburg@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18586711

Exploring the Common Dynamics of Homologous Proteins. Application to the Globin Family

PubMed Central

Maguid, Sandra; Fernandez-Alberti, Sebastian; Ferrelli, Leticia; Echave, Julian

2005-01-01

We present a procedure to explore the global dynamics shared between members of the same protein family. The method allows the comparison of patterns of vibrational motion obtained by Gaussian network model analysis. After the identification of collective coordinates that were conserved during evolution, we quantify the common dynamics within a family. Representative vectors that describe these dynamics are defined using a singular value decomposition approach. As a test case, the globin heme-binding family is considered. The two lowest normal modes are shown to be conserved within this family. Our results encourage the development of models for protein evolution that take into account the conservation of dynamical features. PMID:15749782

Folding and trimerization of clathrin subunits at the triskelion hub.

PubMed

Näthke, I S; Heuser, J; Lupas, A; Stock, J; Turck, C W; Brodsky, F M

1992-03-06

The triskelion shape of the clathrin molecule enables it to form the polyhedral protein network that covers clathrin-coated pits and vesicles. Domains within the clathrin heavy chain that are responsible for maintaining triskelion shape and function were identified and localized. Sequences that mediate trimerization are distal to the carboxyl terminus and are adjacent to a domain that mediates both light chain binding and clathrin assembly. Structural modeling predicts that within this domain, the region of heavy chain-light chain interaction is a bundle of three or four alpha helices. These studies establish a low resolution model of clathrin subunit folding in the central portion (hub) of the triskelion, thus providing a basis for future mutagenesis experiments.

Opening-assisted coherent transport in the semiclassical regime

NASA Astrophysics Data System (ADS)

Zhang, Yang; Celardo, G. Luca; Borgonovi, Fausto; Kaplan, Lev

2017-02-01

We study quantum enhancement of transport in open systems in the presence of disorder and dephasing. Quantum coherence effects may significantly enhance transport in open systems even in the semiclassical regime (where the decoherence rate is greater than the intersite hopping amplitude), as long as the disorder is sufficiently strong. When the strengths of disorder and dephasing are fixed, there is an optimal opening strength at which the coherent transport enhancement is optimized. Analytic results are obtained in two simple paradigmatic tight-binding models of large systems: the linear chain and the fully connected network. The physical behavior is also reflected in the Fenna-Matthews-Olson (FMO) photosynthetic complex, which may be viewed as intermediate between these paradigmatic models.

Carbohydrate binding specificity of pea lectin studied by NMR spectroscopy and molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Cheong, Youngjoo; Shim, Gyuchang; Kang, Dongil; Kim, Yangmee

1999-02-01

The conformational details of Man( α1,6)Man( α)OMe are investigated through NMR spectroscopy in conjunction with molecular modeling. The lowest energy structure (M1) in the adiabatic energy map calculated with a dielectric constant of 50 has glycosidic dihedral angles of φ=-60°, ψ=180° and ω=180°. The other low energy structure (M2) has glycosidic dihedral angles of φ=-60°, ψ=180° and ω=-60°. Molecular dynamics simulations and NMR experiments prove that Man( α1,6)Man( α)OMe in the free form exists with conformational averaging of M1 and M2 conformers predominantly. Molecular dynamics simulations of the pea lectin-carbohydrate complex with explicit water molecules starting from the X-ray crystallographic structure of pea lectin show that the protein-carbohydrate interaction centers mainly on the hydrogen bonds and van der Waals interactions between protein and carbohydrate. From the molecular dynamics simulation, it is found that the M1 structure can bind to pea lectin better than the M2 structure. The origin of this selectivity is the water- mediated hydrogen bond interactions between the remote mannose and the binding site of pea lectin as well as the direct hydrogen bond interaction between the terminal mannose and pea lectin. Extensive networks of interactions in the carbohydrate binding site and the metal binding site are important in maintaining the carbohydrate binding properties of pea lectin. Especially, the predominant factors of mannose binding specificity of pea lectin are the hydrogen bond interactions between the 4th hydroxyl groups of the terminal sugar ring and the side chains of Asp-81 and Asn-125 in the carbohydrate binding site, and the additional interactions between these side chains of Asp-81 and Asn-125 and the calcium ion in the metal binding site of pea lectin.

Improving the accuracy of Møller-Plesset perturbation theory with neural networks

NASA Astrophysics Data System (ADS)

McGibbon, Robert T.; Taube, Andrew G.; Donchev, Alexander G.; Siva, Karthik; Hernández, Felipe; Hargus, Cory; Law, Ka-Hei; Klepeis, John L.; Shaw, David E.

2017-10-01

Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol-1 (root-mean-square error 0.09 kcal mol-1), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

Improving the accuracy of Møller-Plesset perturbation theory with neural networks.

PubMed

McGibbon, Robert T; Taube, Andrew G; Donchev, Alexander G; Siva, Karthik; Hernández, Felipe; Hargus, Cory; Law, Ka-Hei; Klepeis, John L; Shaw, David E

2017-10-28

Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol -1 (root-mean-square error 0.09 kcal mol -1 ), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

PubMed Central

Liebermeister, Wolfram; Klipp, Edda

2006-01-01

Background Translating a known metabolic network into a dynamic model requires rate laws for all chemical reactions. The mathematical expressions depend on the underlying enzymatic mechanism; they can become quite involved and may contain a large number of parameters. Rate laws and enzyme parameters are still unknown for most enzymes. Results We introduce a simple and general rate law called "convenience kinetics". It can be derived from a simple random-order enzyme mechanism. Thermodynamic laws can impose dependencies on the kinetic parameters. Hence, to facilitate model fitting and parameter optimisation for large networks, we introduce thermodynamically independent system parameters: their values can be varied independently, without violating thermodynamical constraints. We achieve this by expressing the equilibrium constants either by Gibbs free energies of formation or by a set of independent equilibrium constants. The remaining system parameters are mean turnover rates, generalised Michaelis-Menten constants, and constants for inhibition and activation. All parameters correspond to molecular energies, for instance, binding energies between reactants and enzyme. Conclusion Convenience kinetics can be used to translate a biochemical network – manually or automatically - into a dynamical model with plausible biological properties. It implements enzyme saturation and regulation by activators and inhibitors, covers all possible reaction stoichiometries, and can be specified by a small number of parameters. Its mathematical form makes it especially suitable for parameter estimation and optimisation. Parameter estimates can be easily computed from a least-squares fit to Michaelis-Menten values, turnover rates, equilibrium constants, and other quantities that are routinely measured in enzyme assays and stored in kinetic databases. PMID:17173669

Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

NASA Astrophysics Data System (ADS)

Smith, Keith; Ricaud, Benjamin; Shahid, Nauman; Rhodes, Stephen; Starr, John M.; Ibáñez, Augustin; Parra, Mario A.; Escudero, Javier; Vandergheynst, Pierre

2017-02-01

Visual short-term memory binding tasks are a promising early marker for Alzheimer’s disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained from encoding and maintenance periods of tasks performed by healthy young volunteers. We probe the task’s transient physiological underpinnings by contrasting shape only (Shape) and shape-colour binding (Bind) conditions, displayed in the left and right sides of the screen, separately. Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision. We find that connectivity in the Bind condition is less integrated with the global network than in the Shape condition in occipital and frontal modules during the encoding period of the right screen condition. Using MDE we are able to discern driving effects in the occipital module between 100-140 ms, coinciding with the P100 visually evoked potential, followed by a driving effect in the frontal module between 140-180 ms, suggesting that the differences found constitute an information processing difference between these modules. This provides temporally precise information over a heterogeneous population in promising tasks for the detection of AD.

A Digitally Programmable Cytomorphic Chip for Simulation of Arbitrary Biochemical Reaction Networks.

PubMed

Woo, Sung Sik; Kim, Jaewook; Sarpeshkar, Rahul

2018-04-01

Prior work has shown that compact analog circuits can faithfully represent and model fundamental biomolecular circuits via efficient log-domain cytomorphic transistor equivalents. Such circuits have emphasized basis functions that are dominant in genetic transcription and translation networks and deoxyribonucleic acid (DNA)-protein binding. Here, we report a system featuring digitally programmable 0.35 μm BiCMOS analog cytomorphic chips that enable arbitrary biochemical reaction networks to be exactly represented thus enabling compact and easy composition of protein networks as well. Since all biomolecular networks can be represented as chemical reaction networks, our protein networks also include the former genetic network circuits as a special case. The cytomorphic analog protein circuits use one fundamental association-dissociation-degradation building-block circuit that can be configured digitally to exactly represent any zeroth-, first-, and second-order reaction including loading, dynamics, nonlinearity, and interactions with other building-block circuits. To address a divergence issue caused by random variations in chip fabrication processes, we propose a unique way of performing computation based on total variables and conservation laws, which we instantiate at both the circuit and network levels. Thus, scalable systems that operate with finite error over infinite time can be built. We show how the building-block circuits can be composed to form various network topologies, such as cascade, fan-out, fan-in, loop, dimerization, or arbitrary networks using total variables. We demonstrate results from a system that combines interacting cytomorphic chips to simulate a cancer pathway and a glycolysis pathway. Both simulations are consistent with conventional software simulations. Our highly parallel digitally programmable analog cytomorphic systems can lead to a useful design, analysis, and simulation tool for studying arbitrary large-scale biological networks in systems and synthetic biology.

Role of Protein Dimeric Interface in Allosteric Inhibition of N-Acetyl-Aspartate Hydrolysis by Human Aspartoacylase.

PubMed

Kots, Ekaterina D; Lushchekina, Sofya V; Varfolomeev, Sergey D; Nemukhin, Alexander V

2017-08-28

The results of molecular modeling suggest a mechanism of allosteric inhibition upon hydrolysis of N-acetyl-aspartate (NAA), one of the most abundant amino acid derivatives in brain, by human aspartoacylase (hAsp). Details of this reaction are important to suggest the practical ways to control the enzyme activity. Search for allosteric sites using the Allosite web server and SiteMap analysis allowed us to identify substrate binding pockets located at the interface between the subunits of the hAsp dimer molecule. Molecular docking of NAA to the pointed areas at the dimer interface predicted a specific site, in which the substrate molecule interacts with the Gly237, Arg233, Glu290, and Lys292 residues. Analysis of multiple long-scaled molecular dynamics trajectories (the total simulation time exceeded 1.5 μs) showed that binding of NAA to the identified allosteric site induced significant rigidity to the protein loops with the amino acid side chains forming gates to the enzyme active site. Application of the protein dynamical network algorithms showed that substantial reorganization of the signal propagation pathways of intersubunit communication in the dimer occurred upon allosteric NAA binding to the remote site. The modeling approaches provide an explanation to the observed decrease of the reaction rate of NAA hydrolysis by hAsp at high substrate concentrations.

Structural Basis of Cooperative Ligand Binding by the Glycine Riboswitch

DOE Office of Scientific and Technical Information (OSTI.GOV)

E Butler; J Wang; Y Xiong

2011-12-31

The glycine riboswitch regulates gene expression through the cooperative recognition of its amino acid ligand by a tandem pair of aptamers. A 3.6 {angstrom} crystal structure of the tandem riboswitch from the glycine permease operon of Fusobacterium nucleatum reveals the glycine binding sites and an extensive network of interactions, largely mediated by asymmetric A-minor contacts, that serve to communicate ligand binding status between the aptamers. These interactions provide a structural basis for how the glycine riboswitch cooperatively regulates gene expression.

Structure and Dynamics Analysis on Plexin-B1 Rho GTPase Binding Domain as a Monomer and Dimer

PubMed Central

2015-01-01

Plexin-B1 is a single-pass transmembrane receptor. Its Rho GTPase binding domain (RBD) can associate with small Rho GTPases and can also self-bind to form a dimer. In total, more than 400 ns of NAMD molecular dynamics simulations were performed on RBD monomer and dimer. Different analysis methods, such as root mean squared fluctuation (RMSF), order parameters (S2), dihedral angle correlation, transfer entropy, principal component analysis, and dynamical network analysis, were carried out to characterize the motions seen in the trajectories. RMSF results show that after binding, the L4 loop becomes more rigid, but the L2 loop and a number of residues in other regions become slightly more flexible. Calculating order parameters (S2) for CH, NH, and CO bonds on both backbone and side chain shows that the L4 loop becomes essentially rigid after binding, but part of the L1 loop becomes slightly more flexible. Backbone dihedral angle cross-correlation results show that loop regions such as the L1 loop including residues Q25 and G26, the L2 loop including residue R61, and the L4 loop including residues L89–R91, are highly correlated compared to other regions in the monomer form. Analysis of the correlated motions at these residues, such as Q25 and R61, indicate two signal pathways. Transfer entropy calculations on the RBD monomer and dimer forms suggest that the binding process should be driven by the L4 loop and C-terminal. However, after binding, the L4 loop functions as the motion responder. The signal pathways in RBD were predicted based on a dynamical network analysis method using the pathways predicted from the dihedral angle cross-correlation calculations as input. It is found that the shortest pathways predicted from both inputs can overlap, but signal pathway 2 (from F90 to R61) is more dominant and overlaps all of the routes of pathway 1 (from F90 to P111). This project confirms the allosteric mechanism in signal transmission inside the RBD network, which was in part proposed in the previous experimental study. PMID:24901636

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological function.

VAMPnets for deep learning of molecular kinetics.

PubMed

Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noé, Frank

2018-01-02

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.

Structural determinants of ligand binding in the ternary complex of human ileal bile acid binding protein with glycocholate and glycochenodeoxycholate obtained from solution NMR.

PubMed

Horváth, Gergő; Bencsura, Ákos; Simon, Ágnes; Tochtrop, Gregory P; DeKoster, Gregory T; Covey, Douglas F; Cistola, David P; Toke, Orsolya

2016-02-01

Besides aiding digestion, bile salts are important signal molecules exhibiting a regulatory role in metabolic processes. Human ileal bile acid binding protein (I-BABP) is an intracellular carrier of bile salts in the epithelial cells of the distal small intestine and has a key role in the enterohepatic circulation of bile salts. Positive binding cooperativity combined with site selectivity of glycocholate and glycochenodeoxycholate, the two most abundant bile salts in the human body, make human I-BABP a unique member of the family of intracellular lipid binding proteins. Solution NMR structure of the ternary complex of human I-BABP with glycocholate and glycochenodeoxycholate reveals an extensive network of hydrogen bonds and hydrophobic interactions stabilizing the bound bile salts. Conformational changes accompanying bile salt binding affects four major regions in the protein including the C/D, E/F and G/H loops as well as the helical segment. Most of these protein regions coincide with a previously described network of millisecond time scale fluctuations in the apo protein, a motion absent in the bound state. Comparison of the heterotypic doubly ligated complex with the unligated form provides further evidence of a conformation selection mechanism of ligand entry. Structural and dynamic aspects of human I-BABP-bile salt interaction are discussed and compared with characteristics of ligand binding in other members of the intracellular lipid binding protein family. The coordinates of the 10 lowest energy structures of the human I-BABP : GCDA : GCA complex as well as the distance restraints used to calculate the final ensemble have been deposited in the Brookhaven Protein Data Bank with accession number 2MM3. © 2015 FEBS.

UO₂²⁺ uptake by proteins: understanding the binding features of the super uranyl binding protein and design of a protein with higher affinity.

PubMed

Odoh, Samuel O; Bondarevsky, Gary D; Karpus, Jason; Cui, Qiang; He, Chuan; Spezia, Riccardo; Gagliardi, Laura

2014-12-17

The capture of uranyl, UO2(2+), by a recently engineered protein (Zhou et al. Nat. Chem. 2014, 6, 236) with high selectivity and femtomolar sensitivity has been examined by a combination of density functional theory, molecular dynamics, and free-energy simulations. It was found that UO2(2+) is coordinated to five carboxylate oxygen atoms from four amino acid residues of the super uranyl binding protein (SUP). A network of hydrogen bonds between the amino acid residues coordinated to UO2(2+) and residues in its second coordination sphere also affects the protein's uranyl binding affinity. Free-energy simulations show how UO2(2+) capture is governed by the nature of the amino acid residues in the binding site, the integrity and strength of the second-sphere hydrogen bond network, and the number of water molecules in the first coordination sphere. Alteration of any of these three factors through mutations generally results in a reduction of the binding free energy of UO2(2+) to the aqueous protein as well as of the difference between the binding free energies of UO2(2+) and other ions (Ca(2+), Cu(2+), Mg(2+), and Zn(2+)), a proxy for the protein's selectivity over these ions. The results of our free-energy simulations confirmed the previously reported experimental results and allowed us to discover a mutant of SUP, specifically the GLU64ASP mutant, that not only binds UO2(2+) more strongly than SUP but that is also more selective for UO2(2+) over other ions. The predictions from the computations were confirmed experimentally.

The natural naphthoquinone plumbagin exhibits antiproliferative activity and disrupts the microtubule network through tubulin binding.

PubMed

Acharya, Bipul R; Bhattacharyya, Bhabatarak; Chakrabarti, Gopal

2008-07-29

Plumbagin (5-hydroxy-2-methyl-1,4-naphthoquinone), a naphthoquinone isolated from the roots of Plumbaginaceae plants, has potential antiproliferative activity against several tumor types. We have examined the effects of plumbagin on cellular microtubules ex vivo as well as its binding with purified tubulin and microtubules in vitro. Cell viability experiments using human non-small lung epithelium carcinoma cells (A549) indicated that the IC 50 value for plumbagin is 14.6 microM. Immunofluorescence studies using an antitubulin FITC conjugated antibody showed a significant perturbation of the interphase microtubule network in a dose dependent manner. In vitro polymerization of purified tubulin into microtubules is inhibited by plumbagin with an IC 50 value of 38 +/- 0.5 microM. Its binding to tubulin quenches protein tryptophan fluorescence in a time and concentration dependent manner. Binding of plumbagin to tubulin is slow, taking 60 min for equilibration at 25 degrees C. The association reaction kinetics is biphasic in nature, and the association rate constants for fast and slow phases are 235.12 +/- 36 M (-1) s (-1) and 11.63 +/- 11 M (-1) s (-1) at 25 degrees C respectively. The stoichiometry of plumbagin binding to tubulin is 1:1 (mole:mole) with a dissociation constant of 0.936 +/- 0.71 microM at 25 degrees C. Plumbagin competes for the colchicine binding site with a K i of 7.5 microM as determined from a modified Dixon plot. Based on these data we conclude that plumbagin recognizes the colchicine binding site to tubulin. Further study is necessary to locate the pharmacophoric point of attachment of the inhibitor to the colchicine binding site of tubulin.

Real-Time Speaker Detection for User-Device Binding

DTIC Science & Technology

2010-12-01

31 xi THIS PAGE INTENTIONALLY LEFT BLANK xii CHAPTER 1: Introduction The roll-out of commercial wireless networks continues to rise worldwide...in a secured facility. It could also be connected to the call server via a Virtual Private Network (VPN) or public lines if security is not a top...communications network [25]. Yet, James Arden Barnett, Jr., Chief of the Public Safety and Homeland Security Bureau, argues that emergency communications

Strengthening the Ties That Bind: Integrating Undergraduate Liberal and Professional Study. Report of the Professional Preparation Network.

ERIC Educational Resources Information Center

Stark, Joan S.; Lowther, Malcolm A.

The integration of undergraduate liberal and professional education is discussed in the following chapters: (1) "A Call to Action"; (2) "Binding Values, Changing Times and Curricular Balance"; (3) "Seizing Opportunities for Excellence"; (4) "Defining the Educated Professional"; (5) "Extending a Constructive Debate"; and (6) "A New Spirit of…

Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

PubMed

Szalay, Kristóf Z; Nussinov, Ruth; Csermely, Peter

2014-06-01

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multiscale Modelling for investigating single molecule effects on the mechanics of actin filaments

NASA Astrophysics Data System (ADS)

A, Deriu Marco; C, Bidone Tamara; Laura, Carbone; Cristina, Bignardi; M, Montevecchi Franco; Umberto, Morbiducci

2011-12-01

This work presents a preliminary multiscale computational investigation of the effects of nucleotides and cations on the mechanics of actin filaments (F-actin). At the molecular level, Molecular Dynamics (MD) simulations are employed to characterize the rearrangements of the actin monomers (G-actin) in terms of secondary structures evolution in physiological conditions. At the mesoscale level, a coarse grain (CG) procedure is adopted where each monomer is represented by means of Elastic Network Modeling (ENM) technique. At the macroscale level, actin filaments up to hundreds of nanometers are assumed as isotropic and elastic beams and characterized via Rotation Translation Block (RTB) analysis. F-actin bound to adenosine triphosphate (ATP) shows a persistence length around 5 μm, while actin filaments bound to adenosine diphosphate (ADP) have a persistence length of about 3 μm. With magnesium bound to the high affinity binding site of G-actin, the persistence length of F-actin decreases to about 2 μm only in the ADP-bound form of the filament, while the same ion has no effects, in terms of stiffness variation, on the ATP-bound form of F-actin. The molecular mechanisms behind these changes in flexibility are herein elucidated. Thus, this study allows to analyze how the local binding of cations and nucleotides on G-actin induce molecular rearrangements that transmit to the overall F-actin, characterizing shifts of mechanical properties, that can be related with physiological and pathological cellular phenomena, as cell migration and spreading. Further, this study provides the basis for upcoming investigating of network and cellular remodelling at higher length scales.

A Relay Network of Extracellular Heme-Binding Proteins Drives C. albicans Iron Acquisition from Hemoglobin

PubMed Central

Kuznets, Galit; Vigonsky, Elena; Weissman, Ziva; Lalli, Daniela; Gildor, Tsvia; Kauffman, Sarah J.; Turano, Paola; Becker, Jeffrey; Lewinson, Oded; Kornitzer, Daniel

2014-01-01

Iron scavenging constitutes a crucial challenge for survival of pathogenic microorganisms in the iron-poor host environment. Candida albicans, like many microbial pathogens, is able to utilize iron from hemoglobin, the largest iron pool in the host's body. Rbt5 is an extracellular glycosylphosphatidylinositol (GPI)-anchored heme-binding protein of the CFEM family that facilitates heme-iron uptake by an unknown mechanism. Here, we characterize an additional C. albicans CFEM protein gene, PGA7, deletion of which elicits a more severe heme-iron utilization phenotype than deletion of RBT5. The virulence of the pga7−/− mutant is reduced in a mouse model of systemic infection, consistent with a requirement for heme-iron utilization for C. albicans pathogenicity. The Pga7 and Rbt5 proteins exhibit distinct cell wall attachment, and discrete localization within the cell envelope, with Rbt5 being more exposed than Pga7. Both proteins are shown here to efficiently extract heme from hemoglobin. Surprisingly, while Pga7 has a higher affinity for heme in vitro, we find that heme transfer can occur bi-directionally between Pga7 and Rbt5, supporting a model in which they cooperate in a heme-acquisition relay. Together, our data delineate the roles of Pga7 and Rbt5 in a cell surface protein network that transfers heme from extracellular hemoglobin to the endocytic pathway, and provide a paradigm for how receptors embedded in the cell wall matrix can mediate nutrient uptake across the fungal cell envelope. PMID:25275454

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.

PubMed

Reinharz, Vladimir; Soulé, Antoine; Westhof, Eric; Waldispühl, Jérôme; Denise, Alain

2018-05-04

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

CoryneRegNet 3.0--an interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli.

PubMed

Baumbach, Jan; Wittkop, Tobias; Rademacher, Katrin; Rahmann, Sven; Brinkrolf, Karina; Tauch, Andreas

2007-04-30

CoryneRegNet is an ontology-based data warehouse for the reconstruction and visualization of transcriptional regulatory interactions in prokaryotes. To extend the biological content of CoryneRegNet, we added comprehensive data on transcriptional regulations in the model organism Escherichia coli K-12, originally deposited in the international reference database RegulonDB. The enhanced web interface of CoryneRegNet offers several types of search options. The results of a search are displayed in a table-based style and include a visualization of the genetic organization of the respective gene region. Information on DNA binding sites of transcriptional regulators is depicted by sequence logos. The results can also be displayed by several layouters implemented in the graphical user interface GraphVis, allowing, for instance, the visualization of genome-wide network reconstructions and the homology-based inter-species comparison of reconstructed gene regulatory networks. In an application example, we compare the composition of the gene regulatory networks involved in the SOS response of E. coli and Corynebacterium glutamicum. CoryneRegNet is available at the following URL: http://www.cebitec.uni-bielefeld.de/groups/gi/software/coryneregnet/.

The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

PubMed

Hermjakob, Henning; Montecchi-Palazzi, Luisa; Bader, Gary; Wojcik, Jérôme; Salwinski, Lukasz; Ceol, Arnaud; Moore, Susan; Orchard, Sandra; Sarkans, Ugis; von Mering, Christian; Roechert, Bernd; Poux, Sylvain; Jung, Eva; Mersch, Henning; Kersey, Paul; Lappe, Michael; Li, Yixue; Zeng, Rong; Rana, Debashis; Nikolski, Macha; Husi, Holger; Brun, Christine; Shanker, K; Grant, Seth G N; Sander, Chris; Bork, Peer; Zhu, Weimin; Pandey, Akhilesh; Brazma, Alvis; Jacq, Bernard; Vidal, Marc; Sherman, David; Legrain, Pierre; Cesareni, Gianni; Xenarios, Ioannis; Eisenberg, David; Steipe, Boris; Hogue, Chris; Apweiler, Rolf

2004-02-01

A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).

Potential Regulators Driving the Transition in Nonalcoholic Fatty Liver Disease: a Stage-Based View.

PubMed

Lou, Yi; Chen, Yi-Dan; Sun, Fu-Rong; Shi, Jun-Ping; Song, Yu; Yang, Jin

2017-01-01

The incidence of nonalcoholic fatty liver disease (NAFLD), ranging from mild steatosis to hepatocellular injury and inflammation, increases with the rise of obesity. However, the implications of transcription factors network in progressive NAFLD remain to be determined. A co-regulatory network approach by combining gene expression and transcription influence was utilized to dissect transcriptional regulators in different NAFLD stages. In vivo, mice models of NAFLD were used to investigate whether dysregulated expression be undertaken by transcriptional regulators. Through constructing a large-scale co-regulatory network, sample-specific regulator activity was estimated. The combinations of active regulators that drive the progression of NAFLD were identified. Next, top regulators in each stage of NAFLD were determined, and the results were validated using the different experiments and bariatric surgical samples. In particular, Adipocyte enhancer-binding protein 1 (AEBP1) showed increased transcription activity in nonalcoholic steatohepatitis (NASH). Further characterization of the AEBP1 related transcription program defined its co-regulators, targeted genes, and functional organization. The dynamics of AEBP1 and its potential targets were verified in an animal model of NAFLD. This study identifies putative functions for several transcription factors in the pathogenesis of NAFLD and may thus point to potential targets for therapeutic interventions. © 2017 The Author(s) Published by S. Karger AG, Basel.

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

NASA Astrophysics Data System (ADS)

Rodríguez, Ana R.; O'Neill, Kate M.; Swiatkowski, Przemyslaw; Patel, Mihir V.; Firestein, Bonnie L.

2018-02-01

Objective. This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. Approach. We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ PSD-95 non-binding) expression cellularly and network-wide, respectively. Main results. Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. Significance. Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

Linker histone H1.0 interacts with an extensive network of proteins found in the nucleolus

PubMed Central

Kalashnikova, Anna A.; Winkler, Duane D.; McBryant, Steven J.; Henderson, Ryan K.; Herman, Jacob A.; DeLuca, Jennifer G.; Luger, Karolin; Prenni, Jessica E.; Hansen, Jeffrey C.

2013-01-01

The H1 linker histones are abundant chromatin-associated DNA-binding proteins. Recent evidence suggests that linker histones also may function through protein–protein interactions. To gain a better understanding of the scope of linker histone involvement in protein–protein interactions, we used a proteomics approach to identify H1-binding proteins in human nuclear extracts. Full-length H1.0 and H1.0 lacking its C-terminal domain (CTD) were used for protein pull-downs. A total of 107 candidate H1.0 binding proteins were identified by LC-MS/MS. About one-third of the H1.0-dependent interactions were mediated by the CTD, and two-thirds by the N-terminal domain-globular domain fragment. Many of the proteins pulled down by H1.0 were core splicing factors. Another group of H1-binding proteins functions in rRNA biogenesis. H1.0 also pulled down numerous ribosomal proteins and proteins involved in cellular transport. Strikingly, nearly all of the H1.0-binding proteins are found in the nucleolus. Quantitative biophysical studies with recombinant proteins confirmed that H1.0 directly binds to FACT and the splicing factors SF2/ASF and U2AF65. Our results demonstrate that H1.0 interacts with an extensive network of proteins that function in RNA metabolism in the nucleolus, and suggest that a new paradigm for linker histone action is in order. PMID:23435226

Structural Insights into the Phospholipid Binding Specificity of Human Evectin-2

NASA Astrophysics Data System (ADS)

Okazaki, Seiji; Kato, Ryuichi; Wakatsuki, Soichi; Uchida, Yasunori; Taguchi, Tomohiko; Arai, Hiroyuki

Evectin-2 is a recycling endosomal protein and plays an essential role in retrograde transport from recycling endosomes to the trans-Golgi network. The pleckstrin homology (PH) domain of Evectin-2 can specifically binds to phosphatidylserine (PS), which is enriched in recycling endosomes. To elucidate the molecular mechanism how it specifically binds to PS, we solved the crystal structures of human Evectin-2 PH domain for apo and O-phospho-L-serine complexed forms at 1.75 and 1.00 Å resolution, respectively. These structural analyses clearly show that PS-induced conformational change of Evectin-2 PH domain effectively explains the strict phospholipid binding specificity.

A distal point mutation in the streptavidin-biotin complex preserves structure but diminishes binding affinity: experimental evidence of electronic polarization effects?

PubMed

Baugh, Loren; Le Trong, Isolde; Cerutti, David S; Gülich, Susanne; Stayton, Patrick S; Stenkamp, Ronald E; Lybrand, Terry P

2010-06-08

We have identified a distal point mutation in streptavidin that causes a 1000-fold reduction in biotin binding affinity without disrupting the equilibrium complex structure. The F130L mutation creates a small cavity occupied by a water molecule; however, all neighboring side chain positions are preserved, and protein-biotin hydrogen bonds are unperturbed. Molecular dynamics simulations reveal a reduced mobility of biotin binding residues but no observable destabilization of protein-ligand interactions. Our combined structural and computational studies suggest that the additional water molecule may affect binding affinity through an electronic polarization effect that impacts the highly cooperative hydrogen bonding network in the biotin binding pocket.

Proteomic characterization of the nucleolar linker histone H1 interaction network

PubMed Central

Szerlong, Heather J.; Herman, Jacob A.; Krause, Christine M.; DeLuca, Jennifer G.; Skoultchi, Arthur; Winger, Quinton A.; Prenni, Jessica E.; Hansen, Jeffrey C.

2015-01-01

To investigate the relationship between linker histone H1 and protein-protein interactions in the nucleolus, biochemical and proteomics approaches were used to characterize nucleoli purified from cultured human and mouse cells. Mass spectrometry identified 175 proteins in human T-cell nucleolar extracts that bound to sepharose-immobilized H1 in vitro. Gene ontology analysis found significant enrichment for H1 binding proteins with functions related to nucleolar chromatin structure and RNA polymerase I transcription regulation, rRNA processing, and mRNA splicing. Consistent with the affinity binding results, H1 existed in large (400 to >650 kDa) macromolecular complexes in human T cell nucleolar extracts. To complement the biochemical experiments, the effects of in vivo H1 depletion on protein content and structural integrity of the nucleolus were investigated using the H1 triple isoform knock out (H1ΔTKO) mouse embryonic stem cell (mESC) model system. Proteomic profiling of purified wild type mESC nucleoli identified a total of 613 proteins, only ~60% of which were detected in the H1 mutant nucleoli. Within the affected group, spectral counting analysis quantitated 135 specific nucleolar proteins whose levels were significantly altered in H1ΔTKO mESC. Importantly, the functions of the affected proteins in mESC closely overlapped with those of the human T cell nucleolar H1 binding proteins. Immunofluorescence microscopy of intact H1ΔTKO mESC demonstrated both a loss of nucleolar RNA content and altered nucleolar morphology resulting from in vivo H1 depletion. We conclude that H1 organizes and maintains an extensive protein-protein interaction network in the nucleolus required for nucleolar structure and integrity. PMID:25584861

Inference of developmental gene regulatory networks beyond classical model systems: new approaches in the post-genomic era.

PubMed

Fernandez-Valverde, Selene L; Aguilera, Felipe; Ramos-Díaz, René Alexander

2018-06-18

The advent of high-throughput sequencing technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyse cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in high-throughput sequencing technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global of gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.

Coordinating Resource Usage through Adaptive Service Provisioning in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Fok, Chien-Liang; Roman, Gruia-Catalin; Lu, Chenyang

Wireless sensor networks (WSNs) exhibit high levels of network dynamics and consist of devices with limited energy. This results in the need to coordinate applications not only at the functional level, as is traditionally done, but also in terms of resource utilization. In this paper, we present a middleware that does this using adaptive service provisioning. Novel service binding strategies automatically adapt application behavior when opportunities for energy savings surface, and switch providers when the network topology changes. The former is accomplished by providing limited information about the energy consumption associated with using various services, systematically exploiting opportunities for sharing service invocations, and exploiting the broadcast nature of wireless communication in WSNs. The middleware has been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can enable energy-aware service binding decisions that result in increased energy efficiency and significantly increase service availability, while imposing minimal additional burden on the application, service, and device developers. Two applications, medical patient monitoring and structural health monitoring, demonstrate the middleware's efficacy.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Monte Carlo simulation of a simple gene network yields new evolutionary insights.

PubMed

Andrecut, M; Cloud, D; Kauffman, S A

2008-02-07

Monte Carlo simulations of a genetic toggle switch show that its behavior can be more complex than analytic models would suggest. We show here that as a result of the interplay between frequent and infrequent reaction events, such a switch can have more stable states than an analytic model would predict, and that the number and character of these states depend to a large extent on the propensity of transcription factors to bind to and dissociate from promoters. The effects of gene duplications differ even more; in analytic models, these seem to result in the disappearance of bi-stability and thus a loss of the switching function, but a Monte Carlo simulation shows that they can result in the appearance of new stable states without the loss of old ones, and thus in an increase of the complexity of the switch's behavior which may facilitate the evolution of new cellular functions. These differences are of interest with respect to the evolution of gene networks, particularly in clonal lines of cancer cells, where the duplication of active genes is an extremely common event, and often seems to result in the appearance of viable new cellular phenotypes.

In silico reconstitution of Listeria propulsion exhibits nano-saltation.

PubMed

Alberts, Jonathan B; Odell, Garrett M

2004-12-01

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein-protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our "in silico reconstitution" produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes,in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.

Insights into the Mutation-Induced HHH Syndrome from Modeling Human Mitochondrial Ornithine Transporter-1

PubMed Central

Wang, Jing-Fang; Chou, Kuo-Chen

2012-01-01

Human mitochondrial ornithine transporter-1 is reported in coupling with the hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome, which is a rare autosomal recessive disorder. For in-depth understanding of the molecular mechanism of the disease, it is crucially important to acquire the 3D structure of human mitochondrial ornithine transporter-1. Since no such structure is available in the current protein structure database, we have developed it via computational approaches based on the recent NMR structure of human mitochondrial uncoupling protein (Berardi MJ, Chou JJ, et al. Nature 2011, 476:109–113). Subsequently, we docked the ligand L-ornithine into the computational structure to search for the favorable binding mode. It was observed that the binding interaction for the most favorable binding mode is featured by six remarkable hydrogen bonds between the receptor and ligand, and that the most favorable binding mode shared the same ligand-binding site with most of the homologous mitochondrial carriers from different organisms, implying that the ligand-binding sites are quite conservative in the mitochondrial carriers family although their sequences similarity is very low with 20% or so. Moreover, according to our structural analysis, the relationship between the disease-causing mutations of human mitochondrial ornithine transporter-1 and the HHH syndrome can be classified into the following three categories: (i) the mutation occurs in the pseudo-repeat regions so as to change the region of the protein closer to the mitochondrial matrix; (ii) the mutation is directly affecting the substrate binding pocket so as to reduce the substrate binding affinity; (iii) the mutation is located in the structural region closer to the intermembrane space that can significantly break the salt bridge networks of the protein. These findings may provide useful insights for in-depth understanding of the molecular mechanism of the HHH syndrome and developing effective drugs against the disease. PMID:22292090

The Transcription Factor AlsR Binds and Regulates the Promoter of the alsSD Operon Responsible for Acetoin Formation in Bacillus subtilis

PubMed Central

Frädrich, Claudia; March, Anika; Fiege, Kerstin; Hartmann, Anja; Jahn, Dieter

2012-01-01

Bacillus subtilis forms acetoin under anaerobic fermentative growth conditions and as a product of the aerobic carbon overflow metabolism. Acetoin formation from pyruvate requires α-acetolactate synthase and acetolactate decarboxylase, both encoded by the alsSD operon. The alsR gene, encoding the LysR-type transcriptional regulator AlsR, was found to be essential for the in vivo expression of alsSD in response to anaerobic acetate accumulation, the addition of acetate, low pH, and the aerobic stationary phase. The expressions of the alsSD operon and the alsR regulatory gene were independent of other regulators of the anaerobic regulatory network, including ResDE, Fnr, and ArfM. A negative autoregulation of alsR was observed. In vitro transcription from the alsSD promoter using purified B. subtilis RNA polymerase required AlsR. DNA binding studies with purified recombinant AlsR in combination with promoter mutagenesis experiments identified a 19-bp high-affinity palindromic binding site (TAAT-N11-ATTA) at positions −76 to −58 (regulatory binding site [RBS]) and a low-affinity site (AT-N11-AT) at positions −41 to −27 (activator binding site [ABS]) upstream of the transcriptional start site of alsSD. The RBS and ABS were found to be essential for in vivo alsSD transcription. AlsR binding to both sites induced the formation of higher-order, transcription-competent complexes. The AlsR protein carrying the S100A substitution at the potential coinducer binding site still bound to the RBS and ABS. However, AlsR(S100A) failed to form the higher-order complex and to initiate in vivo and in vitro transcription. A model for AlsR promoter binding and transcriptional activation was deduced. PMID:22178965

The role of CH/π interactions in the high affinity binding of streptavidin and biotin.

PubMed

Ozawa, Motoyasu; Ozawa, Tomonaga; Nishio, Motohiro; Ueda, Kazuyoshi

2017-08-01

The streptavidin-biotin complex has an extraordinarily high affinity (Ka: 10 15 mol -1 ) and contains one of the strongest non-covalent interactions known. This strong interaction is widely used in biological tools, including for affinity tags, detection, and immobilization of proteins. Although hydrogen bond networks and hydrophobic interactions have been proposed to explain this high affinity, the reasons for it remain poorly understood. Inspired by the deceased affinity of biotin observed for point mutations of streptavidin at tryptophan residues, we hypothesized that a CH/π interaction may also contribute to the strong interaction between streptavidin and biotin. CH/π interactions were explored and analyzed at the biotin-binding site and at the interface of the subunits by the fragment molecular orbital method (FMO) and extended applications: PIEDA and FMO4. The results show that CH/π interactions are involved in the high affinity for biotin at the binding site of streptavidin. We further suggest that the involvement of CH/π interactions at the subunit interfaces and an extended CH/π network play more critical roles in determining the high affinity, rather than involvement at the binding site. Copyright © 2017 Elsevier Inc. All rights reserved.

Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

PubMed

Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

2018-02-20

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

Potassium and the K+/H+ Exchanger Kha1p Promote Binding of Copper to ApoFet3p Multi-copper Ferroxidase*

PubMed Central

Wu, Xiaobin; Kim, Heejeong; Seravalli, Javier; Barycki, Joseph J.; Hart, P. John; Gohara, David W.; Di Cera, Enrico; Jung, Won Hee; Kosman, Daniel J.; Lee, Jaekwon

2016-01-01

Acquisition and distribution of metal ions support a number of biological processes. Here we show that respiratory growth of and iron acquisition by the yeast Saccharomyces cerevisiae relies on potassium (K+) compartmentalization to the trans-Golgi network via Kha1p, a K+/H+ exchanger. K+ in the trans-Golgi network facilitates binding of copper to the Fet3p multi-copper ferroxidase. The effect of K+ is not dependent on stable binding with Fet3p or alteration of the characteristics of the secretory pathway. The data suggest that K+ acts as a chemical factor in Fet3p maturation, a role similar to that of cations in folding of nucleic acids. Up-regulation of KHA1 gene in response to iron limitation via iron-specific transcription factors indicates that K+ compartmentalization is linked to cellular iron homeostasis. Our study reveals a novel functional role of K+ in the binding of copper to apoFet3p and identifies a K+/H+ exchanger at the secretory pathway as a new molecular factor associated with iron uptake in yeast. PMID:26966178

Elucidation of the binding preferences of peptide recognition modules: SH3 and PDZ domains.

PubMed

Teyra, Joan; Sidhu, Sachdev S; Kim, Philip M

2012-08-14

Peptide-binding domains play a critical role in regulation of cellular processes by mediating protein interactions involved in signalling. In recent years, the development of large-scale technologies has enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. These efforts have provided significant insights into the binding specificities of these modular domains. Many research groups have taken advantage of this unprecedented volume of specificity data and have developed a variety of new algorithms for the prediction of binding specificities of peptide-binding domains and for the prediction of their natural binding targets. This knowledge has also been applied to the design of synthetic peptide-binding domains in order to rewire protein-protein interaction networks. Here, we describe how these experimental technologies have impacted on our understanding of peptide-binding domain specificities and on the elucidation of their natural ligands. We discuss SH3 and PDZ domains as well characterized examples, and we explore the feasibility of expanding high-throughput experiments to other peptide-binding domains. Copyright © 2012. Published by Elsevier B.V.

Essential Requirements for Robust Signaling in Hfq Dependent Small RNA Networks

PubMed Central

Adamson, David N.; Lim, Han N.

2011-01-01

Bacteria possess networks of small RNAs (sRNAs) that are important for modulating gene expression. At the center of many of these sRNA networks is the Hfq protein. Hfq's role is to quickly match cognate sRNAs and target mRNAs from among a large number of possible combinations and anneal them to form duplexes. Here we show using a kinetic model that Hfq can efficiently and robustly achieve this difficult task by minimizing the sequestration of sRNAs and target mRNAs in Hfq complexes. This sequestration can be reduced by two non-mutually exclusive kinetic mechanisms. The first mechanism involves heterotropic cooperativity (where sRNA and target mRNA binding to Hfq is influenced by other RNAs bound to Hfq); this cooperativity can selectively decrease singly-bound Hfq complexes and ternary complexes with non-cognate sRNA-target mRNA pairs while increasing cognate ternary complexes. The second mechanism relies on frequent RNA dissociation enabling the rapid cycling of sRNAs and target mRNAs among different Hfq complexes; this increases the probability the cognate ternary complex forms before the sRNAs and target mRNAs degrade. We further demonstrate that the performance of sRNAs in isolation is not predictive of their performance within a network. These findings highlight the importance of experimentally characterizing duplex formation in physiologically relevant contexts with multiple RNAs competing for Hfq. The model will provide a valuable framework for guiding and interpreting these experiments. PMID:21876666

Extensive cross-regulation of post-transcriptional regulatory networks in Drosophila

DOE PAGES

Stoiber, Marcus H.; Olson, Sara; May, Gemma E.; ...

2015-08-20

In eukaryotic cells, RNAs exist as ribonucleoprotein particles (RNPs). Despite the importance of these complexes in many biological processes, including splicing, polyadenylation, stability, transportation, localization, and translation, their compositions are largely unknown. We affinity-purified 20 distinct RNA-binding proteins (RBPs) from cultured Drosophila melanogaster cells under native conditions and identified both the RNA and protein compositions of these RNP complexes. We identified “high occupancy target” (HOT) RNAs that interact with the majority of the RBPs we surveyed. HOT RNAs encode components of the nonsense-mediated decay and splicing machinery, as well as RNA-binding and translation initiation proteins. The RNP complexes contain proteinsmore » and mRNAs involved in RNA binding and post-transcriptional regulation. Genes with the capacity to produce hundreds of mRNA isoforms, ultracomplex genes, interact extensively with heterogeneous nuclear ribonuclear proteins (hnRNPs). Our data are consistent with a model in which subsets of RNPs include mRNA and protein products from the same gene, indicating the widespread existence of auto-regulatory RNPs. Lastly, from the simultaneous acquisition and integrative analysis of protein and RNA constituents of RNPs, we identify extensive cross-regulatory and hierarchical interactions in post-transcriptional control.« less

Extensive cross-regulation of post-transcriptional regulatory networks in Drosophila

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stoiber, Marcus H.; Olson, Sara; May, Gemma E.

In eukaryotic cells, RNAs exist as ribonucleoprotein particles (RNPs). Despite the importance of these complexes in many biological processes, including splicing, polyadenylation, stability, transportation, localization, and translation, their compositions are largely unknown. We affinity-purified 20 distinct RNA-binding proteins (RBPs) from cultured Drosophila melanogaster cells under native conditions and identified both the RNA and protein compositions of these RNP complexes. We identified “high occupancy target” (HOT) RNAs that interact with the majority of the RBPs we surveyed. HOT RNAs encode components of the nonsense-mediated decay and splicing machinery, as well as RNA-binding and translation initiation proteins. The RNP complexes contain proteinsmore » and mRNAs involved in RNA binding and post-transcriptional regulation. Genes with the capacity to produce hundreds of mRNA isoforms, ultracomplex genes, interact extensively with heterogeneous nuclear ribonuclear proteins (hnRNPs). Our data are consistent with a model in which subsets of RNPs include mRNA and protein products from the same gene, indicating the widespread existence of auto-regulatory RNPs. Lastly, from the simultaneous acquisition and integrative analysis of protein and RNA constituents of RNPs, we identify extensive cross-regulatory and hierarchical interactions in post-transcriptional control.« less

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy.

PubMed

Xu, Yao; Havenith, Martina

2015-11-07

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

Perspective: Watching low-frequency vibrations of water in biomolecular recognition by THz spectroscopy

NASA Astrophysics Data System (ADS)

Xu, Yao; Havenith, Martina

2015-11-01

Terahertz (THz) spectroscopy has turned out to be a powerful tool which is able to shed new light on the role of water in biomolecular processes. The low frequency spectrum of the solvated biomolecule in combination with MD simulations provides deep insights into the collective hydrogen bond dynamics on the sub-ps time scale. The absorption spectrum between 1 THz and 10 THz of solvated biomolecules is sensitive to changes in the fast fluctuations of the water network. Systematic studies on mutants of antifreeze proteins indicate a direct correlation between biological activity and a retardation of the (sub)-ps hydration dynamics at the protein binding site, i.e., a "hydration funnel." Kinetic THz absorption studies probe the temporal changes of THz absorption during a biological process, and give access to the kinetics of the coupled protein-hydration dynamics. When combined with simulations, the observed results can be explained in terms of a two-tier model involving a local binding and a long range influence on the hydration bond dynamics of the water around the binding site that highlights the significance of the changes in the hydration dynamics at recognition site for biomolecular recognition. Water is shown to assist molecular recognition processes.

The spatial architecture of protein function and adaptation

PubMed Central

McLaughlin, Richard N.; Poelwijk, Frank J.; Raman, Arjun; Gosal, Walraj S.; Ranganathan, Rama

2014-01-01

Statistical analysis of protein evolution suggests a design for natural proteins in which sparse networks of coevolving amino acids (termed sectors) comprise the essence of three-dimensional structure and function1, 2, 3, 4, 5. However, proteins are also subject to pressures deriving from the dynamics of the evolutionary process itself—the ability to tolerate mutation and to be adaptive to changing selection pressures6, 7, 8, 9, 10. To understand the relationship of the sector architecture to these properties, we developed a high-throughput quantitative method for a comprehensive single-mutation study in which every position is substituted individually to every other amino acid. Using a PDZ domain (PSD95pdz3) model system, we show that sector positions are functionally sensitive to mutation, whereas non-sector positions are more tolerant to substitution. In addition, we find that adaptation to a new binding specificity initiates exclusively through variation within sector residues. A combination of just two sector mutations located near and away from the ligand-binding site suffices to switch the binding specificity of PSD95pdz3 quantitatively towards a class-switching ligand. The localization of functional constraint and adaptive variation within the sector has important implications for understanding and engineering proteins. PMID:23041932

Cross-Linker Unbinding and Self-Similarity in Bundled Cytoskeletal Networks

NASA Astrophysics Data System (ADS)

Lieleg, O.; Bausch, A. R.

2007-10-01

The macromechanical properties of purely bundled in vitro actin networks are not only determined by the micromechanical properties of individual bundles but also by molecular unbinding events of the actin-binding protein (ABP) fascin. Under high mechanical load the network elasticity depends on the forced unbinding of individual ABPs in a rate dependent manner. Cross-linker unbinding in combination with the structural self-similarity of the network enables the introduction of a concentration-time superposition principle—broadening the mechanically accessible frequency range over 8 orders of magnitude.

Vitamin K3 disrupts the microtubule networks by binding to tubulin: a novel mechanism of its antiproliferative activity.

PubMed

Acharya, Bipul R; Choudhury, Diptiman; Das, Amlan; Chakrabarti, Gopal

2009-07-28

Vitamin K3 (2-methyl-1,4-naphthoquinone), also known as menadione, is the synthetic precursor of all the naturally occurring vitamin K in the body. Vitamin K is necessary for the production of prothrombin and five other blood-clotting factors in humans. We have examined the effects of menadione on cellular microtubules ex vivo as well as its binding with purified tubulin and microtubules in vitro. Cell viability experiments using human cervical epithelial cancer cells (HeLa) and human oral epithelial cancer cells (KB) indicated that the IC(50) values for menadione are 25.6 +/- 0.6 and 64.3 +/- 0.36 microM, respectively, in those cells. Mendione arrests HeLa cells in mitosis. Immunofluorescence studies using an anti-alpha-tubulin antibody showed a significant irreversible depolymeriztion of the interphase microtubule network and spindle microtubule in a dose-dependent manner. In vitro polymerization of purified tubulin into microtubules is inhibited by menadione with an IC(50) value of 47 +/- 0.65 microM. The binding of menadione with tubulin was studied using menadione fluorescence and intrinsic tryptophan fluorescence of tubulin. Binding of menadione to tubulin is slow, taking 35 min for equilibration at 25 degrees C. The association reaction kinetics is biphasic in nature, and the association rate constants for fast and slow phases are 189.12 +/- 17 and 32.44 +/- 21 M(-1) s(-1) at 25 degrees C, respectively. The stoichiometry of menadione binding to tubulin is 1:1 (molar ratio) with a dissociation constant from 2.44 +/- 0.34 to 3.65 +/- 0.25 microM at 25 degrees C. Menadione competes for the colchicine binding site with a K(i) of 2.5 muM as determined from a modified Dixon plot. The obtained data suggested that menadione binds at the colchicine binding site to tubulin. Thus, we can conclude one novel mechanism of inhibition of cancer cell proliferation by menadione is through tubulin binding.

Neural basis for dynamic updating of object representation in visual working memory.

PubMed

Takahama, Sachiko; Miyauchi, Satoru; Saiki, Jun

2010-02-15

In real world, objects have multiple features and change dynamically. Thus, object representations must satisfy dynamic updating and feature binding. Previous studies have investigated the neural activity of dynamic updating or feature binding alone, but not both simultaneously. We investigated the neural basis of feature-bound object representation in a dynamically updating situation by conducting a multiple object permanence tracking task, which required observers to simultaneously process both the maintenance and dynamic updating of feature-bound objects. Using an event-related design, we separated activities during memory maintenance and change detection. In the search for regions showing selective activation in dynamic updating of feature-bound objects, we identified a network during memory maintenance that was comprised of the inferior precentral sulcus, superior parietal lobule, and middle frontal gyrus. In the change detection period, various prefrontal regions, including the anterior prefrontal cortex, were activated. In updating object representation of dynamically moving objects, the inferior precentral sulcus closely cooperates with a so-called "frontoparietal network", and subregions of the frontoparietal network can be decomposed into those sensitive to spatial updating and feature binding. The anterior prefrontal cortex identifies changes in object representation by comparing memory and perceptual representations rather than maintaining object representations per se, as previously suggested. Copyright 2009 Elsevier Inc. All rights reserved.

Modeling Complex Equilibria in ITC Experiments: Thermodynamic Parameters Estimation for a Three Binding Site Model

PubMed Central

Le, Vu H.; Buscaglia, Robert; Chaires, Jonathan B.; Lewis, Edwin A.

2013-01-01

Isothermal Titration Calorimetry, ITC, is a powerful technique that can be used to estimate a complete set of thermodynamic parameters (e.g. Keq (or ΔG), ΔH, ΔS, and n) for a ligand binding interaction described by a thermodynamic model. Thermodynamic models are constructed by combination of equilibrium constant, mass balance, and charge balance equations for the system under study. Commercial ITC instruments are supplied with software that includes a number of simple interaction models, for example one binding site, two binding sites, sequential sites, and n-independent binding sites. More complex models for example, three or more binding sites, one site with multiple binding mechanisms, linked equilibria, or equilibria involving macromolecular conformational selection through ligand binding need to be developed on a case by case basis by the ITC user. In this paper we provide an algorithm (and a link to our MATLAB program) for the non-linear regression analysis of a multiple binding site model with up to four overlapping binding equilibria. Error analysis demonstrates that fitting ITC data for multiple parameters (e.g. up to nine parameters in the three binding site model) yields thermodynamic parameters with acceptable accuracy. PMID:23262283

Mutations in extracellular matrix genes NID1 and LAMC1 cause autosomal dominant Dandy-Walker malformation and occipital cephaloceles.

PubMed

Darbro, Benjamin W; Mahajan, Vinit B; Gakhar, Lokesh; Skeie, Jessica M; Campbell, Elizabeth; Wu, Shu; Bing, Xinyu; Millen, Kathleen J; Dobyns, William B; Kessler, John A; Jalali, Ali; Cremer, James; Segre, Alberto; Manak, J Robert; Aldinger, Kimerbly A; Suzuki, Satoshi; Natsume, Nagato; Ono, Maya; Hai, Huynh Dai; Viet, Le Thi; Loddo, Sara; Valente, Enza M; Bernardini, Laura; Ghonge, Nitin; Ferguson, Polly J; Bassuk, Alexander G

2013-08-01

We performed whole-exome sequencing of a family with autosomal dominant Dandy-Walker malformation and occipital cephaloceles and detected a mutation in the extracellular matrix (ECM) protein-encoding gene NID1. In a second family, protein interaction network analysis identified a mutation in LAMC1, which encodes a NID1-binding partner. Structural modeling of the NID1-LAMC1 complex demonstrated that each mutation disrupts the interaction. These findings implicate the ECM in the pathogenesis of Dandy-Walker spectrum disorders. © 2013 WILEY PERIODICALS, INC.

Molecular simulation to investigate the cofactor specificity for pichia stipitis Xylose reductase.

PubMed

Xia, Xiao-Le; Cong, Shan; Weng, Xiao-Rong; Chen, Jin-Hua; Wang, Jing-Fang; Chou, Kuo-Chen

2013-11-01

Xylose is one of the most abundant carbohydrates in nature, and widely used to produce bioethanol via fermentation in industry. Xylulose can produce two key enzymes: xylose reductase and xylitol dehydrogenase. Owing to the disparate cofactor specificities of xylose reductase and xylitol dehydrogenase, intracellular redox imbalance is detected during the xylose fermentation, resulting in low ethanol yields. To overcome this barrier, a common strategy is applied to artificially modify the cofactor specificity of xylose reductase. In this study, we utilized molecular simulation approaches to construct a 3D (three-dimensional) structural model for the NADP-dependent Pichia stipitis xylose reductase (PsXR). Based on the 3D model, the favourable binding modes for both cofactors NAD and NADP were obtained using the flexible docking procedure and molecular dynamics simulation. Structural analysis of the favourable binding modes showed that the cofactor binding site of PsXR was composed of 3 major components: a hydrophilic pocket, a hydrophobic pocket as well as a linker channel between the aforementioned two pockets. The hydrophilic pocket could recognize the nicotinamide moiety of the cofactors by hydrogen bonding networks, while the hydrophobic pocket functioned to position the adenine moiety of the cofactors by hydrophobic and Π-Π stacking interactions. The linker channel contained some key residues for ligand-binding; their mutation could have impact to the specificity of PsXR. Finally, it was found that any of the two single mutations, K21A and K270N, might reverse the cofactor specificity of PsXR from major NADP- to NADdependent, which was further confirmed by the additional experiments. Our findings may provide useful insights into the cofactor specificity of PsXR, stimulating new strategies for better designing xylose reductase and improving ethanol production in industry.

Exploring the structural basis of the selective inhibition of monoamine oxidase A by dicarbonitrile aminoheterocycles: role of Asn181 and Ile335 validated by spectroscopic and computational studies.

PubMed

Juárez-Jiménez, Jordi; Mendes, Eduarda; Galdeano, Carles; Martins, Carla; Silva, Daniel B; Marco-Contelles, José; do Carmo Carreiras, Maria; Luque, F Javier; Ramsay, Rona R

2014-02-01

Since cyanide potentiates the inhibitory activity of several monoamine oxidase (MAO) inhibitors, a series of carbonitrile-containing aminoheterocycles was examined to explore the role of nitriles in determining the inhibitory activity against MAO. Dicarbonitrile aminofurans were found to be potent, selective inhibitors against MAO A. The origin of the MAO A selectivity was identified by combining spectroscopic and computational methods. Spectroscopic changes induced in MAO A by mono- and dicarbonitrile inhibitors were different, providing experimental evidence for distinct binding modes to the enzyme. Similar differences were also found between the binding of dicarbonitrile compounds to MAO A and to MAO B. Stabilization of the flavin anionic semiquinone by monocarbonitrile compounds, but destabilization by dicarbonitriles, provided further support to the distinct binding modes of these compounds and their interaction with the flavin ring. Molecular modeling studies supported the role played by the nitrile and amino groups in anchoring the inhibitor to the binding cavity. In particular, the results highlight the role of Asn181 and Ile335 in assisting the interaction of the nitrile-containing aminofuran ring. The network of interactions afforded by the specific attachment of these functional groups provides useful guidelines for the design of selective, reversible MAO A inhibitors. Copyright © 2013 Elsevier B.V. All rights reserved.

Comparative analyses of the thermodynamic RNA binding signatures of different types of RNA recognition motifs

PubMed Central

Cléry, Antoine; Allain, Frédéric H-T

2017-01-01

Abstract RNA recognition motifs (RRMs) are structurally versatile domains important in regulation of alternative splicing. Structural mechanisms of sequence-specific recognition of single-stranded RNAs (ssRNAs) by RRMs are well understood. The thermodynamic strategies are however unclear. Therefore, we utilized microcalorimetry and semi-empirical analyses to comparatively analyze the cognate ssRNA binding thermodynamics of four different RRM domains, each with a different RNA binding mode. The different binding modes are: canonical binding to the β-sheet surface; canonical binding with involvement of N- and C-termini; binding to conserved loops; and binding to an α-helix. Our results identify enthalpy as the sole and general force driving association at physiological temperatures. Also, networks of weak interactions are a general feature regulating stability of the different RRM–ssRNA complexes. In agreement, non-polyelectrolyte effects contributed between ∼75 and 90% of the overall free energy of binding in the considered complexes. The various RNA binding modes also displayed enormous heat capacity differences, that upon dissection revealed large differential changes in hydration, conformations and dynamics upon binding RNA. Altogether, different modes employed by RRMs to bind cognate ssRNAs utilize various thermodynamics strategies during the association process. PMID:28334819

Improving community healthcare for patients with Parkinson's disease: the dutch model.

PubMed

Keus, S H J; Oude Nijhuis, L B; Nijkrake, M J; Bloem, B R; Munneke, M

2012-01-01

Because of the complex nature of Parkinson's disease, a wide variety of health professionals are involved in care. Stepwise, we have addressed the challenges in the provision of multidisciplinary care for this patient group. As a starting point, we have gained detailed insight into the current delivery of allied healthcare, as well as the barriers and facilitators for optimal care. To overcome the identified barriers, a tertiary referral centre was founded; evidence-based guidelines were developed and cost-effectively implemented within regional community networks of specifically trained allied health professionals (the ParkinsonNet concept). We increasingly use ICT to bind these professional networks together and also to empower and engage patients in making decisions about their health. This comprehensive approach is likely to be feasible for other countries as well, so we currently collaborate in a European collaboration to improve community care for persons with Parkinson's disease.

Transcriptional Networks Controlled by NKX2-1 in the Development of Forebrain GABAergic Neurons

DOE PAGES

Sandberg, Magnus; Flandin, Pierre; Silberberg, Shanni; ...

2016-09-21

The embryonic basal ganglia generates multiple projection neurons and interneuron subtypes from distinct progenitor domains. Combinatorial interactions of transcription factors and chromatin are thought to regulate gene expression. In the medial ganglionic eminence, the NKX2-1 transcription factor controls regional identity and, with LHX6, is necessary to specify pallidal projection neurons and forebrain interneurons. Here, we dissected the molecular functions of NKX2-1 by defining its chromosomal binding, regulation of gene expression, and epigenetic state. NKX2-1 binding at distal regulatory elements led to a repressed epigenetic state and transcriptional repression in the ventricular zone. Conversely, NKX2-1 is required to establish a permissivemore » chromatin state and transcriptional activation in the sub-ventricular and mantle zones. Moreover, combinatorial binding of NKX2-1 and LHX6 promotes transcriptionally permissive chromatin and activates genes expressed in cortical migrating interneurons. Our integrated approach gives a foundation for elucidating transcriptional networks guiding the development of the MGE and its descendants.« less

Discriminating tastes

PubMed Central

Storz, Gisela

2011-01-01

Hfq-binding small RNAs (sRNAs) are critical regulators that form limited base-pairing interactions with target mRNAs in bacteria. These sRNAs have been linked to diverse environmental responses, yet little is known how Hfq-binding sRNAs participate in the regulatory networks associated with each response. We recently described how the Hfq-binding sRNA Spot 42 in Escherichia coli contributes to catabolite repression, a regulatory phenomenon that allows bacteria to consume some carbon sources over others. Spot 42 base pairs with numerous mRNAs encoding enzymes in central and secondary metabolism, redox balancing, and the uptake and consumption of non-preferred carbon sources. Many of the corresponding genes are transcriptionally activated by the Spot 42-repressor CRP, forming a regulatory circuit called a multi-output feedforward loop. We found that this loop influences both the steady-state levels and dynamics of gene regulation. In this article, we discuss how the CRP-Spot 42 feedforward loop is integrated into encompassing networks and how this loop may benefit enteric bacteria facing uncertain and changing nutrient conditions. PMID:21788732

RNA regulatory networks diversified through curvature of the PUF protein scaffold

DOE PAGES

Wilinski, Daniel; Qiu, Chen; Lapointe, Christopher P.; ...

2015-09-14

Proteins bind and control mRNAs, directing their localization, translation and stability. Members of the PUF family of RNA-binding proteins control multiple mRNAs in a single cell, and play key roles in development, stem cell maintenance and memory formation. Here we identified the mRNA targets of a S. cerevisiae PUF protein, Puf5p, by ultraviolet-crosslinking-affinity purification and high-throughput sequencing (HITS-CLIP). The binding sites recognized by Puf5p are diverse, with variable spacer lengths between two specific sequences. Each length of site correlates with a distinct biological function. Crystal structures of Puf5p–RNA complexes reveal that the protein scaffold presents an exceptionally flat and extendedmore » interaction surface relative to other PUF proteins. In complexes with RNAs of different lengths, the protein is unchanged. A single PUF protein repeat is sufficient to induce broadening of specificity. Changes in protein architecture, such as alterations in curvature, may lead to evolution of mRNA regulatory networks.« less

RNA regulatory networks diversified through curvature of the PUF protein scaffold

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilinski, Daniel; Qiu, Chen; Lapointe, Christopher P.

Proteins bind and control mRNAs, directing their localization, translation and stability. Members of the PUF family of RNA-binding proteins control multiple mRNAs in a single cell, and play key roles in development, stem cell maintenance and memory formation. Here we identified the mRNA targets of a S. cerevisiae PUF protein, Puf5p, by ultraviolet-crosslinking-affinity purification and high-throughput sequencing (HITS-CLIP). The binding sites recognized by Puf5p are diverse, with variable spacer lengths between two specific sequences. Each length of site correlates with a distinct biological function. Crystal structures of Puf5p–RNA complexes reveal that the protein scaffold presents an exceptionally flat and extendedmore » interaction surface relative to other PUF proteins. In complexes with RNAs of different lengths, the protein is unchanged. A single PUF protein repeat is sufficient to induce broadening of specificity. Changes in protein architecture, such as alterations in curvature, may lead to evolution of mRNA regulatory networks.« less

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

NASA Technical Reports Server (NTRS)

Huang, S.; Ingber, D. E.

2000-01-01

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells. Copyright 2000 Academic Press.

Water molecules in the nucleotide binding cleft of actin: effects on subunit conformation and implications for ATP hydrolysis.

PubMed

Saunders, Marissa G; Voth, Gregory A

2011-10-14

In the monomeric actin crystal structure, the positions of a highly organized network of waters are clearly visible within the active site. However, the recently proposed models of filamentous actin (F-actin) did not extend to including these waters. Since the water network is important for ATP hydrolysis, information about water position is critical to understanding the increased rate of catalysis upon filament formation. Here, we show that waters in the active site are essential for intersubdomain rotational flexibility and that they organize the active-site structure. Including the crystal structure waters during simulation setup allows us to observe distinct changes in the active-site structure upon the flattening of the actin subunit, as proposed in the Oda model for F-actin. We identify changes in both protein position and water position relative to the phosphate tail that suggest a mechanism for accelerating the rate of nucleotide hydrolysis in F-actin by stabilizing charge on the β-phosphate and by facilitating deprotonation of catalytic water. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sequential Logic Model Deciphers Dynamic Transcriptional Control of Gene Expressions

PubMed Central

Yeo, Zhen Xuan; Wong, Sum Thai; Arjunan, Satya Nanda Vel; Piras, Vincent; Tomita, Masaru; Selvarajoo, Kumar; Giuliani, Alessandro; Tsuchiya, Masa

2007-01-01

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method. PMID:17712424

Coding of odors by temporal binding within a model network of the locust antennal lobe.

PubMed

Patel, Mainak J; Rangan, Aaditya V; Cai, David

2013-01-01

The locust olfactory system interfaces with the external world through antennal receptor neurons (ORNs), which represent odors in a distributed, combinatorial manner. ORN axons bundle together to form the antennal nerve, which relays sensory information centrally to the antennal lobe (AL). Within the AL, an odor generates a dynamically evolving ensemble of active cells, leading to a stimulus-specific temporal progression of neuronal spiking. This experimental observation has led to the hypothesis that an odor is encoded within the AL by a dynamically evolving trajectory of projection neuron (PN) activity that can be decoded piecewise to ascertain odor identity. In order to study information coding within the locust AL, we developed a scaled-down model of the locust AL using Hodgkin-Huxley-type neurons and biologically realistic connectivity parameters and current components. Using our model, we examined correlations in the precise timing of spikes across multiple neurons, and our results suggest an alternative to the dynamic trajectory hypothesis. We propose that the dynamical interplay of fast and slow inhibition within the locust AL induces temporally stable correlations in the spiking activity of an odor-dependent neural subset, giving rise to a temporal binding code that allows rapid stimulus detection by downstream elements.

Active and passive transport of cargo in a corrugated channel: A lattice model study

NASA Astrophysics Data System (ADS)

Dey, Supravat; Ching, Kevin; Das, Moumita

2018-04-01

Inside cells, cargos such as vesicles and organelles are transported by molecular motors to their correct locations via active motion on cytoskeletal tracks and passive, Brownian diffusion. During the transportation of cargos, motor-cargo complexes (MCCs) navigate the confining and crowded environment of the cytoskeletal network and other macromolecules. Motivated by this, we study a minimal two-state model of motor-driven cargo transport in confinement and predict transport properties that can be tested in experiments. We assume that the motion of the MCC is directly affected by the entropic barrier due to confinement if it is in the passive, unbound state but not in the active, bound state where it moves with a constant bound velocity. We construct a lattice model based on a Fokker Planck description of the two-state system, study it using a kinetic Monte Carlo method and compare our numerical results with analytical expressions for a mean field limit. We find that the effect of confinement strongly depends on the bound velocity and the binding kinetics of the MCC. Confinement effectively reduces the effective diffusivity and average velocity, except when it results in an enhanced average binding rate and thereby leads to a larger average velocity than when unconfined.

From allosteric drugs to allo-network drugs: state of the art and trends of design, synthesis and computational methods.

PubMed

Csermely, Peter; Nussinov, Ruth; Szilágyi, András

2013-01-01

Allosteric drugs bind to sites which are usually less conserved evolutionarily as compared to orthosteric sites. As such, they can discriminate between closely related proteins, have fewer side effects, and a consequent lower concentration can convey a lesser likelihood of receptor desensitization. However, an allosteric mode of action may also make the results of preclinical and animal experiments less predictive. The sensitivity of the allosteric consequences to the environment further increases the importance of accounting for patient population diversity. Even subtle differences in protein sequence, in cellular metabolic states or in target tissues, can result in different outcomes. This mini-hot-topic issue of CTMC showcases some successes and challenges of allosteric drug development through the examples of seventransmembrane (GPCR), AMPA, NMDA and metabotropic glutamate receptors, as well as the morpheein model of allosterism involved in inherent metabolic errors. Finally, the development of allo-network drugs, which are allosteric drugs acting indirectly on the neighborhood of the pharmacological target in protein-protein interaction or signaling networks, is described.

Hot Spots in a Network of Functional Sites

PubMed Central

Ozbek, Pemra; Soner, Seren; Haliloglu, Turkan

2013-01-01

It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation. PMID:24023934

Ligand Binding to Macromolecules: Allosteric and Sequential Models of Cooperativity.

ERIC Educational Resources Information Center

Hess, V. L.; Szabo, Attila

1979-01-01

A simple model is described for the binding of ligands to macromolecules. The model is applied to the cooperative binding by hemoglobin and aspartate transcarbamylase. The sequential and allosteric models of cooperative binding are considered. (BB)

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

PubMed Central

Galeazzi, Juan M.; Navajas, Joaquín; Mender, Bedeho M. W.; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M.

2016-01-01

ABSTRACT Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant’s gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views. PMID:27253452

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes.

PubMed

Galeazzi, Juan M; Navajas, Joaquín; Mender, Bedeho M W; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M

2016-01-01

Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

PubMed

Zafar, Mohd; Van Vinh, N; Behera, Shishir Kumar; Park, Hung-Suck

2017-04-01

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box-Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand-metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model-genetic algorithm (RSM-GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47μg/g) is facilitated at 30.22mg C/L of EtOH with initial As(III) concentration of 196.77μg/L at pH5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM. Copyright © 2016. Published by Elsevier B.V.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

PubMed

Meng, Hu; Liu, Ying; Lai, Luhua

2015-08-18

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Predicting HIV-1 broadly neutralizing antibody epitope networks using neutralization titers and a novel computational method

PubMed Central

2014-01-01

Background Recent efforts in HIV-1 vaccine design have focused on immunogens that evoke potent neutralizing antibody responses to a broad spectrum of viruses circulating worldwide. However, the development of effective vaccines will depend on the identification and characterization of the neutralizing antibodies and their epitopes. We developed bioinformatics methods to predict epitope networks and antigenic determinants using structural information, as well as corresponding genotypes and phenotypes generated by a highly sensitive and reproducible neutralization assay. 282 clonal envelope sequences from a multiclade panel of HIV-1 viruses were tested in viral neutralization assays with an array of broadly neutralizing monoclonal antibodies (mAbs: b12, PG9,16, PGT121 - 128, PGT130 - 131, PGT135 - 137, PGT141 - 145, and PGV04). We correlated IC50 titers with the envelope sequences, and used this information to predict antibody epitope networks. Structural patches were defined as amino acid groups based on solvent-accessibility, radius, atomic depth, and interaction networks within 3D envelope models. We applied a boosted algorithm consisting of multiple machine-learning and statistical models to evaluate these patches as possible antibody epitope regions, evidenced by strong correlations with the neutralization response for each antibody. Results We identified patch clusters with significant correlation to IC50 titers as sites that impact neutralization sensitivity and therefore are potentially part of the antibody binding sites. Predicted epitope networks were mostly located within the variable loops of the envelope glycoprotein (gp120), particularly in V1/V2. Site-directed mutagenesis experiments involving residues identified as epitope networks across multiple mAbs confirmed association of these residues with loss or gain of neutralization sensitivity. Conclusions Computational methods were implemented to rapidly survey protein structures and predict epitope networks associated with response to individual monoclonal antibodies, which resulted in the identification and deeper understanding of immunological hotspots targeted by broadly neutralizing HIV-1 antibodies. PMID:24646213

Systems pharmacology - Towards the modeling of network interactions.

PubMed

Danhof, Meindert

2016-10-30

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Sulfated hyaluronan alters fibronectin matrix assembly and promotes osteogenic differentiation of human bone marrow stromal cells

NASA Astrophysics Data System (ADS)

Vogel, Sarah; Arnoldini, Simon; Möller, Stephanie; Schnabelrauch, Matthias; Hempel, Ute

2016-11-01

Extracellular matrix (ECM) composition and structural integrity is one of many factors that influence cellular differentiation. Fibronectin (FN) which is in many tissues the most abundant ECM protein forms a unique fibrillary network. FN homes several binding sites for sulfated glycosaminoglycans (sGAG), such as heparin (Hep), which was previously shown to influence FN conformation and protein binding. Synthetically sulfated hyaluronan derivatives (sHA) can serve as model molecules with a well characterized sulfation pattern to study sGAG-FN interaction. Here is shown that the low-sulfated sHA (sHA1) interacts with FN and influences fibril assembly. The interaction of FN fibrils with sHA1 and Hep, but not with non-sulfated HA was visualized by immunofluorescent co-staining. FRET analysis of FN confirmed the presence of more extended fibrils in human bone marrow stromal cells (hBMSC)-derived ECM in response to sHA1 and Hep. Although both sHA1 and Hep affected FN conformation, exclusively sHA1 increased FN protein level and led to thinner fibrils. Further, only sHA1 had a pro-osteogenic effect and enhanced the activity of tissue non-specific alkaline phosphatase. We hypothesize that the sHA1-triggered change in FN assembly influences the entire ECM network and could be the underlying mechanism for the pro-osteogenic effect of sHA1 on hBMSC.

The Gene Regulatory Network of Lens Induction Is Wired through Meis-Dependent Shadow Enhancers of Pax6

PubMed Central

Antosova, Barbora; Smolikova, Jana; Klimova, Lucie; Lachova, Jitka; Bendova, Michaela; Kozmikova, Iryna; Machon, Ondrej; Kozmik, Zbynek

2016-01-01

Lens induction is a classical developmental model allowing investigation of cell specification, spatiotemporal control of gene expression, as well as how transcription factors are integrated into highly complex gene regulatory networks (GRNs). Pax6 represents a key node in the gene regulatory network governing mammalian lens induction. Meis1 and Meis2 homeoproteins are considered as essential upstream regulators of Pax6 during lens morphogenesis based on their interaction with the ectoderm enhancer (EE) located upstream of Pax6 transcription start site. Despite this generally accepted regulatory pathway, Meis1-, Meis2- and EE-deficient mice have surprisingly mild eye phenotypes at placodal stage of lens development. Here, we show that simultaneous deletion of Meis1 and Meis2 in presumptive lens ectoderm results in arrested lens development in the pre-placodal stage, and neither lens placode nor lens is formed. We found that in the presumptive lens ectoderm of Meis1/Meis2 deficient embryos Pax6 expression is absent. We demonstrate using chromatin immunoprecipitation (ChIP) that in addition to EE, Meis homeoproteins bind to a remote, ultraconserved SIMO enhancer of Pax6. We further show, using in vivo gene reporter analyses, that the lens-specific activity of SIMO enhancer is dependent on the presence of three Meis binding sites, phylogenetically conserved from man to zebrafish. Genetic ablation of EE and SIMO enhancers demostrates their requirement for lens induction and uncovers an apparent redundancy at early stages of lens development. These findings identify a genetic requirement for Meis1 and Meis2 during the early steps of mammalian eye development. Moreover, they reveal an apparent robustness in the gene regulatory mechanism whereby two independent "shadow enhancers" maintain critical levels of a dosage-sensitive gene, Pax6, during lens induction. PMID:27918583

Multiple myeloma phosphotyrosine proteomic profile associated with FGFR3 expression, ligand activation, and drug inhibition

PubMed Central

St-Germain, Jonathan R.; Taylor, Paul; Tong, Jiefei; Jin, Lily L.; Nikolic, Ana; Stewart, Ian I.; Ewing, Robert M.; Dharsee, Moyez; Li, Zhihua; Trudel, Suzanne; Moran, Michael F.

2009-01-01

Signaling by growth factor receptor tyrosine kinases is manifest through networks of proteins that are substrates and/or bind to the activated receptors. FGF receptor-3 (FGFR3) is a drug target in a subset of human multiple myelomas (MM) and is mutationally activated in some cervical and colon and many bladder cancers and in certain skeletal dysplasias. To define the FGFR3 network in multiple myeloma, mass spectrometry was used to identify and quantify phosphotyrosine (pY) sites modulated by FGFR3 activation and inhibition in myeloma-derived KMS11 cells. Label-free quantification of peptide ion currents indicated the activation of FGFR3 by phosphorylation of tandem tyrosines in the kinase domain activation loop when cellular pY phosphatases were inhibited by pervanadate. Among the 175 proteins that accumulated pY in response to pervanadate was a subset of 52 including FGFR3 that contained a total of 61 pY sites that were sensitive to inhibition by the FGFR3 inhibitor PD173074. The FGFR3 isoform containing the tandem pY motif in its activation loop was targeted by PD173074. Forty of the drug-sensitive pY sites, including two located within the 35-residue cytoplasmic domain of the transmembrane growth factor binding proteoglycan (and multiple myeloma biomarker) Syndecan-1/CD138, were also stimulated in cells treated with the ligand FGF1, providing additional validation of their link to FGFR3. The identification of these overlapping sets of co-modulated tyrosine phosphorylations presents an outline of an FGFR3 network in the MM model and demonstrates the potential for pharmacodynamic monitoring by label-free quantitative phospho-proteomics. PMID:19901323

Phenotypic Robustness and the Assortativity Signature of Human Transcription Factor Networks

PubMed Central

Pechenick, Dov A.; Payne, Joshua L.; Moore, Jason H.

2014-01-01

Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs — such as their degree distribution — with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness. PMID:25121490

Relating microstructure to rheology of a bundled and cross-linked F-actin network in vitro

NASA Astrophysics Data System (ADS)

Shin, J. H.; Gardel, M. L.; Mahadevan, L.; Matsudaira, P.; Weitz, D. A.

2004-06-01

The organization of individual actin filaments into higher-order structures is controlled by actin-binding proteins (ABPs). Although the biological significance of the ABPs is well documented, little is known about how bundling and cross-linking quantitatively affect the microstructure and mechanical properties of actin networks. Here we quantify the effect of the ABP scruin on actin networks by using imaging techniques, cosedimentation assays, multiparticle tracking, and bulk rheology. We show how the structure of the actin network is modified as the scruin concentration is varied, and we correlate these structural changes to variations in the resultant network elasticity.

An ensemble model of competitive multi-factor binding of the genome

PubMed Central

Wasson, Todd; Hartemink, Alexander J.

2009-01-01

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not. Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an “occupancy profile,” a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding. Importantly, the sequence preferences our model takes as input are derived from in vitro experiments. This ensures that the calculated occupancy profiles are the result of the forces of competition represented explicitly in our model and the inherent sequence affinities of the constituent DBFs. PMID:19720867

The role of charged surface residues in the binding ability of small hubs in protein-protein interaction networks

PubMed Central

Patil, Ashwini; Nakamura, Haruki

2007-01-01

Hubs are highly connected proteins in a protein-protein interaction network. Previous work has implicated disordered domains and high surface charge as the properties significant in the ability of hubs to bind multiple proteins. While conformational flexibility of disordered domains plays an important role in the binding ability of large hubs, high surface charge is the dominant property in small hubs. In this study, we further investigate the role of the high surface charge in the binding ability of small hubs in the absence of disordered domains. Using multipole expansion, we find that the charges are highly distributed over the hub surfaces. Residue enrichment studies show that the charged residues in hubs are more prevalent on the exposed surface, with the exception of Arg, which is predominantly found at the interface, as compared to non-hubs. This suggests that the charged residues act primarily from the exposed surface rather than the interface to affect the binding ability of small hubs. They do this through (i) enhanced intra-molecular electrostatic interactions to lower the desolvation penalty, (ii) indirect long – range intermolecular interactions with charged residues on the partner proteins for better complementarity and electrostatic steering, and (iii) increased solubility for enhanced diffusion-controlled rate of binding. Along with Arg, we also find a high prevalence of polar residues Tyr, Gln and His and the hydrophobic residue Met at the interfaces of hubs, all of which have the ability to form multiple types of interactions, indicating that the interfaces of hubs are optimized to participate in multiple interactions. PMID:27857564

The role of charged surface residues in the binding ability of small hubs in protein-protein interaction networks.

PubMed

Patil, Ashwini; Nakamura, Haruki

2007-01-01

Hubs are highly connected proteins in a protein-protein interaction network. Previous work has implicated disordered domains and high surface charge as the properties significant in the ability of hubs to bind multiple proteins. While conformational flexibility of disordered domains plays an important role in the binding ability of large hubs, high surface charge is the dominant property in small hubs. In this study, we further investigate the role of the high surface charge in the binding ability of small hubs in the absence of disordered domains. Using multipole expansion, we find that the charges are highly distributed over the hub surfaces. Residue enrichment studies show that the charged residues in hubs are more prevalent on the exposed surface, with the exception of Arg, which is predominantly found at the interface, as compared to non-hubs. This suggests that the charged residues act primarily from the exposed surface rather than the interface to affect the binding ability of small hubs. They do this through (i) enhanced intra-molecular electrostatic interactions to lower the desolvation penalty, (ii) indirect long - range intermolecular interactions with charged residues on the partner proteins for better complementarity and electrostatic steering, and (iii) increased solubility for enhanced diffusion-controlled rate of binding. Along with Arg, we also find a high prevalence of polar residues Tyr, Gln and His and the hydrophobic residue Met at the interfaces of hubs, all of which have the ability to form multiple types of interactions, indicating that the interfaces of hubs are optimized to participate in multiple interactions.

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

PubMed

Lautz, Jonathan D; Brown, Emily A; VanSchoiack, Alison A Williams; Smith, Stephen E P

2018-05-27

Cells utilize dynamic, network level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of preexisting multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Magnetic properties and core electron binding energies of liquid water

NASA Astrophysics Data System (ADS)

Galamba, N.; Cabral, Benedito J. C.

2018-01-01

The magnetic properties and the core and inner valence electron binding energies of liquid water are investigated. The adopted methodology relies on the combination of molecular dynamics and electronic structure calculations. Born-Oppenheimer molecular dynamics with the Becke and Lee-Yang-Parr functionals for exchange and correlation, respectively, and includes an empirical correction (BLYP-D3) functional and classical molecular dynamics with the TIP4P/2005-F model were carried out. The Keal-Tozer functional was applied for predicting magnetic shielding and spin-spin coupling constants. Core and inner valence electron binding energies in liquid water were calculated with symmetry adapted cluster-configuration interaction. The relationship between the magnetic shielding constant σ(17O), the role played by the oxygen atom as a proton acceptor and donor, and the tetrahedral organisation of liquid water are investigated. The results indicate that the deshielding of the oxygen atom in water is very dependent on the order parameter (q) describing the tetrahedral organisation of the hydrogen bond network. The strong sensitivity of magnetic properties on changes of the electronic density in the nuclei environment is illustrated by a correlation between σ(17O) and the energy gap between the 1a1[O1s] (core) and the 2a1 (inner valence) orbitals of water. Although several studies discussed the eventual connection between magnetic properties and core electron binding energies, such a correlation could not be clearly established. Here, we demonstrate that for liquid water this correlation exists although involving the gap between electron binding energies of core and inner valence orbitals.

Photonic crystal biosensor microplates with integrated fluid networks for high throughput applications in drug discovery

NASA Astrophysics Data System (ADS)

Choi, Charles J.; Chan, Leo L.; Pineda, Maria F.; Cunningham, Brian T.

2007-09-01

Assays used in pharmaceutical research require a system that can not only detect biochemical interactions with high sensitivity, but that can also perform many measurements in parallel while consuming low volumes of reagents. While nearly all label-free biosensor transducers to date have been interfaced with a flow channel, the liquid handling system is typically aligned and bonded to the transducer for supplying analytes to only a few sensors in parallel. In this presentation, we describe a fabrication approach for photonic crystal biosensors that utilizes nanoreplica molding to produce a network of sensors that are automatically self-aligned with a microfluidic network in a single process step. The sensor/fluid network is inexpensively produced on large surface areas upon flexible plastic substrates, allowing the device to be incorporated into standard format 96-well microplates. A simple flow scheme using hydrostatic pressure applied through a single control point enables immobilization of capture ligands upon a large number of sensors with 220 nL of reagent, and subsequent exposure of the sensors to test samples. A high resolution imaging detection instrument is capable of monitoring the binding within parallel channels at rates compatible with determining kinetic binding constants between the immobilized ligands and the analytes. The first implementation of this system is capable of monitoring the kinetic interactions of 11 flow channels at once, and a total of 88 channels within an integrated biosensor microplate in rapid succession. The system was initially tested to characterize the interaction between sets of proteins with known binding behavior.

PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory.

PubMed

Niknam, Niloofar; Khakzad, Hamed; Arab, Seyed Shahriar; Naderi-Manesh, Hossein

2016-05-01

The integrative and cooperative nature of protein structure involves the assessment of topological and global features of constituent parts. Network concept takes complete advantage of both of these properties in the analysis concomitantly. High compatibility to structural concepts or physicochemical properties in addition to exploiting a remarkable simplification in the system has made network an ideal tool to explore biological systems. There are numerous examples in which different protein structural and functional characteristics have been clarified by the network approach. Here, we present an interactive and user-friendly Matlab-based toolbox, PDB2Graph, devoted to protein structure network construction, visualization, and analysis. Moreover, PDB2Graph is an appropriate tool for identifying critical nodes involved in protein structural robustness and function based on centrality indices. It maps critical amino acids in protein networks and can greatly aid structural biologists in selecting proper amino acid candidates for manipulating protein structures in a more reasonable and rational manner. To introduce the capability and efficiency of PDB2Graph in detail, the structural modification of Calmodulin through allosteric binding of Ca(2+) is considered. In addition, a mutational analysis for three well-identified model proteins including Phage T4 lysozyme, Barnase and Ribonuclease HI, was performed to inspect the influence of mutating important central residues on protein activity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Building New Bridges between In Vitro and In Vivo in Early Drug Discovery: Where Molecular Modeling Meets Systems Biology.

PubMed

Pearlstein, Robert A; McKay, Daniel J J; Hornak, Viktor; Dickson, Callum; Golosov, Andrei; Harrison, Tyler; Velez-Vega, Camilo; Duca, José

2017-01-01

Cellular drug targets exist within networked function-generating systems whose constituent molecular species undergo dynamic interdependent non-equilibrium state transitions in response to specific perturbations (i.e.. inputs). Cellular phenotypic behaviors are manifested through the integrated behaviors of such networks. However, in vitro data are frequently measured and/or interpreted with empirical equilibrium or steady state models (e.g. Hill, Michaelis-Menten, Briggs-Haldane) relevant to isolated target populations. We propose that cells act as analog computers, "solving" sets of coupled "molecular differential equations" (i.e. represented by populations of interacting species)via "integration" of the dynamic state probability distributions among those populations. Disconnects between biochemical and functional/phenotypic assays (cellular/in vivo) may arise with targetcontaining systems that operate far from equilibrium, and/or when coupled contributions (including target-cognate partner binding and drug pharmacokinetics) are neglected in the analysis of biochemical results. The transformation of drug discovery from a trial-and-error endeavor to one based on reliable design criteria depends on improved understanding of the dynamic mechanisms powering cellular function/dysfunction at the systems level. Here, we address the general mechanisms of molecular and cellular function and pharmacological modulation thereof. We outline a first principles theory on the mechanisms by which free energy is stored and transduced into biological function, and by which biological function is modulated by drug-target binding. We propose that cellular function depends on dynamic counter-balanced molecular systems necessitated by the exponential behavior of molecular state transitions under non-equilibrium conditions, including positive versus negative mass action kinetics and solute-induced perturbations to the hydrogen bonds of solvating water versus kT. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

N-3 polyunsaturated fatty acid regulation of hepatic gene transcription

PubMed Central

Jump, Donald B.

2009-01-01

Purpose of review The liver plays a central role in whole body lipid metabolism and adapts rapidly to changes in dietary fat composition. This adaption involves changes in the expression of genes involved in glycolysis, de-novo lipogenesis, fatty acid elongation, desaturation and oxidation. This review brings together metabolic and molecular studies that help explain n-3 (omega-3) polyunsaturated fatty acid regulation of hepatic gene transcription. Recent findings Dietary n-3 polyunsaturated fatty acid regulates hepatic gene expression by targeting three major transcriptional regulatory networks: peroxisome proliferator-activated receptor α, sterol regulatory element binding protein-1 and the carbohydrate regulatory element binding protein/Max-like factor X heterodimer. 22 : 6,n-3, the most prominent n-3 polyunsaturated fatty acid in tissues, is a weak activator of peroxisome proliferator-activated receptor α. Hepatic metabolism of 22 : 6,n-3, however, generates 20 : 5,n-3, a strong peroxisome proliferator-activated receptor α activator. In contrast to peroxisome proliferator-activated receptor α, 22 : 6,n-3 is the most potent fatty acid regulator of hepatic sterol regulatory element binding protein-1. 22 : 6,n-3 suppresses sterol regulatory element binding protein-1 gene expression while enhancing degradation of nuclear sterol regulatory element binding protein-1 through 26S proteasome and Erk1/2-dependent mechanisms. Both n-3 and n-6 polyunsaturated fatty acid suppress carbohydrate regulatory element binding protein and Max-like factor X nuclear abundance and interfere with glucose-regulated hepatic metabolism. Summary These studies have revealed unique mechanisms by which specific polyunsaturated fatty acids control peroxisome proliferator activated receptor α, sterol regulatory element binding protein-1 and carbohydrate regulatory element binding protein/Max-like factor X function. As such, specific metabolic and signal transduction pathways contribute significantly to the fatty acid regulation of these transcription factors and their corresponding regulatory networks. PMID:18460914

Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains

NASA Astrophysics Data System (ADS)

Stoop, Ralph; Saase, Victor; Wagner, Clemens; Stoop, Britta; Stoop, Ruedi

2013-03-01

We study to what extent cortical columns with their particular wiring boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of enhancement. It is on a mesoscopic—intercolumnar—scale that the existence of columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks.

Study on spin filtering and switching action in a double-triangular network chain

NASA Astrophysics Data System (ADS)

Zhang, Yongmei

2018-04-01

Spin transport properties of a double-triangular quantum network with local magnetic moment on backbones and magnetic flux penetrating the network plane are studied. Numerical simulation results show that such a quantum network will be a good candidate for spin filter and spin switch. Local dispersion and density of states are considered in the framework of tight-binding approximation. Transmission coefficients are calculated by the method of transfer matrix. Spin transmission is regulated by substrate magnetic moment and magnetic flux piercing those triangles. Experimental realization of such theoretical research will be conducive to designing of new spintronic devices.

Alternative Polyadenylation Regulates CELF1/CUGBP1 Target Transcripts Following T Cell Activation

PubMed Central

Beisang, Daniel; Reilly, Cavan; Bohjanen, Paul R.

2014-01-01

Alternative polyadenylation (APA) is an evolutionarily conserved mechanism for regulating gene expression. Transcript 3′ end shortening through changes in polyadenylation site usage occurs following T cell activation, but the consequences of APA on gene expression are poorly understood. We previously showed that GU-rich elements (GREs) found in the 3′ untranslated regions of select transcripts mediate rapid mRNA decay by recruiting the protein CELF1/CUGBP1. Using a global RNA sequencing approach, we found that a network of CELF1 target transcripts involved in cell division underwent preferential 3′ end shortening via APA following T cell activation, resulting in decreased inclusion of CELF1 binding sites and increased transcript expression. We present a model whereby CELF1 regulates APA site selection following T cell activation through reversible binding to nearby GRE sequences. These findings provide insight into the role of APA in controlling cellular proliferation during biological processes such as development, oncogenesis and T cell activation PMID:25123787

Transcription initiation complex structures elucidate DNA opening.

PubMed

Plaschka, C; Hantsche, M; Dienemann, C; Burzinski, C; Plitzko, J; Cramer, P

2016-05-19

Transcription of eukaryotic protein-coding genes begins with assembly of the RNA polymerase (Pol) II initiation complex and promoter DNA opening. Here we report cryo-electron microscopy (cryo-EM) structures of yeast initiation complexes containing closed and open DNA at resolutions of 8.8 Å and 3.6 Å, respectively. DNA is positioned and retained over the Pol II cleft by a network of interactions between the TATA-box-binding protein TBP and transcription factors TFIIA, TFIIB, TFIIE, and TFIIF. DNA opening occurs around the tip of the Pol II clamp and the TFIIE 'extended winged helix' domain, and can occur in the absence of TFIIH. Loading of the DNA template strand into the active centre may be facilitated by movements of obstructing protein elements triggered by allosteric binding of the TFIIE 'E-ribbon' domain. The results suggest a unified model for transcription initiation with a key event, the trapping of open promoter DNA by extended protein-protein and protein-DNA contacts.

Conformational Heterogeneity of the HIV Envelope Glycan Shield.

PubMed

Yang, Mingjun; Huang, Jing; Simon, Raphael; Wang, Lai-Xi; MacKerell, Alexander D

2017-06-30

To better understand the conformational properties of the glycan shield covering the surface of the HIV gp120/gp41 envelope (Env) trimer, and how the glycan shield impacts the accessibility of the underlying protein surface, we performed enhanced sampling molecular dynamics (MD) simulations of a model glycosylated HIV Env protein and related systems. Our simulation studies revealed a conformationally heterogeneous glycan shield with a network of glycan-glycan interactions more extensive than those observed to date. We found that partial preorganization of the glycans potentially favors binding by established broadly neutralizing antibodies; omission of several specific glycans could increase the accessibility of other glycans or regions of the protein surface to antibody or CD4 receptor binding; the number of glycans that can potentially interact with known antibodies is larger than that observed in experimental studies; and specific glycan conformations can maximize or minimize interactions with individual antibodies. More broadly, the enhanced sampling MD simulations described here provide a valuable tool to guide the engineering of specific Env glycoforms for HIV vaccine design.

Allosteric binding sites in Rab11 for potential drug candidates

PubMed Central

2018-01-01

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously. PMID:29874286

A Hop-Sensitive Mechanism to Establish Route Optimization in Mobile Networks

NASA Astrophysics Data System (ADS)

Gnanaraj, J. Isac; Newton, P. Calduwel; Arockiam, L.; Kim, Tai-Hoon

The mobile network plays a vital role in mobile communications. It supports both host mobility and network mobility. The mobile network which supports network mobility can be called as NEMO. The NEMO refers to NEtwork MObility or mobile network that moves. Though NEMO provides many supports, it also suffers due to Quality of Service (QoS) issues. One such issue is Route Optimization (RO). When a Mobile Node (MN) is away from Home Network (HN), it will send a binding update to Home Agent (HA) in HN to inform its movement. If the Correspondent Node (CN) wants to send data to MN, it will send data through HA. In this scenario, three networks such as HN, Foreign Network (FN) and Correspondent Network are involved in data transfer. The involvement of these three networks affects the QoS. This paper concentrates on some of the QoS parameters to propose a QoS mechanism to establish RO among these three networks and significantly increases performance of the mobile network.

A portable structural analysis library for reaction networks.

PubMed

Bedaso, Yosef; Bergmann, Frank T; Choi, Kiri; Medley, Kyle; Sauro, Herbert M

2018-07-01

The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper, we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with extensive on-line documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub (https://github.com/sys-bio/Libstructural). Copyright © 2018 Elsevier B.V. All rights reserved.

Genome-wide inference of regulatory networks in Streptomyces coelicolor.

PubMed

Castro-Melchor, Marlene; Charaniya, Salim; Karypis, George; Takano, Eriko; Hu, Wei-Shou

2010-10-18

The onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses. In this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements. Despite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.

Dissolving variables in connectionist combinatory logic

NASA Technical Reports Server (NTRS)

Barnden, John; Srinivas, Kankanahalli

1990-01-01

A connectionist system which can represent and execute combinator expressions to elegantly solve the variable binding problem in connectionist networks is presented. This system is a graph reduction machine utilizing graph representations and traversal mechanisms similar to ones described in the BoltzCONS system of Touretzky (1986). It is shown that, as combinators eliminate variables by introducing special functions, these functions can be connectionistically implemented without reintroducing variable binding. This approach 'dissolves' an important part of the variable binding problem, in that a connectionist system still has to manipulate complex data structures, but those structures and their manipulations are rendered more uniform.

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

PubMed

González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

2012-03-01

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

New insights into the catalytic mechanism of Bombyx mori prostaglandin E synthase gained from structure–function analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yamamoto, Kohji, E-mail: yamamok@agr.kyushu-u.ac.jp; Suzuki, Mamoru; Higashiura, Akifumi

2013-11-01

Highlights: •Structure of Bombyx mori prostaglandin E synthase is determined. •Bound glutathione sulfonic acid is located at the glutathione-binding site. •Electron-sharing network is present in this protein. •This network includes Asn95, Asp96, and Arg98. •Site-directed mutagenesis reveals that the residues contribute to the catalytic activity. -- Abstract: Prostaglandin E synthase (PGES) catalyzes the isomerization of PGH{sub 2} to PGE{sub 2}. We previously reported the identification and structural characterization of Bombyx mori PGES (bmPGES), which belongs to Sigma-class glutathione transferase. Here, we extend these studies by determining the structure of bmPGES in complex with glutathione sulfonic acid (GTS) at a resolutionmore » of 1.37 Å using X-ray crystallography. GTS localized to the glutathione-binding site. We found that electron-sharing network of bmPGES includes Asn95, Asp96, and Arg98. Site-directed mutagenesis of these residues to create mutant forms of bmPGES mutants indicate that they contribute to catalytic activity. These results are, to our knowledge, the first to reveal the presence of an electron-sharing network in bmPGES.« less

Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.

PubMed

Karamzadeh, Razieh; Karimi-Jafari, Mohammad Hossein; Sharifi-Zarchi, Ali; Chitsaz, Hamidreza; Salekdeh, Ghasem Hosseini; Moosavi-Movahedi, Ali Akbar

2017-06-16

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

An introduction to deep learning on biological sequence data: examples and solutions.

PubMed

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Cytoskeletal mechanics: Structure and Dynamics

NASA Astrophysics Data System (ADS)

Bausch, Andreas

2008-03-01

The actin cytoskeleton, a dynamic network of semiflexible filaments and associated regulatory proteins, is responsible for the extraordinary viscoelastic properties of cells. Especially for cellular motility the controlled self assembly to defined structures and the dynamic reorganization on different time scales are of outstanding importance. A prominent example for the controlled self assembly are actin bundles: in many cytoskeletal processes cells rely on the tight control of the structural and mechanical properties of the actin bundles. Using an in vitro model system we show that size control relies on a mismatch between the helical structure of individual actin filaments and the packing symmetry within bundles. While such self assembled structure may evoke the picture of a static network the contrary is the case: the cytoskeleton is highly dynamic and a constant remodeling takes place in vivo. Such dynamic reorganization of the cytoskeleton relies on the non-static nature of single actin/ABP bonds. Here, we study the thermal and forced unbinding events of individual ABP in such in vitro networks. The binding kinetics of the transient crosslinkers determines the mechanical response of such networks -- in the linear as well in the non-linear regime. These effects are important prerequisites for the high adaptability of cells and at the same time might be the molecular mechanism employed by them for mechanosensing.

Genome-Wide Mapping of Collier In Vivo Binding Sites Highlights Its Hierarchical Position in Different Transcription Regulatory Networks

PubMed Central

Dubois, Laurence; Bataillé, Laetitia; Painset, Anaïs; Le Gras, Stéphanie; Jost, Bernard; Crozatier, Michèle; Vincent, Alain

2015-01-01

Collier, the single Drosophila COE (Collier/EBF/Olf-1) transcription factor, is required in several developmental processes, including head patterning and specification of muscle and neuron identity during embryogenesis. To identify direct Collier (Col) targets in different cell types, we used ChIP-seq to map Col binding sites throughout the genome, at mid-embryogenesis. In vivo Col binding peaks were associated to 415 potential direct target genes. Gene Ontology analysis revealed a strong enrichment in proteins with DNA binding and/or transcription-regulatory properties. Characterization of a selection of candidates, using transgenic CRM-reporter assays, identified direct Col targets in dorso-lateral somatic muscles and specific neuron types in the central nervous system. These data brought new evidence that Col direct control of the expression of the transcription regulators apterous and eyes-absent (eya) is critical to specifying neuronal identities. They also showed that cross-regulation between col and eya in muscle progenitor cells is required for specification of muscle identity, revealing a new parallel between the myogenic regulatory networks operating in Drosophila and vertebrates. Col regulation of eya, both in specific muscle and neuronal lineages, may illustrate one mechanism behind the evolutionary diversification of Col biological roles. PMID:26204530

A novel functional domain of Cdc15 kinase is required for its interaction with Tem1 GTPase in Saccharomyces cerevisiae.

PubMed Central

Asakawa, K; Yoshida, S; Otake, F; Toh-e, A

2001-01-01

Exit from mitosis requires the inactivation of cyclin-dependent kinase (CDK) activity. In the budding yeast Saccharomyces cerevisiae, a number of gene products have been identified as components of the signal transduction network regulating inactivation of CDK (called the MEN, for the mitotic exit network). Cdc15, one of such components of the MEN, is an essential protein kinase. By the two-hybrid screening, we identified Cdc15 as a binding protein of Tem1 GTPase, another essential regulator of the MEN. Coprecipitation experiments revealed that Tem1 binds to Cdc15 in vivo. By deletion analysis, we found that the Tem1-binding domain resides near the conserved kinase domain of Cdc15. The cdc15-LF mutation, which was introduced into the Tem1-binding domain, reduced the interaction with Cdc15 and Tem1 and caused temperature-sensitive growth.The kinase activity of Cdc15 was not so much affected by the cdc15-LF mutation. However, Cdc15-LF failed to localize to the SPB at the restrictive temperature. Our data show that the interaction with Tem1 is important for the function of Cdc15 and that Cdc15 and Tem1 function in a complex to direct the exit from mitosis. PMID:11290702

SH2 Domains Serve as Lipid-Binding Modules for pTyr-Signaling Proteins.

PubMed

Park, Mi-Jeong; Sheng, Ren; Silkov, Antonina; Jung, Da-Jung; Wang, Zhi-Gang; Xin, Yao; Kim, Hyunjin; Thiagarajan-Rosenkranz, Pallavi; Song, Seohyeon; Yoon, Youngdae; Nam, Wonhee; Kim, Ilshin; Kim, Eui; Lee, Dong-Gyu; Chen, Yong; Singaram, Indira; Wang, Li; Jang, Myoung Ho; Hwang, Cheol-Sang; Honig, Barry; Ryu, Sungho; Lorieau, Justin; Kim, You-Me; Cho, Wonhwa

2016-04-07

The Src-homology 2 (SH2) domain is a protein interaction domain that directs myriad phosphotyrosine (pY)-signaling pathways. Genome-wide screening of human SH2 domains reveals that ∼90% of SH2 domains bind plasma membrane lipids and many have high phosphoinositide specificity. They bind lipids using surface cationic patches separate from pY-binding pockets, thus binding lipids and the pY motif independently. The patches form grooves for specific lipid headgroup recognition or flat surfaces for non-specific membrane binding and both types of interaction are important for cellular function and regulation of SH2 domain-containing proteins. Cellular studies with ZAP70 showed that multiple lipids bind its C-terminal SH2 domain in a spatiotemporally specific manner and thereby exert exquisite spatiotemporal control over its protein binding and signaling activities in T cells. Collectively, this study reveals how lipids control SH2 domain-mediated cellular protein-protein interaction networks and suggest a new strategy for therapeutic modulation of pY-signaling pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

Network recruitment to coherent oscillations in a hippocampal computer model

PubMed Central

Krieger, Abba; Litt, Brian

2011-01-01

Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. “Epileptic” high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. “Normal” HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy. PMID:21273309

CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors

PubMed Central

Sugathan, Aarathi; Biagioli, Marta; Golzio, Christelle; Erdin, Serkan; Blumenthal, Ian; Manavalan, Poornima; Ragavendran, Ashok; Brand, Harrison; Lucente, Diane; Miles, Judith; Sheridan, Steven D.; Stortchevoi, Alexei; Kellis, Manolis; Haggarty, Stephen J.; Katsanis, Nicholas; Gusella, James F.; Talkowski, Michael E.

2014-01-01

Truncating mutations of chromodomain helicase DNA-binding protein 8 (CHD8), and of many other genes with diverse functions, are strong-effect risk factors for autism spectrum disorder (ASD), suggesting multiple mechanisms of pathogenesis. We explored the transcriptional networks that CHD8 regulates in neural progenitor cells (NPCs) by reducing its expression and then integrating transcriptome sequencing (RNA sequencing) with genome-wide CHD8 binding (ChIP sequencing). Suppressing CHD8 to levels comparable with the loss of a single allele caused altered expression of 1,756 genes, 64.9% of which were up-regulated. CHD8 showed widespread binding to chromatin, with 7,324 replicated sites that marked 5,658 genes. Integration of these data suggests that a limited array of direct regulatory effects of CHD8 produced a much larger network of secondary expression changes. Genes indirectly down-regulated (i.e., without CHD8-binding sites) reflect pathways involved in brain development, including synapse formation, neuron differentiation, cell adhesion, and axon guidance, whereas CHD8-bound genes are strongly associated with chromatin modification and transcriptional regulation. Genes associated with ASD were strongly enriched among indirectly down-regulated loci (P < 10−8) and CHD8-bound genes (P = 0.0043), which align with previously identified coexpression modules during fetal development. We also find an intriguing enrichment of cancer-related gene sets among CHD8-bound genes (P < 10−10). In vivo suppression of chd8 in zebrafish produced macrocephaly comparable to that of humans with inactivating mutations. These data indicate that heterozygous disruption of CHD8 precipitates a network of gene-expression changes involved in neurodevelopmental pathways in which many ASD-associated genes may converge on shared mechanisms of pathogenesis. PMID:25294932

Free-energy simulations reveal that both hydrophobic and polar interactions are important for influenza hemagglutinin antibody binding.

PubMed

Xia, Zhen; Huynh, Tien; Kang, Seung-gu; Zhou, Ruhong

2012-03-21

Antibodies binding to conserved epitopes can provide a broad range of neutralization to existing influenza subtypes and may also prevent the propagation of potential pandemic viruses by fighting against emerging strands. Here we propose a computational framework to study structural binding patterns and detailed molecular mechanisms of viral surface glycoprotein hemagglutinin (HA) binding with a broad spectrum of neutralizing monoclonal antibody fragments (Fab). We used rigorous free-energy perturbation (FEP) methods to calculate the antigen-antibody binding affinities, with an aggregate underlying molecular-dynamics simulation time of several microseconds (∼2 μs) using all-atom, explicit-solvent models. We achieved a high accuracy in the validation of our FEP protocol against a series of known binding affinities for this complex system, with <0.5 kcal/mol errors on average. We then introduced what to our knowledge are novel mutations into the interfacial region to further study the binding mechanism. We found that the stacking interaction between Trp-21 in HA2 and Phe-55 in the CDR-H2 of Fab is crucial to the antibody-antigen association. A single mutation of either W21A or F55A can cause a binding affinity decrease of ΔΔG > 4.0 kcal/mol (equivalent to an ∼1000-fold increase in the dissociation constant K(d)). Moreover, for group 1 HA subtypes (which include both the H1N1 swine flu and the H5N1 bird flu), the relative binding affinities change only slightly (< ±1 kcal/mol) when nonpolar residues at the αA helix of HA mutate to conservative amino acids of similar size, which explains the broad neutralization capability of antibodies such as F10 and CR6261. Finally, we found that the hydrogen-bonding network between His-38 (in HA1) and Ser-30/Gln-64 (in Fab) is important for preserving the strong binding of Fab against group 1 HAs, whereas the lack of such hydrogen bonds with Asn-38 in most group 2 HAs may be responsible for the escape of antibody neutralization. These large-scale simulations may provide new insight into the antigen-antibody binding mechanism at the atomic level, which could be essential for designing more-effective vaccines for influenza. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.