Selection of peptides interfering with protein-protein interaction.

PubMed

Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

2009-01-01

Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

Coarsening of protein clusters on subcellular drops exhibits strong and sudden size selectivity

NASA Astrophysics Data System (ADS)

Brown, Aidan; Rutenberg, Andrew

2015-03-01

Autophagy is an important process for the degradation of cellular components, with receptor proteins targeting substrates to downstream autophagy machinery. An important question is how receptor protein interactions lead to their selective accumulation on autophagy substrates. Receptor proteins have recently been observed in clusters, raising the possibility that clustering could affect autophagy selectivity. We investigate the clustering dynamics of the autophagy receptor protein NBR1. In addition to standard receptor protein domains, NBR1 has a ``J'' domain that anchors it to membranes, and a coiled-coil domain that enhances self-interaction. We model coarsening clusters of NBR1 on the surfaces of a polydisperse collection of drops, representing organelles. Despite the disconnected nature of the drop surfaces, we recover dynamical scaling of cluster sizes. Significantly, we find that at a well-defined time after coarsening begins, clusters evaporate from smaller drops and grow on larger drops. Thus, coarsening-driven size selection will localize protein clusters to larger substrates, leaving smaller substrates without clusters. This provides a possible physical mechanism for autophagy selectivity, and can explain reports of size selection during peroxisome degradation.

Hierarchy and extremes in selections from pools of randomized proteins

PubMed Central

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-01-01

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution). PMID:26969726

Hierarchy and extremes in selections from pools of randomized proteins.

PubMed

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-03-29

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different "frameworks" typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).

Large-scale model quality assessment for improving protein tertiary structure prediction.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-06-15

Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. © The Author 2015. Published by Oxford University Press.

Selective molecular transport through the protein shell of a bacterial microcompartment organelle

DOE PAGES

Chowdhury, Chiranjit; Chun, Sunny; Pang, Allan; ...

2015-02-23

Bacterial microcompartments are widespread prokaryotic organelles that have important and diverse roles ranging from carbon fixation to enteric pathogenesis. Current models for microcompartment function propose that their outer protein shell is selectively permeable to small molecules, but whether a protein shell can mediate selective permeability and how this occurs are unresolved questions. In this paper, biochemical and physiological studies of structure-guided mutants are used to show that the hexameric PduA shell protein of the 1,2-propanediol utilization (Pdu) microcompartment forms a selectively permeable pore tailored for the influx of 1,2-propanediol (the substrate of the Pdu microcompartment) while restricting the efflux ofmore » propionaldehyde, a toxic intermediate of 1,2-propanediol catabolism. Crystal structures of various PduA mutants provide a foundation for interpreting the observed biochemical and phenotypic data in terms of molecular diffusion across the shell. Finally and overall, these studies provide a basis for understanding a class of selectively permeable channels formed by nonmembrane proteins.« less

Evolution of sparsity and modularity in a model of protein allostery

NASA Astrophysics Data System (ADS)

Hemery, Mathieu; Rivoire, Olivier

2015-04-01

The sequence of a protein is not only constrained by its physical and biochemical properties under current selection, but also by features of its past evolutionary history. Understanding the extent and the form that these evolutionary constraints may take is important to interpret the information in protein sequences. To study this problem, we introduce a simple but physical model of protein evolution where selection targets allostery, the functional coupling of distal sites on protein surfaces. This model shows how the geometrical organization of couplings between amino acids within a protein structure can depend crucially on its evolutionary history. In particular, two scenarios are found to generate a spatial concentration of functional constraints: high mutation rates and fluctuating selective pressures. This second scenario offers a plausible explanation for the high tolerance of natural proteins to mutations and for the spatial organization of their least tolerant amino acids, as revealed by sequence analysis and mutagenesis experiments. It also implies a faculty to adapt to new selective pressures that is consistent with observations. The model illustrates how several independent functional modules may emerge within the same protein structure, depending on the nature of past environmental fluctuations. Our model thus relates the evolutionary history of proteins to the geometry of their functional constraints, with implications for decoding and engineering protein sequences.

Directional Darwinian Selection in proteins.

PubMed

McClellan, David A

2013-01-01

Molecular evolution is a very active field of research, with several complementary approaches, including dN/dS, HON90, MM01, and others. Each has documented strengths and weaknesses, and no one approach provides a clear picture of how natural selection works at the molecular level. The purpose of this work is to present a simple new method that uses quantitative amino acid properties to identify and characterize directional selection in proteins. Inferred amino acid replacements are viewed through the prism of a single physicochemical property to determine the amount and direction of change caused by each replacement. This allows the calculation of the probability that the mean change in the single property associated with the amino acid replacements is equal to zero (H0: μ = 0; i.e., no net change) using a simple two-tailed t-test. Example data from calanoid and cyclopoid copepod cytochrome oxidase subunit I sequence pairs are presented to demonstrate how directional selection may be linked to major shifts in adaptive zones, and that convergent evolution at the whole organism level may be the result of convergent protein adaptations. Rather than replace previous methods, this new method further complements existing methods to provide a holistic glimpse of how natural selection shapes protein structure and function over evolutionary time.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

PubMed

Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

2017-11-27

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Modelling of DNA-Mediated of Two- and -Three dimensional Protein-Protein and Protein-Nanoparticle Self-Assembly

NASA Astrophysics Data System (ADS)

Millan, Jaime; McMillan, Janet; Brodin, Jeff; Lee, Byeongdu; Mirkin, Chad; Olvera de La Cruz, Monica

Programmable DNA interactions represent a robust scheme to self-assemble a rich variety of tunable superlattices, where intrinsic and in some cases non-desirable nano-scale building blocks interactions are substituted for DNA hybridization events. Recent advances in synthesis has allowed the extension of this successful scheme to proteins, where DNA distribution can be tuned independently of protein shape by selectively addressing surface residues, giving rise to assembly properties in three dimensional protein-nanoparticle superlattices dependent on DNA distribution. In parallel to this advances, we introduced a scalable coarse-grained model that faithfully reproduces the previously observed co-assemblies from nanoparticles and proteins conjugates. Herein, we implement this numerical model to explain the stability of complex protein-nanoparticle binary superlattices and to elucidate experimentally inaccessible features such as protein orientation. Also, we will discuss systematic studies that highlight the role of DNA distribution and sequence on two-dimensional protein-protein and protein-nanoparticle superlattices.

The importance of protein in leaf selection of folivorous primates.

PubMed

Ganzhorn, Joerg U; Arrigo-Nelson, Summer J; Carrai, Valentina; Chalise, Mukesh K; Donati, Giuseppe; Droescher, Iris; Eppley, Timothy M; Irwin, Mitchell T; Koch, Flávia; Koenig, Andreas; Kowalewski, Martin M; Mowry, Christopher B; Patel, Erik R; Pichon, Claire; Ralison, Jose; Reisdorff, Christoph; Simmen, Bruno; Stalenberg, Eleanor; Starrs, Danswell; Terboven, Juana; Wright, Patricia C; Foley, William J

2017-04-01

Protein limitation has been considered a key factor in hypotheses on the evolution of life history and animal communities, suggesting that animals should prioritize protein in their food choice. This contrasts with the limited support that food selection studies have provided for such a priority in nonhuman primates, particularly for folivores. Here, we suggest that this discrepancy can be resolved if folivores only need to select for high protein leaves when average protein concentration in the habitat is low. To test the prediction, we applied meta-analyses to analyze published and unpublished results of food selection for protein and fiber concentrations from 24 studies (some with multiple species) of folivorous primates. To counter potential methodological flaws, we differentiated between methods analyzing total nitrogen and soluble protein concentrations. We used a meta-analysis to test for the effect of protein on food selection by primates and found a significant effect of soluble protein concentrations, but a non-significant effect for total nitrogen. Furthermore, selection for soluble protein was reinforced in forests where protein was less available. Selection for low fiber content was significant but unrelated to the fiber concentrations in representative leaf samples of a given forest. There was no relationship (either negative or positive) between the concentration of protein and fiber in the food or in representative samples of leaves. Overall our study suggests that protein selection is influenced by the protein availability in the environment, explaining the sometimes contradictory results in previous studies on protein selection. Am. J. Primatol. 79:e22550, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

NASA Astrophysics Data System (ADS)

Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

2015-11-01

Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

Modeling phase separation in mixtures of intrinsically-disordered proteins

NASA Astrophysics Data System (ADS)

Gu, Chad; Zilman, Anton

Phase separation in a pure or mixed solution of intrinsically-disordered proteins (IDPs) and its role in various biological processes has generated interest from the theoretical biophysics community. Phase separation of IDPs has been implicated in the formation of membrane-less organelles such as nucleoli, as well as in a mechanism of selectivity in transport through the nuclear pore complex. Based on a lattice model of polymers, we study the phase diagram of IDPs in a mixture and describe the selective exclusion of soluble proteins from the dense-phase IDP aggregates. The model captures the essential behaviour of phase separation by a minimal set of coarse-grained parameters, corresponding to the average monomer-monomer and monomer-protein attraction strength, as well as the protein-to-monomer size ratio. Contrary to the intuition that strong monomer-monomer interaction increases exclusion of soluble proteins from the dense IDP aggregates, our model predicts that the concentration of soluble proteins in the aggregate phase as a function of monomer-monomer attraction is non-monotonic. We corroborate the predictions of the lattice model using Langevin dynamics simulations of grafted polymers in planar and cylindrical geometries, mimicking various in-vivo and in-vitro conditions.

DeepQA: improving the estimation of single protein model quality with deep belief networks.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring*

PubMed Central

Lawless, Craig; Holman, Stephen W.; Brownridge, Philip; Lanthaler, Karin; Harman, Victoria M.; Watkins, Rachel; Hammond, Dean E.; Miller, Rebecca L.; Sims, Paul F. G.; Grant, Christopher M.; Eyers, Claire E.; Beynon, Robert J.

2016-01-01

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800 Saccharomyces cerevisiae proteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a “gold-standard” reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies. PMID:26750110

Selective Advantage of Recombination in Evolving Protein Populations:. a Lattice Model Study

NASA Astrophysics Data System (ADS)

Williams, Paul D.; Pollock, David D.; Goldstein, Richard A.

Recent research has attempted to clarify the contributions of several mutational processes, such as substitutions or homologous recombination. Simplistic, tractable protein models, which determine the compact native structure phenotype from the sequence genotype, are well-suited to such studies. In this paper, we use a lattice-protein model to examine the effects of point mutation and homologous recombination on evolving populations of proteins. We find that while the majority of mutation and recombination events are neutral or deleterious, recombination is far more likely to be beneficial. This results in a faster increase in fitness during evolution, although the final fitness level is not significantly changed. This transient advantage provides an evolutionary advantage to subpopulations that undergo recombination, allowing fixation of recombination to occur in the population.

Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination

PubMed Central

Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.

2010-01-01

A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624

Site-selective post-translational modification of proteins using an unnatural amino acid, 3-azidotyrosine.

PubMed

Ohno, Satoshi; Matsui, Megumi; Yokogawa, Takashi; Nakamura, Masashi; Hosoya, Takamitsu; Hiramatsu, Toshiyuki; Suzuki, Masaaki; Hayashi, Nobuhiro; Nishikawa, Kazuya

2007-03-01

An efficient method for site-selective modification of proteins using an unnatural amino acid, 3-azidotyrosine has been developed. This method utilizes the yeast amber suppressor tRNA(Tyr)/mutated tyrosyl-tRNA synthetase pair as a carrier of 3-azidotyrosine in an Escherichia coli cell-free translation system, and triarylphosphine derivatives for specific modification of the azido group. Using rat calmodulin (CaM) as a model protein, we prepared several unnatural CaM molecules, each carrying an azidotyrosine at predetermined positions 72, 78, 80 or 100, respectively. Post-translational modification of these proteins with a conjugate compound of triarylphosphine and biotin produced site-selectively biotinylated CaM molecules. Reaction efficiency was similar among these proteins irrespective of the position of introduction, and site-specificity of biotinylation was confirmed using mass spectrometry. In addition, CBP-binding activity of the biotinylated CaMs was confirmed to be similar to that of wild-type CaM. This method is intrinsically versatile in that it should be easily applicable to introducing any other desirable compounds (e.g., probes and cross-linkers) into selected sites of proteins as far as appropriate derivative compounds of triarylphosphine could be chemically synthesized. Elucidation of molecular mechanisms of protein functions and protein-to-protein networks will be greatly facilitated by making use of these site-selectively modified proteins.

Selection of stably folded proteins by phage-display with proteolysis.

PubMed

Bai, Yawen; Feng, Hanqiao

2004-05-01

To facilitate the process of protein design and learn the basic rules that control the structure and stability of proteins, combinatorial methods have been developed to select or screen proteins with desired properties from libraries of mutants. One such method uses phage-display and proteolysis to select stably folded proteins. This method does not rely on specific properties of proteins for selection. Therefore, in principle it can be applied to any protein. Since its first demonstration in 1998, the method has been used to create hyperthermophilic proteins, to evolve novel folded domains from a library generated by combinatorial shuffling of polypeptide segments and to convert a partially unfolded structure to a fully folded protein.

SELECTIVE ADVANTAGE OF RECOMBINATION IN EVOLVING PROTEIN POPULATIONS: A LATTICE MODEL STUDY

PubMed Central

WILLIAMS, PAUL D.; POLLOCK, DAVID D.

2010-01-01

Recent research has attempted to clarify the contributions of several mutational processes, such as substitutions or homologous recombination. Simplistic, tractable protein models, which determine the compact native structure phenotype from the sequence genotype, are well-suited to such studies. In this paper, we use a lattice-protein model to examine the effects of point mutation and homologous recombination on evolving populations of proteins. We find that while the majority of mutation and recombination events are neutral or deleterious, recombination is far more likely to be beneficial. This results in a faster increase in fitness during evolution, although the final fitness level is not significantly changed. This transient advantage provides an evolutionary advantage to subpopulations that undergo recombination, allowing fixation of recombination to occur in the population. PMID:25473139

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

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

Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco

Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsinmore » kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.« less

Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

DOE PAGES

Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; ...

2015-12-24

Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsinmore » kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.« less

Selecting for Fast Protein-Protein Association As Demonstrated on a Random TEM1 Yeast Library Binding BLIP.

PubMed

Cohen-Khait, Ruth; Schreiber, Gideon

2018-04-27

Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.

Modeling complexes of modeled proteins.

PubMed

Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A

2017-03-01

Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å C α RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

PubMed

Muley, Vijaykumar Yogesh; Ranjan, Akash

2012-01-01

Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.

PubMed

Akhter, Nasrin; Shehu, Amarda

2018-01-19

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

Phytomonas: A non-pathogenic trypanosomatid model for functional expression of proteins.

PubMed

Miranda, Mariana R; Sayé, Melisa; Reigada, Chantal; Carrillo, Carolina; Pereira, Claudio A

2015-10-01

Phytomonas are protozoan parasites from the Trypanosomatidae family which infect a wide variety of plants. Herein, Phytomonas Jma was tested as a model for functional expression of heterologous proteins. Green fluorescent protein expression was evaluated in Phytomonas and compared with Trypanosoma cruzi, the etiological agent of Chagas' disease. Phytomonas was able to express GFP at levels similar to T. cruzi although the transgenic selection time was higher. It was possible to establish an efficient transfection and selection protocol for protein expression. These results demonstrate that Phytomonas can be a good model for functional expression of proteins from other trypanosomatids, presenting the advantage of being completely safe for humans. Copyright © 2015 Elsevier Inc. All rights reserved.

Detecting consistent patterns of directional adaptation using differential selection codon models.

PubMed

Parto, Sahar; Lartillot, Nicolas

2017-06-23

Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

A selection that reports on protein-protein interactions within a thermophilic bacterium.

PubMed

Nguyen, Peter Q; Silberg, Jonathan J

2010-07-01

Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

Profiling Charge Complementarity and Selectivity for Binding at the Protein Surface

PubMed Central

Sulea, Traian; Purisima, Enrico O.

2003-01-01

A novel analysis and representation of the protein surface in terms of electrostatic binding complementarity and selectivity is presented. The charge optimization methodology is applied in a probe-based approach that simulates the binding process to the target protein. The molecular surface is color coded according to calculated optimal charge or according to charge selectivity, i.e., the binding cost of deviating from the optimal charge. The optimal charge profile depends on both the protein shape and charge distribution whereas the charge selectivity profile depends only on protein shape. High selectivity is concentrated in well-shaped concave pockets, whereas solvent-exposed convex regions are not charge selective. This suggests the synergy of charge and shape selectivity hot spots toward molecular selection and recognition, as well as the asymmetry of charge selectivity at the binding interface of biomolecular systems. The charge complementarity and selectivity profiles map relevant electrostatic properties in a readily interpretable way and encode information that is quite different from that visualized in the standard electrostatic potential map of unbound proteins. PMID:12719221

Profiling charge complementarity and selectivity for binding at the protein surface.

PubMed

Sulea, Traian; Purisima, Enrico O

2003-05-01

A novel analysis and representation of the protein surface in terms of electrostatic binding complementarity and selectivity is presented. The charge optimization methodology is applied in a probe-based approach that simulates the binding process to the target protein. The molecular surface is color coded according to calculated optimal charge or according to charge selectivity, i.e., the binding cost of deviating from the optimal charge. The optimal charge profile depends on both the protein shape and charge distribution whereas the charge selectivity profile depends only on protein shape. High selectivity is concentrated in well-shaped concave pockets, whereas solvent-exposed convex regions are not charge selective. This suggests the synergy of charge and shape selectivity hot spots toward molecular selection and recognition, as well as the asymmetry of charge selectivity at the binding interface of biomolecular systems. The charge complementarity and selectivity profiles map relevant electrostatic properties in a readily interpretable way and encode information that is quite different from that visualized in the standard electrostatic potential map of unbound proteins.

Zeolite Nanoparticles for Selective Sorption of Plasma Proteins

NASA Astrophysics Data System (ADS)

Rahimi, M.; Ng, E.-P.; Bakhtiari, K.; Vinciguerra, M.; Ahmad, H. Ali; Awala, H.; Mintova, S.; Daghighi, M.; Bakhshandeh Rostami, F.; de Vries, M.; Motazacker, M. M.; Peppelenbosch, M. P.; Mahmoudi, M.; Rezaee, F.

2015-11-01

The affinity of zeolite nanoparticles (diameter of 8-12 nm) possessing high surface area and high pore volume towards human plasma proteins has been investigated. The protein composition (corona) of zeolite nanoparticles has been shown to be more dependent on the plasma protein concentrations and the type of zeolites than zeolite nanoparticles concentration. The number of proteins present in the corona of zeolite nanoparticles at 100% plasma (in vivo state) is less than with 10% plasma exposure. This could be due to a competition between the proteins to occupy the corona of the zeolite nanoparticles. Moreover, a high selective adsorption for apolipoprotein C-III (APOC-III) and fibrinogen on the zeolite nanoparticles at high plasma concentration (100%) was observed. While the zeolite nanoparticles exposed to low plasma concentration (10%) exhibited a high selective adsorption for immunoglobulin gamma (i.e. IGHG1, IGHG2 and IGHG4) proteins. The zeolite nanoparticles can potentially be used for selectively capture of APOC-III in order to reduce the activation of lipoprotein lipase inhibition during hypertriglyceridemia treatment. The zeolite nanoparticles can be adapted to hemophilic patients (hemophilia A (F-VIII deficient) and hemophilia B (F-IX deficient)) with a risk of bleeding, and thus might be potentially used in combination with the existing therapy.

Zeolite Nanoparticles for Selective Sorption of Plasma Proteins.

PubMed

Rahimi, M; Ng, E-P; Bakhtiari, K; Vinciguerra, M; Ali Ahmad, H; Awala, H; Mintova, S; Daghighi, M; Bakhshandeh Rostami, F; de Vries, M; Motazacker, M M; Peppelenbosch, M P; Mahmoudi, M; Rezaee, F

2015-11-30

The affinity of zeolite nanoparticles (diameter of 8-12 nm) possessing high surface area and high pore volume towards human plasma proteins has been investigated. The protein composition (corona) of zeolite nanoparticles has been shown to be more dependent on the plasma protein concentrations and the type of zeolites than zeolite nanoparticles concentration. The number of proteins present in the corona of zeolite nanoparticles at 100% plasma (in vivo state) is less than with 10% plasma exposure. This could be due to a competition between the proteins to occupy the corona of the zeolite nanoparticles. Moreover, a high selective adsorption for apolipoprotein C-III (APOC-III) and fibrinogen on the zeolite nanoparticles at high plasma concentration (100%) was observed. While the zeolite nanoparticles exposed to low plasma concentration (10%) exhibited a high selective adsorption for immunoglobulin gamma (i.e. IGHG1, IGHG2 and IGHG4) proteins. The zeolite nanoparticles can potentially be used for selectively capture of APOC-III in order to reduce the activation of lipoprotein lipase inhibition during hypertriglyceridemia treatment. The zeolite nanoparticles can be adapted to hemophilic patients (hemophilia A (F-VIII deficient) and hemophilia B (F-IX deficient)) with a risk of bleeding, and thus might be potentially used in combination with the existing therapy.

Zeolite Nanoparticles for Selective Sorption of Plasma Proteins

PubMed Central

Rahimi, M.; Ng, E.-P.; Bakhtiari, K.; Vinciguerra, M.; Ahmad, H. Ali; Awala, H.; Mintova, S.; Daghighi, M.; Bakhshandeh Rostami, F.; de Vries, M.; Motazacker, M. M.; Peppelenbosch, M. P.; Mahmoudi, M.; Rezaee, F.

2015-01-01

The affinity of zeolite nanoparticles (diameter of 8–12 nm) possessing high surface area and high pore volume towards human plasma proteins has been investigated. The protein composition (corona) of zeolite nanoparticles has been shown to be more dependent on the plasma protein concentrations and the type of zeolites than zeolite nanoparticles concentration. The number of proteins present in the corona of zeolite nanoparticles at 100% plasma (in vivo state) is less than with 10% plasma exposure. This could be due to a competition between the proteins to occupy the corona of the zeolite nanoparticles. Moreover, a high selective adsorption for apolipoprotein C-III (APOC-III) and fibrinogen on the zeolite nanoparticles at high plasma concentration (100%) was observed. While the zeolite nanoparticles exposed to low plasma concentration (10%) exhibited a high selective adsorption for immunoglobulin gamma (i.e. IGHG1, IGHG2 and IGHG4) proteins. The zeolite nanoparticles can potentially be used for selectively capture of APOC-III in order to reduce the activation of lipoprotein lipase inhibition during hypertriglyceridemia treatment. The zeolite nanoparticles can be adapted to hemophilic patients (hemophilia A (F-VIII deficient) and hemophilia B (F-IX deficient)) with a risk of bleeding, and thus might be potentially used in combination with the existing therapy. PMID:26616161

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

PubMed Central

Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L.; Scheffler, Konrad

2012-01-01

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance. PMID:22589711

Modeling HIV-1 drug resistance as episodic directional selection.

PubMed

Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L; Scheffler, Konrad

2012-01-01

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

Protein solubility modeling

NASA Technical Reports Server (NTRS)

Agena, S. M.; Pusey, M. L.; Bogle, I. D.

1999-01-01

A thermodynamic framework (UNIQUAC model with temperature dependent parameters) is applied to model the salt-induced protein crystallization equilibrium, i.e., protein solubility. The framework introduces a term for the solubility product describing protein transfer between the liquid and solid phase and a term for the solution behavior describing deviation from ideal solution. Protein solubility is modeled as a function of salt concentration and temperature for a four-component system consisting of a protein, pseudo solvent (water and buffer), cation, and anion (salt). Two different systems, lysozyme with sodium chloride and concanavalin A with ammonium sulfate, are investigated. Comparison of the modeled and experimental protein solubility data results in an average root mean square deviation of 5.8%, demonstrating that the model closely follows the experimental behavior. Model calculations and model parameters are reviewed to examine the model and protein crystallization process. Copyright 1999 John Wiley & Sons, Inc.

A Theoretical Lower Bound for Selection on the Expression Levels of Proteins

DOE PAGES

Price, Morgan N.; Arkin, Adam P.

2016-06-11

We use simple models of the costs and benefits of microbial gene expression to show that changing a protein's expression away from its optimum by 2-fold should reduce fitness by at least [Formula: see text], where P is the fraction the cell's protein that the gene accounts for. As microbial genes are usually expressed at above 5 parts per million, and effective population sizes are likely to be above 10(6), this implies that 2-fold changes to gene expression levels are under strong selection, as [Formula: see text], where Ne is the effective population size and s is the selection coefficient.more » Thus, most gene duplications should be selected against. On the other hand, we predict that for most genes, small changes in the expression will be effectively neutral.« less

A Theoretical Lower Bound for Selection on the Expression Levels of Proteins

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

Price, Morgan N.; Arkin, Adam P.

We use simple models of the costs and benefits of microbial gene expression to show that changing a protein's expression away from its optimum by 2-fold should reduce fitness by at least [Formula: see text], where P is the fraction the cell's protein that the gene accounts for. As microbial genes are usually expressed at above 5 parts per million, and effective population sizes are likely to be above 10(6), this implies that 2-fold changes to gene expression levels are under strong selection, as [Formula: see text], where Ne is the effective population size and s is the selection coefficient.more » Thus, most gene duplications should be selected against. On the other hand, we predict that for most genes, small changes in the expression will be effectively neutral.« less

Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations

PubMed Central

Johnson, David K.; Karanicolas, John

2015-01-01

Small-molecules that inhibit interactions between specific pairs of proteins have long represented a promising avenue for therapeutic intervention in a variety of settings. Structural studies have shown that in many cases, the inhibitor-bound protein adopts a conformation that is distinct from its unbound and its protein-bound conformations. This plasticity of the protein surface presents a major challenge in predicting which members of a protein family will be inhibited by a given ligand. Here, we use biased simulations of Bcl-2-family proteins to generate ensembles of low-energy conformations that contain surface pockets suitable for small molecule binding. We find that the resulting conformational ensembles include surface pockets that mimic those observed in inhibitor-bound crystal structures. Next, we find that the ensembles generated using different members of this protein family are overlapping but distinct, and that the activity of a given compound against a particular family member (ligand selectivity) can be predicted from whether the corresponding ensemble samples a complementary surface pocket. Finally, we find that each ensemble includes certain surface pockets that are not shared by any other family member: while no inhibitors have yet been identified to take advantage of these pockets, we expect that chemical scaffolds complementing these “distinct” pockets will prove highly selective for their targets. The opportunity to achieve target selectivity within a protein family by exploiting differences in surface fluctuations represents a new paradigm that may facilitate design of family-selective small-molecule inhibitors of protein-protein interactions. PMID:25706586

Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS)

PubMed Central

Holman, Stephen W.; Hammond, Dean E.; Simpson, Deborah M.; Waters, John; Hurst, Jane L.

2016-01-01

Protein turnover represents an important mechanism in the functioning of cells, with deregulated synthesis and degradation of proteins implicated in many diseased states. Therefore, proteomics strategies to measure turnover rates with high confidence are of vital importance to understanding many biological processes. In this study, the more widely used approach of non-targeted precursor ion signal intensity (MS1) quantification is compared with selected reaction monitoring (SRM), a data acquisition strategy that records data for specific peptides, to determine if improved quantitative data would be obtained using a targeted quantification approach. Using mouse liver as a model system, turnover measurement of four tricarboxylic acid cycle proteins was performed using both MS1 and SRM quantification strategies. SRM outperformed MS1 in terms of sensitivity and selectivity of measurement, allowing more confident determination of protein turnover rates. SRM data are acquired using cheaper and more widely available tandem quadrupole mass spectrometers, making the approach accessible to a larger number of researchers than MS1 quantification, which is best performed on high mass resolution instruments. SRM acquisition is ideally suited to focused studies where the turnover of tens of proteins is measured, making it applicable in determining the dynamics of proteins complexes and complete metabolic pathways. This article is part of the themed issue ‘Quantitative mass spectrometry’. PMID:27644981

@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.

PubMed

Pons, Jean-Luc; Labesse, Gilles

2009-07-01

@TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/

Selective dye-labeling of newly synthesized proteins in bacterial cells.

PubMed

Beatty, Kimberly E; Xie, Fang; Wang, Qian; Tirrell, David A

2005-10-19

We describe fluorescence labeling of newly synthesized proteins in Escherichia coli cells by means of Cu(I)-catalyzed cycloaddition between alkynyl amino acid side chains and the fluorogenic dye 3-azido-7-hydroxycoumarin. The method involves co-translational labeling of proteins by the non-natural amino acids homopropargylglycine (Hpg) or ethynylphenylalanine (Eth) followed by treatment with the dye. As a demonstration, the model protein barstar was expressed and treated overnight with Cu(I) and 3-azido-7-hydroxycoumarin. Examination of treated cells by confocal microscopy revealed that strong fluorescence enhancement was observed only for alkynyl-barstar treated with Cu(I) and the reactive dye. The cellular fluorescence was punctate, and gel electrophoresis confirmed that labeled barstar was localized in inclusion bodies. Other proteins showed little fluorescence. Examination of treated cells by fluorimetry demonstrated that cultures supplemented with Eth or Hpg showed an 8- to 14-fold enhancement in fluorescence intensity after labeling. Addition of a protein synthesis inhibitor reduced the emission intensity to levels slightly above background, confirming selective labeling of newly synthesized proteins in the bacterial cell.

Nonparametric density estimation and optimal bandwidth selection for protein unfolding and unbinding data

NASA Astrophysics Data System (ADS)

Bura, E.; Zhmurov, A.; Barsegov, V.

2009-01-01

Dynamic force spectroscopy and steered molecular simulations have become powerful tools for analyzing the mechanical properties of proteins, and the strength of protein-protein complexes and aggregates. Probability density functions of the unfolding forces and unfolding times for proteins, and rupture forces and bond lifetimes for protein-protein complexes allow quantification of the forced unfolding and unbinding transitions, and mapping the biomolecular free energy landscape. The inference of the unknown probability distribution functions from the experimental and simulated forced unfolding and unbinding data, as well as the assessment of analytically tractable models of the protein unfolding and unbinding requires the use of a bandwidth. The choice of this quantity is typically subjective as it draws heavily on the investigator's intuition and past experience. We describe several approaches for selecting the "optimal bandwidth" for nonparametric density estimators, such as the traditionally used histogram and the more advanced kernel density estimators. The performance of these methods is tested on unimodal and multimodal skewed, long-tailed distributed data, as typically observed in force spectroscopy experiments and in molecular pulling simulations. The results of these studies can serve as a guideline for selecting the optimal bandwidth to resolve the underlying distributions from the forced unfolding and unbinding data for proteins.

Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement.

PubMed

Jiang, Hanlun; Sheong, Fu Kit; Zhu, Lizhe; Gao, Xin; Bernauer, Julie; Huang, Xuhui

2015-07-01

Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

MODBASE, a database of annotated comparative protein structure models

PubMed Central

Pieper, Ursula; Eswar, Narayanan; Stuart, Ashley C.; Ilyin, Valentin A.; Sali, Andrej

2002-01-01

MODBASE (http://guitar.rockefeller.edu/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on PSI-BLAST, IMPALA and MODELLER. MODBASE uses the MySQL relational database management system for flexible and efficient querying, and the MODVIEW Netscape plugin for viewing and manipulating multiple sequences and structures. It is updated regularly to reflect the growth of the protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different datasets. The largest dataset contains models for domains in 304 517 out of 539 171 unique protein sequences in the complete TrEMBL database (23 March 2001); only models based on significant alignments (PSI-BLAST E-value < 10–4) and models assessed to have the correct fold are included. Other datasets include models for target selection and structure-based annotation by the New York Structural Genomics Research Consortium, models for prediction of genes in the Drosophila melanogaster genome, models for structure determination of several ribosomal particles and models calculated by the MODWEB comparative modeling web server. PMID:11752309

The selectivity of protein-imprinted gels and its relation to protein properties: A computer simulation study.

PubMed

Yankelov, Rami; Yungerman, Irena; Srebnik, Simcha

2017-07-01

Polymer-based protein recognition systems have enormous potential within clinical and diagnostic fields due to their reusability, biocompatibility, ease of manufacturing, and potential specificity. Imprinted polymer matrices have been extensively studied and applied as a simple technique for creating artificial polymer-based recognition gels for a target molecule. Although this technique has been proven effective when targeting small molecules (such as drugs), imprinting of proteins have so far resulted in materials with limited selectivity due to the large molecular size of the protein and aqueous environment. Using coarse-grained molecular simulation, we investigate the relation between protein makeup, polymer properties, and the selectivity of imprinted gels. Nonspecific binding that results in poor selectivity is shown to be strongly dependent on surface chemistry of the template and competitor proteins as well as on polymer chemistry. Residence time distributions of proteins diffusing within the gels provide a transparent picture of the relation between polymer constitution, protein properties, and the nonspecific interactions with the imprinted gel. The pronounced effect of protein surface chemistry on imprinted gel specificity is demonstrated. Copyright © 2017 John Wiley & Sons, Ltd.

pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life.

PubMed

Spassov, Velin Z; Yan, Lisa

2013-04-01

Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein-protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism. Copyright © 2013 Wiley Periodicals, Inc.

Selective detection of target proteins by peptide-enabled graphene biosensor.

PubMed

Khatayevich, Dmitriy; Page, Tamon; Gresswell, Carolyn; Hayamizu, Yuhei; Grady, William; Sarikaya, Mehmet

2014-04-24

Direct molecular detection of biomarkers is a promising approach for diagnosis and monitoring of numerous diseases, as well as a cornerstone of modern molecular medicine and drug discovery. Currently, clinical applications of biomarkers are limited by the sensitivity, complexity and low selectivity of available indirect detection methods. Electronic 1D and 2D nano-materials such as carbon nanotubes and graphene, respectively, offer unique advantages as sensing substrates for simple, fast and ultrasensitive detection of biomolecular binding. Versatile methods, however, have yet to be developed for simultaneous functionalization and passivation of the sensor surface to allow for enhanced detection and selectivity of the device. Herein, we demonstrate selective detection of a model protein against a background of serum protein using a graphene sensor functionalized via self-assembling multifunctional short peptides. The two peptides are engineered to bind to graphene and undergo co-assembly in the form of an ordered monomolecular film on the substrate. While the probe peptide displays the bioactive molecule, the passivating peptide prevents non-specific protein adsorption onto the device surface, ensuring target selectivity. In particular, we demonstrate a graphene field effect transistor (gFET) biosensor which can detect streptavidin against a background of serum bovine albumin at less than 50 ng/ml. Our nano-sensor design, allows us to restore the graphene surface and utilize each sensor in multiple experiments. The peptide-enabled gFET device has great potential to address a variety of bio-sensing problems, such as studying ligand-receptor interactions, or detection of biomarkers in a clinical setting. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Selective autophagy mediated by autophagic adapter proteins

PubMed Central

Lamark, Trond

2011-01-01

Mounting evidence suggests that autophagy is a more selective process than originally anticipated. The discovery and characterization of autophagic adapters, like p62 and NBR1, has provided mechanistic insight into this process. p62 and NBR1 are both selectively degraded by autophagy and able to act as cargo receptors for degradation of ubiquitinated substrates. A direct interaction between these autophagic adapters and the autophagosomal marker protein LC3, mediated by a so-called LIR (LC3-interacting region) motif, their inherent ability to polymerize or aggregate as well as their ability to specifically recognize substrates are required for efficient selective autophagy. These three required features of autophagic cargo receptors are evolutionarily conserved and also employed in the yeast cytoplasm-to-vacuole targeting (Cvt) pathway and in the degradation of P granules in C. elegans. Here, we review the mechanistic basis of selective autophagy in mammalian cells discussing the degradation of misfolded proteins, p62 bodies, aggresomes, mitochondria and invading bacteria. The emerging picture of selective autophagy affecting the regulation of cell signaling with consequences for oxidative stress responses, tumorigenesis and innate immunity is also addressed. PMID:21189453

Models of protein-ligand crystal structures: trust, but verify.

PubMed

Deller, Marc C; Rupp, Bernhard

2015-09-01

X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.

Comparative Protein Structure Modeling Using MODELLER.

PubMed

Webb, Benjamin; Sali, Andrej

2014-09-08

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Copyright © 2014 John Wiley & Sons, Inc.

Purification-Free, Target-Selective Immobilization of a Protein from Cell Lysates.

PubMed

Cha, Jaehyun; Kwon, Inchan

2018-02-27

Protein immobilization has been widely used for laboratory experiments and industrial processes. Preparation of a recombinant protein for immobilization usually requires laborious and expensive purification steps. Here, a novel purification-free, target-selective immobilization technique of a protein from cell lysates is reported. Purification steps are skipped by immobilizing a target protein containing a clickable non-natural amino acid (p-azidophenylalanine) in cell lysates onto alkyne-functionalized solid supports via bioorthogonal azide-alkyne cycloaddition. In order to achieve a target protein-selective immobilization, p-azidophenylalanine was introduced into an exogenous target protein, but not into endogenous non-target proteins using host cells with amber codon-free genomic DNAs. Immobilization of superfolder fluorescent protein (sfGFP) from cell lysates is as efficient as that of the purified sfGFP. Using two fluorescent proteins (sfGFP and mCherry), the authors also demonstrated that the target proteins are immobilized with a minimal immobilization of non-target proteins (target-selective immobilization). © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Binding free energy analysis of protein-protein docking model structures by evERdock.

PubMed

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-14

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

Binding free energy analysis of protein-protein docking model structures by evERdock

NASA Astrophysics Data System (ADS)

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-01

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

A feature-based approach to modeling protein-protein interaction hot spots.

PubMed

Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-05-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

PubMed Central

CHENG, JIANLIN; EICKHOLT, JESSE; WANG, ZHENG; DENG, XIN

2013-01-01

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques — template-based modeling and template-free modeling — have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can signicantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction. PMID:22809379

Conformational selection in protein binding and function

PubMed Central

Weikl, Thomas R; Paul, Fabian

2014-01-01

Protein binding and function often involves conformational changes. Advanced nuclear magnetic resonance (NMR) experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may “select” protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transition times for ligand binding and unbinding that are small compared to the dwell times of proteins in different conformations, which is plausible for small ligand molecules. Such a separation of timescales leads to a decoupling and temporal ordering of binding/unbinding events and conformational changes. We propose that conformational-selection and induced-change processes (such as induced fit) are two sides of the same coin, because the temporal ordering is reversed in binding and unbinding direction. Conformational-selection processes can be characterized by a conformational excitation that occurs prior to a binding or unbinding event, while induced-change processes exhibit a characteristic conformational relaxation that occurs after a binding or unbinding event. We discuss how the ordering of events can be determined from relaxation rates and effective on- and off-rates determined in mixing experiments, and from the conformational exchange rates measured in advanced NMR or single-molecule fluorescence resonance energy transfer experiments. For larger ligand molecules such as peptides, conformational changes and binding events can be intricately coupled and exhibit aspects of conformational-selection and induced-change processes in both binding and unbinding direction. PMID:25155241

Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

NASA Astrophysics Data System (ADS)

Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

2018-01-01

The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

Computational Design of Ligand Binding Proteins with High Affinity and Selectivity

PubMed Central

Dou, Jiayi; Doyle, Lindsey; Nelson, Jorgen W.; Schena, Alberto; Jankowski, Wojciech; Kalodimos, Charalampos G.; Johnsson, Kai; Stoddard, Barry L.; Baker, David

2014-01-01

The ability to design proteins with high affinity and selectivity for any given small molecule would have numerous applications in biosensing, diagnostics, and therapeutics, and is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition phenomena. Attempts to design ligand binding proteins have met with little success, however, and the computational design of precise molecular recognition between proteins and small molecules remains an “unsolved problem”1. We describe a general method for the computational design of small molecule binding sites with pre-organized hydrogen bonding and hydrophobic interfaces and high overall shape complementary to the ligand, and use it to design protein binding sites for the steroid digoxigenin (DIG). Of 17 designs that were experimentally characterized, two bind DIG; the highest affinity design has the lowest predicted interaction energy and the most pre-organized binding site in the set. A comprehensive binding-fitness landscape of this design generated by library selection and deep sequencing was used to guide optimization of binding affinity to a picomolar level, and two X-ray co-crystal structures of optimized complexes show atomic level agreement with the design models. The designed binder has a high selectivity for DIG over the related steroids digitoxigenin, progesterone, and β-estradiol, which can be reprogrammed through the designed hydrogen-bonding interactions. Taken together, the binding fitness landscape, co-crystal structures, and thermodynamic binding parameters illustrate how increases in binding affinity can result from distal sequence changes that limit the protein ensemble to conformers making the most energetically favorable interactions with the ligand. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics, and diagnostics. PMID:24005320

Disulfide bridge regulates ligand-binding site selectivity in liver bile acid-binding proteins.

PubMed

Cogliati, Clelia; Tomaselli, Simona; Assfalg, Michael; Pedò, Massimo; Ferranti, Pasquale; Zetta, Lucia; Molinari, Henriette; Ragona, Laura

2009-10-01

Bile acid-binding proteins (BABPs) are cytosolic lipid chaperones that play central roles in driving bile flow, as well as in the adaptation to various pathological conditions, contributing to the maintenance of bile acid homeostasis and functional distribution within the cell. Understanding the mode of binding of bile acids with their cytoplasmic transporters is a key issue in providing a model for the mechanism of their transfer from the cytoplasm to the nucleus, for delivery to nuclear receptors. A number of factors have been shown to modulate bile salt selectivity, stoichiometry, and affinity of binding to BABPs, e.g. chemistry of the ligand, protein plasticity and, possibly, the formation of disulfide bridges. Here, the effects of the presence of a naturally occurring disulfide bridge on liver BABP ligand-binding properties and backbone dynamics have been investigated by NMR. Interestingly, the disulfide bridge does not modify the protein-binding stoichiometry, but has a key role in modulating recognition at both sites, inducing site selectivity for glycocholic and glycochenodeoxycholic acid. Protein conformational changes following the introduction of a disulfide bridge are small and located around the inner binding site, whereas significant changes in backbone motions are observed for several residues distributed over the entire protein, both in the apo form and in the holo form. Site selectivity appears, therefore, to be dependent on protein mobility rather than being governed by steric factors. The detected properties further establish a parallelism with the behaviour of human ileal BABP, substantiating the proposal that BABPs have parallel functions in hepatocytes and enterocytes.

Photoactivable antibody binding protein: site-selective and covalent coupling of antibody.

PubMed

Jung, Yongwon; Lee, Jeong Min; Kim, Jung-won; Yoon, Jeongwon; Cho, Hyunmin; Chung, Bong Hyun

2009-02-01

Here we report new photoactivable antibody binding proteins, which site-selectively capture antibodies and form covalent conjugates with captured antibodies upon irradiation. The proteins allow the site-selective tagging and/or immobilization of antibodies with a highly preferred orientation and omit the need for prior antibody modifications. The minimal Fc-binding domain of protein G, a widely used antibody binding protein, was genetically and chemically engineered to contain a site-specific photo cross-linker, benzophenone. In addition, the domain was further mutated to have an enhanced Fc-targeting ability. This small engineered protein was successfully cross-linked only to the Fc region of the antibody without any nonspecific reactivity. SPR analysis indicated that antibodies can be site-selectively biotinylated through the present photoactivable protein. Furthermore, the system enabled light-induced covalent immobilization of antibodies directly on various solid surfaces, such as those of glass slides, gold chips, and small particles. Antibody coupling via photoactivable antibody binding proteins overcomes several limitations of conventional approaches, such as random chemical reactions or reversible protein binding, and offers a versatile tool for the field of immunosensors.

Stability and the Evolvability of Function in a Model Protein

PubMed Central

Bloom, Jesse D.; Wilke, Claus O.; Arnold, Frances H.; Adami, Christoph

2004-01-01

Functional proteins must fold with some minimal stability to a structure that can perform a biochemical task. Here we use a simple model to investigate the relationship between the stability requirement and the capacity of a protein to evolve the function of binding to a ligand. Although our model contains no built-in tradeoff between stability and function, proteins evolved function more efficiently when the stability requirement was relaxed. Proteins with both high stability and high function evolved more efficiently when the stability requirement was gradually increased than when there was constant selection for high stability. These results show that in our model, the evolution of function is enhanced by allowing proteins to explore sequences corresponding to marginally stable structures, and that it is easier to improve stability while maintaining high function than to improve function while maintaining high stability. Our model also demonstrates that even in the absence of a fundamental biophysical tradeoff between stability and function, the speed with which function can evolve is limited by the stability requirement imposed on the protein. PMID:15111394

Application of RGS box proteins to evaluate G-protein selectivity in receptor-promoted signaling.

PubMed

Hains, Melinda D; Siderovski, David P; Harden, T Kendall

2004-01-01

Regulator of G-protein signaling (RGS) domains bind directly to GTP-bound Galpha subunits and accelerate their intrinsic GTPase activity by up to several thousandfold. The selectivity of RGS proteins for individual Galpha subunits has been illustrated. Thus, the expression of RGS proteins can be used to inhibit signaling pathways activated by specific G protein-coupled receptors (GPCRs). This article describes the use of specific RGS domain constructs to discriminate among G(i/o), Gq-and G(12/13)-mediated activation of phospholipase C (PLC) isozymes in COS-7 cells. Overexpression of the N terminus of GRK2 (amino acids 45-178) or p115 RhoGEF (amino acids 1-240) elicited selective inhibition of Galphaq- or Galpha(12/13)-mediated signaling to PLC activation, respectively. In contrast, RGS2 overexpression was found to inhibit PLC activation by both G(i/o)- and Gq-coupled GPCRs. RGS4 exhibited dramatic receptor selectivity in its inhibitory actions; of the G(i/o)- and Gq-coupled GPCRs tested (LPA1, LPA2, P2Y1, S1P3), only the Gq-coupled lysophosphatidic acid-activated LPA2 receptor was found to be inhibited by RGS4 overexpression.

Parallel cascade selection molecular dynamics for efficient conformational sampling and free energy calculation of proteins

NASA Astrophysics Data System (ADS)

Kitao, Akio; Harada, Ryuhei; Nishihara, Yasutaka; Tran, Duy Phuoc

2016-12-01

Parallel Cascade Selection Molecular Dynamics (PaCS-MD) was proposed as an efficient conformational sampling method to investigate conformational transition pathway of proteins. In PaCS-MD, cycles of (i) selection of initial structures for multiple independent MD simulations and (ii) conformational sampling by independent MD simulations are repeated until the convergence of the sampling. The selection is conducted so that protein conformation gradually approaches a target. The selection of snapshots is a key to enhance conformational changes by increasing the probability of rare event occurrence. Since the procedure of PaCS-MD is simple, no modification of MD programs is required; the selections of initial structures and the restart of the next cycle in the MD simulations can be handled with relatively simple scripts with straightforward implementation. Trajectories generated by PaCS-MD were further analyzed by the Markov state model (MSM), which enables calculation of free energy landscape. The combination of PaCS-MD and MSM is reported in this work.

Select host cell proteins coelute with monoclonal antibodies in protein A chromatography.

PubMed

Nogal, Bartek; Chhiba, Krishan; Emery, Jefferson C

2012-01-01

The most significant factor contributing to the presence of host cell protein (HCP) impurities in Protein A chromatography eluates is their association with the product monoclonal antibodies (mAbs) has been reported previously, and it has been suggested that more efficacious column washes may be developed by targeting the disruption of the mAbs-HCP interaction. However, characterization of this interaction is not straight forward as it is likely to involve multiple proteins and/or types of interaction. This work is an attempt to begin to understand the contribution of HCP subpopulations and/or mAb interaction propensity to the variability in HCP levels in the Protein A eluate. We performed a flowthrough (FT) recycling study with product respiking using two antibody molecules of apparently different HCP interaction propensities. In each case, the ELISA assay showed depletion of select subpopulations of HCP in Protein A eluates in subsequent column runs, while the feedstock HCP in the FTs remained unchanged from its native harvested cell culture fluid (HCCF) levels. In a separate study, the final FT from each molecule's recycling study was cross-spiked with various mAbs. In this case, Protein A eluate levels remained low for all but two molecules which were known as having high apparent HCP interaction propensity. The results of these studies suggest that mAbs may preferentially bind to select subsets of HCPs, and the degree of interaction and/or identity of the associated HCPs may vary depending on the mAb. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

Comparative Protein Structure Modeling Using MODELLER

PubMed Central

Webb, Benjamin; Sali, Andrej

2016-01-01

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. PMID:27322406

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

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

A model of autophagy size selectivity by receptor clustering on peroxisomes

NASA Astrophysics Data System (ADS)

Brown, Aidan I.; Rutenberg, Andrew D.

2017-05-01

Selective autophagy must not only select the correct type of organelle, but also must discriminate between individual organelles of the same kind so that some but not all of the organelles are removed. We propose that physical clustering of autophagy receptor proteins on the organelle surface can provide an appropriate all-or-none signal for organelle degradation. We explore this proposal using a computational model restricted to peroxisomes and the relatively well characterized pexophagy receptor proteins NBR1 and p62. We find that larger peroxisomes nucleate NBR1 clusters first and lose them last through competitive coarsening. This results in significant size-selectivity that favors large peroxisomes, and can explain the increased catalase signal that results from siRNA inhibition of p62. Excess ubiquitin, resulting from damaged organelles, suppresses size-selectivity but not cluster formation. Our proposed selectivity mechanism thus allows all damaged organelles to be degraded, while otherwise selecting only a portion of organelles for degradation.

Human long intrinsically disordered protein regions are frequent targets of positive selection.

PubMed

Afanasyeva, Arina; Bockwoldt, Mathias; Cooney, Christopher R; Heiland, Ines; Gossmann, Toni I

2018-06-01

Intrinsically disordered regions occur frequently in proteins and are characterized by a lack of a well-defined three-dimensional structure. Although these regions do not show a higher order of structural organization, they are known to be functionally important. Disordered regions are rapidly evolving, largely attributed to relaxed purifying selection and an increased role of genetic drift. It has also been suggested that positive selection might contribute to their rapid diversification. However, for our own species, it is currently unknown whether positive selection has played a role during the evolution of these protein regions. Here, we address this question by investigating the evolutionary pattern of more than 6600 human proteins with intrinsically disordered regions and their ordered counterparts. Our comparative approach with data from more than 90 mammalian genomes uses a priori knowledge of disordered protein regions, and we show that this increases the power to detect positive selection by an order of magnitude. We can confirm that human intrinsically disordered regions evolve more rapidly, not only within humans but also across the entire mammalian phylogeny. They have, however, experienced substantial evolutionary constraint, hinting at their fundamental functional importance. We find compelling evidence that disordered protein regions are frequent targets of positive selection and estimate that the relative rate of adaptive substitutions differs fourfold between disordered and ordered protein regions in humans. Our results suggest that disordered protein regions are important targets of genetic innovation and that the contribution of positive selection in these regions is more pronounced than in other protein parts. © 2018 Afanasyeva et al.; Published by Cold Spring Harbor Laboratory Press.

Discrete, continuous, and stochastic models of protein sorting in the Golgi apparatus

PubMed Central

Gong, Haijun; Guo, Yusong; Linstedt, Adam

2017-01-01

The Golgi apparatus plays a central role in processing and sorting proteins and lipids in eukaryotic cells. Golgi compartments constantly exchange material with each other and with other cellular components, allowing them to maintain and reform distinct identities despite dramatic changes in structure and size during cell division, development, and osmotic stress. We have developed three minimal models of membrane and protein exchange in the Golgi—a discrete, stochastic model, a continuous ordinary differential equation model, and a continuous stochastic differential equation model—each based on two fundamental mechanisms: vesicle-coat-mediated selective concentration of cargoes and soluble N-ethylmaleimide-sensitive factor attachment protein receptor SNARE proteins during vesicle formation and SNARE-mediated selective fusion of vesicles. By exploring where the models differ, we hope to discover whether the discrete, stochastic nature of vesicle-mediated transport is likely to have appreciable functional consequences for the Golgi. All three models show similar ability to restore and maintain distinct identities over broad parameter ranges. They diverge, however, in conditions corresponding to collapse and reassembly of the Golgi. The results suggest that a continuum model provides a good description of Golgi maintenance but that considering the discrete nature of vesicle-based traffic is important to understanding assembly and disassembly of the Golgi. Experimental analysis validates a prediction of the models that altering guanine nucleotide exchange factor expression levels will modulate Golgi size. PMID:20365406

Biophysical models of protein evolution: Understanding the patterns of evolutionary sequence divergence

PubMed Central

Echave, Julian; Wilke, Claus O.

2018-01-01

For decades, rates of protein evolution have been interpreted in terms of the vague concept of “functional importance”. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating them has large impacts on protein structure and stability. Here, we review the studies of the emergent field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field. PMID:28301766

Rhesus macaque and mouse models for down-selecting circumsporozoite protein based malaria vaccines differ significantly in immunogenicity and functional outcomes.

PubMed

Phares, Timothy W; May, Anthony D; Genito, Christopher J; Hoyt, Nathan A; Khan, Farhat A; Porter, Michael D; DeBot, Margot; Waters, Norman C; Saudan, Philippe; Dutta, Sheetij

2017-03-13

Non-human primates, such as the rhesus macaques, are the preferred model for down-selecting human malaria vaccine formulations, but the rhesus model is expensive and does not allow for direct efficacy testing of human malaria vaccines. Transgenic rodent parasites expressing genes of human Plasmodium are now routinely used for efficacy studies of human malaria vaccines. Mice have however rarely predicted success in human malaria trials and there is scepticism whether mouse studies alone are sufficient to move a vaccine candidate into the clinic. A comparison of immunogenicity, fine-specificity and functional activity of two Alum-adjuvanted Plasmodium falciparum circumsporozoite protein (CSP)-based vaccines was conducted in mouse and rhesus models. One vaccine was a soluble recombinant protein (CSP) and the other was the same CSP covalently conjugated to the Qβ phage particle (Qβ-CSP). Mice showed different kinetics of antibody responses and different sensitivity to the NANP-repeat and N-terminal epitopes as compared to rhesus. While mice failed to discern differences between the protective efficacy of CSP versus Qβ-CSP vaccine following direct challenge with transgenic Plasmodium berghei parasites, rhesus serum from the Qβ-CSP-vaccinated animals induced higher in vivo sporozoite neutralization activity. Despite some immunologic parallels between models, these data demonstrate that differences between the immune responses induced in the two models risk conflicting decisions regarding potential vaccine utility in humans. In combination with historical observations, the data presented here suggest that although murine models may be useful for some purposes, non-human primate models may be more likely to predict the human response to investigational vaccines.

Formylbenzene diazonium hexafluorophosphate reagent for tyrosine-selective modification of proteins and the introduction of a bioorthogonal aldehyde.

PubMed

Gavrilyuk, Julia; Ban, Hitoshi; Nagano, Masanobu; Hakamata, Wataru; Barbas, Carlos F

2012-12-19

4-Formylbenzene diazonium hexafluorophosphate (FBDP) is a novel bench-stable crystalline diazonium salt that reacts selectively with tyrosine to install a bioorthogonal aldehyde functionality. Model studies with N-acyl-tyrosine methylamide allowed us to identify conditions optimal for tyrosine ligation reactions with small peptides and proteins. FBDP-based conjugation was used for the facile introduction of small molecule tags, poly(ethylene glycol) chains (PEGylation), and functional small molecules onto model proteins and to label the surface of living cells.

Molecular modelling of protein-protein/protein-solvent interactions

NASA Astrophysics Data System (ADS)

Luchko, Tyler

The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule

A model in which heat shock protein 90 targets protein-folding clefts: rationale for a new approach to neuroprotective treatment of protein folding diseases.

PubMed

Pratt, William B; Morishima, Yoshihiro; Gestwicki, Jason E; Lieberman, Andrew P; Osawa, Yoichi

2014-11-01

In an EBM Minireview published in 2010, we proposed that the heat shock protein (Hsp)90/Hsp70-based chaperone machinery played a major role in determining the selection of proteins that have undergone oxidative or other toxic damage for ubiquitination and proteasomal degradation. The proposal was based on a model in which the Hsp90 chaperone machinery regulates signaling by modulating ligand-binding clefts. The model provides a framework for thinking about the development of neuroprotective therapies for protein-folding diseases like Alzheimer's disease (AD), Parkinson's disease (PD), and the polyglutamine expansion disorders, such as Huntington's disease (HD) and spinal and bulbar muscular atrophy (SBMA). Major aberrant proteins that misfold and accumulate in these diseases are "client" proteins of the abundant and ubiquitous stress chaperone Hsp90. These Hsp90 client proteins include tau (AD), α-synuclein (PD), huntingtin (HD), and the expanded glutamine androgen receptor (polyQ AR) (SBMA). In this Minireview, we update our model in which Hsp90 acts on protein-folding clefts and show how it forms a rational basis for developing drugs that promote the targeted elimination of these aberrant proteins. © 2014 by the Society for Experimental Biology and Medicine.

Dynamics of human protein kinase Aurora A linked to drug selectivity

DOE PAGES

Pitsawong, Warintra; Buosi, Vanessa; Otten, Renee; ...

2018-06-14

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinases Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Auroramore » A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome.« less

Dynamics of human protein kinase Aurora A linked to drug selectivity

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

Pitsawong, Warintra; Buosi, Vanessa; Otten, Renee

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinases Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Auroramore » A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome.« less

Formylbenzene diazonium hexafluorophosphate reagent for tyrosine-selective modification of proteins and the introduction of a bioorthogonal aldehyde

PubMed Central

Gavrilyuk, Julia; Ban, Hitoshi; Nagano, Masanobu; Hakamata, Wataru; Barbas, Carlos F.

2012-01-01

4-Formylbenzene diazonium hexafluorophosphate (FBDP) is a novel bench-stable crystalline diazonium salt that reacts selectively with tyrosine to install a bioorthogonal aldehyde functionality. Model studies with N-acyl-tyrosine methylamide allowed us to identify conditions optimal for tyrosine ligation reactions with small peptides and proteins. FBDP-based conjugation was used for the facile introduction of small molecule tags, poly(ethylene) glycol chains (PEGylation), and functional small molecules onto model proteins and to label the surface of living cells. PMID:23181702

Absolute Quantification of Selected Proteins in the Human Osteoarthritic Secretome

PubMed Central

Peffers, Mandy J.; Beynon, Robert J.; Clegg, Peter D.

2013-01-01

Osteoarthritis (OA) is characterized by a loss of extracellular matrix which is driven by catabolic cytokines. Proteomic analysis of the OA cartilage secretome enables the global study of secreted proteins. These are an important class of molecules with roles in numerous pathological mechanisms. Although cartilage studies have identified profiles of secreted proteins, quantitative proteomics techniques have been implemented that would enable further biological questions to be addressed. To overcome this limitation, we used the secretome from human OA cartilage explants stimulated with IL-1β and compared proteins released into the media using a label-free LC-MS/MS-based strategy. We employed QconCAT technology to quantify specific proteins using selected reaction monitoring. A total of 252 proteins were identified, nine were differentially expressed by IL-1 β stimulation. Selected protein candidates were quantified in absolute amounts using QconCAT. These findings confirmed a significant reduction in TIMP-1 in the secretome following IL-1β stimulation. Label-free and QconCAT analysis produced equivocal results indicating no effect of cytokine stimulation on aggrecan, cartilage oligomeric matrix protein, fibromodulin, matrix metalloproteinases 1 and 3 or plasminogen release. This study enabled comparative protein profiling and absolute quantification of proteins involved in molecular pathways pertinent to understanding the pathogenesis of OA. PMID:24132152

LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.

PubMed

Ma, Chao; Wang, Lirong; Yang, Peng; Myint, Kyaw Z; Xie, Xiang-Qun

2013-01-28

The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.

Lifetime of muscarinic receptor-G-protein complexes determines coupling efficiency and G-protein subtype selectivity.

PubMed

Ilyaskina, Olga S; Lemoine, Horst; Bünemann, Moritz

2018-05-08

G-protein-coupled receptors (GPCRs) are essential for the detection of extracellular stimuli by cells and transfer the encoded information via the activation of functionally distinct subsets of heterotrimeric G proteins into intracellular signals. Despite enormous achievements toward understanding GPCR structures, major aspects of the GPCR-G-protein selectivity mechanism remain unresolved. As this can be attributed to the lack of suitable and broadly applicable assays, we set out to develop a quantitative FRET-based assay to study kinetics and affinities of G protein binding to activated GPCRs in membranes of permeabilized cells in the absence of nucleotides. We measured the association and dissociation kinetics of agonist-induced binding of G i/o , G q/11 , G s , and G 12/13 proteins to muscarinic M 1 , M 2 , and M 3 receptors in the absence of nucleotides between fluorescently labeled G proteins and receptors expressed in mammalian cells. Our results show a strong quantitative correlation between not the on-rates of G-protein-M 3 -R interactions but rather the affinities of G q and G o proteins to M 3 -Rs, their GPCR-G-protein lifetime and their coupling efficiencies determined in intact cells, suggesting that the G-protein subtype-specific affinity to the activated receptor in the absence of nucleotides is, in fact, a major determinant of the coupling efficiency. Our broadly applicable FRET-based assay represents a fast and reliable method to quantify the intrinsic affinity and relative coupling selectivity of GPCRs toward all G-protein subtypes.

Modeling chain folding in protein-constrained circular DNA.

PubMed Central

Martino, J A; Olson, W K

1998-01-01

An efficient method for sampling equilibrium configurations of DNA chains binding one or more DNA-bending proteins is presented. The technique is applied to obtain the tertiary structures of minimal bending energy for a selection of dinucleosomal minichromosomes that differ in degree of protein-DNA interaction, protein spacing along the DNA chain contour, and ring size. The protein-bound portions of the DNA chains are represented by tight, left-handed supercoils of fixed geometry. The protein-free regions are modeled individually as elastic rods. For each random spatial arrangement of the two nucleosomes assumed during a stochastic search for the global minimum, the paths of the flexible connecting DNA segments are determined through a numerical solution of the equations of equilibrium for torsionally relaxed elastic rods. The minimal energy forms reveal how protein binding and spacing and plasmid size differentially affect folding and offer new insights into experimental minichromosome systems. PMID:9591675

Membrane proteins bind lipids selectively to modulate their structure and function.

PubMed

Laganowsky, Arthur; Reading, Eamonn; Allison, Timothy M; Ulmschneider, Martin B; Degiacomi, Matteo T; Baldwin, Andrew J; Robinson, Carol V

2014-06-05

Previous studies have established that the folding, structure and function of membrane proteins are influenced by their lipid environments and that lipids can bind to specific sites, for example, in potassium channels. Fundamental questions remain however regarding the extent of membrane protein selectivity towards lipids. Here we report a mass spectrometry approach designed to determine the selectivity of lipid binding to membrane protein complexes. We investigate the mechanosensitive channel of large conductance (MscL) from Mycobacterium tuberculosis and aquaporin Z (AqpZ) and the ammonia channel (AmtB) from Escherichia coli, using ion mobility mass spectrometry (IM-MS), which reports gas-phase collision cross-sections. We demonstrate that folded conformations of membrane protein complexes can exist in the gas phase. By resolving lipid-bound states, we then rank bound lipids on the basis of their ability to resist gas phase unfolding and thereby stabilize membrane protein structure. Lipids bind non-selectively and with high avidity to MscL, all imparting comparable stability; however, the highest-ranking lipid is phosphatidylinositol phosphate, in line with its proposed functional role in mechanosensation. AqpZ is also stabilized by many lipids, with cardiolipin imparting the most significant resistance to unfolding. Subsequently, through functional assays we show that cardiolipin modulates AqpZ function. Similar experiments identify AmtB as being highly selective for phosphatidylglycerol, prompting us to obtain an X-ray structure in this lipid membrane-like environment. The 2.3 Å resolution structure, when compared with others obtained without lipid bound, reveals distinct conformational changes that re-position AmtB residues to interact with the lipid bilayer. Our results demonstrate that resistance to unfolding correlates with specific lipid-binding events, enabling a distinction to be made between lipids that merely bind from those that modulate membrane

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

[Crystal structure of SMU.2055 protein from Streptococcus mutans and its small molecule inhibitors design and selection].

PubMed

Xiaodan, Chen; Xiurong, Zhan; Xinyu, Wu; Chunyan, Zhao; Wanghong, Zhao

2015-04-01

The aim of this study is to analyze the three-dimensional crystal structure of SMU.2055 protein, a putative acetyltransferase from the major caries pathogen Streptococcus mutans (S. mutans). The design and selection of the structure-based small molecule inhibitors are also studied. The three-dimensional crystal structure of SMU.2055 protein was obtained by structural genomics research methods of gene cloning and expression, protein purification with Ni²⁺-chelating affinity chromatography, crystal screening, and X-ray diffraction data collection. An inhibitor virtual model matching with its target protein structure was set up using computer-aided drug design methods, virtual screening and fine docking, and Libdock and Autodock procedures. The crystal of SMU.2055 protein was obtained, and its three-dimensional crystal structure was analyzed. This crystal was diffracted to a resolution of 0.23 nm. It belongs to orthorhombic space group C222(1), with unit cell parameters of a = 9.20 nm, b = 9.46 nm, and c = 19.39 nm. The asymmetric unit contained four molecules, with a solvent content of 56.7%. Moreover, five small molecule compounds, whose structure matched with that of the target protein in high degree, were designed and selected. Protein crystallography research of S. mutans SMU.2055 helps to understand the structures and functions of proteins from S. mutans at the atomic level. These five compounds may be considered as effective inhibitors to SMU.2055. The virtual model of small molecule inhibitors we built will lay a foundation to the anticaries research based on the crystal structure of proteins.

Site-selective protein-modification chemistry for basic biology and drug development.

PubMed

Krall, Nikolaus; da Cruz, Filipa P; Boutureira, Omar; Bernardes, Gonçalo J L

2016-02-01

Nature has produced intricate machinery to covalently diversify the structure of proteins after their synthesis in the ribosome. In an attempt to mimic nature, chemists have developed a large set of reactions that enable post-expression modification of proteins at pre-determined sites. These reactions are now used to selectively install particular modifications on proteins for many biological and therapeutic applications. For example, they provide an opportunity to install post-translational modifications on proteins to determine their exact biological roles. Labelling of proteins in live cells with fluorescent dyes allows protein uptake and intracellular trafficking to be tracked and also enables physiological parameters to be measured optically. Through the conjugation of potent cytotoxicants to antibodies, novel anti-cancer drugs with improved efficacy and reduced side effects may be obtained. In this Perspective, we highlight the most exciting current and future applications of chemical site-selective protein modification and consider which hurdles still need to be overcome for more widespread use.

Site-selective protein-modification chemistry for basic biology and drug development

NASA Astrophysics Data System (ADS)

Krall, Nikolaus; da Cruz, Filipa P.; Boutureira, Omar; Bernardes, Gonçalo J. L.

2016-02-01

Nature has produced intricate machinery to covalently diversify the structure of proteins after their synthesis in the ribosome. In an attempt to mimic nature, chemists have developed a large set of reactions that enable post-expression modification of proteins at pre-determined sites. These reactions are now used to selectively install particular modifications on proteins for many biological and therapeutic applications. For example, they provide an opportunity to install post-translational modifications on proteins to determine their exact biological roles. Labelling of proteins in live cells with fluorescent dyes allows protein uptake and intracellular trafficking to be tracked and also enables physiological parameters to be measured optically. Through the conjugation of potent cytotoxicants to antibodies, novel anti-cancer drugs with improved efficacy and reduced side effects may be obtained. In this Perspective, we highlight the most exciting current and future applications of chemical site-selective protein modification and consider which hurdles still need to be overcome for more widespread use.

Membrane Compartmentalization Reducing the Mobility of Lipids and Proteins within a Model Plasma Membrane.

PubMed

Koldsø, Heidi; Reddy, Tyler; Fowler, Philip W; Duncan, Anna L; Sansom, Mark S P

2016-09-01

The cytoskeleton underlying cell membranes may influence the dynamic organization of proteins and lipids within the bilayer by immobilizing certain transmembrane (TM) proteins and forming corrals within the membrane. Here, we present coarse-grained resolution simulations of a biologically realistic membrane model of asymmetrically organized lipids and TM proteins. We determine the effects of a model of cytoskeletal immobilization of selected membrane proteins using long time scale coarse-grained molecular dynamics simulations. By introducing compartments with varying degrees of restraints within the membrane models, we are able to reveal how compartmentalization caused by cytoskeletal immobilization leads to reduced and anomalous diffusional mobility of both proteins and lipids. This in turn results in a reduced rate of protein dimerization within the membrane and of hopping of membrane proteins between compartments. These simulations provide a molecular realization of hierarchical models often invoked to explain single-molecule imaging studies of membrane proteins.

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification

PubMed Central

Murphy, Patrick J. M.; Stone, Orrin J.; Anderson, Michelle E.

2011-01-01

should be employed for future, more exhaustive optimization experiments and protein purification runs 4. The specific protein being purified here is recombinant green fluorescent protein (GFP); however, the approach may be adapted for purifying other proteins with one or more hydrophobic surface regions. GFP serves as a useful model protein, due to its stability, unique light absorbance peak at 397 nm, and fluorescence when exposed to UV light 5. Bacterial lysate containing wild type GFP was prepared in a high-salt buffer, loaded into a Bio-Rad DuoFlow medium pressure liquid chromatography system, and adsorbed to HiTrap HIC columns containing different HIC media. The protein was eluted from the columns and analyzed by in-line and post-run detection methods. Buffer blending, dynamic sample loop injection, sequential column selection, multi-wavelength analysis, and split fraction eluate collection increased the functionality of the system and reproducibility of the experimental approach. PMID:21968976

Protocols for efficient simulations of long-time protein dynamics using coarse-grained CABS model.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2014-01-01

Coarse-grained (CG) modeling is a well-acknowledged simulation approach for getting insight into long-time scale protein folding events at reasonable computational cost. Depending on the design of a CG model, the simulation protocols vary from highly case-specific-requiring user-defined assumptions about the folding scenario-to more sophisticated blind prediction methods for which only a protein sequence is required. Here we describe the framework protocol for the simulations of long-term dynamics of globular proteins, with the use of the CABS CG protein model and sequence data. The simulations can start from a random or a selected (e.g., native) structure. The described protocol has been validated using experimental data for protein folding model systems-the prediction results agreed well with the experimental results.

Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

PubMed

Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

2017-12-15

Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

Analysis of differential selective forces acting on the coat protein (P3) of the plant virus family Luteoviridae.

PubMed

Torres, Marina W; Corrêa, Régis L; Schrago, Carlos G

2005-12-30

The coat protein (CP) of the family Luteoviridae is directly associated with the success of infection. It participates in various steps of the virus life cycle, such as virion assembly, stability, systemic infection, and transmission. Despite its importance, extensive studies on the molecular evolution of this protein are lacking. In the present study, we investigate the action of differential selective forces on the CP coding region using maximum likelihood methods. We found that the protein is subjected to heterogeneous selective pressures and some sites may be evolving near neutrality. Based on the proposed 3-D model of the CP S-domain, we showed that nearly neutral sites are predominantly located in the region of the protein that faces the interior of the capsid, in close contact with the viral RNA, while highly conserved sites are mainly part of beta-strands, in the protein's major framework.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

The PMDB Protein Model Database

PubMed Central

Castrignanò, Tiziana; De Meo, Paolo D'Onorio; Cozzetto, Domenico; Talamo, Ivano Giuseppe; Tramontano, Anna

2006-01-01

The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins. The database is designed to provide access to models published in the scientific literature, together with validating experimental data. It is a relational database and it currently contains >74 000 models for ∼240 proteins. The system is accessible at and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface. Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein. Each model is assigned a unique identifier that allows interested users to directly access the data. PMID:16381873

Effects of protein transduction domain (PTD) selection and position for improved intracellular delivery of PTD-Hsp27 fusion protein formulations.

PubMed

Ul Ain, Qurrat; Lee, Jong Hwan; Woo, Young Sun; Kim, Yong-Hee

2016-09-01

Protein drugs have attracted considerable attention as therapeutic agents due to their diversity and biocompatibility. However, hydrophilic proteins possess difficulty in penetrating lipophilic cell membrane. Although protein transduction domains (PTDs) have shown effectiveness in protein delivery, the importance of selection and position of PTDs in recombinant protein vector constructs has not been investigated. This study intends to investigate the significance of PTD selection and position for therapeutic protein delivery. Heat shock protein 27 (Hsp27) would be a therapeutic protein for the treatment of ischemic heart diseases, but itself is insufficient to prevent systemic degradation and overcoming biochemical barriers during cellular transport. Among all PTD-Hsp27 fusion proteins we cloned, Tat-Hsp27 fusion protein showed the highest efficacy. Nona-arginine (9R) conjugation to the N-terminal of Hsp27 (Hsp27-T) showed higher efficacy than C-terminal. To test the synergistic effect of two PTDs, Tat was inserted to the N-terminal of Hsp27-9R. Tat-Hsp27-9R exhibited enhanced transduction efficiency and significant improvement against oxidative stress and apoptosis. PTD-Hsp27 fusion proteins have strong potential to be developed as therapeutic proteins for the treatment of ischemic heart diseases and selection and position of PTDs for improved efficacy of PTD-fusion proteins need to be optimized considering protein's nature, transduction efficiency and stability.

Widespread Positive Selection Drives Differentiation of Centromeric Proteins in the Drosophila melanogaster subgroup.

PubMed

Beck, Emily A; Llopart, Ana

2015-11-25

Rapid evolution of centromeric satellite repeats is thought to cause compensatory amino acid evolution in interacting centromere-associated kinetochore proteins. Cid, a protein that mediates kinetochore/centromere interactions, displays particularly high amino acid turnover. Rapid evolution of both Cid and centromeric satellite repeats led us to hypothesize that the apparent compensatory evolution may extend to interacting partners in the Condensin I complex (i.e., SMC2, SMC4, Cap-H, Cap-D2, and Cap-G) and HP1s. Missense mutations in these proteins often result in improper centromere formation and aberrant chromosome segregation, thus selection for maintained function and coevolution among proteins of the complex is likely strong. Here, we report evidence of rapid evolution and recurrent positive selection in seven centromere-associated proteins in species of the Drosophila melanogaster subgroup, and further postulate that positive selection on these proteins could be a result of centromere drive and compensatory changes, with kinetochore proteins competing for optimal spindle attachment.

Computer Models of Proteins

NASA Technical Reports Server (NTRS)

2000-01-01

Dr. Marc Pusey (seated) and Dr. Craig Kundrot use computers to analyze x-ray maps and generate three-dimensional models of protein structures. With this information, scientists at Marshall Space Flight Center can learn how proteins are made and how they work. The computer screen depicts a proten structure as a ball-and-stick model. Other models depict the actual volume occupied by the atoms, or the ribbon-like structures that are crucial to a protein's function.

A Discontinuous Potential Model for Protein-Protein Interactions.

PubMed

Shao, Qing; Hall, Carol K

2016-01-01

Protein-protein interactions play an important role in many biologic and industrial processes. In this work, we develop a two-bead-per-residue model that enables us to account for protein-protein interactions in a multi-protein system using discontinuous molecular dynamics simulations. This model deploys discontinuous potentials to describe the non-bonded interactions and virtual bonds to keep proteins in their native state. The geometric and energetic parameters are derived from the potentials of mean force between sidechain-sidechain, sidechain-backbone, and backbone-backbone pairs. The energetic parameters are scaled with the aim of matching the second virial coefficient of lysozyme reported in experiment. We also investigate the performance of several bond-building strategies.

Selective precipitation and purification of monovalent proteins using oligovalent ligands and ammonium sulfate.

PubMed

Mirica, Katherine A; Lockett, Matthew R; Snyder, Phillip W; Shapiro, Nathan D; Mack, Eric T; Nam, Sarah; Whitesides, George M

2012-02-15

This paper describes a method for the selective precipitation and purification of a monovalent protein (carbonic anhydrase is used as a demonstration) from cellular lysate using ammonium sulfate and oligovalent ligands. The oligovalent ligands induce the formation of protein-ligand aggregates, and at an appropriate concentration of dissolved ammonium sulfate, these complexes precipitate. The purification involves three steps: (i) the removal of high-molecular-weight impurities through the addition of ammonium sulfate to the crude cell lysate; (ii) the introduction of an oligovalent ligand and the selective precipitation of the target protein-ligand aggregates from solution; and (iii) the removal of the oligovalent ligand from the precipitate by dialysis to release the target protein. The increase of mass and volume of the proteins upon aggregate formation reduces their solubility, and results in the selective precipitation of these aggregates. We recovered human carbonic anhydrase, from crude cellular lysate, in 82% yield and 95% purity with a trivalent benzene sulfonamide ligand. This method provides a chromatography-free strategy of purifying monovalent proteins--for which appropriate oligovalent ligands can be synthesized--and combines the selectivity of affinity-based purification with the convenience of salt-induced precipitation.

Modeling of chemical inhibition from amyloid protein aggregation kinetics.

PubMed

Vázquez, José Antonio

2014-02-27

The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. In this manuscript, experimental data of insulin, Aβ42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH = 7.4/T = 37°C) and taiwaniaflavone for Aβ42 were the compounds studied that shown the greatest inhibition capacity. An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

Selected wheat seed defense proteins exhibit competitive binding to model microbial lipid interfaces.

PubMed

Sanders, Michael R; Clifton, Luke A; Neylon, Cameron; Frazier, Richard A; Green, Rebecca J

2013-07-17

Puroindolines (Pins) and purothionins (Pths) are basic, amphiphilic, cysteine-rich wheat proteins that play a role in plant defense against microbial pathogens. This study examined the co-adsorption and sequential addition of Pins (Pin-a, Pin-b, and a mutant form of Pin-b with Trp-44 to Arg-44 substitution) and β-purothionin (β-Pth) model anionic lipid layers using a combination of surface pressure measurements, external reflection FTIR spectroscopy, and neutron reflectometry. Results highlighted differences in the protein binding mechanisms and in the competitive binding and penetration of lipid layers between respective Pins and β-Pth. Pin-a formed a blanket-like layer of protein below the lipid surface that resulted in the reduction or inhibition of β-Pth penetration of the lipid layer. Wild-type Pin-b participated in co-operative binding with β-Pth, whereas the mutant Pin-b did not bind to the lipid layer in the presence of β-Pth. The results provide further insight into the role of hydrophobic and cationic amino acid residues in antimicrobial activity.

Exploiting translational coupling for the selection of cells producing toxic recombinant proteins from expression vectors.

PubMed

Tagliavia, Marcello; Cuttitta, Angela

2016-01-01

High rates of plasmid instability are associated with the use of some expression vectors in Escherichia coli, resulting in the loss of recombinant protein expression. This is due to sequence alterations in vector promoter elements caused by the background expression of the cloned gene, which leads to the selection of fast-growing, plasmid-containing cells that do not express the target protein. This phenomenon, which is worsened when expressing toxic proteins, results in preparations containing very little or no recombinant protein, or even in clone loss; however, no methods to prevent loss of recombinant protein expression are currently available. We have exploited the phenomenon of translational coupling, a mechanism of prokaryotic gene expression regulation, in order to select cells containing plasmids still able to express recombinant proteins. Here we designed an expression vector in which the cloned gene and selection marker are co-expressed. Our approach allowed for the selection of the recombinant protein-expressing cells and proved effective even for clones encoding toxic proteins.

Data-assisted protein structure modeling by global optimization in CASP12.

PubMed

Joo, Keehyoung; Heo, Seungryong; Joung, InSuk; Hong, Seung Hwan; Lee, Sung Jong; Lee, Jooyoung

2018-03-01

In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling. © 2018 Wiley Periodicals, Inc.

Selectivity of protein oxidative damage during aging in Drosophila melanogaster.

PubMed Central

Das, N; Levine, R L; Orr, W C; Sohal, R S

2001-01-01

The purpose of the present study was to determine whether oxidation of various proteins during the aging process occurs selectively or randomly, and whether the same proteins are damaged in different species. Protein oxidative damage to the proteins, present in the matrix of mitochondria in the flight muscles of Drosophila melanogaster and manifested as carbonyl modifications, was detected immunochemically with anti-dinitrophenyl-group antibodies. Aconitase was found to be the only protein in the mitochondrial matrix that exhibited an age-associated increase in carbonylation. The accrual of oxidative damage was accompanied by an approx. 50% loss in aconitase activity. An increase in ambient temperature, which elevates the rate of metabolism and shortens the life span of flies, caused an elevation in the amount of aconitase carbonylation and an accelerated loss in its activity. Exposure to 100% ambient oxygen showed that aconitase was highly susceptible to undergo oxidative damage and loss of activity under oxidative stress. Administration of fluoroacetate, a competitive inhibitor of aconitase activity, resulted in a dose-dependent decrease in the life span of the flies. Results of the present study demonstrate that protein oxidative damage during aging is a selective phenomenon, and might constitute a mechanism by which oxidative stress causes age-associated losses in specific biochemical functions. PMID:11696009

Selective sweeps in Cryptocercus woodroach antifungal proteins.

PubMed

Velenovsky, Joseph F; Kalisch, Jessica; Bulmer, Mark S

2016-10-01

We identified the antifungal gene termicin in three species of Cryptocercus woodroaches. Cryptocercus represents the closest living cockroach lineage of termites, which suggests that the antifungal role of termicin evolved prior to the divergence of termites from other cockroaches. An analysis of Cryptocercus termicin and two β-1,3-glucanase genes (GNBP1 and GNBP2), which appear to work synergistically with termicin in termites, revealed evidence of selection in these proteins. We identified the signature of past selective sweeps within GNBP2 from Cryptocercus punctulatus and Cryptocercus wrighti. The signature of past selective sweeps was also found within termicin from Cryptocercus punctulatus and Cryptocercus darwini. Our analysis further suggests a phenotypically identical variant of GNBP2 was maintained within Cryptocercus punctulatus, Cryptocercus wrighti, and Cryptocercus darwini while synonymous sites diverged. Cryptocercus termicin and GNBP2 appear to have experienced similar selective pressure to that of their termite orthologues in Reticulitermes. This selective pressure may be a result of ubiquitous entomopathogenic fungal pathogens such as Metarhizium. This study further reveals the similarities between Cryptocercus woodroaches and termites.

Protein Models Docking Benchmark 2

PubMed Central

Anishchenko, Ivan; Kundrotas, Petras J.; Tuzikov, Alexander V.; Vakser, Ilya A.

2015-01-01

Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have pre-defined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native Cα RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the “real case scenario,” as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu. PMID:25712716

Responsive Photonic Crystal Carbohydrate Hydrogel Sensor Materials for Selective and Sensitive Lectin Protein Detection.

PubMed

Cai, Zhongyu; Sasmal, Aniruddha; Liu, Xinyu; Asher, Sanford A

2017-10-27

Lectin proteins, such as the highly toxic lectin protein, ricin, and the immunochemically important lectin, jacalin, play significant roles in many biological functions. It is highly desirable to develop a simple but efficient method to selectively detect lectin proteins. Here we report the development of carbohydrate containing responsive hydrogel sensing materials for the selective detection of lectin proteins. The copolymerization of a vinyl linked carbohydrate monomer with acrylamide and acrylic acid forms a carbohydrate hydrogel that shows specific "multivalent" binding to lectin proteins. The resulting carbohydrate hydrogels are attached to 2-D photonic crystals (PCs) that brightly diffract visible light. This diffraction provides an optical readout that sensitively monitors the hydrogel volume. We utilize lactose, galactose, and mannose containing hydrogels to fabricate a series of 2-D PC sensors that show strong selective binding to the lectin proteins ricin, jacalin, and concanavalin A (Con A). This binding causes a carbohydrate hydrogel shrinkage which significantly shifts the diffraction wavelength. The resulting 2-D PC sensors can selectively detect the lectin proteins ricin, jacalin, and Con A. These unoptimized 2-D PC hydrogel sensors show a limit of detection (LoD) of 7.5 × 10 -8 M for ricin, a LoD of 2.3 × 10 -7 M for jacalin, and a LoD of 3.8 × 10 -8 M for Con A, respectively. This sensor fabrication approach may enable numerous sensors for the selective detection of numerous lectin proteins.

Coarse-Grained Simulations of Membrane Insertion and Folding of Small Helical Proteins Using the CABS Model.

PubMed

Pulawski, Wojciech; Jamroz, Michal; Kolinski, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2016-11-28

The CABS coarse-grained model is a well-established tool for modeling globular proteins (predicting their structure, dynamics, and interactions). Here we introduce an extension of the CABS representation and force field (CABS-membrane) to the modeling of the effect of the biological membrane environment on the structure of membrane proteins. We validate the CABS-membrane model in folding simulations of 10 short helical membrane proteins not using any knowledge about their structure. The simulations start from random protein conformations placed outside the membrane environment and allow for full flexibility of the modeled proteins during their spontaneous insertion into the membrane. In the resulting trajectories, we have found models close to the experimental membrane structures. We also attempted to select the correctly folded models using simple filtering followed by structural clustering combined with reconstruction to the all-atom representation and all-atom scoring. The CABS-membrane model is a promising approach for further development toward modeling of large protein-membrane systems.

Protein Hydrogel Microbeads for Selective Uranium Mining from Seawater.

PubMed

Kou, Songzi; Yang, Zhongguang; Sun, Fei

2017-01-25

Practical methods for oceanic uranium extraction have yet to be developed in order to tap into the vast uranium reserve in the ocean as an alternative energy. Here we present a protein hydrogel system containing a network of recently engineered super uranyl binding proteins (SUPs) that is assembled through thiol-maleimide click chemistry under mild conditions. Monodisperse SUP hydrogel microbeads fabricated by a microfluidic device further enable uranyl (UO 2 2+ ) enrichment from natural seawater with great efficiency (enrichment index, K = 2.5 × 10 3 ) and selectivity. Our results demonstrate the feasibility of using protein hydrogels to extract uranium from the ocean.

Functional selectivity of G-protein-coupled receptors: from recombinant systems to native human cells.

PubMed

Seifert, Roland

2013-10-01

In the mid 1990s, it was assumed that a two-state model, postulating an inactive (R) state and an active (R*) state provides the molecular basis for GPCR activation. However, it became clear that this model could not accommodate many experimental observations. Accordingly, the two-state model was superseded by a multi-state model according to which any given ligand stabilizes a unique receptor conformation with distinct capabilities of activating down-stream G-proteins and β-arrestin. Much of this research was conducted with the β2-adrenoceptor in recombinant systems. At the molecular level, there is now no doubt anymore that ligand-specific receptor conformations, also referred to as functional selectivity, exist. This concept holds great potential for drug discovery in terms of developing drugs with higher selectivity for specific cells and/or cell functions and fewer side effects. A major challenge is the analysis for functional selectivity in native cells. Here, I discuss our current knowledge on functional selectivity of three representative GPCRs, the β2-adrenoceptor and the histamine H2- and H4-receptors, in recombinant systems and native human cells. Studies with human neutrophils and eosinophils support the concept of functional selectivity. A major strategy for the analysis of functional selectivity in native cells is to generate complete concentration/response curves with a large set of structurally diverse ligands for multiple parameters. Next, correlations of potencies and efficacies are analyzed, and deviations of the correlations from linearity are indicative for functional selectivity. Additionally, pharmacological inhibitors are used to dissect cell functions from each other. Copyright © 2013 Elsevier Inc. All rights reserved.

Selective Precipitation and Purification of Monovalent Proteins Using Oligovalent Ligands and Ammonium Sulfate

PubMed Central

Mirica, Katherine A.; Lockett, Matthew R.; Snyder, Phillip W.; Shapiro, Nathan D.; Mack, Eric T.; Nam, Sarah; Whitesides, George M.

2012-01-01

This paper describes a method for the selective precipitation and purification of a monovalent protein (carbonic anhydrase is used as a demonstration) from cellular lysate using ammonium sulfate and oligovalent ligands. The oligovalent ligands induce the formation of protein-ligand aggregates, and at an appropriate concentration of dissolved ammonium sulfate, these complexes precipitate. The purification involves three steps: i) the removal of high-molecular weight impurities through the addition of ammonium sulfate to the crude cell lysate; ii) the introduction of an oligovalent ligand and the selective precipitation of the target protein-ligand aggregates from solution; and iii) the removal of the oligovalent ligand from the precipitate by dialysis to release the target protein. The increase of mass and volume of the proteins upon aggregate formation reduces their solubility, and results in the selective precipitation of these aggregates. We recovered human carbonic anhydrase, from crude cellular lysate, in 82% yield and 95% purity with a trivalent benzene sulfonamide ligand. This method provides a chromatography-free strategy of purifying monovalent proteins—for which appropriate oligovalent ligands can be synthesized—and combines the selectivity of affinity-based purification with the convenience of salt-induced precipitation. PMID:22188202

Spatial structure of disordered proteins dictates conductance and selectivity in nuclear pore complex mimics

PubMed Central

Frey, Steffen; Dwarkasing, Arvind; Versloot, Roderick; van der Giessen, Erik

2018-01-01

Nuclear pore complexes (NPCs) lined with intrinsically disordered FG-domains act as selective gatekeepers for molecular transport between the nucleus and the cytoplasm in eukaryotic cells. The underlying physical mechanism of the intriguing selectivity is still under debate. Here, we probe the transport of ions and transport receptors through biomimetic NPCs consisting of Nsp1 domains attached to the inner surface of solid-state nanopores. We examine both wildtype FG-domains and hydrophilic SG-mutants. FG-nanopores showed a clear selectivity as transport receptors can translocate across the pore whereas other proteins cannot. SG mutant pores lack such selectivity. To unravel this striking difference, we present coarse-grained molecular dynamics simulations that reveal that FG-pores exhibit a high-density, nonuniform protein distribution, in contrast to a uniform and significantly less-dense protein distribution in the SG-mutant. We conclude that the sequence-dependent density distribution of disordered proteins inside the NPC plays a key role for its conductivity and selective permeability. PMID:29442997

Positive selection in the N-terminal extramembrane domain of lung surfactant protein C (SP-C) in marine mammals.

PubMed

Foot, Natalie J; Orgeig, Sandra; Donnellan, Stephen; Bertozzi, Terry; Daniels, Christopher B

2007-07-01

Maximum-likelihood models of codon and amino acid substitution were used to analyze the lung-specific surfactant protein C (SP-C) from terrestrial, semi-aquatic, and diving mammals to identify lineages and amino acid sites under positive selection. Site models used the nonsynonymous/synonymous rate ratio (omega) as an indicator of selection pressure. Mechanistic models used physicochemical distances between amino acid substitutions to specify nonsynonymous substitution rates. Site models strongly identified positive selection at different sites in the polar N-terminal extramembrane domain of SP-C in the three diving lineages: site 2 in the cetaceans (whales and dolphins), sites 7, 9, and 10 in the pinnipeds (seals and sea lions), and sites 2, 9, and 10 in the sirenians (dugongs and manatees). The only semi-aquatic contrast to indicate positive selection at site 10 was that including the polar bear, which had the largest body mass of the semi-aquatic species. Analysis of the biophysical properties that were influential in determining the amino acid substitutions showed that isoelectric point, chemical composition of the side chain, polarity, and hydrophobicity were the crucial determinants. Amino acid substitutions at these sites may lead to stronger binding of the N-terminal domain to the surfactant phospholipid film and to increased adsorption of the protein to the air-liquid interface. Both properties are advantageous for the repeated collapse and reinflation of the lung upon diving and resurfacing and may reflect adaptations to the high hydrostatic pressures experienced during diving.

In vitro Selection and Interaction Studies of a DNA Aptamer Targeting Protein A

PubMed Central

Stoltenburg, Regina; Schubert, Thomas; Strehlitz, Beate

2015-01-01

A new DNA aptamer targeting Protein A is presented. The aptamer was selected by use of the FluMag-SELEX procedure. The SELEX technology (Systematic Evolution of Ligands by EXponential enrichment) is widely applied as an in vitro selection and amplification method to generate target-specific aptamers and exists in various modified variants. FluMag-SELEX is one of them and is characterized by the use of magnetic beads for target immobilization and fluorescently labeled oligonucleotides for monitoring the aptamer selection progress. Structural investigations and sequence truncation experiments of the selected aptamer for Protein A led to the conclusion, that a stem-loop structure at its 5’-end including the 5’-primer binding site is essential for aptamer-target binding. Extensive interaction analyses between aptamer and Protein A were performed by methods like surface plasmon resonance, MicroScale Thermophoresis and bead-based binding assays using fluorescence measurements. The binding of the aptamer to its target was thus investigated in assays with immobilization of one of the binding partners each, and with both binding partners in solution. Affinity constants were determined in the low micromolar to submicromolar range, increasing to the nanomolar range under the assumption of avidity. Protein A provides more than one binding site for the aptamer, which may overlap with the known binding sites for immunoglobulins. The aptamer binds specifically to both native and recombinant Protein A, but not to other immunoglobulin-binding proteins like Protein G and L. Cross specificity to other proteins was not found. The application of the aptamer is directed to Protein A detection or affinity purification. Moreover, whole cells of Staphylococcus aureus, presenting Protein A on the cell surface, could also be bound by the aptamer. PMID:26221730

In vitro Selection and Interaction Studies of a DNA Aptamer Targeting Protein A.

PubMed

Stoltenburg, Regina; Schubert, Thomas; Strehlitz, Beate

2015-01-01

A new DNA aptamer targeting Protein A is presented. The aptamer was selected by use of the FluMag-SELEX procedure. The SELEX technology (Systematic Evolution of Ligands by EXponential enrichment) is widely applied as an in vitro selection and amplification method to generate target-specific aptamers and exists in various modified variants. FluMag-SELEX is one of them and is characterized by the use of magnetic beads for target immobilization and fluorescently labeled oligonucleotides for monitoring the aptamer selection progress. Structural investigations and sequence truncation experiments of the selected aptamer for Protein A led to the conclusion, that a stem-loop structure at its 5'-end including the 5'-primer binding site is essential for aptamer-target binding. Extensive interaction analyses between aptamer and Protein A were performed by methods like surface plasmon resonance, MicroScale Thermophoresis and bead-based binding assays using fluorescence measurements. The binding of the aptamer to its target was thus investigated in assays with immobilization of one of the binding partners each, and with both binding partners in solution. Affinity constants were determined in the low micromolar to submicromolar range, increasing to the nanomolar range under the assumption of avidity. Protein A provides more than one binding site for the aptamer, which may overlap with the known binding sites for immunoglobulins. The aptamer binds specifically to both native and recombinant Protein A, but not to other immunoglobulin-binding proteins like Protein G and L. Cross specificity to other proteins was not found. The application of the aptamer is directed to Protein A detection or affinity purification. Moreover, whole cells of Staphylococcus aureus, presenting Protein A on the cell surface, could also be bound by the aptamer.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2016-09-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Modeling repetitive, non‐globular proteins

PubMed Central

Basu, Koli; Campbell, Robert L.; Guo, Shuaiqi; Sun, Tianjun

2016-01-01

Abstract While ab initio modeling of protein structures is not routine, certain types of proteins are more straightforward to model than others. Proteins with short repetitive sequences typically exhibit repetitive structures. These repetitive sequences can be more amenable to modeling if some information is known about the predominant secondary structure or other key features of the protein sequence. We have successfully built models of a number of repetitive structures with novel folds using knowledge of the consensus sequence within the sequence repeat and an understanding of the likely secondary structures that these may adopt. Our methods for achieving this success are reviewed here. PMID:26914323

Amino acid selective unlabeling for sequence specific resonance assignments in proteins

PubMed Central

Krishnarjuna, B.; Jaipuria, Garima; Thakur, Anushikha

2010-01-01

Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective ‘unlabeling’ or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly 13C/15N labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {12COi–15Ni+1}-filtered HSQC, which aids in linking the 1HN/15N resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i − 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to 2H labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of 14N at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies. Electronic supplementary material The online version of this article (doi:10.1007/s10858-010-9459-z) contains supplementary material, which is available to authorized users. PMID:21153044

Rapid NMR Assignments of Proteins by Using Optimized Combinatorial Selective Unlabeling.

PubMed

Dubey, Abhinav; Kadumuri, Rajashekar Varma; Jaipuria, Garima; Vadrevu, Ramakrishna; Atreya, Hanudatta S

2016-02-15

A new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling is presented. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific assignments, without requiring a contiguously stretch of amino acid residues to be linked, and is applicable to deuterated proteins. We show that a 2D [(15) N,(1) H] HSQC spectrum with two 2D spectra can result in ∼50 % assignments. The methodology was applied to two proteins: an intrinsically disordered protein (12 kDa) and the 29 kDa (268 residue) α-subunit of Escherichia coli tryptophan synthase, which presents a challenging case with spectral overlaps and missing peaks. The method can augment existing approaches and will be useful for applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions, even prior to complete resonance assignments. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

PubMed Central

Zheng, Lu-Lu; Niu, Shen; Hao, Pei; Feng, KaiYan; Cai, Yu-Dong; Li, Yixue

2011-01-01

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations. PMID:22174779

Quantitative Rheological Model Selection

NASA Astrophysics Data System (ADS)

Freund, Jonathan; Ewoldt, Randy

2014-11-01

The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.

Carbohydrate-protein interactions: molecular modeling insights.

PubMed

Pérez, Serge; Tvaroška, Igor

2014-01-01

The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses. © 2014 Elsevier Inc. All rights reserved.

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

PubMed Central

Beleva Guthrie, Violeta; Masica, David L; Fraser, Andrew; Federico, Joseph; Fan, Yunfan; Camps, Manel; Karchin, Rachel

2018-01-01

Abstract The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure. PMID:29522102

Comparative vesicle proteomics reveals selective regulation of protein expression in chestnut blight fungus by a hypovirus.

PubMed

Wang, Jinzi; Wang, Fangzhen; Feng, Youjun; Mi, Ke; Chen, Qi; Shang, Jinjie; Chen, Baoshan

2013-01-14

The chestnut blight fungus (Cryphonectria parasitica) and hypovirus constitute a model system to study fungal pathogenesis and mycovirus-host interaction. Knowledge in this field has been gained largely from investigations at gene transcription level so far. Here we report a systematic analysis of the vesicle proteins of the host fungus with/without hypovirus infection. Thirty-three differentially expressed protein spots were identified in the purified vesicle protein samples by two-dimensional electrophoresis and mass spectrometry. Down-regulated proteins were mostly cargo proteins involved in primary metabolism and energy generation and up-regulated proteins were mostly vesicle associated proteins and ABC transporter. A virus-encoded protein p48 was found to have four forms with different molecular mass in vesicles from the virus-infected strain. While a few of the randomly selected differentially expressed proteins were in accordance with their transcription profiles, majority were not in agreement with their mRNA accumulation patterns, suggesting that an extensive post-transcriptional regulation may have occurred in the host fungus upon a hypovirus infection. Copyright © 2012 Elsevier B.V. All rights reserved.

Selective Destruction of Protein Function by Chromophore-Assisted Laser Inactivation

NASA Astrophysics Data System (ADS)

Jay, Daniel G.

1988-08-01

Chromophore-assisted laser inactivation of protein function has been achieved. After a protein binds a specific ligand or antibody conjugated with malachite green (C.I. 42000), it is selectively inactivated by laser irradiation at a wavelength of light absorbed by the dye but not significantly absorbed by cellular components. Ligand-bound proteins in solution and on the surfaces of cells can be denatured without other proteins in the same samples being affected. Chromophore-assisted laser inactivation can be used to study cell surface phenomena by inactivating the functions of single proteins on living cells, a molecular extension of cellular laser ablation. It has an advantage over genetics and the use of specific inhibitors in that the protein function of a single cell within the organism can be inactivated by focusing the laser beam.

Superoxide dismutase 1 is positively selected to minimize protein aggregation in great apes.

PubMed

Dasmeh, Pouria; Kepp, Kasper P

2017-08-01

Positive (adaptive) selection has recently been implied in human superoxide dismutase 1 (SOD1), a highly abundant antioxidant protein with energy signaling and antiaging functions, one of very few examples of direct selection on a human protein product (exon); the molecular drivers of this selection are unknown. We mapped 30 extant SOD1 sequences to the recently established mammalian species tree and inferred ancestors, key substitutions, and signatures of selection during the protein's evolution. We detected elevated substitution rates leading to great apes (Hominidae) at ~1 per 2 million years, significantly higher than in other primates and rodents, although these paradoxically generally evolve much faster. The high evolutionary rate was partly due to relaxation of some selection pressures and partly to distinct positive selection of SOD1 in great apes. We then show that higher stability and net charge and changes at the dimer interface were selectively introduced upon separation from old world monkeys and lesser apes (gibbons). Consequently, human, chimpanzee and gorilla SOD1s have a net charge of -6 at physiological pH, whereas the closely related gibbons and macaques have -3. These features consistently point towards selection against the malicious aggregation effects of elevated SOD1 levels in long-living great apes. The findings mirror the impact of human SOD1 mutations that reduce net charge and/or stability and cause ALS, a motor neuron disease characterized by oxidative stress and SOD1 aggregates and triggered by aging. Our study thus marks an example of direct selection for a particular chemical phenotype (high net charge and stability) in a single human protein with possible implications for the evolution of aging.

InterPred: A pipeline to identify and model protein-protein interactions.

PubMed

Mirabello, Claudio; Wallner, Björn

2017-06-01

Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred Proteins 2017; 85:1159-1170. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Protein model discrimination using mutational sensitivity derived from deep sequencing.

PubMed

Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

2012-02-08

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

Folding and stability of helical bundle proteins from coarse-grained models.

PubMed

Kapoor, Abhijeet; Travesset, Alex

2013-07-01

We develop a coarse-grained model where solvent is considered implicitly, electrostatics are included as short-range interactions, and side-chains are coarse-grained to a single bead. The model depends on three main parameters: hydrophobic, electrostatic, and side-chain hydrogen bond strength. The parameters are determined by considering three level of approximations and characterizing the folding for three selected proteins (training set). Nine additional proteins (containing up to 126 residues) as well as mutated versions (test set) are folded with the given parameters. In all folding simulations, the initial state is a random coil configuration. Besides the native state, some proteins fold into an additional state differing in the topology (structure of the helical bundle). We discuss the stability of the native states, and compare the dynamics of our model to all atom molecular dynamics simulations as well as some general properties on the interactions governing folding dynamics. Copyright © 2013 Wiley Periodicals, Inc.

Application of model bread baking in the examination of arabinoxylan-protein complexes in rye bread.

PubMed

Buksa, Krzysztof

2016-09-05

The changes in molecular mass of arabinoxylan (AX) and protein caused by bread baking process were examined using a model rye bread. Instead of the normal flour, the dough contained starch, water-extractable AX and protein which were isolated from rye wholemeal. From the crumb of selected model breads, starch was removed releasing AX-protein complexes, which were further examined by size exclusion chromatography. On the basis of the research, it was concluded that optimum model mix can be composed of 3-6% AX and 3-6% rye protein isolate at 94-88% of rye starch meaning with the most similar properties to low extraction rye flour. Application of model rye bread allowed to examine the interactions between AX and proteins. Bread baked with a share of AX, rye protein and starch, from which the complexes of the highest molar mass were isolated, was characterized by the strongest structure of the bread crumb. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multilabel learning via random label selection for protein subcellular multilocations prediction.

PubMed

Wang, Xiao; Li, Guo-Zheng

2013-01-01

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multilocation proteins to multiple proteins with single location, which does not take correlations among different subcellular locations into account. In this paper, a novel method named random label selection (RALS) (multilabel learning via RALS), which extends the simple binary relevance (BR) method, is proposed to learn from multilocation proteins in an effective and efficient way. RALS does not explicitly find the correlations among labels, but rather implicitly attempts to learn the label correlations from data by augmenting original feature space with randomly selected labels as its additional input features. Through the fivefold cross-validation test on a benchmark data set, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark data sets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multilocations of proteins. The prediction web server is available at >http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins.

PubMed

Naranjo, Yandi; Pons, Miquel; Konrat, Robert

2012-01-01

The number of existing protein sequences spans a very small fraction of sequence space. Natural proteins have overcome a strong negative selective pressure to avoid the formation of insoluble aggregates. Stably folded globular proteins and intrinsically disordered proteins (IDPs) use alternative solutions to the aggregation problem. While in globular proteins folding minimizes the access to aggregation prone regions, IDPs on average display large exposed contact areas. Here, we introduce the concept of average meta-structure correlation maps to analyze sequence space. Using this novel conceptual view we show that representative ensembles of folded and ID proteins show distinct characteristics and respond differently to sequence randomization. By studying the way evolutionary constraints act on IDPs to disable a negative function (aggregation) we might gain insight into the mechanisms by which function-enabling information is encoded in IDPs.

Role of Water in the Selection of Stable Proteins at Ambient and Extreme Thermodynamic Conditions

NASA Astrophysics Data System (ADS)

Bianco, Valentino; Franzese, Giancarlo; Dellago, Christoph; Coluzza, Ivan

2017-04-01

Proteins that are functional at ambient conditions do not necessarily work at extreme conditions of temperature T and pressure P . Furthermore, there are limits of T and P above which no protein has a stable functional state. Here, we show that these limits and the selection mechanisms for working proteins depend on how the properties of the surrounding water change with T and P . We find that proteins selected at high T are superstable and are characterized by a nonextreme segregation of a hydrophilic surface and a hydrophobic core. Surprisingly, a larger segregation reduces the stability range in T and P . Our computer simulations, based on a new protein design protocol, explain the hydropathy profile of proteins as a consequence of a selection process influenced by water. Our results, potentially useful for engineering proteins and drugs working far from ambient conditions, offer an alternative rationale to the evolutionary action exerted by the environment in extreme conditions.

Automated selection of stabilizing mutations in designed and natural proteins.

PubMed

Borgo, Benjamin; Havranek, James J

2012-01-31

The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins.

Automated selection of stabilizing mutations in designed and natural proteins

PubMed Central

Borgo, Benjamin; Havranek, James J.

2012-01-01

The ability to engineer novel protein folds, conformations, and enzymatic activities offers enormous potential for the development of new protein therapeutics and biocatalysts. However, many de novo and redesigned proteins exhibit poor hydrophobic packing in their predicted structures, leading to instability or insolubility. The general utility of rational, structure-based design would greatly benefit from an improved ability to generate well-packed conformations. Here we present an automated protocol within the RosettaDesign framework that can identify and improve poorly packed protein cores by selecting a series of stabilizing point mutations. We apply our method to previously characterized designed proteins that exhibited a decrease in stability after a full computational redesign. We further demonstrate the ability of our method to improve the thermostability of a well-behaved native protein. In each instance, biophysical characterization reveals that we were able to stabilize the original proteins against chemical and thermal denaturation. We believe our method will be a valuable tool for both improving upon designed proteins and conferring increased stability upon native proteins. PMID:22307603

Structure-Based Design of Highly Selective Inhibitors of the CREB Binding Protein Bromodomain.

PubMed

Denny, R Aldrin; Flick, Andrew C; Coe, Jotham; Langille, Jonathan; Basak, Arindrajit; Liu, Shenping; Stock, Ingrid; Sahasrabudhe, Parag; Bonin, Paul; Hay, Duncan A; Brennan, Paul E; Pletcher, Mathew; Jones, Lyn H; Chekler, Eugene L Piatnitski

2017-07-13

Chemical probes are required for preclinical target validation to interrogate novel biological targets and pathways. Selective inhibitors of the CREB binding protein (CREBBP)/EP300 bromodomains are required to facilitate the elucidation of biology associated with these important epigenetic targets. Medicinal chemistry optimization that paid particular attention to physiochemical properties delivered chemical probes with desirable potency, selectivity, and permeability attributes. An important feature of the optimization process was the successful application of rational structure-based drug design to address bromodomain selectivity issues (particularly against the structurally related BRD4 protein).

Evaluation of protein-protein docking model structures using all-atom molecular dynamics simulations combined with the solution theory in the energy representation

NASA Astrophysics Data System (ADS)

Takemura, Kazuhiro; Guo, Hao; Sakuraba, Shun; Matubayasi, Nobuyuki; Kitao, Akio

2012-12-01

We propose a method to evaluate binding free energy differences among distinct protein-protein complex model structures through all-atom molecular dynamics simulations in explicit water using the solution theory in the energy representation. Complex model structures are generated from a pair of monomeric structures using the rigid-body docking program ZDOCK. After structure refinement by side chain optimization and all-atom molecular dynamics simulations in explicit water, complex models are evaluated based on the sum of their conformational and solvation free energies, the latter calculated from the energy distribution functions obtained from relatively short molecular dynamics simulations of the complex in water and of pure water based on the solution theory in the energy representation. We examined protein-protein complex model structures of two protein-protein complex systems, bovine trypsin/CMTI-1 squash inhibitor (PDB ID: 1PPE) and RNase SA/barstar (PDB ID: 1AY7), for which both complex and monomer structures were determined experimentally. For each system, we calculated the energies for the crystal complex structure and twelve generated model structures including the model most similar to the crystal structure and very different from it. In both systems, the sum of the conformational and solvation free energies tended to be lower for the structure similar to the crystal. We concluded that our energy calculation method is useful for selecting low energy complex models similar to the crystal structure from among a set of generated models.

Evaluation of protein-protein docking model structures using all-atom molecular dynamics simulations combined with the solution theory in the energy representation.

PubMed

Takemura, Kazuhiro; Guo, Hao; Sakuraba, Shun; Matubayasi, Nobuyuki; Kitao, Akio

2012-12-07

We propose a method to evaluate binding free energy differences among distinct protein-protein complex model structures through all-atom molecular dynamics simulations in explicit water using the solution theory in the energy representation. Complex model structures are generated from a pair of monomeric structures using the rigid-body docking program ZDOCK. After structure refinement by side chain optimization and all-atom molecular dynamics simulations in explicit water, complex models are evaluated based on the sum of their conformational and solvation free energies, the latter calculated from the energy distribution functions obtained from relatively short molecular dynamics simulations of the complex in water and of pure water based on the solution theory in the energy representation. We examined protein-protein complex model structures of two protein-protein complex systems, bovine trypsin/CMTI-1 squash inhibitor (PDB ID: 1PPE) and RNase SA/barstar (PDB ID: 1AY7), for which both complex and monomer structures were determined experimentally. For each system, we calculated the energies for the crystal complex structure and twelve generated model structures including the model most similar to the crystal structure and very different from it. In both systems, the sum of the conformational and solvation free energies tended to be lower for the structure similar to the crystal. We concluded that our energy calculation method is useful for selecting low energy complex models similar to the crystal structure from among a set of generated models.

Advances in Homology Protein Structure Modeling

PubMed Central

Xiang, Zhexin

2007-01-01

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function. PMID:16787261

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

PubMed

Zhu, Guanhua; Liu, Wei; Bao, Chenglong; Tong, Dudu; Ji, Hui; Shen, Zuowei; Yang, Daiwen; Lu, Lanyuan

2018-05-01

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates. © 2018 Wiley Periodicals, Inc.

Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models

NASA Astrophysics Data System (ADS)

Zang, Tianwu

Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with 3˜5A root-mean-square deviation (RMSD) from the solution, the progress of refining these models is quite slow. It is therefore urgently needed to develop effective methods to bring low-quality models to higher-accuracy ranges (e.g., less than 2 A RMSD). In this thesis, I present several novel computational methods to address the high-accuracy refinement problem. First, an enhanced sampling method, named parallel continuous simulated tempering (PCST), is developed to accelerate the molecular dynamics (MD) simulation. Second, two energy biasing methods, Structure-Based Model (SBM) and Ensemble-Based Model (EBM), are introduced to perform targeted sampling around important conformations. Third, a three-step method is developed to blindly select high-quality models along the MD simulation. These methods work together to make significant refinement of low-quality models without any knowledge of the solution. The effectiveness of these methods is examined in different applications. Using the PCST-SBM method, models with higher global distance test scores (GDT_TS) are generated and selected in the MD simulation of 18 targets from the refinement category of the 10th Critical Assessment of Structure Prediction (CASP10). In addition, in the refinement test of two CASP10 targets using the PCST-EBM method, it is indicated that EBM may bring the initial model to even higher-quality levels. Furthermore, a multi-round refinement protocol of PCST-SBM improves the model quality of a protein to the level that is sufficient high for the molecular replacement in X-ray crystallography. Our results justify the crucial position of enhanced sampling in the protein structure prediction and demonstrate that a considerable improvement of low-accuracy structures is still achievable with current force fields.

DNA cleavage site selection by Type III restriction enzymes provides evidence for head-on protein collisions following 1D bidirectional motion

PubMed Central

Schwarz, Friedrich W.; van Aelst, Kara; Tóth, Júlia; Seidel, Ralf; Szczelkun, Mark D.

2011-01-01

DNA cleavage by the Type III Restriction–Modification enzymes requires communication in 1D between two distant indirectly-repeated recognitions sites, yet results in non-specific dsDNA cleavage close to only one of the two sites. To test a recently proposed ATP-triggered DNA sliding model, we addressed why one site is selected over another during cleavage. We examined the relative cleavage of a pair of identical sites on DNA substrates with different distances to a free or protein blocked end, and on a DNA substrate using different relative concentrations of protein. Under these conditions a bias can be induced in the cleavage of one site over the other. Monte-Carlo simulations based on the sliding model reproduce the experimentally observed behaviour. This suggests that cleavage site selection simply reflects the dynamics of the preceding stochastic enzyme events that are consistent with bidirectional motion in 1D and DNA cleavage following head-on protein collision. PMID:21724613

Sexual selection and the adaptive evolution of PKDREJ protein in primates and rodents

PubMed Central

Vicens, Alberto; Gómez Montoto, Laura; Couso-Ferrer, Francisco; Sutton, Keith A.; Roldan, Eduardo R.S.

2015-01-01

PKDREJ is a testis-specific protein thought to be located on the sperm surface. Functional studies in the mouse revealed that loss of PKDREJ has effects on sperm transport and the ability to undergo an induced acrosome reaction. Thus, PKDREJ has been considered a potential target of post-copulatory sexual selection in the form of sperm competition. Proteins involved in reproductive processes often show accelerated evolution. In many cases, this rapid divergence is promoted by positive selection which may be driven, at least in part, by post-copulatory sexual selection. We analysed the evolution of the PKDREJ protein in primates and rodents and assessed whether PKDREJ divergence is associated with testes mass relative to body mass, which is a reliable proxy of sperm competition levels. Evidence of an association between the evolutionary rate of the PKDREJ gene and testes mass relative to body mass was not found in primates. Among rodents, evidence of positive selection was detected in the Pkdrej gene in the family Cricetidae but not in Muridae. We then assessed whether Pkdrej divergence is associated with episodes of sperm competition in these families. We detected a positive significant correlation between the evolutionary rates of Pkdrej and testes mass relative to body mass in cricetids. These findings constitute the first evidence of post-copulatory sexual selection influencing the evolution of a protein that participates in the mechanisms regulating sperm transport and the acrosome reaction, strongly suggesting that positive selection may act on these fertilization steps, leading to advantages in situations of sperm competition. PMID:25304980

Homology Modeling of Class A G Protein-Coupled Receptors

PubMed Central

Costanzi, Stefano

2012-01-01

G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the three-dimensional models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments. PMID:22323225

Animal models of protein allergenicity: potential benefits, pitfalls and challenges.

PubMed

Dearman, R J; Kimber, I

2009-04-01

Food allergy is an important health issue. With an increasing interest in novel foods derived from transgenic crop plants, there is a growing need for the development of approaches suitable for the characterization of the allergenic potential of proteins. There are methods available currently (such as homology searches and serological testing) that are very effective at identifying proteins that are likely to cross-react with known allergens. However, animal models may play a role in the identification of truly novel proteins, such as bacterial or fungal proteins, that have not been experienced previously in the diet. We consider here the potential benefits, pitfalls and challenges of the selection of various animal models, including the mouse, the rat, the dog and the neonatal swine. The advantages and disadvantages of various experimental end-points are discussed, including the measurement of specific IgE by ELISA, Western blotting or functional tests such as the passive cutaneous anaphylaxis assay, and the assessment of challenge-induced clinical symptoms in previously sensitized animals. The experimental variables of route of exposure to test proteins and the incorporation of adjuvant to increase the sensitivity of the responses are considered also. It is important to emphasize that currently none of these approaches has been validated for the purposes of hazard identification in the context of a safety assessment. However, the available evidence suggests that the judicious use of an accurate and robust animal model could provide important additional data that would contribute significantly to the assessment of the potential allergenicity of novel proteins.

Insight into the Intermolecular Recognition Mechanism between Keap1 and IKKβ Combining Homology Modelling, Protein-Protein Docking, Molecular Dynamics Simulations and Virtual Alanine Mutation

PubMed Central

Jiang, Zheng-Yu; Chu, Hong-Xi; Xi, Mei-Yang; Yang, Ting-Ting; Jia, Jian-Min; Huang, Jing-Jie; Guo, Xiao-Ke; Zhang, Xiao-Jin; You, Qi-Dong; Sun, Hao-Peng

2013-01-01

Degradation of certain proteins through the ubiquitin-proteasome pathway is a common strategy taken by the key modulators responsible for stress responses. Kelch-like ECH-associated protein-1(Keap1), a substrate adaptor component of the Cullin3 (Cul3)-based ubiquitin E3 ligase complex, mediates the ubiquitination of two key modulators, NF-E2-related factor 2 (Nrf2) and IκB kinase β (IKKβ), which are involved in the redox control of gene transcription. However, compared to the Keap1-Nrf2 protein-protein interaction (PPI), the intermolecular recognition mechanism of Keap1 and IKKβ has been poorly investigated. In order to explore the binding pattern between Keap1 and IKKβ, the PPI model of Keap1 and IKKβ was investigated. The structure of human IKKβ was constructed by means of the homology modeling method and using reported crystal structure of Xenopus laevis IKKβ as the template. A protein-protein docking method was applied to develop the Keap1-IKKβ complex model. After the refinement and visual analysis of docked proteins, the chosen pose was further optimized through molecular dynamics simulations. The resulting structure was utilized to conduct the virtual alanine mutation for the exploration of hot-spots significant for the intermolecular interaction. Overall, our results provided structural insights into the PPI model of Keap1-IKKβ and suggest that the substrate specificity of Keap1 depend on the interaction with the key tyrosines, namely Tyr525, Tyr574 and Tyr334. The study presented in the current project may be useful to design molecules that selectively modulate Keap1. The selective recognition mechanism of Keap1 with IKKβ or Nrf2 will be helpful to further know the crosstalk between NF-κB and Nrf2 signaling. PMID:24066166

Insight into the intermolecular recognition mechanism between Keap1 and IKKβ combining homology modelling, protein-protein docking, molecular dynamics simulations and virtual alanine mutation.

PubMed

Jiang, Zheng-Yu; Chu, Hong-Xi; Xi, Mei-Yang; Yang, Ting-Ting; Jia, Jian-Min; Huang, Jing-Jie; Guo, Xiao-Ke; Zhang, Xiao-Jin; You, Qi-Dong; Sun, Hao-Peng

2013-01-01

Degradation of certain proteins through the ubiquitin-proteasome pathway is a common strategy taken by the key modulators responsible for stress responses. Kelch-like ECH-associated protein-1(Keap1), a substrate adaptor component of the Cullin3 (Cul3)-based ubiquitin E3 ligase complex, mediates the ubiquitination of two key modulators, NF-E2-related factor 2 (Nrf2) and IκB kinase β (IKKβ), which are involved in the redox control of gene transcription. However, compared to the Keap1-Nrf2 protein-protein interaction (PPI), the intermolecular recognition mechanism of Keap1 and IKKβ has been poorly investigated. In order to explore the binding pattern between Keap1 and IKKβ, the PPI model of Keap1 and IKKβ was investigated. The structure of human IKKβ was constructed by means of the homology modeling method and using reported crystal structure of Xenopus laevis IKKβ as the template. A protein-protein docking method was applied to develop the Keap1-IKKβ complex model. After the refinement and visual analysis of docked proteins, the chosen pose was further optimized through molecular dynamics simulations. The resulting structure was utilized to conduct the virtual alanine mutation for the exploration of hot-spots significant for the intermolecular interaction. Overall, our results provided structural insights into the PPI model of Keap1-IKKβ and suggest that the substrate specificity of Keap1 depend on the interaction with the key tyrosines, namely Tyr525, Tyr574 and Tyr334. The study presented in the current project may be useful to design molecules that selectively modulate Keap1. The selective recognition mechanism of Keap1 with IKKβ or Nrf2 will be helpful to further know the crosstalk between NF-κB and Nrf2 signaling.

Phage display selection of peptides that target calcium-binding proteins.

PubMed

Vetter, Stefan W

2013-01-01

Phage display allows to rapidly identify peptide sequences with binding affinity towards target proteins, for example, calcium-binding proteins (CBPs). Phage technology allows screening of 10(9) or more independent peptide sequences and can identify CBP binding peptides within 2 weeks. Adjusting of screening conditions allows selecting CBPs binding peptides that are either calcium-dependent or independent. Obtained peptide sequences can be used to identify CBP target proteins based on sequence homology or to quickly obtain peptide-based CBP inhibitors to modulate CBP-target interactions. The protocol described here uses a commercially available phage display library, in which random 12-mer peptides are displayed on filamentous M13 phages. The library was screened against the calcium-binding protein S100B.

Aptamer-based potentiometric measurements of proteins using ion-selective microelectrodes.

PubMed

Numnuam, Apon; Chumbimuni-Torres, Karin Y; Xiang, Yun; Bash, Ralph; Thavarungkul, Panote; Kanatharana, Proespichaya; Pretsch, Ernö; Wang, Joseph; Bakker, Eric

2008-02-01

We here report on the first example of an aptamer-based potentiometric sandwich assay of proteins. The measurements are based on CdS quantum dot labels of the secondary aptamer, which were determined with a novel solid-contact Cd2+-selective polymer membrane electrode after dissolution with hydrogen peroxide. The electrode exhibited cadmium ion detection limits of 100 pM in 100 mL samples and of 1 nM in 200 microL microwells, using a calcium-selective electrode as a pseudoreference electrode. As a prototype example, thrombin was measured in 200 microL samples with a lower detection limit of 0.14 nM corresponding to 28 fmol of analyte. The results show great promise for the potentiometric determination of proteins at very low concentrations in microliter samples.

A selection that reports on protein–protein interactions within a thermophilic bacterium

PubMed Central

Nguyen, Peter Q.; Silberg, Jonathan J.

2010-01-01

Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein–protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein–protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AKTn). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75°C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78°C by a vector that coexpresses polypeptides corresponding to residues 1–79 and 80–220 of AKTn. In contrast, PQN1 growth was not complemented by AKTn fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein–protein interactions, since AKTn-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein–protein interactions. PMID:20418388

Genetic variability and natural selection at the ligand domain of the Duffy binding protein in brazilian Plasmodium vivax populations

PubMed Central

2010-01-01

Background Plasmodium vivax malaria is a major public health challenge in Latin America, Asia and Oceania, with 130-435 million clinical cases per year worldwide. Invasion of host blood cells by P. vivax mainly depends on a type I membrane protein called Duffy binding protein (PvDBP). The erythrocyte-binding motif of PvDBP is a 170 amino-acid stretch located in its cysteine-rich region II (PvDBPII), which is the most variable segment of the protein. Methods To test whether diversifying natural selection has shaped the nucleotide diversity of PvDBPII in Brazilian populations, this region was sequenced in 122 isolates from six different geographic areas. A Bayesian method was applied to test for the action of natural selection under a population genetic model that incorporates recombination. The analysis was integrated with a structural model of PvDBPII, and T- and B-cell epitopes were localized on the 3-D structure. Results The results suggest that: (i) recombination plays an important role in determining the haplotype structure of PvDBPII, and (ii) PvDBPII appears to contain neutrally evolving codons as well as codons evolving under natural selection. Diversifying selection preferentially acts on sites identified as epitopes, particularly on amino acid residues 417, 419, and 424, which show strong linkage disequilibrium. Conclusions This study shows that some polymorphisms of PvDBPII are present near the erythrocyte-binding domain and might serve to elude antibodies that inhibit cell invasion. Therefore, these polymorphisms should be taken into account when designing vaccines aimed at eliciting antibodies to inhibit erythrocyte invasion. PMID:21092207

Deciphering the selectivity of Bombyx mori pheromone binding protein for bombykol over bombykal: a theoretical approach.

PubMed

Charlier, Landry; Antonczak, Serge; Jacquin-Joly, Emmanuelle; Cabrol-Bass, Daniel; Golebiowski, Jérôme

2008-12-22

In this article we report calculations dedicated to estimate the selectivity of the Bombyx mori pheromone binding protein towards the two closely related pheromonal components Bombykol and Bombykal. The selectivity is quantified by the binding free-energy difference, obtained either by the thermodynamic integration or by the MM-GBSA approach. In the latter, the selectivity is decomposed on a per-residue basis, which identifies the residues considered crucial for the selectivity of the protein for Bombykol over Bombykal. A discussion on the role of Bombyx mori pheromone binding protein is provided on the basis of these results.

Determination of Multiple φ-Torsion Angles in Proteins by Selective and Extensive 13C Labeling and Two-Dimensional Solid-State NMR

NASA Astrophysics Data System (ADS)

Hong, Mei

1999-08-01

We describe an approach to efficiently determine the backbone conformation of solid proteins that utilizes selective and extensive 13C labeling in conjunction with two-dimensional magic-angle-spinning NMR. The selective 13C labeling approach aims to reduce line broadening and other multispin complications encountered in solid-state NMR of uniformly labeled proteins while still enhancing the sensitivity of NMR spectra. It is achieved by using specifically labeled glucose or glycerol as the sole carbon source in the protein expression medium. For amino acids synthesized in the linear part of the biosynthetic pathways, [1-13C]glucose preferentially labels the ends of the side chains, while [2-13C]glycerol labels the Cα of these residues. Amino acids produced from the citric-acid cycle are labeled in a more complex manner. Information on the secondary structure of such a labeled protein was obtained by measuring multiple backbone torsion angles φ simultaneously, using an isotropic-anisotropic 2D correlation technique, the HNCH experiment. Initial experiments for resonance assignment of a selectively 13C labeled protein were performed using 15N-13C 2D correlation spectroscopy. From the time dependence of the 15N-13C dipolar coherence transfer, both intraresidue and interresidue connectivities can be observed, thus yielding partial sequential assignment. We demonstrate the selective 13C labeling and these 2D NMR experiments on a 8.5-kDa model protein, ubiquitin. This isotope-edited NMR approach is expected to facilitate the structure determination of proteins in the solid state.

Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling.

PubMed

Meier, Armin; Söding, Johannes

2015-10-01

Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches. We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins' atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed. We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.

Autocrine selection of a GLP-1R G-protein biased agonist with potent antidiabetic effects.

PubMed

Zhang, Hongkai; Sturchler, Emmanuel; Zhu, Jiang; Nieto, Ainhoa; Cistrone, Philip A; Xie, Jia; He, LinLing; Yea, Kyungmoo; Jones, Teresa; Turn, Rachel; Di Stefano, Peter S; Griffin, Patrick R; Dawson, Philip E; McDonald, Patricia H; Lerner, Richard A

2015-12-01

Glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) agonists have emerged as treatment options for type 2 diabetes mellitus (T2DM). GLP-1R signals through G-protein-dependent, and G-protein-independent pathways by engaging the scaffold protein β-arrestin; preferential signalling of ligands through one or the other of these branches is known as 'ligand bias'. Here we report the discovery of the potent and selective GLP-1R G-protein-biased agonist, P5. We identified P5 in a high-throughput autocrine-based screening of large combinatorial peptide libraries, and show that P5 promotes G-protein signalling comparable to GLP-1 and Exendin-4, but exhibited a significantly reduced β-arrestin response. Preclinical studies using different mouse models of T2DM demonstrate that P5 is a weak insulin secretagogue. Nevertheless, chronic treatment of diabetic mice with P5 increased adipogenesis, reduced adipose tissue inflammation as well as hepatic steatosis and was more effective at correcting hyperglycaemia and lowering haemoglobin A1c levels than Exendin-4, suggesting that GLP-1R G-protein-biased agonists may provide a novel therapeutic approach to T2DM.

Light scattering evidence of selective protein fouling on biocompatible block copolymer micelles

NASA Astrophysics Data System (ADS)

Giacomelli, Fernando C.; Stepánek, Petr; Schmidt, Vanessa; Jäger, Eliézer; Jäger, Alessandro; Giacomelli, Cristiano

2012-07-01

Selective protein fouling on block copolymer micelles with well-known potential for tumour-targeting drug delivery was evidenced by using dynamic light scattering measurements. The stability and interaction of block copolymer micelles with model proteins (BSA, IgG, lysozyme and CytC) is reported for systems featuring a hydrophobic (poly[2-(diisopropylamino)-ethyl methacrylate]) (PDPA) core and hydrophilic coronas comprising poly(ethylene oxide)/poly(glycerol monomethacrylate) (PEO-b-PG2MA) or poly[2-(methacryloyloxy)ethyl phosphorylcholine] (PMPC). The results revealed that protein size and hydrophilic chain density play important roles in the observed interactions. The PEO113-b-PG2MA30-b-PDPA50 nanoparticles are stable and protein adsorption is prevented at all investigated protein environments. The successful protein-repellent characteristic of these nanoparticles is attributed to a high hydrophilic surface chain density (>0.1 chains per nm2) and to the length of the hydrophilic chains. On the other hand, although PMPC also has protein-repellent characteristics, the low surface chain density of the hydrophilic shell is supposed to enable interactions with small proteins. The PMPC40-b-PDPA70 micelles are stable in BSA and IgG environments due to weak repulsion forces between PMPC and the proteins, to the hydration layer, and particularly to a size-effect where the large BSA (RH = 4.2 nm) and IgG (RH = 7.0 nm) do not easily diffuse within the PMPC shell. Conversely, a clear interaction was observed with the 2.1 nm radius lysozyme. The lysozyme protein can diffuse within the PMPC micellar shell towards the PDPA hydrophobic core in a process favored by its smaller size and the low hydrophilic PMPC surface chain density (~0.049 chains per nm2) as compared to PEO-b-PG2MA (~0.110 chains per nm2). The same behavior was not evidenced with the 2.3 nm radius positively charged CytC, probably due to its higher surface hydrophilicity and the consequent chemical

The use of experimental structures to model protein dynamics.

PubMed

Katebi, Ataur R; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L

2015-01-01

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step

The Ebola virus matrix protein VP40 selectively induces vesiculation from phosphatidylserine-enriched membranes.

PubMed

Soni, Smita P; Stahelin, Robert V

2014-11-28

Ebola virus is from the Filoviridae family of viruses and is one of the most virulent pathogens known with ∼ 60% clinical fatality. The Ebola virus negative sense RNA genome encodes seven proteins including viral matrix protein 40 (VP40), which is the most abundant protein found in the virions. Within infected cells VP40 localizes at the inner leaflet of the plasma membrane (PM), binds lipids, and regulates formation of new virus particles. Expression of VP40 in mammalian cells is sufficient to form virus-like particles that are nearly indistinguishable from the authentic virions. However, how VP40 interacts with the PM and forms virus-like particles is for the most part unknown. To investigate VP40 lipid specificity in a model of viral egress we employed giant unilamellar vesicles with different lipid compositions. The results demonstrate VP40 selectively induces vesiculation from membranes containing phosphatidylserine (PS) at concentrations of PS that are representative of the PM inner leaflet content. The formation of intraluminal vesicles was not significantly detected in the presence of other important PM lipids including cholesterol and polyvalent phosphoinositides, further demonstrating PS selectivity. Taken together, these studies suggest that PM phosphatidylserine may be an important component of Ebola virus budding and that VP40 may be able to mediate PM scission. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Selection of unadapted, pathogenic SHIVs encoding newly transmitted HIV-1 envelope proteins.

PubMed

Del Prete, Gregory Q; Ailers, Braiden; Moldt, Brian; Keele, Brandon F; Estes, Jacob D; Rodriguez, Anthony; Sampias, Marissa; Oswald, Kelli; Fast, Randy; Trubey, Charles M; Chertova, Elena; Smedley, Jeremy; LaBranche, Celia C; Montefiori, David C; Burton, Dennis R; Shaw, George M; Markowitz, Marty; Piatak, Michael; KewalRamani, Vineet N; Bieniasz, Paul D; Lifson, Jeffrey D; Hatziioannou, Theodora

2014-09-10

Infection of macaques with chimeric viruses based on SIVMAC but expressing the HIV-1 envelope (Env) glycoproteins (SHIVs) remains the most powerful model for evaluating prevention and therapeutic strategies against AIDS. Unfortunately, only a few SHIVs are currently available. Furthermore, their generation has required extensive adaptation of the HIV-1 Env sequences in macaques so they may not accurately represent HIV-1 Env proteins circulating in humans, potentially limiting their translational utility. We developed a strategy for generating large numbers of SHIV constructs expressing Env proteins from newly transmitted HIV-1 strains. By inoculating macaques with cocktails of multiple SHIV variants, we selected SHIVs that can replicate and cause AIDS-like disease in immunologically intact rhesus macaques without requiring animal-to-animal passage. One of these SHIVs could be transmitted mucosally. We demonstrate the utility of the SHIVs generated by this method for evaluating neutralizing antibody administration as a protection against mucosal SHIV challenge. Copyright © 2014 Elsevier Inc. All rights reserved.

Tuning a Protein-Labeling Reaction to Achieve Highly Site Selective Lysine Conjugation.

PubMed

Pham, Grace H; Ou, Weijia; Bursulaya, Badry; DiDonato, Michael; Herath, Ananda; Jin, Yunho; Hao, Xueshi; Loren, Jon; Spraggon, Glen; Brock, Ansgar; Uno, Tetsuo; Geierstanger, Bernhard H; Cellitti, Susan E

2018-04-16

Activated esters are widely used to label proteins at lysine side chains and N termini. These reagents are useful for labeling virtually any protein, but robust reactivity toward primary amines generally precludes site-selective modification. In a unique case, fluorophenyl esters are shown to preferentially label human kappa antibodies at a single lysine (Lys188) within the light-chain constant domain. Neighboring residues His189 and Asp151 contribute to the accelerated rate of labeling at Lys188 relative to the ≈40 other lysine sites. Enriched Lys188 labeling can be enhanced from 50-70 % to >95 % by any of these approaches: lowering reaction temperature, applying flow chemistry, or mutagenesis of specific residues in the surrounding protein environment. Our results demonstrated that activated esters with fluoro-substituted aromatic leaving groups, including a fluoronaphthyl ester, can be generally useful reagents for site-selective lysine labeling of antibodies and other immunoglobulin-type proteins. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Template-based structure modeling of protein-protein interactions

PubMed Central

Szilagyi, Andras; Zhang, Yang

2014-01-01

The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the proteinprotein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement. PMID:24721449

Preservation of protein clefts in comparative models.

PubMed

Piedra, David; Lois, Sergi; de la Cruz, Xavier

2008-01-16

Comparative, or homology, modelling of protein structures is the most widely used prediction method when the target protein has homologues of known structure. Given that the quality of a model may vary greatly, several studies have been devoted to identifying the factors that influence modelling results. These studies usually consider the protein as a whole, and only a few provide a separate discussion of the behaviour of biologically relevant features of the protein. Given the value of the latter for many applications, here we extended previous work by analysing the preservation of native protein clefts in homology models. We chose to examine clefts because of their role in protein function/structure, as they are usually the locus of protein-protein interactions, host the enzymes' active site, or, in the case of protein domains, can also be the locus of domain-domain interactions that lead to the structure of the whole protein. We studied how the largest cleft of a protein varies in comparative models. To this end, we analysed a set of 53507 homology models that cover the whole sequence identity range, with a special emphasis on medium and low similarities. More precisely we examined how cleft quality - measured using six complementary parameters related to both global shape and local atomic environment, depends on the sequence identity between target and template proteins. In addition to this general analysis, we also explored the impact of a number of factors on cleft quality, and found that the relationship between quality and sequence identity varies depending on cleft rank amongst the set of protein clefts (when ordered according to size), and number of aligned residues. We have examined cleft quality in homology models at a range of seq.id. levels. Our results provide a detailed view of how quality is affected by distinct parameters and thus may help the user of comparative modelling to determine the final quality and applicability of his/her cleft models

In silico modeling of the yeast protein and protein family interaction network

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

The deubiquitinating enzyme USP36 controls selective autophagy activation by ubiquitinated proteins.

PubMed

Taillebourg, Emmanuel; Gregoire, Isabel; Viargues, Perrine; Jacomin, Anne-Claire; Thevenon, Dominique; Faure, Mathias; Fauvarque, Marie-Odile

2012-05-01

Initially described as a nonspecific degradation process induced upon starvation, autophagy is now known also to be involved in the degradation of specific ubiquitinated substrates such as mitochondria, bacteria and aggregated proteins, ensuring crucial functions in cell physiology and immunity. We report here that the deubiquitinating enzyme USP36 controls selective autophagy activation in Drosophila and in human cells. We show that dUsp36 loss of function autonomously inhibits cell growth while activating autophagy. Despite the phenotypic similarity, dUSP36 is not part of the TOR signaling pathway. Autophagy induced by dUsp36 loss of function depends on p62/SQSTM1, an adaptor for delivering cargo marked by polyubiquitin to autophagosomes. Consistent with p62 requirement, dUsp36 mutant cells display nuclear aggregates of ubiquitinated proteins, including Histone H2B, and cytoplasmic ubiquitinated proteins; the latter are eliminated by autophagy. Importantly, USP36 function in p62-dependent selective autophagy is conserved in human cells. Our work identifies a novel, crucial role for a deubiquitinating enzyme in selective autophagy.

Input variable selection and calibration data selection for storm water quality regression models.

PubMed

Sun, Siao; Bertrand-Krajewski, Jean-Luc

2013-01-01

Storm water quality models are useful tools in storm water management. Interest has been growing in analyzing existing data for developing models for urban storm water quality evaluations. It is important to select appropriate model inputs when many candidate explanatory variables are available. Model calibration and verification are essential steps in any storm water quality modeling. This study investigates input variable selection and calibration data selection in storm water quality regression models. The two selection problems are mutually interacted. A procedure is developed in order to fulfil the two selection tasks in order. The procedure firstly selects model input variables using a cross validation method. An appropriate number of variables are identified as model inputs to ensure that a model is neither overfitted nor underfitted. Based on the model input selection results, calibration data selection is studied. Uncertainty of model performances due to calibration data selection is investigated with a random selection method. An approach using the cluster method is applied in order to enhance model calibration practice based on the principle of selecting representative data for calibration. The comparison between results from the cluster selection method and random selection shows that the former can significantly improve performances of calibrated models. It is found that the information content in calibration data is important in addition to the size of calibration data.

QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information.

PubMed

Benkert, Pascal; Schwede, Torsten; Tosatto, Silvio Ce

2009-05-20

The selection of the most accurate protein model from a set of alternatives is a crucial step in protein structure prediction both in template-based and ab initio approaches. Scoring functions have been developed which can either return a quality estimate for a single model or derive a score from the information contained in the ensemble of models for a given sequence. Local structural features occurring more frequently in the ensemble have a greater probability of being correct. Within the context of the CASP experiment, these so called consensus methods have been shown to perform considerably better in selecting good candidate models, but tend to fail if the best models are far from the dominant structural cluster. In this paper we show that model selection can be improved if both approaches are combined by pre-filtering the models used during the calculation of the structural consensus. Our recently published QMEAN composite scoring function has been improved by including an all-atom interaction potential term. The preliminary model ranking based on the new QMEAN score is used to select a subset of reliable models against which the structural consensus score is calculated. This scoring function called QMEANclust achieves a correlation coefficient of predicted quality score and GDT_TS of 0.9 averaged over the 98 CASP7 targets and perform significantly better in selecting good models from the ensemble of server models than any other groups participating in the quality estimation category of CASP7. Both scoring functions are also benchmarked on the MOULDER test set consisting of 20 target proteins each with 300 alternatives models generated by MODELLER. QMEAN outperforms all other tested scoring functions operating on individual models, while the consensus method QMEANclust only works properly on decoy sets containing a certain fraction of near-native conformations. We also present a local version of QMEAN for the per-residue estimation of model quality (QMEANlocal

Selective Sensitization of Zinc Finger Protein Oxidation by Reactive Oxygen Species through Arsenic Binding*

PubMed Central

Zhou, Xixi; Cooper, Karen L.; Sun, Xi; Liu, Ke J.; Hudson, Laurie G.

2015-01-01

Cysteine oxidation induced by reactive oxygen species (ROS) on redox-sensitive targets such as zinc finger proteins plays a critical role in redox signaling and subsequent biological outcomes. We found that arsenic exposure led to oxidation of certain zinc finger proteins based on arsenic interaction with zinc finger motifs. Analysis of zinc finger proteins isolated from arsenic-exposed cells and zinc finger peptides by mass spectrometry demonstrated preferential oxidation of C3H1 and C4 zinc finger configurations. C2H2 zinc finger proteins that do not bind arsenic were not oxidized by arsenic-generated ROS in the cellular environment. The findings suggest that selectivity in arsenic binding to zinc fingers with three or more cysteines defines the target proteins for oxidation by ROS. This represents a novel mechanism of selective protein oxidation and demonstrates how an environmental factor may sensitize certain target proteins for oxidation, thus altering the oxidation profile and redox regulation. PMID:26063799

Anti-inflammatory properties of Gö 6850: a selective inhibitor of protein kinase C.

PubMed

Jacobson, P B; Kuchera, S L; Metz, A; Schächtele, C; Imre, K; Schrier, D J

1995-11-01

Protein kinase C (PKC) regulates a variety of signal transduction events implicated in the pathogenesis of inflammation, including the biosynthesis of inflammatory cytokines and superoxide and the activation of phospholipase A2. Because of the significant role of PKC in these inflammatory processes, we evaluated a specific and potent inhibitor of C kinase for efficacy in several in vitro and in vivo murine models of inflammation. Unlike the relatively nonspecific kinase inhibitor staurosporine, the bisindolylmaleimide 3-[1-[-3-(dimethylaminopropyl]-1H-indol-3-yl]- 4-(1H-indol-3-yl)-1H-pyrrole-2,5-dione monohydrochloride (Gö 6850) demonstrated increased selectivity for C kinase in purified enzyme assays (respective IC50 values (microM) for Gö 6850 and staurosporine: protein kinase C (0.032, 0.009); myosin light-chain kinase (0.6, 0.01); protein kinase G (4.6, 0.018); protein kinase A (33, 0.04); tyrosine kinase1 (94, 0.4); tyrosine kinase2 (> 100, > 1)). Topically applied Gö 6850 inhibited phorbol myristate acetate-induced edema, neutrophil influx and vascular permeability in murine epidermis in a dose- and time-dependent manner at levels comparable to indomethacin. In a murine model of delayed type hypersensitivity, Gö 6850 inhibited dinitrofluorobenzene-induced contact dermatitis with and ID50 value of 150 micrograms/ear. Cellular studies in mouse peritoneal macrophages demonstrated that Gö 6850 was a potent inhibitor of phorbol myristate acetate-induced prostaglandin E2 production. Superoxide production in phorbol myristate acetate-stimulated murine neutrophils was also inhibited by Gö 6850 (IC50 = 88 nM).(ABSTRACT TRUNCATED AT 250 WORDS)

Protein subcellular location pattern classification in cellular images using latent discriminative models.

PubMed

Li, Jieyue; Xiong, Liang; Schneider, Jeff; Murphy, Robert F

2012-06-15

Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood. In the experiments, we show that the proposed models help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this article for classifying 942 proteins into 13 classes of patterns is about 84.6%, which to our knowledge is the best so far. In addition, the dependencies learned are consistent with prior knowledge of cell organization. http://murphylab.web.cmu.edu/software/.

Protein Carriers for Glycoconjugate Vaccines: History, Selection Criteria, Characterization and New Trends.

PubMed

Micoli, Francesca; Adamo, Roberto; Costantino, Paolo

2018-06-15

Currently licensed glycoconjugate vaccines are composed of a carbohydrate moiety covalently linked to a protein carrier. Polysaccharides are T-cell independent antigens able to directly stimulate B cells to produce antibodies. Disease burden caused by polysaccharide-encapsulated bacteria is highest in the first year of life, where plain polysaccharides are not generally immunogenic, limiting their use as vaccines. This limitation has been overcome by covalent coupling carbohydrate antigens to proteins that provide T cell epitopes. In addition to the protein carriers currently used in licensed glycoconjugate vaccines, there is a search for new protein carriers driven by several considerations: (i) concerns that pre-exposure or co-exposure to a given carrier can lead to immune interference and reduction of the anti-carbohydrate immune response; (ii) increasing interest to explore the dual role of proteins as carrier and protective antigen; and (iii) new ways to present carbohydrates antigens to the immune system. Protein carriers can be directly coupled to activated glycans or derivatized to introduce functional groups for subsequent conjugation. Proteins can be genetically modified to pre-determine the site of glycans attachment by insertion of unnatural amino acids bearing specific functional groups, or glycosylation consensus sequences for in vivo expression of the glycoconjugate. A large portion of the new protein carriers under investigation are recombinant ones, but more complex systems such as Outer Membrane Vesicles and other nanoparticles are being investigated. Selection criteria for new protein carriers are based on several aspects including safety, manufacturability, stability, reactivity toward conjugation, and preclinical evidence of immunogenicity of corresponding glycoconjugates. Characterization panels of protein carriers include tests before conjugation, after derivatization when applicable, and after conjugation. Glycoconjugate vaccines based on

Rats free to select between pure protein and a fat-carbohydrate mix ingest high-protein mixed meals during the dark period and protein meals during the light period.

PubMed

Makarios-Lahham, Lina; Roseau, Suzanne M; Fromentin, Gilles; Tome, Daniel; Even, Patrick C

2004-03-01

Rats that are allowed to select their diets [dietary self- selection (DSS)] often ingest >30% of their daily energy in the form of protein. Such an intake may seem unhealthy, but the consistency of this choice suggests that it is motivated by physiologic drives. To gain a clearer understanding of how protein selection is structured during DSS, we adapted 12 rats to a standard diet (14% Protein) and then allowed them to choose between two diets, i.e., total milk protein (P) and a mix of carbohydrates and lipids (FC). The protein intake during DSS rose above 40%; assuming an intermeal interval of 10 min, 70% of the energy intake occurred with meals that included both P and FC, with the sequence of FC followed by P preferred to the sequence of P followed by FC (70 vs. 30%, P < 0.001). In addition, energy intake during the light period was reduced to only 10% of the daily energy intake [vs. 30% with the control P14 diet or a with a high-protein diet (50%)], and 90% of the intake was in the form of pure protein meals. In complementary studies, we verified that the high protein intake also occurred when rats were offered casein and whey and was not due to the high palatability of the milk protein. We conclude that a specific feeding pattern accompanies high protein intake in rats allowed DSS. The mechanisms underlying this behavior and its potential beneficial/adverse consequences over the long term still must be clarified.

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

PubMed Central

2011-01-01

Background Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. Results The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations. Conclusions We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific

Mixture models for protein structure ensembles.

PubMed

Hirsch, Michael; Habeck, Michael

2008-10-01

Protein structure ensembles provide important insight into the dynamics and function of a protein and contain information that is not captured with a single static structure. However, it is not clear a priori to what extent the variability within an ensemble is caused by internal structural changes. Additional variability results from overall translations and rotations of the molecule. And most experimental data do not provide information to relate the structures to a common reference frame. To report meaningful values of intrinsic dynamics, structural precision, conformational entropy, etc., it is therefore important to disentangle local from global conformational heterogeneity. We consider the task of disentangling local from global heterogeneity as an inference problem. We use probabilistic methods to infer from the protein ensemble missing information on reference frames and stable conformational sub-states. To this end, we model a protein ensemble as a mixture of Gaussian probability distributions of either entire conformations or structural segments. We learn these models from a protein ensemble using the expectation-maximization algorithm. Our first model can be used to find multiple conformers in a structure ensemble. The second model partitions the protein chain into locally stable structural segments or core elements and less structured regions typically found in loops. Both models are simple to implement and contain only a single free parameter: the number of conformers or structural segments. Our models can be used to analyse experimental ensembles, molecular dynamics trajectories and conformational change in proteins. The Python source code for protein ensemble analysis is available from the authors upon request.

GAL4 transactivation-based assay for the detection of selective intercellular protein movement.

PubMed

Kumar, Dhinesh; Chen, Huan; Rim, Yeonggil; Kim, Jae-Yean

2015-01-01

Several plant proteins function as intercellular messenger to specify cell fate and coordinate plant development. Such intercellular communication can be achieved by direct, selective, or nonselective (diffusion-based) trafficking through plasmodesmata (PD), the symplasmic membrane-lined nanochannels adjoining two cells. A trichome rescue trafficking assay was reported to allow the detection of protein movement in Arabidopsis leaf tissue using transgenic gene expression. Here, we provide a protocol to dissect the mode of intercellular protein movement in Arabidopsis root. This assay system involves a root ground tissue-specific GAL4/UAS transactivation expression system in combination with fluorescent reporter proteins. In this system, mCherry, a red fluorescent protein, can move cell to cell via diffusion, while mCherry-H2B is tightly cell autonomous. Thus, a protein fused to mCherry-H2B that can move out from the site of synthesis likely contains a selective trafficking signal to impart a cell-to-cell gain-of-trafficking function to the cell-autonomous mCherry-H2B. This approach can be adapted to investigate the cell-to-cell trafficking properties of any protein of interest.

Ensemble-based evaluation for protein structure models.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2016-06-15

Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts' intuitive assessment of computational models and provides information of practical usefulness of models. https://bitbucket.org/mjamroz/flexscore dkihara@purdue.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Ensemble-based evaluation for protein structure models

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2016-01-01

Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models. Availability and implementation: https://bitbucket.org/mjamroz/flexscore Contact: dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307633

A Selective Review of Group Selection in High-Dimensional Models

PubMed Central

Huang, Jian; Breheny, Patrick; Ma, Shuangge

2013-01-01

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study. PMID:24174707

Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches.

PubMed

Yugandhar, K; Gromiha, M Michael

2014-09-01

Protein-protein interactions are intrinsic to virtually every cellular process. Predicting the binding affinity of protein-protein complexes is one of the challenging problems in computational and molecular biology. In this work, we related sequence features of protein-protein complexes with their binding affinities using machine learning approaches. We set up a database of 185 protein-protein complexes for which the interacting pairs are heterodimers and their experimental binding affinities are available. On the other hand, we have developed a set of 610 features from the sequences of protein complexes and utilized Ranker search method, which is the combination of Attribute evaluator and Ranker method for selecting specific features. We have analyzed several machine learning algorithms to discriminate protein-protein complexes into high and low affinity groups based on their Kd values. Our results showed a 10-fold cross-validation accuracy of 76.1% with the combination of nine features using support vector machines. Further, we observed accuracy of 83.3% on an independent test set of 30 complexes. We suggest that our method would serve as an effective tool for identifying the interacting partners in protein-protein interaction networks and human-pathogen interactions based on the strength of interactions. © 2014 Wiley Periodicals, Inc.

Reactibodies generated by kinetic selection couple chemical reactivity with favorable protein dynamics

PubMed Central

Smirnov, Ivan; Carletti, Eugénie; Kurkova, Inna; Nachon, Florian; Nicolet, Yvain; Mitkevich, Vladimir A.; Débat, Hélène; Avalle, Bérangère; Belogurov, Alexey A.; Kuznetsov, Nikita; Reshetnyak, Andrey; Masson, Patrick; Tonevitsky, Alexander G.; Ponomarenko, Natalia; Makarov, Alexander A.; Friboulet, Alain; Tramontano, Alfonso; Gabibov, Alexander

2011-01-01

Igs offer a versatile template for combinatorial and rational design approaches to the de novo creation of catalytically active proteins. We have used a covalent capture selection strategy to identify biocatalysts from within a human semisynthetic antibody variable fragment library that uses a nucleophilic mechanism. Specific phosphonylation at a single tyrosine within the variable light-chain framework was confirmed in a recombinant IgG construct. High-resolution crystallographic structures of unmodified and phosphonylated Fabs display a 15-Å-deep two-chamber cavity at the interface of variable light (VL) and variable heavy (VH) fragments having a nucleophilic tyrosine at the base of the site. The depth and structure of the pocket are atypical of antibodies in general but can be compared qualitatively with the catalytic site of cholinesterases. A structurally disordered heavy chain complementary determining region 3 loop, constituting a wall of the cleft, is stabilized after covalent modification by hydrogen bonding to the phosphonate tropinol moiety. These features and presteady state kinetics analysis indicate that an induced fit mechanism operates in this reaction. Mutations of residues located in this stabilized loop do not interfere with direct contacts to the organophosphate ligand but can interrogate second shell interactions, because the H3 loop has a conformation adjusted for binding. Kinetic and thermodynamic parameters along with computational docking support the active site model, including plasticity and simple catalytic components. Although relatively uncomplicated, this catalytic machinery displays both stereo- and chemical selectivity. The organophosphate pesticide paraoxon is hydrolyzed by covalent catalysis with rate-limiting dephosphorylation. This reactibody is, therefore, a kinetically selected protein template that has enzyme-like catalytic attributes. PMID:21896761

Using Denatured Egg White as a Macroscopic Model for Teaching Protein Structure and Introducing Protein Synthesis for High School Students

NASA Astrophysics Data System (ADS)

Correia, Paulo R. M.; Torres, Bayardo B.

2007-12-01

The success of teaching molecular and atomic phenomena depends on the didactical strategy and the media selection adopted, in consideration of the level of abstraction of the subject to be taught and the students' capability to deal with abstract operations. Dale's cone of experience was employed to plan three 50-minute classes to discuss protein denaturation from a chemical point of view. Only low abstraction level activities were selected: (i) two demonstrations showing the denaturation of albumin by heating and by changing the solvent, (ii) the assembly of a macroscopic model representing the protein molecule, and (iii) a role-play for simulating glucagon synthesis. A student-centered approach and collaborative learning were used throughout the classes. The use of macroscopic models is a powerful didactical strategy to represent molecular and atomic events. They can convert microscopic entities into touchable objects, reducing the abstraction level required to discuss chemistry with high school students. Thus, interesting topics involving molecules and their behavior can take place efficiently when mediated by concrete experiences.

PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

PubMed Central

2013-01-01

Background Assessment of potential allergenicity of protein is necessary whenever transgenic proteins are introduced into the food chain. Bioinformatics approaches in allergen prediction have evolved appreciably in recent years to increase sophistication and performance. However, what are the critical features for protein's allergenicity have been not fully investigated yet. Results We presented a more comprehensive model in 128 features space for allergenic proteins prediction by integrating various properties of proteins, such as biochemical and physicochemical properties, sequential features and subcellular locations. The overall accuracy in the cross-validation reached 93.42% to 100% with our new method. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) procedure were applied to obtain which features are essential for allergenicity. Results of the performance comparisons showed the superior of our method to the existing methods used widely. More importantly, it was observed that the features of subcellular locations and amino acid composition played major roles in determining the allergenicity of proteins, particularly extracellular/cell surface and vacuole of the subcellular locations for wheat and soybean. To facilitate the allergen prediction, we implemented our computational method in a web application, which can be available at http://gmobl.sjtu.edu.cn/PREAL/index.php. Conclusions Our new approach could improve the accuracy of allergen prediction. And the findings may provide novel insights for the mechanism of allergies. PMID:24565053

A Proteomics Approach to the Protein Normalization Problem: Selection of Unvarying Proteins for MS-Based Proteomics and Western Blotting.

PubMed

Wiśniewski, Jacek R; Mann, Matthias

2016-07-01

Proteomics and other protein-based analysis methods such as Western blotting all face the challenge of discriminating changes in the levels of proteins of interest from inadvertent changes in the amount loaded for analysis. Mass-spectrometry-based proteomics can now estimate the relative and absolute amounts of thousands of proteins across diverse biological systems. We reasoned that this new technology could prove useful for selection of very stably expressed proteins that could serve as better loading controls than those traditionally employed. Large-scale proteomic analyses of SDS lysates of cultured cells and tissues revealed deglycase DJ-1 as the protein with the lowest variability in abundance among different cell types in human, mouse, and amphibian cells. The protein constitutes 0.069 ± 0.017% of total cellular protein and occurs at a specific concentration of 34.6 ± 8.7 pmol/mg of total protein. Since DJ-1 is ubiquitous and therefore easily detectable with several peptides, it can be helpful in normalization of proteomic data sets. In addition, DJ-1 appears to be an advantageous loading control for Western blot that is superior to those used commonly used, allowing comparisons between tissues and cells originating from evolutionarily distant vertebrate species. Notably, this is not possible by the detection and quantitation of housekeeping proteins, which are often used in the Western blot technique. The approach introduced here can be applied to select the most appropriate loading controls for MS-based proteomics or Western blotting in any biological system.

Sexual selection and the adaptive evolution of PKDREJ protein in primates and rodents.

PubMed

Vicens, Alberto; Gómez Montoto, Laura; Couso-Ferrer, Francisco; Sutton, Keith A; Roldan, Eduardo R S

2015-02-01

PKDREJ is a testis-specific protein thought to be located on the sperm surface. Functional studies in the mouse revealed that loss of PKDREJ has effects on sperm transport and the ability to undergo an induced acrosome reaction. Thus, PKDREJ has been considered a potential target of post-copulatory sexual selection in the form of sperm competition. Proteins involved in reproductive processes often show accelerated evolution. In many cases, this rapid divergence is promoted by positive selection which may be driven, at least in part, by post-copulatory sexual selection. We analysed the evolution of the PKDREJ protein in primates and rodents and assessed whether PKDREJ divergence is associated with testes mass relative to body mass, which is a reliable proxy of sperm competition levels. Evidence of an association between the evolutionary rate of the PKDREJ gene and testes mass relative to body mass was not found in primates. Among rodents, evidence of positive selection was detected in the Pkdrej gene in the family Cricetidae but not in Muridae. We then assessed whether Pkdrej divergence is associated with episodes of sperm competition in these families. We detected a positive significant correlation between the evolutionary rates of Pkdrej and testes mass relative to body mass in cricetids. These findings constitute the first evidence of post-copulatory sexual selection influencing the evolution of a protein that participates in the mechanisms regulating sperm transport and the acrosome reaction, strongly suggesting that positive selection may act on these fertilization steps, leading to advantages in situations of sperm competition. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

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

Booe, Jason M.; Walker, Christopher S.; Barwell, James

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind relatedmore » GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. Lastly, the structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes.« less

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

DOE PAGES

Booe, Jason M.; Walker, Christopher S.; Barwell, James; ...

2015-05-14

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind relatedmore » GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. Lastly, the structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes.« less

A feature-based approach to modeling protein–protein interaction hot spots

PubMed Central

Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-01-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

A Primer for Model Selection: The Decisive Role of Model Complexity

NASA Astrophysics Data System (ADS)

Höge, Marvin; Wöhling, Thomas; Nowak, Wolfgang

2018-03-01

Selecting a "best" model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the "best" trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

Usefulness of a Darwinian System in a Biotechnological Application: Evolution of Optical Window Fluorescent Protein Variants under Selective Pressure

PubMed Central

Ng, David; Pauli, Jutta; Resch-Genger, Ute; Kühn, Enrico; Heuer, Steffen; Beisker, Wolfgang; Köster, Reinhard W.; Zitzelsberger, Horst; Caldwell, Randolph B

2014-01-01

With rare exceptions, natural evolution is an extremely slow process. One particularly striking exception in the case of protein evolution is in the natural production of antibodies. Developing B cells activate and diversify their immunoglobulin (Ig) genes by recombination, gene conversion (GC) and somatic hypermutation (SHM). Iterative cycles of hypermutation and selection continue until antibodies of high antigen binding specificity emerge (affinity maturation). The avian B cell line DT40, a cell line which is highly amenable to genetic manipulation and exhibits a high rate of targeted integration, utilizes both GC and SHM. Targeting the DT40's diversification machinery onto transgenes of interest inserted into the Ig loci and coupling selective pressure based on the desired outcome mimics evolution. Here we further demonstrate the usefulness of this platform technology by selectively pressuring a large shift in the spectral properties of the fluorescent protein eqFP615 into the highly stable and advanced optical imaging expediting fluorescent protein Amrose. The method is advantageous as it is time and cost effective and no prior knowledge of the outcome protein's structure is necessary. Amrose was evolved to have high excitation at 633 nm and excitation/emission into the far-red, which is optimal for whole-body and deep tissue imaging as we demonstrate in the zebrafish and mouse model. PMID:25192257

Usefulness of a Darwinian system in a biotechnological application: evolution of optical window fluorescent protein variants under selective pressure.

PubMed

Schoetz, Ulrike; Deliolanis, Nikolaos C; Ng, David; Pauli, Jutta; Resch-Genger, Ute; Kühn, Enrico; Heuer, Steffen; Beisker, Wolfgang; Köster, Reinhard W; Zitzelsberger, Horst; Caldwell, Randolph B

2014-01-01

With rare exceptions, natural evolution is an extremely slow process. One particularly striking exception in the case of protein evolution is in the natural production of antibodies. Developing B cells activate and diversify their immunoglobulin (Ig) genes by recombination, gene conversion (GC) and somatic hypermutation (SHM). Iterative cycles of hypermutation and selection continue until antibodies of high antigen binding specificity emerge (affinity maturation). The avian B cell line DT40, a cell line which is highly amenable to genetic manipulation and exhibits a high rate of targeted integration, utilizes both GC and SHM. Targeting the DT40's diversification machinery onto transgenes of interest inserted into the Ig loci and coupling selective pressure based on the desired outcome mimics evolution. Here we further demonstrate the usefulness of this platform technology by selectively pressuring a large shift in the spectral properties of the fluorescent protein eqFP615 into the highly stable and advanced optical imaging expediting fluorescent protein Amrose. The method is advantageous as it is time and cost effective and no prior knowledge of the outcome protein's structure is necessary. Amrose was evolved to have high excitation at 633 nm and excitation/emission into the far-red, which is optimal for whole-body and deep tissue imaging as we demonstrate in the zebrafish and mouse model.

Fast and Selective Modification of Thiol Proteins/Peptides by N-(Phenylseleno)phthalimide

NASA Astrophysics Data System (ADS)

Wang, Zhengfang; Zhang, Yun; Zhang, Hao; Harrington, Peter B.; Chen, Hao

2012-03-01

We previously reported that selenamide reagents such as ebselen and N-(phenylseleno)phthalimide (NPSP) can be used to selectively derivatize thiols for mass spectrometric analysis, and the introduced selenium tags are useful as they could survive or removed with collision-induced dissociation (CID). Described herein is the further study of the reactivity of various protein/peptide thiols toward NPSP and its application to derivatize thiol peptides in protein digests. With a modified protocol (i.e., dissolving NPSP in acetonitrile instead of aqueous solvent), we found that quantitative conversion of thiols can be obtained in seconds, using NPSP in a slight excess amount (NPSP:thiol of 1.1-2:1). Further investigation shows that the thiol reactivity toward NPSP reflects its chemical environment and accessibility in proteins/peptides. For instance, adjacent basic amino acid residues increase the thiol reactivity, probably because they could stabilize the thiolate form to facilitate the nucleophilic attack of thiol on NPSP. In the case of creatine phosphokinase, the native protein predominately has one thiol reacted with NPSP while all of four thiol groups of the denatured protein can be derivatized, in accordance with the corresponding protein conformation. In addition, thiol peptides in protein/peptide enzymatic digests can be quickly and effectively tagged by NPSP following tri- n-butylphosphine (TBP) reduction. Notably, all three thiols of the peptide QCCASVCSL in the insulin peptic digest can be modified simultaneously by NPSP. These results suggest a novel and selective method for protecting thiols in the bottom-up approach for protein structure analysis.

Positive selection and propeptide repeats promote rapid interspecific divergence of a gastropod sperm protein.

PubMed

Hellberg, M E; Moy, G W; Vacquier, V D

2000-03-01

Male-specific proteins have increasingly been reported as targets of positive selection and are of special interest because of the role they may play in the evolution of reproductive isolation. We report the rapid interspecific divergence of cDNA encoding a major acrosomal protein of unknown function (TMAP) of sperm from five species of teguline gastropods. A mitochondrial DNA clock (calibrated by congeneric species divided by the Isthmus of Panama) estimates that these five species diverged 2-10 MYA. Inferred amino acid sequences reveal a propeptide that has diverged rapidly between species. The mature protein has diverged faster still due to high nonsynonymous substitution rates (> 25 nonsynonymous substitutions per site per 10(9) years). cDNA encoding the mature protein (89-100 residues) shows evidence of positive selection (Dn/Ds > 1) for 4 of 10 pairwise species comparisons. cDNA and predicted secondary-structure comparisons suggest that TMAP is neither orthologous nor paralogous to abalone lysin, and thus marks a second, phylogenetically independent, protein subject to strong positive selection in free-spawning marine gastropods. In addition, an internal repeat in one species (Tegula aureotincta) produces a duplicated cleavage site which results in two alternatively processed mature proteins differing by nine amino acid residues. Such alternative processing may provide a mechanism for introducing novel amino acid sequence variation at the amino-termini of proteins. Highly divergent TMAP N-termini from two other tegulines (Tegula regina and Norrisia norrisii) may have originated by such a mechanism.

Highly selective isolation and purification of heme proteins in biological samples using multifunctional magnetic nanospheres.

PubMed

Liu, Yating; Li, Yan; Wei, Yun

2014-12-01

Magnetic particles with suitable surface modification are capable of binding proteins selectively, and magnetic separations have advantages of rapidity, convenience, and high selectivity. In this paper, new magnetic nanoparticles modified with imidazolium ionic liquid (Fe3O4 @SiO2 @ILs) were successfully fabricated. N-Methylimidazolium was immobilized onto silica-coated magnetic nanoparticles via γ-chloropropyl modification as a magnetic nanoadsorbent for heme protein separation. The particle size was about 90 nm without significant aggregation during the preparation process. Hemoglobin as one of heme proteins used in this experiment was compared with other nonheme proteins. It has been found that the magnetic nanoparticles can be used for more rapid, efficient, and specific adsorption of hemoglobin with a binding capacity as high as 5.78 mg/mg. In comparison with other adsorption materials of proteins in the previous reports, Fe3 O4 @SiO2 @ILs magnetic nanoparticles exhibit the excellent performance in isolation of heme proteins with higher binding capacity and selectivity. In addition, a short separation time makes the functionalized nanoparticles suitable for purifying unstable proteins, as well as having other potential applications in a variety of biomedical fields. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A quantitative model of optimal data selection in Wason's selection task.

PubMed

Hattori, Masasi

2002-10-01

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

No Photon Wasted: An Efficient and Selective Singlet Oxygen Photosensitizing Protein.

PubMed

Westberg, Michael; Bregnhøj, Mikkel; Etzerodt, Michael; Ogilby, Peter R

2017-10-12

Optogenetics has been, and will continue to be, a boon to mechanistic studies of cellular processes. Genetically encodable proteins that sensitize the production of reactive oxygen species (ROS) are expected to play an increasingly important role, particularly in elucidating mechanisms of temporally and spatially dependent cell signaling. However, a substantial challenge in developing such photosensitizing proteins has been to funnel the optical excitation energy into the initial selective production of only one ROS. Singlet molecular oxygen, O 2 (a 1 Δ g ), is a ROS known to have a wide range of effects on cell function. Nevertheless, mechanistic details of singlet oxygen's behavior in a cell are lacking. On the basis of the rational optimization of a LOV-derived flavoprotein, we now report the development and photophysical characterization of a protein-encased photosensitizer that efficiently and selectively produces singlet oxygen at the expense of other ROS, especially ROS that derive from photoinduced electron transfer reactions. These results set the stage for a plethora of new experiments to elucidate ROS-mediated events in cells.

The Use of Experimental Structures to Model Protein Dynamics

PubMed Central

Katebi, Ataur R.; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L.

2014-01-01

Summary The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high – for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods – Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to

Selective Sorting of Cargo Proteins into Bacterial Membrane Vesicles*

PubMed Central

Haurat, M. Florencia; Aduse-Opoku, Joseph; Rangarajan, Minnie; Dorobantu, Loredana; Gray, Murray R.; Curtis, Michael A.; Feldman, Mario F.

2011-01-01

In contrast to the well established multiple cellular roles of membrane vesicles in eukaryotic cell biology, outer membrane vesicles (OMV) produced via blebbing of prokaryotic membranes have frequently been regarded as cell debris or microscopy artifacts. Increasingly, however, bacterial membrane vesicles are thought to play a role in microbial virulence, although it remains to be determined whether OMV result from a directed process or from passive disintegration of the outer membrane. Here we establish that the human oral pathogen Porphyromonas gingivalis has a mechanism to selectively sort proteins into OMV, resulting in the preferential packaging of virulence factors into OMV and the exclusion of abundant outer membrane proteins from the protein cargo. Furthermore, we show a critical role for lipopolysaccharide in directing this sorting mechanism. The existence of a process to package specific virulence factors into OMV may significantly alter our current understanding of host-pathogen interactions. PMID:21056982

Molecular Mechanism of Selectivity among G Protein-Coupled Receptor Kinase 2 Inhibitors

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

Thal, David M.; Yeow, Raymond Y.; Schoenau, Christian

2012-07-11

G protein-coupled receptors (GPCRs) are key regulators of cell physiology and control processes ranging from glucose homeostasis to contractility of the heart. A major mechanism for the desensitization of activated GPCRs is their phosphorylation by GPCR kinases (GRKs). Overexpression of GRK2 is strongly linked to heart failure, and GRK2 has long been considered a pharmaceutical target for the treatment of cardiovascular disease. Several lead compounds developed by Takeda Pharmaceuticals show high selectivity for GRK2 and therapeutic potential for the treatment of heart failure. To understand how these drugs achieve their selectivity, we determined crystal structures of the bovine GRK2-G{beta}{gamma} complexmore » in the presence of two of these inhibitors. Comparison with the apoGRK2-G{beta}{gamma} structure demonstrates that the compounds bind in the kinase active site in a manner similar to that of the AGC kinase inhibitor balanol. Both balanol and the Takeda compounds induce a slight closure of the kinase domain, the degree of which correlates with the potencies of the inhibitors. Based on our crystal structures and homology modeling, we identified five amino acids surrounding the inhibitor binding site that we hypothesized could contribute to inhibitor selectivity. However, our results indicate that these residues are not major determinants of selectivity among GRK subfamilies. Rather, selectivity is achieved by the stabilization of a unique inactive conformation of the GRK2 kinase domain.« less

Comparison of selected analytical techniques for protein sizing, quantitation and molecular weight determination.

PubMed

Goetz, H; Kuschel, M; Wulff, T; Sauber, C; Miller, C; Fisher, S; Woodward, C

2004-09-30

Protein analysis techniques are developing fast due to the growing number of proteins obtained by recombinant DNA techniques. In the present paper we compare selected techniques, which are used for protein sizing, quantitation and molecular weight determination: sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE), lab-on-a-chip or microfluidics technology (LoaC), size exclusion chromatography (SEC) and mass spectrometry (MS). We compare advantages and limitations of each technique in respect to different application areas, analysis time, protein sizing and quantitation performance.

Dissecting protein:protein interactions between transcription factors with an RNA aptamer.

PubMed Central

Tian, Y; Adya, N; Wagner, S; Giam, C Z; Green, M R; Ellington, A D

1995-01-01

Nucleic acid aptamers isolated from random sequence pools have generally proven useful at inhibiting the interactions of nucleic acid binding proteins with their cognate nucleic acids. In order to develop reagents that could also be used to study protein:protein interactions, we have used in vitro selection to search for RNA aptamers that could interact with the transactivating protein Tax from human T-cell leukemia virus. Tax does not normally bind to nucleic acids, but instead stimulates transcription by interacting with a variety of cellular transcription factors, including the cyclic AMP-response element binding protein (CREB), NF-kappa B, and the serum response factor (SRF). Starting from a pool of greater than 10(13) different RNAs with a core of 120 random sequence positions, RNAs were selected for their ability to be co-retained on nitrocellulose filters with Tax. After five cycles of selection and amplification, a single nucleic acid species remained. This aptamer was found to bind Tax with high affinity and specificity, and could disrupt complex formation between Tax and NF-kappa B, but not with SRF. The differential effects of our aptamer probe on protein:protein interactions suggest a model for how the transcription factor binding sites on the surface of the Tax protein are organized. This model is consistent with data from a variety of other studies. PMID:7489503

The different roles of selective autophagic protein degradation in mammalian cells.

PubMed

Wang, Da-wei; Peng, Zhen-ju; Ren, Guang-fang; Wang, Guang-xin

2015-11-10

Autophagy is an intracellular pathway for bulk protein degradation and the removal of damaged organelles by lysosomes. Autophagy was previously thought to be unselective; however, studies have increasingly confirmed that autophagy-mediated protein degradation is highly regulated. Abnormal autophagic protein degradation has been associated with multiple human diseases such as cancer, neurological disability and cardiovascular disease; therefore, further elucidation of protein degradation by autophagy may be beneficial for protein-based clinical therapies. Macroautophagy and chaperone-mediated autophagy (CMA) can both participate in selective protein degradation in mammalian cells, but the process is quite different in each case. Here, we summarize the various types of macroautophagy and CMA involved in determining protein degradation. For this summary, we divide the autophagic protein degradation pathways into four categories: the post-translational modification dependent and independent CMA pathways and the ubiquitin dependent and independent macroautophagy pathways, and describe how some non-canonical pathways and modifications such as phosphorylation, acetylation and arginylation can influence protein degradation by the autophagy lysosome system (ALS). Finally, we comment on why autophagy can serve as either diagnostics or therapeutic targets in different human diseases.

Odorants selectively activate distinct G protein subtypes in olfactory cilia.

PubMed

Schandar, M; Laugwitz, K L; Boekhoff, I; Kroner, C; Gudermann, T; Schultz, G; Breer, H

1998-07-03

Chemoelectrical signal transduction in olfactory neurons appears to involve intracellular reaction cascades mediated by heterotrimeric GTP-binding proteins. In this study attempts were made to identify the G protein subtype(s) in olfactory cilia that are activated by the primary (odorant) signal. Antibodies directed against the alpha subunits of distinct G protein subtypes interfered specifically with second messenger reponses elicited by defined subsets of odorants; odor-induced cAMP-formation was attenuated by Galphas antibodies, whereas Galphao antibodies blocked odor-induced inositol 1,4, 5-trisphosphate (IP3) formation. Activation-dependent photolabeling of Galpha subunits with [alpha-32P]GTP azidoanilide followed by immunoprecipitation using subtype-specific antibodies enabled identification of particular individual G protein subtypes that were activated upon stimulation of isolated olfactory cilia by chemically distinct odorants. For example odorants that elicited a cAMP response resulted in labeling of a Galphas-like protein, whereas odorants that elicited an IP3 response led to the labeling of a Galphao-like protein. Since odorant-induced IP3 formation was also blocked by Gbeta antibodies, activation of olfactory phospholipase C might be mediated by betagamma subunits of a Go-like G protein. These results indicate that different subsets of odorants selectively trigger distinct reaction cascades and provide evidence for dual transduction pathways in olfactory signaling.

Selection of specific protein binders for pre-defined targets from an optimized library of artificial helicoidal repeat proteins (alphaRep).

PubMed

Guellouz, Asma; Valerio-Lepiniec, Marie; Urvoas, Agathe; Chevrel, Anne; Graille, Marc; Fourati-Kammoun, Zaineb; Desmadril, Michel; van Tilbeurgh, Herman; Minard, Philippe

2013-01-01

We previously designed a new family of artificial proteins named αRep based on a subgroup of thermostable helicoidal HEAT-like repeats. We have now assembled a large optimized αRep library. In this library, the side chains at each variable position are not fully randomized but instead encoded by a distribution of codons based on the natural frequency of side chains of the natural repeats family. The library construction is based on a polymerization of micro-genes and therefore results in a distribution of proteins with a variable number of repeats. We improved the library construction process using a "filtration" procedure to retain only fully coding modules that were recombined to recreate sequence diversity. The final library named Lib2.1 contains 1.7×10(9) independent clones. Here, we used phage display to select, from the previously described library or from the new library, new specific αRep proteins binding to four different non-related predefined protein targets. Specific binders were selected in each case. The results show that binders with various sizes are selected including relatively long sequences, with up to 7 repeats. ITC-measured affinities vary with Kd values ranging from micromolar to nanomolar ranges. The formation of complexes is associated with a significant thermal stabilization of the bound target protein. The crystal structures of two complexes between αRep and their cognate targets were solved and show that the new interfaces are established by the variable surfaces of the repeated modules, as well by the variable N-cap residues. These results suggest that αRep library is a new and versatile source of tight and specific binding proteins with favorable biophysical properties.

Succination is Increased on Select Proteins in the Brainstem of the NADH dehydrogenase (ubiquinone) Fe-S protein 4 (Ndufs4) Knockout Mouse, a Model of Leigh Syndrome*

PubMed Central

Piroli, Gerardo G.; Manuel, Allison M.; Clapper, Anna C.; Walla, Michael D.; Baatz, John E.; Palmiter, Richard D.; Quintana, Albert; Frizzell, Norma

2016-01-01

Elevated fumarate concentrations as a result of Krebs cycle inhibition lead to increases in protein succination, an irreversible post-translational modification that occurs when fumarate reacts with cysteine residues to generate S-(2-succino)cysteine (2SC). Metabolic events that reduce NADH re-oxidation can block Krebs cycle activity; therefore we hypothesized that oxidative phosphorylation deficiencies, such as those observed in some mitochondrial diseases, would also lead to increased protein succination. Using the Ndufs4 knockout (Ndufs4 KO) mouse, a model of Leigh syndrome, we demonstrate for the first time that protein succination is increased in the brainstem (BS), particularly in the vestibular nucleus. Importantly, the brainstem is the most affected region exhibiting neurodegeneration and astrocyte and microglial proliferation, and these mice typically die of respiratory failure attributed to vestibular nucleus pathology. In contrast, no increases in protein succination were observed in the skeletal muscle, corresponding with the lack of muscle pathology observed in this model. 2D SDS-PAGE followed by immunoblotting for succinated proteins and MS/MS analysis of BS proteins allowed us to identify the voltage-dependent anion channels 1 and 2 as specific targets of succination in the Ndufs4 knockout. Using targeted mass spectrometry, Cys77 and Cys48 were identified as endogenous sites of succination in voltage-dependent anion channels 2. Given the important role of voltage-dependent anion channels isoforms in the exchange of ADP/ATP between the cytosol and the mitochondria, and the already decreased capacity for ATP synthesis in the Ndufs4 KO mice, we propose that the increased protein succination observed in the BS of these animals would further decrease the already compromised mitochondrial function. These data suggest that fumarate is a novel biochemical link that may contribute to the progression of the neuropathology in this mitochondrial disease model

Models of globular proteins in aqueous solutions

NASA Astrophysics Data System (ADS)

Wentzel, Nathaniel James

Protein crystallization is a continuing area of research. Currently, there is no universal theory for the conditions required to crystallize proteins. A better understanding of protein crystallization will be helpful in determining protein structure and preventing and treating certain diseases. In this thesis, we will extend the understanding of globular proteins in aqueous solutions by analyzing various models for protein interactions. Experiments have shown that the liquid-liquid phase separation curves for lysozyme in solution with salt depend on salt type and salt concentration. We analyze a simple square well model for this system whose well depth depends on salt type and salt concentration, to determine the phase coexistence surfaces from experimental data. The surfaces, calculated from a single Monte Carlo simulation and a simple scaling argument, are shown as a function of temperature, salt concentration and protein concentration for two typical salts. Urate Oxidase from Asperigillus flavus is a protein used for studying the effects of polymers on the crystallization of large proteins. Experiments have determined some aspects of the phase diagram. We use Monte Carlo techniques and perturbation theory to predict the phase diagram for a model of urate oxidase in solution with PEG. The model used includes an electrostatic interaction, van der Waals attraction, and a polymerinduced depletion interaction. The results agree quantitatively with experiments. Anisotropy plays a role in globular protein interactions, including the formation of hemoglobin fibers in sickle cell disease. Also, the solvent conditions have been shown to play a strong role in the phase behavior of some aqueous protein solutions. Each has previously been treated separately in theoretical studies. Here we propose and analyze a simple, combined model that treats both anisotropy and solvent effects. We find that this model qualitatively explains some phase behavior, including the existence of

Mathematical model of zinc absorption: effects of dietary calcium, protein and iron on zinc absorption

PubMed Central

Miller, Leland V.; Krebs, Nancy F.; Hambidge, K. Michael

2013-01-01

A previously described mathematical model of Zn absorption as a function of total daily dietary Zn and phytate was fitted to data from studies in which dietary Ca, Fe and protein were also measured. An analysis of regression residuals indicated statistically significant positive relationships between the residuals and Ca, Fe and protein, suggesting that the presence of any of these dietary components enhances Zn absorption. Based on the hypotheses that (1) Ca and Fe both promote Zn absorption by binding with phytate and thereby making it unavailable for binding Zn and (2) protein enhances the availability of Zn for transporter binding, the model was modified to incorporate these effects. The new model of Zn absorption as a function of dietary Zn, phytate, Ca, Fe and protein was then fitted to the data. The proportion of variation in absorbed Zn explained by the new model was 0·88, an increase from 0·82 with the original model. A reduced version of the model without Fe produced an equally good fit to the data and an improved value for the model selection criterion, demonstrating that when dietary Ca and protein are controlled for, there is no evidence that dietary Fe influences Zn absorption. Regression residuals and testing with additional data supported the validity of the new model. It was concluded that dietary Ca and protein modestly enhanced Zn absorption and Fe had no statistically discernable effect. Furthermore, the model provides a meaningful foundation for efforts to model nutrient interactions in mineral absorption. PMID:22617116

Mathematical model of zinc absorption: effects of dietary calcium, protein and iron on zinc absorption.

PubMed

Miller, Leland V; Krebs, Nancy F; Hambidge, K Michael

2013-02-28

A previously described mathematical model of Zn absorption as a function of total daily dietary Zn and phytate was fitted to data from studies in which dietary Ca, Fe and protein were also measured. An analysis of regression residuals indicated statistically significant positive relationships between the residuals and Ca, Fe and protein, suggesting that the presence of any of these dietary components enhances Zn absorption. Based on the hypotheses that (1) Ca and Fe both promote Zn absorption by binding with phytate and thereby making it unavailable for binding Zn and (2) protein enhances the availability of Zn for transporter binding, the model was modified to incorporate these effects. The new model of Zn absorption as a function of dietary Zn, phytate, Ca, Fe and protein was then fitted to the data. The proportion of variation in absorbed Zn explained by the new model was 0·88, an increase from 0·82 with the original model. A reduced version of the model without Fe produced an equally good fit to the data and an improved value for the model selection criterion, demonstrating that when dietary Ca and protein are controlled for, there is no evidence that dietary Fe influences Zn absorption. Regression residuals and testing with additional data supported the validity of the new model. It was concluded that dietary Ca and protein modestly enhanced Zn absorption and Fe had no statistically discernable effect. Furthermore, the model provides a meaningful foundation for efforts to model nutrient interactions in mineral absorption.

Protein folding simulations: from coarse-grained model to all-atom model.

PubMed

Zhang, Jian; Li, Wenfei; Wang, Jun; Qin, Meng; Wu, Lei; Yan, Zhiqiang; Xu, Weixin; Zuo, Guanghong; Wang, Wei

2009-06-01

Protein folding is an important and challenging problem in molecular biology. During the last two decades, molecular dynamics (MD) simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics, and structure-stability-function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions such as the minimal number of amino acid or the evolution principle of protein families. Nowadays, the MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse-grained models and studying larger and more complex molecular systems such as protein-protein complex and their assembling process, amyloid related aggregations, and structure and motion of chaperons, motors, channels and virus capsides; the second trend is toward building high resolution models and explore more detailed and accurate pictures of protein folding and the associated processes, such as the coordination bond or disulfide bond involved folding, the polarization, charge transfer and protonate/deprotonate process involved in metal coupled folding, and the ion permeation and its coupling with the kinetics of channels. On these new territories, MD simulations have given many promising results and will continue to offer exciting views. Here, we review several new subjects investigated by using MD simulations as well as the corresponding developments of appropriate protein models. These include but are not limited to the attempt to go beyond the topology based Gō-like model and characterize the energetic factors in protein structures and dynamics, the study of the thermodynamics and kinetics of disulfide bond involved protein folding, the modeling of the interactions between chaperonin and the encapsulated protein and the protein folding under this circumstance, the effort to clarify the important yet still elusive folding mechanism of protein BBL

Modelling and simulation of biased agonism dynamics at a G protein-coupled receptor.

PubMed

Bridge, L J; Mead, J; Frattini, E; Winfield, I; Ladds, G

2018-04-07

Theoretical models of G protein-coupled receptor (GPCR) concentration-response relationships often assume an agonist producing a single functional response via a single active state of the receptor. These models have largely been analysed assuming steady-state conditions. There is now much experimental evidence to suggest that many GPCRs can exist in multiple receptor conformations and elicit numerous functional responses, with ligands having the potential to activate different signalling pathways to varying extents-a concept referred to as biased agonism, functional selectivity or pluri-dimensional efficacy. Moreover, recent experimental results indicate a clear possibility for time-dependent bias, whereby an agonist's bias with respect to different pathways may vary dynamically. Efforts towards understanding the implications of temporal bias by characterising and quantifying ligand effects on multiple pathways will clearly be aided by extending current equilibrium binding and biased activation models to include G protein activation dynamics. Here, we present a new model of time-dependent biased agonism, based on ordinary differential equations for multiple cubic ternary complex activation models with G protein cycle dynamics. This model allows simulation and analysis of multi-pathway activation bias dynamics at a single receptor for the first time, at the level of active G protein (α GTP ), towards the analysis of dynamic functional responses. The model is generally applicable to systems with N G G proteins and N* active receptor states. Numerical simulations for N G =N * =2 reveal new insights into the effects of system parameters (including cooperativities, and ligand and receptor concentrations) on bias dynamics, highlighting new phenomena including the dynamic inter-conversion of bias direction. Further, we fit this model to 'wet' experimental data for two competing G proteins (G i and G s ) that become activated upon stimulation of the adenosine A 1

Model selection for multi-component frailty models.

PubMed

Ha, Il Do; Lee, Youngjo; MacKenzie, Gilbert

2007-11-20

Various frailty models have been developed and are now widely used for analysing multivariate survival data. It is therefore important to develop an information criterion for model selection. However, in frailty models there are several alternative ways of forming a criterion and the particular criterion chosen may not be uniformly best. In this paper, we study an Akaike information criterion (AIC) on selecting a frailty structure from a set of (possibly) non-nested frailty models. We propose two new AIC criteria, based on a conditional likelihood and an extended restricted likelihood (ERL) given by Lee and Nelder (J. R. Statist. Soc. B 1996; 58:619-678). We compare their performance using well-known practical examples and demonstrate that the two criteria may yield rather different results. A simulation study shows that the AIC based on the ERL is recommended, when attention is focussed on selecting the frailty structure rather than the fixed effects.

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

Site‐Selective Disulfide Modification of Proteins: Expanding Diversity beyond the Proteome

PubMed Central

Kuan, Seah Ling; Wang, Tao

2016-01-01

Abstract The synthetic transformation of polypeptides with molecular accuracy holds great promise for providing functional and structural diversity beyond the proteome. Consequently, the last decade has seen an exponential growth of site‐directed chemistry to install additional features into peptides and proteins even inside living cells. The disulfide rebridging strategy has emerged as a powerful tool for site‐selective modifications since most proteins contain disulfide bonds. In this Review, we present the chemical design, advantages and limitations of the disulfide rebridging reagents, while summarizing their relevance for synthetic customization of functional protein bioconjugates, as well as the resultant impact and advancement for biomedical applications. PMID:27778400

DockQ: A Quality Measure for Protein-Protein Docking Models

PubMed Central

Basu, Sankar

2016-01-01

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure

Transgenic Parasites Stably Expressing Full-Length Plasmodium falciparum Circumsporozoite Protein as a Model for Vaccine Down-Selection in Mice Using Sterile Protection as an Endpoint

PubMed Central

Porter, Michael D.; Nicki, Jennifer; Pool, Christopher D.; DeBot, Margot; Illam, Ratish M.; Brando, Clara; Bozick, Brooke; De La Vega, Patricia; Angra, Divya; Spaccapelo, Roberta; Crisanti, Andrea; Murphy, Jittawadee R.; Bennett, Jason W.; Schwenk, Robert J.; Ockenhouse, Christian F.

2013-01-01

Circumsporozoite protein (CSP) of Plasmodium falciparum is a protective human malaria vaccine candidate. There is an urgent need for models that can rapidly down-select novel CSP-based vaccine candidates. In the present study, the mouse-mosquito transmission cycle of a transgenic Plasmodium berghei malaria parasite stably expressing a functional full-length P. falciparum CSP was optimized to consistently produce infective sporozoites for protection studies. A minimal sporozoite challenge dose was established, and protection was defined as the absence of blood-stage parasites 14 days after intravenous challenge. The specificity of protection was confirmed by vaccinating mice with multiple CSP constructs of differing lengths and compositions. Constructs that induced high NANP repeat-specific antibody titers in enzyme-linked immunosorbent assays were protective, and the degree of protection was dependent on the antigen dose. There was a positive correlation between antibody avidity and protection. The antibodies in the protected mice recognized the native CSP on the parasites and showed sporozoite invasion inhibitory activity. Passive transfer of anti-CSP antibodies into naive mice also induced protection. Thus, we have demonstrated the utility of a mouse efficacy model to down-select human CSP-based vaccine formulations. PMID:23536694

Self-assembled near-infrared dye nanoparticles as a selective protein sensor by activation of a dormant fluorophore.

PubMed

Anees, Palapuravan; Sreejith, Sivaramapanicker; Ajayaghosh, Ayyappanpillai

2014-09-24

Design of selective sensors for a specific analyte in blood serum, which contains a large number of proteins, small molecules, and ions, is important in clinical diagnostics. While metal and polymeric nanoparticle conjugates have been used as sensors, small molecular assemblies have rarely been exploited for the selective sensing of a protein in blood serum. Herein we demonstrate how a nonspecific small molecular fluorescent dye can be empowered to form a selective protein sensor as illustrated with a thiol-sensitive near-IR squaraine (Sq) dye (λabs= 670 nm, λem= 700 nm). The dye self-assembles to form nonfluorescent nanoparticles (Dh = 200 nm) which selectively respond to human serum albumin (HSA) in the presence of other thiol-containing molecules and proteins by triggering a green fluorescence. This selective response of the dye nanoparticles allowed detection and quantification of HSA in blood serum with a sensitivity limit of 3 nM. Notably, the Sq dye in solution state is nonselective and responds to any thiol-containing proteins and small molecules. The sensing mechanism involves HSA specific controlled disassembly of the Sq nanoparticles to the molecular dye by a noncovalent binding process and its subsequent reaction with the thiol moiety of the protein, triggering the green emission of a dormant fluorophore present in the dye. This study demonstrates the power of a self-assembled small molecular fluorophore for protein sensing and is a simple chemical tool for the clinical diagnosis of blood serum.

Cophenetic correlation analysis as a strategy to select phylogenetically informative proteins: an example from the fungal kingdom

PubMed Central

Kuramae, Eiko E; Robert, Vincent; Echavarri-Erasun, Carlos; Boekhout, Teun

2007-01-01

Background The construction of robust and well resolved phylogenetic trees is important for our understanding of many, if not all biological processes, including speciation and origin of higher taxa, genome evolution, metabolic diversification, multicellularity, origin of life styles, pathogenicity and so on. Many older phylogenies were not well supported due to insufficient phylogenetic signal present in the single or few genes used in phylogenetic reconstructions. Importantly, single gene phylogenies were not always found to be congruent. The phylogenetic signal may, therefore, be increased by enlarging the number of genes included in phylogenetic studies. Unfortunately, concatenation of many genes does not take into consideration the evolutionary history of each individual gene. Here, we describe an approach to select informative phylogenetic proteins to be used in the Tree of Life (TOL) and barcoding projects by comparing the cophenetic correlation coefficients (CCC) among individual protein distance matrices of proteins, using the fungi as an example. The method demonstrated that the quality and number of concatenated proteins is important for a reliable estimation of TOL. Approximately 40–45 concatenated proteins seem needed to resolve fungal TOL. Results In total 4852 orthologous proteins (KOGs) were assigned among 33 fungal genomes from the Asco- and Basidiomycota and 70 of these represented single copy proteins. The individual protein distance matrices based on 531 concatenated proteins that has been used for phylogeny reconstruction before [14] were compared one with another in order to select those with the highest CCC, which then was used as a reference. This reference distance matrix was compared with those of the 70 single copy proteins selected and their CCC values were calculated. Sixty four KOGs showed a CCC above 0.50 and these were further considered for their phylogenetic potential. Proteins belonging to the cellular processes and signaling

Modelling Transcapillary Transport of Fluid and Proteins in Hemodialysis Patients

PubMed Central

Pietribiasi, Mauro; Waniewski, Jacek; Załuska, Alicja; Załuska, Wojciech; Lindholm, Bengt

2016-01-01

Background The kinetics of protein transport to and from the vascular compartment play a major role in the determination of fluid balance and plasma refilling during hemodialysis (HD) sessions. In this study we propose a whole-body mathematical model describing water and protein shifts across the capillary membrane during HD and compare its output to clinical data while evaluating the impact of choosing specific values for selected parameters. Methods The model follows a two-compartment structure (vascular and interstitial space) and is based on balance equations of protein mass and water volume in each compartment. The capillary membrane was described according to the three-pore theory. Two transport parameters, the fractional contribution of large pores (αLP) and the total hydraulic conductivity (LpS) of the capillary membrane, were estimated from patient data. Changes in the intensity and direction of individual fluid and solute flows through each part of the transport system were analyzed in relation to the choice of different values of small pores radius and fractional conductivity, lymphatic sensitivity to hydraulic pressure, and steady-state interstitial-to-plasma protein concentration ratio. Results The estimated values of LpS and αLP were respectively 10.0 ± 8.4 mL/min/mmHg (mean ± standard deviation) and 0.062 ± 0.041. The model was able to predict with good accuracy the profiles of plasma volume and serum total protein concentration in most of the patients (average root-mean-square deviation < 2% of the measured value). Conclusions The applied model provides a mechanistic interpretation of fluid transport processes induced by ultrafiltration during HD, using a minimum of tuned parameters and assumptions. The simulated values of individual flows through each kind of pore and lymphatic absorption rate yielded by the model may suggest answers to unsolved questions on the relative impact of these not-measurable quantities on total vascular refilling and

Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms.

PubMed

Lin, Xiaotong; Liu, Mei; Chen, Xue-wen

2009-04-29

Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more

Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection.

PubMed

Rapp, M; Lein, V; Lacoudre, F; Lafferty, J; Müller, E; Vida, G; Bozhanova, V; Ibraliu, A; Thorwarth, P; Piepho, H P; Leiser, W L; Würschum, T; Longin, C F H

2018-06-01

Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

Modeling disordered protein interactions from biophysical principles

PubMed Central

Christoffer, Charles; Terashi, Genki

2017-01-01

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs. PMID:28394890

Discovery-2: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

PubMed Central

2013-01-01

Background Drug resistance to anti-malarial compounds remains a serious problem, with resistance to newer pharmaceuticals developing at an alarming rate. The development of new anti-malarials remains a priority, and the rational selection of putative targets is a key element of this process. Discovery-2 is an update of the original Discovery in silico resource for the rational selection of putative drug target proteins, enabling researchers to obtain information for a protein which may be useful for the selection of putative drug targets, and to perform advanced filtering of proteins encoded by the malaria genome based on a series of molecular properties. Methods An updated in silico resource has been developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein properties used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions. Newly added features include drugability measures from ChEMBL, automated literature relations and links to clinical trial information. Searching by chemical structure is also available. Results The updated functionality of the Discovery-2 resource is presented, together with a detailed case study of the Plasmodium falciparum S-adenosyl-L-homocysteine hydrolase (PfSAHH) protein. A short example of a chemical search with pyrimethamine is also illustrated. Conclusion The updated Discovery-2 resource allows researchers to obtain detailed properties of proteins from the malaria genome, which may be of interest in the target selection process, and to perform advanced filtering and selection of proteins based on a relevant range of molecular characteristics. PMID:23537208

Decarboxylative alkylation for site-selective bioconjugation of native proteins via oxidation potentials.

PubMed

Bloom, Steven; Liu, Chun; Kölmel, Dominik K; Qiao, Jennifer X; Zhang, Yong; Poss, Michael A; Ewing, William R; MacMillan, David W C

2018-02-01

The advent of antibody-drug conjugates as pharmaceuticals has fuelled a need for reliable methods of site-selective protein modification that furnish homogeneous adducts. Although bioorthogonal methods that use engineered amino acids often provide an elegant solution to the question of selective functionalization, achieving homogeneity using native amino acids remains a challenge. Here, we explore visible-light-mediated single-electron transfer as a mechanism towards enabling site- and chemoselective bioconjugation. Specifically, we demonstrate the use of photoredox catalysis as a platform to selectivity wherein the discrepancy in oxidation potentials between internal versus C-terminal carboxylates can be exploited towards obtaining C-terminal functionalization exclusively. This oxidation potential-gated technology is amenable to endogenous peptides and has been successfully demonstrated on the protein insulin. As a fundamentally new approach to bioconjugation this methodology provides a blueprint toward the development of photoredox catalysis as a generic platform to target other redox-active side chains for native conjugation.

Decarboxylative alkylation for site-selective bioconjugation of native proteins via oxidation potentials

NASA Astrophysics Data System (ADS)

Bloom, Steven; Liu, Chun; Kölmel, Dominik K.; Qiao, Jennifer X.; Zhang, Yong; Poss, Michael A.; Ewing, William R.; MacMillan, David W. C.

2018-02-01

The advent of antibody-drug conjugates as pharmaceuticals has fuelled a need for reliable methods of site-selective protein modification that furnish homogeneous adducts. Although bioorthogonal methods that use engineered amino acids often provide an elegant solution to the question of selective functionalization, achieving homogeneity using native amino acids remains a challenge. Here, we explore visible-light-mediated single-electron transfer as a mechanism towards enabling site- and chemoselective bioconjugation. Specifically, we demonstrate the use of photoredox catalysis as a platform to selectivity wherein the discrepancy in oxidation potentials between internal versus C-terminal carboxylates can be exploited towards obtaining C-terminal functionalization exclusively. This oxidation potential-gated technology is amenable to endogenous peptides and has been successfully demonstrated on the protein insulin. As a fundamentally new approach to bioconjugation this methodology provides a blueprint toward the development of photoredox catalysis as a generic platform to target other redox-active side chains for native conjugation.

Selection of Specific Protein Binders for Pre-Defined Targets from an Optimized Library of Artificial Helicoidal Repeat Proteins (alphaRep)

PubMed Central

Chevrel, Anne; Graille, Marc; Fourati-Kammoun, Zaineb; Desmadril, Michel; van Tilbeurgh, Herman; Minard, Philippe

2013-01-01

We previously designed a new family of artificial proteins named αRep based on a subgroup of thermostable helicoidal HEAT-like repeats. We have now assembled a large optimized αRep library. In this library, the side chains at each variable position are not fully randomized but instead encoded by a distribution of codons based on the natural frequency of side chains of the natural repeats family. The library construction is based on a polymerization of micro-genes and therefore results in a distribution of proteins with a variable number of repeats. We improved the library construction process using a “filtration” procedure to retain only fully coding modules that were recombined to recreate sequence diversity. The final library named Lib2.1 contains 1.7×109 independent clones. Here, we used phage display to select, from the previously described library or from the new library, new specific αRep proteins binding to four different non-related predefined protein targets. Specific binders were selected in each case. The results show that binders with various sizes are selected including relatively long sequences, with up to 7 repeats. ITC-measured affinities vary with Kd values ranging from micromolar to nanomolar ranges. The formation of complexes is associated with a significant thermal stabilization of the bound target protein. The crystal structures of two complexes between αRep and their cognate targets were solved and show that the new interfaces are established by the variable surfaces of the repeated modules, as well by the variable N-cap residues. These results suggest that αRep library is a new and versatile source of tight and specific binding proteins with favorable biophysical properties. PMID:24014183

Selective Enrichment and Direct Analysis of Protein S-Palmitoylation Sites.

PubMed

Thinon, Emmanuelle; Fernandez, Joseph P; Molina, Henrik; Hang, Howard C

2018-05-04

S-Fatty-acylation is the covalent attachment of long chain fatty acids, predominately palmitate (C16:0, S-palmitoylation), to cysteine (Cys) residues via a thioester linkage on proteins. This post-translational and reversible lipid modification regulates protein function and localization in eukaryotes and is important in mammalian physiology and human diseases. While chemical labeling methods have improved the detection and enrichment of S-fatty-acylated proteins, mapping sites of modification and characterizing the endogenously attached fatty acids are still challenging. Here, we describe the integration and optimization of fatty acid chemical reporter labeling with hydroxylamine-mediated enrichment of S-fatty-acylated proteins and direct tagging of modified Cys residues to selectively map lipid modification sites. This afforded improved enrichment and direct identification of many protein S-fatty-acylation sites compared to previously described methods. Notably, we directly identified the S-fatty-acylation sites of IFITM3, an important interferon-stimulated inhibitor of virus entry, and we further demonstrated that the highly conserved Cys residues are primarily modified by palmitic acid. The methods described here should facilitate the direct analysis of protein S-fatty-acylation sites and their endogenously attached fatty acids in diverse cell types and activation states important for mammalian physiology and diseases.

The interface of protein structure, protein biophysics, and molecular evolution

PubMed Central

Liberles, David A; Teichmann, Sarah A; Bahar, Ivet; Bastolla, Ugo; Bloom, Jesse; Bornberg-Bauer, Erich; Colwell, Lucy J; de Koning, A P Jason; Dokholyan, Nikolay V; Echave, Julian; Elofsson, Arne; Gerloff, Dietlind L; Goldstein, Richard A; Grahnen, Johan A; Holder, Mark T; Lakner, Clemens; Lartillot, Nicholas; Lovell, Simon C; Naylor, Gavin; Perica, Tina; Pollock, David D; Pupko, Tal; Regan, Lynne; Roger, Andrew; Rubinstein, Nimrod; Shakhnovich, Eugene; Sjölander, Kimmen; Sunyaev, Shamil; Teufel, Ashley I; Thorne, Jeffrey L; Thornton, Joseph W; Weinreich, Daniel M; Whelan, Simon

2012-01-01

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction. PMID:22528593

Toward the description of electrostatic interactions between globular proteins: potential of mean force in the primitive model.

PubMed

Dahirel, Vincent; Jardat, Marie; Dufrêche, Jean-François; Turq, Pierre

2007-09-07

Monte Carlo simulations are used to calculate the exact potential of mean force between charged globular proteins in aqueous solution. The aim of the present paper is to study the influence of the ions of the added salt on the effective interaction between these nanoparticles. The charges of the model proteins, either identical or opposite, are either central or distributed on a discrete pattern. Contrarily to Poisson-Boltzmann predictions, attractive, and repulsive direct forces between proteins are not screened similarly. Moreover, it has been shown that the relative orientations of the charge patterns strongly influence salt-mediated interactions. More precisely, for short distances between the proteins, ions enhance the difference of the effective forces between (i) like-charged and oppositely charged proteins, (ii) attractive and repulsive relative orientations of the proteins, which may affect the selectivity of protein/protein recognition. Finally, such results observed with the simplest models are applied to a more elaborate one to demonstrate their generality.

An automated method for modeling proteins on known templates using distance geometry.

PubMed

Srinivasan, S; March, C J; Sudarsanam, S

1993-02-01

We present an automated method incorporated into a software package, FOLDER, to fold a protein sequence on a given three-dimensional (3D) template. Starting with the sequence alignment of a family of homologous proteins, tertiary structures are modeled using the known 3D structure of one member of the family as a template. Homologous interatomic distances from the template are used as constraints. For nonhomologous regions in the model protein, the lower and the upper bounds for the interatomic distances are imposed by steric constraints and the globular dimensions of the template, respectively. Distance geometry is used to embed an ensemble of structures consistent with these distance bounds. Structures are selected from this ensemble based on minimal distance error criteria, after a penalty function optimization step. These structures are then refined using energy optimization methods. The method is tested by simulating the alpha-chain of horse hemoglobin using the alpha-chain of human hemoglobin as the template and by comparing the generated models with the crystal structure of the alpha-chain of horse hemoglobin. We also test the packing efficiency of this method by reconstructing the atomic positions of the interior side chains beyond C beta atoms of a protein domain from a known 3D structure. In both test cases, models retain the template constraints and any additionally imposed constraints while the packing of the interior residues is optimized with no short contacts or bond deformations. To demonstrate the use of this method in simulating structures of proteins with nonhomologous disulfides, we construct a model of murine interleukin (IL)-4 using the NMR structure of human IL-4 as the template. The resulting geometry of the nonhomologous disulfide in the model structure for murine IL-4 is consistent with standard disulfide geometry.

Analysis of Species-Selectivity of Human, Mouse and Rat Cytochrome P450 1A and 2B Subfamily Enzymes using Molecular Modeling, Docking and Dynamics Simulations.

PubMed

Karthikeyan, Bagavathy Shanmugam; Suvaithenamudhan, Suvaiyarasan; Akbarsha, Mohammad Abdulkader; Parthasarathy, Subbiah

2018-06-01

Cytochrome P450 (CYP) 1A and 2B subfamily enzymes are important drug metabolizing enzymes, and are highly conserved across species in terms of sequence homology. However, there are major to minor structural and macromolecular differences which provide for species-selectivity and substrate-selectivity. Therefore, species-selectivity of CYP1A and CYP2B subfamily proteins across human, mouse and rat was analyzed using molecular modeling, docking and dynamics simulations when the chiral molecules quinine and quinidine were used as ligands. The three-dimensional structures of 17 proteins belonging to CYP1A and CYP2B subfamilies of mouse and rat were predicted by adopting homology modeling using the available structures of human CYP1A and CYP2B proteins as templates. Molecular docking and dynamics simulations of quinine and quinidine with CYP1A subfamily proteins revealed the existence of species-selectivity across the three species. On the other hand, in the case of CYP2B subfamily proteins, no role for chirality of quinine and quinidine in forming complexes with CYP2B subfamily proteins of the three species was indicated. Our findings reveal the roles of active site amino acid residues of CYP1A and CYP2B subfamily proteins and provide insights into species-selectivity of these enzymes across human, mouse, and rat.

Integration of carboxyl modified magnetic particles and aqueous two-phase extraction for selective separation of proteins.

PubMed

Gai, Qingqing; Qu, Feng; Zhang, Tao; Zhang, Yukui

2011-07-15

Both of the magnetic particle adsorption and aqueous two-phase extraction (ATPE) were simple, fast and low-cost method for protein separation. Selective proteins adsorption by carboxyl modified magnetic particles was investigated according to protein isoelectric point, solution pH and ionic strength. Aqueous two-phase system of PEG/sulphate exhibited selective separation and extraction for proteins before and after magnetic adsorption. The two combination ways, magnetic adsorption followed by ATPE and ATPE followed by magnetic adsorption, for the separation of proteins mixture of lysozyme, bovine serum albumin, trypsin, cytochrome C and myloglobin were discussed and compared. The way of magnetic adsorption followed by ATPE was also applied to human serum separation. Copyright © 2011 Elsevier B.V. All rights reserved.

Variable selection and model choice in geoadditive regression models.

PubMed

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Succination is Increased on Select Proteins in the Brainstem of the NADH dehydrogenase (ubiquinone) Fe-S protein 4 (Ndufs4) Knockout Mouse, a Model of Leigh Syndrome.

PubMed

Piroli, Gerardo G; Manuel, Allison M; Clapper, Anna C; Walla, Michael D; Baatz, John E; Palmiter, Richard D; Quintana, Albert; Frizzell, Norma

2016-02-01

Elevated fumarate concentrations as a result of Krebs cycle inhibition lead to increases in protein succination, an irreversible post-translational modification that occurs when fumarate reacts with cysteine residues to generate S-(2-succino)cysteine (2SC). Metabolic events that reduce NADH re-oxidation can block Krebs cycle activity; therefore we hypothesized that oxidative phosphorylation deficiencies, such as those observed in some mitochondrial diseases, would also lead to increased protein succination. Using the Ndufs4 knockout (Ndufs4 KO) mouse, a model of Leigh syndrome, we demonstrate for the first time that protein succination is increased in the brainstem (BS), particularly in the vestibular nucleus. Importantly, the brainstem is the most affected region exhibiting neurodegeneration and astrocyte and microglial proliferation, and these mice typically die of respiratory failure attributed to vestibular nucleus pathology. In contrast, no increases in protein succination were observed in the skeletal muscle, corresponding with the lack of muscle pathology observed in this model. 2D SDS-PAGE followed by immunoblotting for succinated proteins and MS/MS analysis of BS proteins allowed us to identify the voltage-dependent anion channels 1 and 2 as specific targets of succination in the Ndufs4 knockout. Using targeted mass spectrometry, Cys(77) and Cys(48) were identified as endogenous sites of succination in voltage-dependent anion channels 2. Given the important role of voltage-dependent anion channels isoforms in the exchange of ADP/ATP between the cytosol and the mitochondria, and the already decreased capacity for ATP synthesis in the Ndufs4 KO mice, we propose that the increased protein succination observed in the BS of these animals would further decrease the already compromised mitochondrial function. These data suggest that fumarate is a novel biochemical link that may contribute to the progression of the neuropathology in this mitochondrial disease

Selection by drug resistance proteins located in the mitochondria of mammalian cells.

PubMed

Yoon, Young Geol; Koob, Michael D

2008-12-01

Transformation of mitochondria in mammalian cells is now a technical challenge. In this report, we demonstrate that the standard drug resistant genes encoding neomycin and hygromycin phosphotransferases can potentially be used as selectable markers for mammalian mitochondrial transformation. We re-engineered the drug resistance genes to express proteins targeted to the mitochondrial matrix and confirmed the location of the proteins in the cells by fusing them with GFP and by Western blot and mitochondrial content mixing analyses. We found that the mitochondrially targeted-drug resistance proteins confer resistance to high levels of G418 and hygromycin without affecting the viability of cells.

Postprocessing of docked protein-ligand complexes using implicit solvation models.

PubMed

Lindström, Anton; Edvinsson, Lotta; Johansson, Andreas; Andersson, C David; Andersson, Ida E; Raubacher, Florian; Linusson, Anna

2011-02-28

Molecular docking plays an important role in drug discovery as a tool for the structure-based design of small organic ligands for macromolecules. Possible applications of docking are identification of the bioactive conformation of a protein-ligand complex and the ranking of different ligands with respect to their strength of binding to a particular target. We have investigated the effect of implicit water on the postprocessing of binding poses generated by molecular docking using MM-PB/GB-SA (molecular mechanics Poisson-Boltzmann and generalized Born surface area) methodology. The investigation was divided into three parts: geometry optimization, pose selection, and estimation of the relative binding energies of docked protein-ligand complexes. Appropriate geometry optimization afforded more accurate binding poses for 20% of the complexes investigated. The time required for this step was greatly reduced by minimizing the energy of the binding site using GB solvation models rather than minimizing the entire complex using the PB model. By optimizing the geometries of docking poses using the GB(HCT+SA) model then calculating their free energies of binding using the PB implicit solvent model, binding poses similar to those observed in crystal structures were obtained. Rescoring of these poses according to their calculated binding energies resulted in improved correlations with experimental binding data. These correlations could be further improved by applying the postprocessing to several of the most highly ranked poses rather than focusing exclusively on the top-scored pose. The postprocessing protocol was successfully applied to the analysis of a set of Factor Xa inhibitors and a set of glycopeptide ligands for the class II major histocompatibility complex (MHC) A(q) protein. These results indicate that the protocol for the postprocessing of docked protein-ligand complexes developed in this paper may be generally useful for structure-based design in drug discovery.

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Exploring Several Methods of Groundwater Model Selection

NASA Astrophysics Data System (ADS)

Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar

2017-04-01

Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).

Peptide selection and antibody generation for the prospective immunorecognition of Cry1Ab16 protein of transgenic maize.

PubMed

Costa, Joana; Marani, Mariela M; Grazina, Liliana; Villa, Caterina; Meira, Liliana; Oliveira, M Beatriz P P; Leite, José R S A; Mafra, Isabel

2017-09-15

The introduction of genes isolated from different Bacillus thuringiensis strains to express Cry-type toxins in transgenic crops is a common strategy to confer insect resistance traits. This work intended to extensively in silico analyse Cry1A(b)16 protein for the identification of peptide markers for the biorecognition of transgenic crops. By combining two different strategies based on several bioinformatic tools for linear epitope prediction, a set of seven peptides was successfully selected as potential Cry1A(b)16 immunogens. For the prediction of conformational epitopes, Cry1A(b)16 models were built on the basis of three independent templates of homologue proteins of Cry1A(a) and Cry1A(c) using an integrated approach. PcH_736-746 and PcH_876-886 peptides were selected as the best candidates, being synthesised and used for the production of polyclonal antibodies. To the best of our knowledge, this is the first attempt of selecting and defining linear peptides as immunogenic markers of Cry1A(b)-type toxins in transgenic maize. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

USGS Publications Warehouse

Thomas, D.L.; Johnson, D.; Griffith, B.

2006-01-01

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

Protein and gene model inference based on statistical modeling in k-partite graphs.

PubMed

Gerster, Sarah; Qeli, Ermir; Ahrens, Christian H; Bühlmann, Peter

2010-07-06

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

Molecular interaction of selected phytochemicals under the charged environment of Plasmodium falciparum chloroquine resistance transporter (PfCRT) model.

PubMed

Patel, Saumya K; Khedkar, Vijay M; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A; George, Linz-Buoy; Highland, Hyacinth N; Skelton, Adam A

2016-01-01

Phytochemicals of Catharanthus roseus Linn. and Tylophora indica have been known for their inhibition of malarial parasite, Plasmodium falciparum in cell culture. Resistance to chloroquine (CQ), a widely used antimalarial drug, is due to the CQ resistance transporter (CRT) system. The present study deals with computational modeling of Plasmodium falciparum chloroquine resistance transporter (PfCRT) protein and development of charged environment to mimic a condition of resistance. The model of PfCRT was developed using Protein homology/analogy engine (PHYRE ver 0.2) and was validated based on the results obtained using PSI-PRED. Subsequently, molecular interactions of selected phytochemicals extracted from C. roseus Linn. and T. indica were studied using multiple-iterated genetic algorithm-based docking protocol in order to investigate the translocation of these legends across the PfCRT protein. Further, molecular dynamics studies exhibiting interaction energy estimates of these compounds within the active site of the protein showed that compounds are more selective toward PfCRT. Clusters of conformations with the free energy of binding were estimated which clearly demonstrated the potential channel and by this means the translocation across the PfCRT is anticipated.

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the

Unifying the microscopic picture of His-containing turns: from gas phase model peptides to crystallized proteins.

PubMed

Sohn, Woon Yong; Habka, Sana; Gloaguen, Eric; Mons, Michel

2017-07-14

The presence in crystallized proteins of a local anchoring between the side chain of a His residue, located in the central position of a γ- or β-turn, and its local main chain environment, was assessed by the comparison of protein structures with relevant isolated model peptides. Gas phase laser spectroscopy, combined with relevant quantum chemistry methods, was used to characterize the γ- and β-turn structures in these model peptides. A conformer-selective NH stretch infrared study provided evidence for the formation in vacuo of two types of short-range H-bonded motifs, labelled ε-6 δ and δ- δ 7/π H , bridging the His side chain (in its gauche+ rotamer) to the neighbouring NH(i) and CO(i) sites of the backbone; each side chain-backbone motif was found to be specific of the tautomer (ε or δ) adopted by the His side chain in its neutral form. A close comparison between β- and γ-turns, selected from the Protein Data Bank, and the gas phase models demonstrated that a significant proportion of the gauche+ His rotamer distribution of proteins was well described by the corresponding gas phase H-bonded structures. This is consistent with the persistence of local 6 δ and δ 7/π H intramolecular interactions in proteins, emphasizing the relevance of gas phase data to secondary structures that are poorly accessible to solvents, e.g., in the case of a specific compact topology (Xxx-His β-turns). Deviations from the gas phase structures were also observed, mainly in His-Xxx β-turns, and assigned to solvent accessible turn structures. They were well accounted for by theoretical models of microhydrated turns, in which a few solvent molecules take over the gas phase motifs, constituting a water-mediated local anchoring of the His side chain to the backbone. Finally, the present gas phase benchmark models also pinpointed weaknesses in the protein structure determination by X-ray diffraction analysis; in particular, besides the lack of tautomer information

Investigation of protein selectivity in multimodal chromatography using in silico designed Fab fragment variants.

PubMed

Karkov, Hanne Sophie; Krogh, Berit Olsen; Woo, James; Parimal, Siddharth; Ahmadian, Haleh; Cramer, Steven M

2015-11-01

In this study, a unique set of antibody Fab fragments was designed in silico and produced to examine the relationship between protein surface properties and selectivity in multimodal chromatographic systems. We hypothesized that multimodal ligands containing both hydrophobic and charged moieties would interact strongly with protein surface regions where charged groups and hydrophobic patches were in close spatial proximity. Protein surface property characterization tools were employed to identify the potential multimodal ligand binding regions on the Fab fragment of a humanized antibody and to evaluate the impact of mutations on surface charge and hydrophobicity. Twenty Fab variants were generated by site-directed mutagenesis, recombinant expression, and affinity purification. Column gradient experiments were carried out with the Fab variants in multimodal, cation-exchange, and hydrophobic interaction chromatographic systems. The results clearly indicated that selectivity in the multimodal system was different from the other chromatographic modes examined. Column retention data for the reduced charge Fab variants identified a binding site comprising light chain CDR1 as the main electrostatic interaction site for the multimodal and cation-exchange ligands. Furthermore, the multimodal ligand binding was enhanced by additional hydrophobic contributions as evident from the results obtained with hydrophobic Fab variants. The use of in silico protein surface property analyses combined with molecular biology techniques, protein expression, and chromatographic evaluations represents a previously undescribed and powerful approach for investigating multimodal selectivity with complex biomolecules. © 2015 Wiley Periodicals, Inc.

Knowledge-based model building of proteins: concepts and examples.

PubMed Central

Bajorath, J.; Stenkamp, R.; Aruffo, A.

1993-01-01

We describe how to build protein models from structural templates. Methods to identify structural similarities between proteins in cases of significant, moderate to low, or virtually absent sequence similarity are discussed. The detection and evaluation of structural relationships is emphasized as a central aspect of protein modeling, distinct from the more technical aspects of model building. Computational techniques to generate and complement comparative protein models are also reviewed. Two examples, P-selectin and gp39, are presented to illustrate the derivation of protein model structures and their use in experimental studies. PMID:7505680

Targeted Proteomics and Absolute Protein Quantification for the Construction of a Stoichiometric Host-Pathogen Surface Density Model*

PubMed Central

Sjöholm, Kristoffer; Kilsgård, Ola; Teleman, Johan; Happonen, Lotta; Malmström, Lars; Malmström, Johan

2017-01-01

Sepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host

Targeted Proteomics and Absolute Protein Quantification for the Construction of a Stoichiometric Host-Pathogen Surface Density Model.

PubMed

Sjöholm, Kristoffer; Kilsgård, Ola; Teleman, Johan; Happonen, Lotta; Malmström, Lars; Malmström, Johan

2017-04-01

Sepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host

Intrinsically disordered proteins--relation to general model expressing the active role of the water environment.

PubMed

Kalinowska, Barbara; Banach, Mateusz; Konieczny, Leszek; Marchewka, Damian; Roterman, Irena

2014-01-01

This work discusses the role of unstructured polypeptide chain fragments in shaping the protein's hydrophobic core. Based on the "fuzzy oil drop" model, which assumes an idealized distribution of hydrophobicity density described by the 3D Gaussian, we can determine which fragments make up the core and pinpoint residues whose location conflicts with theoretical predictions. We show that the structural influence of the water environment determines the positions of disordered fragments, leading to the formation of a hydrophobic core overlaid by a hydrophilic mantle. This phenomenon is further described by studying selected proteins which are known to be unstable and contain intrinsically disordered fragments. Their properties are established quantitatively, explaining the causative relation between the protein's structure and function and facilitating further comparative analyses of various structural models. © 2014 Elsevier Inc. All rights reserved.

A coarse grain model for protein-surface interactions

NASA Astrophysics Data System (ADS)

Wei, Shuai; Knotts, Thomas A.

2013-09-01

The interaction of proteins with surfaces is important in numerous applications in many fields—such as biotechnology, proteomics, sensors, and medicine—but fundamental understanding of how protein stability and structure are affected by surfaces remains incomplete. Over the last several years, molecular simulation using coarse grain models has yielded significant insights, but the formalisms used to represent the surface interactions have been rudimentary. We present a new model for protein surface interactions that incorporates the chemical specificity of both the surface and the residues comprising the protein in the context of a one-bead-per-residue, coarse grain approach that maintains computational efficiency. The model is parameterized against experimental adsorption energies for multiple model peptides on different types of surfaces. The validity of the model is established by its ability to quantitatively and qualitatively predict the free energy of adsorption and structural changes for multiple biologically-relevant proteins on different surfaces. The validation, done with proteins not used in parameterization, shows that the model produces remarkable agreement between simulation and experiment.

ZP Domain Proteins in the Abalone Egg Coat Include a Paralog of VERL under Positive Selection That Binds Lysin and 18-kDa Sperm Proteins

PubMed Central

Aagaard, Jan E.; Vacquier, Victor D.; MacCoss, Michael J.; Swanson, Willie J.

2010-01-01

Identifying fertilization molecules is key to our understanding of reproductive biology, yet only a few examples of interacting sperm and egg proteins are known. One of the best characterized comes from the invertebrate archeogastropod abalone (Haliotis spp.), where sperm lysin mediates passage through the protective egg vitelline envelope (VE) by binding to the VE protein vitelline envelope receptor for lysin (VERL). Rapid adaptive divergence of abalone lysin and VERL are an example of positive selection on interacting fertilization proteins contributing to reproductive isolation. Previously, we characterized a subset of the abalone VE proteins that share a structural feature, the zona pellucida (ZP) domain, which is common to VERL and the egg envelopes of vertebrates. Here, we use additional expressed sequence tag sequencing and shotgun proteomics to characterize this family of proteins in the abalone egg VE. We expand 3-fold the number of known ZP domain proteins present within the VE (now 30 in total) and identify a paralog of VERL (vitelline envelope zona pellucida domain protein [VEZP] 14) that contains a putative lysin-binding motif. We find that, like VERL, the divergence of VEZP14 among abalone species is driven by positive selection on the lysin-binding motif alone and that these paralogous egg VE proteins bind a similar set of sperm proteins including a rapidly evolving 18-kDa paralog of lysin, which may mediate sperm–egg fusion. This work identifies an egg coat paralog of VERL under positive selection and the candidate sperm proteins with which it may interact during abalone fertilization. PMID:19767347

Protein attributes contribute to halo-stability, bioinformatics approach

PubMed Central

2011-01-01

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized rule induction models to search for patterns that code for halo-toleration. Up to 251 protein attributes selected by various attribute weighting algorithms as important features contribute to halo-stability; from them 14 attributes selected by 90% of models and the count of hydrogen gained the highest value (1.0) in 70% of attribute weighting models, showing the importance of this attribute in feature selection modeling. The other attributes mostly were the frequencies of di-peptides. No changes were found in the numbers of groups when K-Means and TwoStep clustering modeling were performed on datasets with or without feature selection filtering. Although the depths of induced trees were not high, the accuracies of trees were higher than 94% and the frequency of hydrophobic residues pointed as the most important feature to build trees. The performance evaluation of decision tree models had the same values and the best correctness percentage recorded with the Exhaustive CHAID and CHAID models. We did not find any significant difference in the percent of correctness, performance evaluation, and mean correctness of various decision tree models with or without feature selection. For the first time, we analyzed the performance of different screening, clustering, and decision tree algorithms for discriminating halophilic and non-halophilic proteins and the results showed that amino acid composition can be used to discriminate between halo-tolerant and halo-sensitive proteins. PMID:21592393

Selective labeling of a single organelle by using two-photon conversion of a photoconvertible fluorescent protein

NASA Astrophysics Data System (ADS)

Watanabe, Wataru; Shimada, Tomoko; Matsunaga, Sachihiro; Kurihara, Daisuke; Arimura, Shin-ichi; Tsutsumi, Nobuhiro; Fukui, Kiichi; Itoh, Kazuyoshi

2008-02-01

We present space-selective labeling of organelles by using two-photon conversion of a photoconvertible fluorescent protein with near-infrared femtosecond laser pulses. Two-photon excitation of photoconvertible fluorescent-protein, Kaede, enables space-selective labeling of organelles. We alter the fluorescence of target mitochondria in a tobacco BY-2 cell from green to red by focusing femtosecond laser pulses with a wavelength of 750 nm.

A Generative Angular Model of Protein Structure Evolution

PubMed Central

Golden, Michael; García-Portugués, Eduardo; Sørensen, Michael; Mardia, Kanti V.; Hamelryck, Thomas; Hein, Jotun

2017-01-01

Abstract Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both “smooth” conformational changes and “catastrophic” conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence–structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof. PMID:28453724

Automated method to differentiate between native and mirror protein models obtained from contact maps.

PubMed

Kurczynska, Monika; Kotulska, Malgorzata

2018-01-01

Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters-the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Predicting bacteriophage proteins located in host cell with feature selection technique.

PubMed

Ding, Hui; Liang, Zhi-Yong; Guo, Feng-Biao; Huang, Jian; Chen, Wei; Lin, Hao

2016-04-01

A bacteriophage is a virus that can infect a bacterium. The fate of an infected bacterium is determined by the bacteriophage proteins located in the host cell. Thus, reliably identifying bacteriophage proteins located in the host cell is extremely important to understand their functions and discover potential anti-bacterial drugs. Thus, in this paper, a computational method was developed to recognize bacteriophage proteins located in host cells based only on their amino acid sequences. The analysis of variance (ANOVA) combined with incremental feature selection (IFS) was proposed to optimize the feature set. Using a jackknife cross-validation, our method can discriminate between bacteriophage proteins located in a host cell and the bacteriophage proteins not located in a host cell with a maximum overall accuracy of 84.2%, and can further classify bacteriophage proteins located in host cell cytoplasm and in host cell membranes with a maximum overall accuracy of 92.4%. To enhance the value of the practical applications of the method, we built a web server called PHPred (〈http://lin.uestc.edu.cn/server/PHPred〉). We believe that the PHPred will become a powerful tool to study bacteriophage proteins located in host cells and to guide related drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement

PubMed Central

Xu, Dong; Zhang, Jian; Roy, Ambrish; Zhang, Yang

2011-01-01

I-TASSER is an automated pipeline for protein tertiary structure prediction using multiple threading alignments and iterative structure assembly simulations. In CASP9 experiments, two new algorithms, QUARK and FG-MD, were added to the I-TASSER pipeline for improving the structural modeling accuracy. QUARK is a de novo structure prediction algorithm used for structure modeling of proteins that lack detectable template structures. For distantly homologous targets, QUARK models are found useful as a reference structure for selecting good threading alignments and guiding the I-TASSER structure assembly simulations. FG-MD is an atomic-level structural refinement program that uses structural fragments collected from the PDB structures to guide molecular dynamics simulation and improve the local structure of predicted model, including hydrogen-bonding networks, torsion angles and steric clashes. Despite considerable progress in both the template-based and template-free structure modeling, significant improvements on protein target classification, domain parsing, model selection, and ab initio folding of beta-proteins are still needed to further improve the I-TASSER pipeline. PMID:22069036

PconsD: ultra rapid, accurate model quality assessment for protein structure prediction.

PubMed

Skwark, Marcin J; Elofsson, Arne

2013-07-15

Clustering methods are often needed for accurately assessing the quality of modeled protein structures. Recent blind evaluation of quality assessment methods in CASP10 showed that there is little difference between many different methods as far as ranking models and selecting best model are concerned. When comparing many models, the computational cost of the model comparison can become significant. Here, we present PconsD, a fast, stream-computing method for distance-driven model quality assessment that runs on consumer hardware. PconsD is at least one order of magnitude faster than other methods of comparable accuracy. The source code for PconsD is freely available at http://d.pcons.net/. Supplementary benchmarking data are also available there. arne@bioinfo.se Supplementary data are available at Bioinformatics online.

fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data.

PubMed

Hung, Ling-Hong; Samudrala, Ram

2014-06-15

fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster) © The Author 2014. Published by Oxford University Press.

Selective staining of proteins with hydrophobic surface sites on a native electrophoretic gel.

PubMed

Bertsch, Martina; Kassner, Richard J

2003-01-01

Chemical proteomics aims to characterize all of the proteins in the proteome with respect to their function, which is associated with their interaction with other molecules. We propose the identification of a subproteomic library of expressed proteins whose native structures are typified by the presence of hydrophobic surface sites, which are often involved in interactions with small molecules, membrane lipids, and other proteins, pertaining to their functions. We demonstrate that soluble globular proteins with hydrophobic surface sites can be detected selectively by staining on an electrophoretic gel run under nondenaturing conditions. The application of these staining techniques may help elucidate new catalytic, transport, and regulatory functionalities in complex proteomic screenings.

Selection by drug resistance proteins located in the mitochondria of mammalian cells

PubMed Central

Yoon, Young Geol; Koob, Michael D.

2008-01-01

Transformation of mitochondria in mammalian cells is now a technical challenge. In this report, we demonstrate that the standard drug resistant genes encoding neomycin and hygromycin phosphotransferases can potentially be used as selectable markers for mammalian mitochondrial transformation. We re-engineered the drug resistance genes to express proteins targeted to the mitochondrial matrix and confirmed the location of the proteins in the cells by fusing them with GFP and by Western blot and mitochondrial content mixing analyses. We found that the mitochondrially targeted-drug resistance proteins confer resistance to high levels of G418 and hygromycin without affecting the viability of cells. PMID:18721905

Fast Geometric Consensus Approach for Protein Model Quality Assessment

PubMed Central

Adamczak, Rafal; Pillardy, Jaroslaw; Vallat, Brinda K.

2011-01-01

Abstract Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well. PMID:21244273

FragFit: a web-application for interactive modeling of protein segments into cryo-EM density maps.

PubMed

Tiemann, Johanna K S; Rose, Alexander S; Ismer, Jochen; Darvish, Mitra D; Hilal, Tarek; Spahn, Christian M T; Hildebrand, Peter W

2018-05-21

Cryo-electron microscopy (cryo-EM) is a standard method to determine the three-dimensional structures of molecular complexes. However, easy to use tools for modeling of protein segments into cryo-EM maps are sparse. Here, we present the FragFit web-application, a web server for interactive modeling of segments of up to 35 amino acids length into cryo-EM density maps. The fragments are provided by a regularly updated database containing at the moment about 1 billion entries extracted from PDB structures and can be readily integrated into a protein structure. Fragments are selected based on geometric criteria, sequence similarity and fit into a given cryo-EM density map. Web-based molecular visualization with the NGL Viewer allows interactive selection of fragments. The FragFit web-application, accessible at http://proteinformatics.de/FragFit, is free and open to all users, without any login requirements.

Study and selection of in vivo protein interactions by coupling bimolecular fluorescence complementation and flow cytometry.

PubMed

Morell, Montse; Espargaro, Alba; Aviles, Francesc Xavier; Ventura, Salvador

2008-01-01

We present a high-throughput approach to study weak protein-protein interactions by coupling bimolecular fluorescent complementation (BiFC) to flow cytometry (FC). In BiFC, the interaction partners (bait and prey) are fused to two rationally designed fragments of a fluorescent protein, which recovers its function upon the binding of the interacting proteins. For weak protein-protein interactions, the detected fluorescence is proportional to the interaction strength, thereby allowing in vivo discrimination between closely related binders with different affinity for the bait protein. FC provides a method for high-speed multiparametric data acquisition and analysis; the assay is simple, thousands of cells can be analyzed in seconds and, if required, selected using fluorescence-activated cell sorting (FACS). The combination of both methods (BiFC-FC) provides a technically straightforward, fast and highly sensitive method to validate weak protein interactions and to screen and identify optimal ligands in biologically synthesized libraries. Once plasmids encoding the protein fusions have been obtained, the evaluation of a specific interaction, the generation of a library and selection of active partners using BiFC-FC can be accomplished in 5 weeks.

A genetic replacement system for selection-based engineering of essential proteins

PubMed Central

2012-01-01

Background Essential genes represent the core of biological functions required for viability. Molecular understanding of essentiality as well as design of synthetic cellular systems includes the engineering of essential proteins. An impediment to this effort is the lack of growth-based selection systems suitable for directed evolution approaches. Results We established a simple strategy for genetic replacement of an essential gene by a (library of) variant(s) during a transformation. The system was validated using three different essential genes and plasmid combinations and it reproducibly shows transformation efficiencies on the order of 107 transformants per microgram of DNA without any identifiable false positives. This allowed for reliable recovery of functional variants out of at least a 105-fold excess of non-functional variants. This outperformed selection in conventional bleach-out strains by at least two orders of magnitude, where recombination between functional and non-functional variants interfered with reliable recovery even in recA negative strains. Conclusions We propose that this selection system is extremely suitable for evaluating large libraries of engineered essential proteins resulting in the reliable isolation of functional variants in a clean strain background which can readily be used for in vivo applications as well as expression and purification for use in in vitro studies. PMID:22898007

Modeling the Crystallization of Proteins

NASA Astrophysics Data System (ADS)

Liu, Hongjun; Kumar, Sanat; Garde, Shekhar

2007-03-01

We have used molecular dynamics and monte carlo simulations to understand the pathway to protein crystallization. We find that models which ignore the patchy nature of protein-protein interactions only crystallize inside the metastable gas-lqiuid coexistence region. In this regime they crystallize through the formation of a critical nucleus. In contrast, when patchiness is introduced we find that there is no need to be inside this metastable gas-liquid boundary. Rather, crystallization occurs through an intermediate which is composed of disordered aggregates. These are formed by patchy interactions. Further, there appears to be no need for the formation of a critical nucleus. Thus the pathways for crystallization are strongly controlled by the nature of protein-protein interactions, in good agreement with current experiments.

Quantification Assays for Total and Polyglutamine-Expanded Huntingtin Proteins

PubMed Central

Boogaard, Ivette; Smith, Melanie; Pulli, Kristiina; Szynol, Agnieszka; Albertus, Faywell; Lamers, Marieke B. A. C.; Dijkstra, Sipke; Kordt, Daniel; Reindl, Wolfgang; Herrmann, Frank; McAllister, George; Fischer, David F.; Munoz-Sanjuan, Ignacio

2014-01-01

The expansion of a CAG trinucleotide repeat in the huntingtin gene, which produces huntingtin protein with an expanded polyglutamine tract, is the cause of Huntington's disease (HD). Recent studies have reported that RNAi suppression of polyglutamine-expanded huntingtin (mutant HTT) in HD animal models can ameliorate disease phenotypes. A key requirement for such preclinical studies, as well as eventual clinical trials, aimed to reduce mutant HTT exposure is a robust method to measure HTT protein levels in select tissues. We have developed several sensitive and selective assays that measure either total human HTT or polyglutamine-expanded human HTT proteins on the electrochemiluminescence Meso Scale Discovery detection platform with an increased dynamic range over other methods. In addition, we have developed an assay to detect endogenous mouse and rat HTT proteins in pre-clinical models of HD to monitor effects on the wild type protein of both allele selective and non-selective interventions. We demonstrate the application of these assays to measure HTT protein in several HD in vitro cellular and in vivo animal model systems as well as in HD patient biosamples. Furthermore, we used purified recombinant HTT proteins as standards to quantitate the absolute amount of HTT protein in such biosamples. PMID:24816435

[Assessment of the effect of selected mixture of food additives on the protein metabolism--model studies].

PubMed

Friedrich, Mariola; Kuchlewska, Magdalena

2012-01-01

Contemporarily, food production without food additives is very rare. Increasingly often, however, scientific works report on adverse effects of specified, single food additives on the body. Data is, in turn, lacking on the synergistic effect of a mixture of different food additives on body functions and its main metabolic pathways. The objective of this study, an animal model, was to evaluate if and in what way the compound of chosen and most frequently used and consumed food additives, along with the change of diet composition to processed, purified, influence the selected markers of protein metabolism. The animals were divided into four groups, which were fed with compound of feed pellets: group I and II with basic compound, group III and IV with modified compound in which part of the full grain was replaced by isocalorie wheat flour type 500 and saccharose. Animals from groups I and III received tap water, which was standing for some time, to drink. Animals from groups II and IV received solution of chosen additives to food and next they were given water to drink. The amount of given food additives was evaluated by taking into consideration their consumption by people recalculated to 1 kg of their body mass. The experiment spanned for 7 weeks. It was ascertained that the applied additives caused significant changes in total protein concentration and its fractions: albumin, alpha1-globulin, alpha2-globulin, beta-globulin and gamma-globulin in the blood serum of the animals under research, which can indicate and contribute to disclosure of creation of undesirable food reaction, especially when recommended levels of consumption of those additives are being exceeded. The organism response to the applied additives and accompanying it change of diet was essentially connected to sex of the animals. Undesirable character of changes taking place under the influence of applied additives, was observed both in animals fed with basic feed and modified feed with various

Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

PubMed

Bauer, Katharina Christin; Hämmerling, Frank; Kittelmann, Jörg; Dürr, Cathrin; Görlich, Fabian; Hubbuch, Jürgen

2017-04-01

Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A class of selective antibacterials derived from a protein kinase inhibitor pharmacophore

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

Miller, J. Richard; Dunham, Steve; Mochalkin, Igor

2009-06-25

As the need for novel antibiotic classes to combat bacterial drug resistance increases, the paucity of leads resulting from target-based antibacterial screening of pharmaceutical compound libraries is of major concern. One explanation for this lack of success is that antibacterial screening efforts have not leveraged the eukaryotic bias resulting from more extensive chemistry efforts targeting eukaryotic gene families such as G protein-coupled receptors and protein kinases. Consistent with a focus on antibacterial target space resembling these eukaryotic targets, we used whole-cell screening to identify a series of antibacterial pyridopyrimidines derived from a protein kinase inhibitor pharmacophore. In bacteria, the pyridopyrimidinesmore » target the ATP-binding site of biotin carboxylase (BC), which catalyzes the first enzymatic step of fatty acid biosynthesis. These inhibitors are effective in vitro and in vivo against fastidious Gram-negative pathogens including Haemophilus influenzae. Although the BC active site has architectural similarity to those of eukaryotic protein kinases, inhibitor binding to the BC ATP-binding site is distinct from the protein kinase-binding mode, such that the inhibitors are selective for bacterial BC. In summary, we have discovered a promising class of potent antibacterials with a previously undescribed mechanism of action. In consideration of the eukaryotic bias of pharmaceutical libraries, our findings also suggest that pursuit of a novel inhibitor leads for antibacterial targets with active-site structural similarity to known human targets will likely be more fruitful than the traditional focus on unique bacterial target space, particularly when structure-based and computational methodologies are applied to ensure bacterial selectivity.« less

Building toy models of proteins using coevolutionary information

NASA Astrophysics Data System (ADS)

Cheng, Ryan; Raghunathan, Mohit; Onuchic, Jose

2015-03-01

Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid positions within the multiple sequence alignment of a protein family. Here, we use Direct Coupling Analysis (DCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family to obtain the sequence-dependent interaction energies of a toy protein model. We demonstrate that this methodology predicts residue-residue interaction energies that are consistent with experimental mutational changes in protein stabilities as well as other computational methodologies. Furthermore, we demonstrate with several examples that DCA could be used to construct a structure-based model that quantitatively agrees with experimental data on folding mechanisms. This work serves as a potential framework for generating models of proteins that are enriched by evolutionary data that can potentially be used to engineer key functional motions and interactions in protein systems. This research has been supported by the NSF INSPIRE award MCB-1241332 and by the CTBP sponsored by the NSF (Grant PHY-1427654).

Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

PubMed Central

Joubert, Fourie; Harrison, Claudia M; Koegelenberg, Riaan J; Odendaal, Christiaan J; de Beer, Tjaart AP

2009-01-01

Background Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties. Methods A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available. Results An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum. Conclusion The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties. PMID:19642978

Selectivity of arsenite interaction with zinc finger proteins.

PubMed

Zhao, Linhong; Chen, Siming; Jia, Liangyuan; Shu, Shi; Zhu, Pingping; Liu, Yangzhong

2012-08-01

Arsenic is a carcinogenic element also used for the treatment of acute promyelocytic leukemia. The reactivity of proteins to arsenic must be associated with the various biological functions of As. Here, we investigated the selectivity of arsenite to zinc finger proteins (ZFPs) with different zinc binding motifs (C2H2, C3H, and C4). Single ZFP domain proteins were used for the direct comparison of the reactivity of different ZFPs. The binding constants and the reaction rates have been studied quantitatively. Results show that both the binding affinity and reaction rates of single-domain ZFPs follow the trend of C4 > C3H ≫ C2H2. Compared with the C2H2 motif ZFPs, the binding affinities of C3H and C4 motif ZFPs are nearly two orders of magnitude higher and the reaction rates are approximately two-fold higher. The formation of multi-domain ZFPs significantly enhances the reactivity of C2H2 type ZFPs, but has negligible effects on C3H and C4 ZFPs. Consequently, the reactivities of the three types of multi-domain ZFPs are rather similar. The 2D NMR spectra indicate that the As(III)-bound ZFPs are also unfolded, suggesting that arsenic binding interferes with the function of ZFPs.

The Coalescent Process in Models with Selection

PubMed Central

Kaplan, N. L.; Darden, T.; Hudson, R. R.

1988-01-01

Statistical properties of the process describing the genealogical history of a random sample of genes are obtained for a class of population genetics models with selection. For models with selection, in contrast to models without selection, the distribution of this process, the coalescent process, depends on the distribution of the frequencies of alleles in the ancestral generations. If the ancestral frequency process can be approximated by a diffusion, then the mean and the variance of the number of segregating sites due to selectively neutral mutations in random samples can be numerically calculated. The calculations are greatly simplified if the frequencies of the alleles are tightly regulated. If the mutation rates between alleles maintained by balancing selection are low, then the number of selectively neutral segregating sites in a random sample of genes is expected to substantially exceed the number predicted under a neutral model. PMID:3066685

Selective cell-surface labeling of the molecular motor protein prestin.

PubMed

McGuire, Ryan M; Silberg, Jonathan J; Pereira, Fred A; Raphael, Robert M

2011-06-24

Prestin, a multipass transmembrane protein whose N- and C-termini are localized to the cytoplasm, must be trafficked to the plasma membrane to fulfill its cellular function as a molecular motor. One challenge in studying prestin sequence-function relationships within living cells is separating the effects of amino acid substitutions on prestin trafficking, plasma membrane localization and function. To develop an approach for directly assessing prestin levels at the plasma membrane, we have investigated whether fusion of prestin to a single pass transmembrane protein results in a functional fusion protein with a surface-exposed N-terminal tag that can be detected in living cells. We find that fusion of the biotin-acceptor peptide (BAP) and transmembrane domain of the platelet-derived growth factor receptor (PDGFR) to the N-terminus of prestin-GFP yields a membrane protein that can be metabolically-labeled with biotin, trafficked to the plasma membrane, and selectively detected at the plasma membrane using fluorescently-tagged streptavidin. Furthermore, we show that the addition of a surface detectable tag and a single-pass transmembrane domain to prestin does not disrupt its voltage-sensitive activity. Copyright © 2011 Elsevier Inc. All rights reserved.

Selective cell-surface labeling of the molecular motor protein prestin

PubMed Central

McGuire, Ryan M.; Silberg, Jonathan J.; Pereira, Fred A.; Raphael, Robert M.

2011-01-01

Prestin, a multipass transmembrane protein whose N- an C-termini are localized to the cytoplasm, must be trafficked to the plasma membrane to fulfill its cellular function as a molecular motor. One challenge in studying prestin sequence-function relationships within living cells is separating the effects of amino acid substitutions on prestin trafficking, plasma membrane localization and function. To develop an approach for directly assessing prestin levels at the plasma membrane, we have investigated whether fusion of prestin to a single pass transmembrane protein results in a functional fusion protein with a surface-exposed N-terminal tag that can be detected in living cells. We find that fusion of the biotin-acceptor peptide (BAP) and transmembrane domain of the platelet-derived growth factor receptor (PDGFR) to the N-terminus of prestin-GFP yields a membrane protein that can be metabolically-labeled with biotin, trafficked to the plasma membrane, and selectively detected at the plasma membrane using fluorescently-tagged streptavidin. Furthermore, we show that the addition of a surface detectable tag and a single-pass transmembrane domain to prestin does not disrupt its voltage-sensitive activity. PMID:21651892

Identification and Structure-Function Analysis of Subfamily Selective G Protein-Coupled Receptor Kinase Inhibitors

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

Homan, Kristoff T.; Larimore, Kelly M.; Elkins, Jonathan M.

2015-02-13

Selective inhibitors of individual subfamilies of G protein-coupled receptor kinases (GRKs) would serve as useful chemical probes as well as leads for therapeutic applications ranging from heart failure to Parkinson’s disease. To identify such inhibitors, differential scanning fluorimetry was used to screen a collection of known protein kinase inhibitors that could increase the melting points of the two most ubiquitously expressed GRKs: GRK2 and GRK5. Enzymatic assays on 14 of the most stabilizing hits revealed that three exhibit nanomolar potency of inhibition for individual GRKs, some of which exhibiting orders of magnitude selectivity. Most of the identified compounds can bemore » clustered into two chemical classes: indazole/dihydropyrimidine-containing compounds that are selective for GRK2 and pyrrolopyrimidine-containing compounds that potently inhibit GRK1 and GRK5 but with more modest selectivity. The two most potent inhibitors representing each class, GSK180736A and GSK2163632A, were cocrystallized with GRK2 and GRK1, and their atomic structures were determined to 2.6 and 1.85 Å spacings, respectively. GSK180736A, developed as a Rho-associated, coiled-coil-containing protein kinase inhibitor, binds to GRK2 in a manner analogous to that of paroxetine, whereas GSK2163632A, developed as an insulin-like growth factor 1 receptor inhibitor, occupies a novel region of the GRK active site cleft that could likely be exploited to achieve more selectivity. However, neither compound inhibits GRKs more potently than their initial targets. This data provides the foundation for future efforts to rationally design even more potent and selective GRK inhibitors.« less

Structural Model for the Interaction of a Designed Ankyrin Repeat Protein with the Human Epidermal Growth Factor Receptor 2

PubMed Central

Epa, V. Chandana; Dolezal, Olan; Doughty, Larissa; Xiao, Xiaowen; Jost, Christian; Plückthun, Andreas; Adams, Timothy E.

2013-01-01

Designed Ankyrin Repeat Proteins are a class of novel binding proteins that can be selected and evolved to bind to targets with high affinity and specificity. We are interested in the DARPin H10-2-G3, which has been evolved to bind with very high affinity to the human epidermal growth factor receptor 2 (HER2). HER2 is found to be over-expressed in 30% of breast cancers, and is the target for the FDA-approved therapeutic monoclonal antibodies trastuzumab and pertuzumab and small molecule tyrosine kinase inhibitors. Here, we use computational macromolecular docking, coupled with several interface metrics such as shape complementarity, interaction energy, and electrostatic complementarity, to model the structure of the complex between the DARPin H10-2-G3 and HER2. We analyzed the interface between the two proteins and then validated the structural model by showing that selected HER2 point mutations at the putative interface with H10-2-G3 reduce the affinity of binding up to 100-fold without affecting the binding of trastuzumab. Comparisons made with a subsequently solved X-ray crystal structure of the complex yielded a backbone atom root mean square deviation of 0.84–1.14 Ångstroms. The study presented here demonstrates the capability of the computational techniques of structural bioinformatics in generating useful structural models of protein-protein interactions. PMID:23527120

FlexPepDock lessons from CAPRI peptide-protein rounds and suggested new criteria for assessment of model quality and utility.

PubMed

Marcu, Orly; Dodson, Emma-Joy; Alam, Nawsad; Sperber, Michal; Kozakov, Dima; Lensink, Marc F; Schueler-Furman, Ora

2017-03-01

CAPRI rounds 28 and 29 included, for the first time, peptide-receptor targets of three different systems, reflecting increased appreciation of the importance of peptide-protein interactions. The CAPRI rounds allowed us to objectively assess the performance of Rosetta FlexPepDock, one of the first protocols to explicitly include peptide flexibility in docking, accounting for peptide conformational changes upon binding. We discuss here successes and challenges in modeling these targets: we obtain top-performing, high-resolution models of the peptide motif for cases with known binding sites but there is a need for better modeling of flanking regions, as well as better selection criteria, in particular for unknown binding sites. These rounds have also provided us the opportunity to reassess the success criteria, to better reflect the quality of a peptide-protein complex model. Using all models submitted to CAPRI, we analyze the correlation between current classification criteria and the ability to retrieve critical interface features, such as hydrogen bonds and hotspots. We find that loosening the backbone (and ligand) RMSD threshold, together with a restriction on the side chain RMSD measure, allows us to improve the selection of high-accuracy models. We also suggest a new measure to assess interface hydrogen bond recovery, which is not assessed by the current CAPRI criteria. Finally, we find that surprisingly much can be learned from rather inaccurate models about binding hotspots, suggesting that the current status of peptide-protein docking methods, as reflected by the submitted CAPRI models, can already have a significant impact on our understanding of protein interactions. Proteins 2017; 85:445-462. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Bayesian Model Selection under Time Constraints

NASA Astrophysics Data System (ADS)

Hoege, M.; Nowak, W.; Illman, W. A.

2017-12-01

Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

Physiologically Based Pharmacokinetic Modeling of Therapeutic Proteins.

PubMed

Wong, Harvey; Chow, Timothy W

2017-09-01

Biologics or therapeutic proteins are becoming increasingly important as treatments for disease. The most common class of biologics are monoclonal antibodies (mAbs). Recently, there has been an increase in the use of physiologically based pharmacokinetic (PBPK) modeling in the pharmaceutical industry in drug development. We review PBPK models for therapeutic proteins with an emphasis on mAbs. Due to their size and similarity to endogenous antibodies, there are distinct differences between PBPK models for small molecules and mAbs. The high-level organization of a typical mAb PBPK model consists of a whole-body PBPK model with organ compartments interconnected by both blood and lymph flows. The whole-body PBPK model is coupled with tissue-level submodels used to describe key mechanisms governing mAb disposition including tissue efflux via the lymphatic system, elimination by catabolism, protection from catabolism binding to the neonatal Fc (FcRn) receptor, and nonlinear binding to specific pharmacological targets of interest. The use of PBPK modeling in the development of therapeutic proteins is still in its infancy. Further application of PBPK modeling for therapeutic proteins will help to define its developing role in drug discovery and development. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model

NASA Astrophysics Data System (ADS)

Knott, Michael; Best, Robert B.

2014-05-01

Many proteins undergo a conformational transition upon binding to their cognate binding partner, with intrinsically disordered proteins (IDPs) providing an extreme example in which a folding transition occurs. However, it is often not clear whether this occurs via an "induced fit" or "conformational selection" mechanism, or via some intermediate scenario. In the first case, transient encounters with the binding partner favour transitions to the bound structure before the two proteins dissociate, while in the second the bound structure must be selected from a subset of unbound structures which are in the correct state for binding, because transient encounters of the incorrect conformation with the binding partner are most likely to result in dissociation. A particularly interesting situation involves those intrinsically disordered proteins which can bind to different binding partners in different conformations. We have devised a multi-state coarse-grained simulation model which is able to capture the binding of IDPs in alternate conformations, and by applying it to the binding of nuclear coactivator binding domain (NCBD) to either ACTR or IRF-3 we are able to determine the binding mechanism. By all measures, the binding of NCBD to either binding partner appears to occur via an induced fit mechanism. Nonetheless, we also show how a scenario closer to conformational selection could arise by choosing an alternative non-binding structure for NCBD.

Modelling protein functional domains in signal transduction using Maude

NASA Technical Reports Server (NTRS)

Sriram, M. G.

2003-01-01

Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

Human Immunodeficiency Virus Integration Protein Expressed in Escherichia Coli Possesses Selective DNA Cleaving Activity

NASA Astrophysics Data System (ADS)

Sherman, Paula A.; Fyfe, James A.

1990-07-01

The human immunodeficiency virus (HIV) integration protein, a potential target for selective antiviral therapy, was expressed in Escherichia coli. The purified protein, free of detectable contaminating endonucleases, selectively cleaved double-stranded DNA oligonucleotides that mimic the U3 and the U5 termini of linear HIV DNA. Two nucleotides were removed from the 3' ends of both the U5 plus strand and the U3 minus strand; in both cases, cleavage was adjacent to a conserved CA dinucleotide. The reaction was metal-ion dependent, with a preference for Mn2+ over Mg2+. Reaction selectivity was further demonstrated by the lack of cleavage of an HIV U5 substrate on the complementary (minus) strand, an analogous substrate that mimics the U3 terminus of an avian retrovirus, and an HIV U5 substrate in which the conserved CA dinucleotide was replaced with a TA dinucleotide. Such an integration protein-mediated cleavage reaction is expected to occur as part of the integration event in the retroviral life cycle, in which a double-stranded DNA copy of the viral RNA genome is inserted into the host cell DNA.

The genealogy of samples in models with selection.

PubMed

Neuhauser, C; Krone, S M

1997-02-01

We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.

The Genealogy of Samples in Models with Selection

PubMed Central

Neuhauser, C.; Krone, S. M.

1997-01-01

We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models, DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case. PMID:9071604

Adaptive Modeling Procedure Selection by Data Perturbation.

PubMed

Zhang, Yongli; Shen, Xiaotong

2015-10-01

Many procedures have been developed to deal with the high-dimensional problem that is emerging in various business and economics areas. To evaluate and compare these procedures, modeling uncertainty caused by model selection and parameter estimation has to be assessed and integrated into a modeling process. To do this, a data perturbation method estimates the modeling uncertainty inherited in a selection process by perturbing the data. Critical to data perturbation is the size of perturbation, as the perturbed data should resemble the original dataset. To account for the modeling uncertainty, we derive the optimal size of perturbation, which adapts to the data, the model space, and other relevant factors in the context of linear regression. On this basis, we develop an adaptive data-perturbation method that, unlike its nonadaptive counterpart, performs well in different situations. This leads to a data-adaptive model selection method. Both theoretical and numerical analysis suggest that the data-adaptive model selection method adapts to distinct situations in that it yields consistent model selection and optimal prediction, without knowing which situation exists a priori. The proposed method is applied to real data from the commodity market and outperforms its competitors in terms of price forecasting accuracy.

A hidden markov model derived structural alphabet for proteins.

PubMed

Camproux, A C; Gautier, R; Tufféry, P

2004-06-04

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

Model Selection with the Linear Mixed Model for Longitudinal Data

ERIC Educational Resources Information Center

Ryoo, Ji Hoon

2011-01-01

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

Structural optimization and structure-functional selectivity relationship studies of G protein-biased EP2 receptor agonists.

PubMed

Ogawa, Seiji; Watanabe, Toshihide; Moriyuki, Kazumi; Goto, Yoshikazu; Yamane, Shinsaku; Watanabe, Akio; Tsuboi, Kazuma; Kinoshita, Atsushi; Okada, Takuya; Takeda, Hiroyuki; Tani, Kousuke; Maruyama, Toru

2016-05-15

The modification of the novel G protein-biased EP2 agonist 1 has been investigated to improve its G protein activity and develop a better understanding of its structure-functional selectivity relationship (SFSR). The optimization of the substituents on the phenyl ring of 1, followed by the inversion of the hydroxyl group on the cyclopentane moiety led to compound 9, which showed a 100-fold increase in its G protein activity compared with 1 without any increase in β-arrestin recruitment. Furthermore, SFSR studies revealed that the combination of meta and para substituents on the phenyl moiety was crucial to the functional selectivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

In silico modeling of the Moniliophthora perniciosa Atg8 protein.

PubMed

Pereira, A C F; Cardoso, T H S; Brendel, M; Pungartnik, C

2013-12-11

Autophagy is defined as an intracellular system of lysosomal degradation in eukaryotic cells, and the genes involved in this process are conserved from yeast to humans. Among these genes, ATG8 encodes a ubiquitin-like protein that is conjugated to a phosphatidylethanolamine (PE) membrane by the ubiquitination system. The Atg8p-PE complex is important in initiating the formation of the autophagosome and thus plays a critical role in autophagy. In silico modeling of Atg8p of Moniliophthora perniciosa revealed its three-dimensional structure and enabled comparison with its Saccharomyces cerevisiae homologue ScAtg8p. Some common and distinct features were observed between these two proteins, including the conservation of residues required to allow the interaction of α-helix1 with the ubiquitin core. However, the electrostatic potential surfaces of these helices differ, implying particular roles in selecting specific binding partners. The proposed structure was validated by the programs PROCHECK 3.4, ANOLEA, and QMEAN, which demonstrated 100% of amino acids located in favorable regions with low total energy. Our results showed that MpAtg8p contains the same functional domains (3 α-helices and 4 β-sheets) and is similar in structure as the ScAtg8p yeast. Both proteins have many conserved sequences in common, and therefore, their proposed three-dimensional models show similar configuration.

Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data.

PubMed

Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P

2007-02-08

Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

2015-06-22

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions.

A Coarse-Grained Protein Model in a Water-like Solvent

NASA Astrophysics Data System (ADS)

Sharma, Sumit; Kumar, Sanat K.; Buldyrev, Sergey V.; Debenedetti, Pablo G.; Rossky, Peter J.; Stanley, H. Eugene

2013-05-01

Simulations employing an explicit atom description of proteins in solvent can be computationally expensive. On the other hand, coarse-grained protein models in implicit solvent miss essential features of the hydrophobic effect, especially its temperature dependence, and have limited ability to capture the kinetics of protein folding. We propose a free space two-letter protein (``H-P'') model in a simple, but qualitatively accurate description for water, the Jagla model, which coarse-grains water into an isotropically interacting sphere. Using Monte Carlo simulations, we design protein-like sequences that can undergo a collapse, exposing the ``Jagla-philic'' monomers to the solvent, while maintaining a ``hydrophobic'' core. This protein-like model manifests heat and cold denaturation in a manner that is reminiscent of proteins. While this protein-like model lacks the details that would introduce secondary structure formation, we believe that these ideas represent a first step in developing a useful, but computationally expedient, means of modeling proteins.

IT vendor selection model by using structural equation model & analytical hierarchy process

NASA Astrophysics Data System (ADS)

Maitra, Sarit; Dominic, P. D. D.

2012-11-01

Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.

PubMed

Spielman, Stephanie J; Wilke, Claus O

2016-11-01

The mutation-selection model of coding sequence evolution has received renewed attention for its use in estimating site-specific amino acid propensities and selection coefficient distributions. Two computationally tractable mutation-selection inference frameworks have been introduced: One framework employs a fixed-effects, highly parameterized maximum likelihood approach, whereas the other employs a random-effects Bayesian Dirichlet Process approach. While both implementations follow the same model, they appear to make distinct predictions about the distribution of selection coefficients. The fixed-effects framework estimates a large proportion of highly deleterious substitutions, whereas the random-effects framework estimates that all substitutions are either nearly neutral or weakly deleterious. It remains unknown, however, how accurately each method infers evolutionary constraints at individual sites. Indeed, selection coefficient distributions pool all site-specific inferences, thereby obscuring a precise assessment of site-specific estimates. Therefore, in this study, we use a simulation-based strategy to determine how accurately each approach recapitulates the selective constraint at individual sites. We find that the fixed-effects approach, despite its extensive parameterization, consistently and accurately estimates site-specific evolutionary constraint. By contrast, the random-effects Bayesian approach systematically underestimates the strength of natural selection, particularly for slowly evolving sites. We also find that, despite the strong differences between their inferred selection coefficient distributions, the fixed- and random-effects approaches yield surprisingly similar inferences of site-specific selective constraint. We conclude that the fixed-effects mutation-selection framework provides the more reliable software platform for model application and future development. © The Author 2016. Published by Oxford University Press on behalf of the

Identification and Validation of Selected Universal Stress Protein Domain Containing Drought-Responsive Genes in Pigeonpea (Cajanus cajan L.)

PubMed Central

Sinha, Pallavi; Pazhamala, Lekha T.; Singh, Vikas K.; Saxena, Rachit K.; Krishnamurthy, L.; Azam, Sarwar; Khan, Aamir W.; Varshney, Rajeev K.

2016-01-01

Pigeonpea is a resilient crop, which is relatively more drought tolerant than many other legume crops. To understand the molecular mechanisms of this unique feature of pigeonpea, 51 genes were selected using the Hidden Markov Models (HMM) those codes for proteins having close similarity to universal stress protein domain. Validation of these genes was conducted on three pigeonpea genotypes (ICPL 151, ICPL 8755, and ICPL 227) having different levels of drought tolerance. Gene expression analysis using qRT-PCR revealed 6, 8, and 18 genes to be ≥2-fold differentially expressed in ICPL 151, ICPL 8755, and ICPL 227, respectively. A total of 10 differentially expressed genes showed ≥2-fold up-regulation in the more drought tolerant genotype, which encoded four different classes of proteins. These include plant U-box protein (four genes), universal stress protein A-like protein (four genes), cation/H(+) antiporter protein (one gene) and an uncharacterized protein (one gene). Genes C.cajan_29830 and C.cajan_33874 belonging to uspA, were found significantly expressed in all the three genotypes with ≥2-fold expression variations. Expression profiling of these two genes on the four other legume crops revealed their specific role in pigeonpea. Therefore, these genes seem to be promising candidates for conferring drought tolerance specifically to pigeonpea. PMID:26779199

Modeling and simulating networks of interdependent protein interactions.

PubMed

Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

2018-05-21

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package

Effects of urea on selectivity and protein-ligand interactions in multimodal cation exchange chromatography.

PubMed

Holstein, Melissa A; Parimal, Siddharth; McCallum, Scott A; Cramer, Steven M

2013-01-08

Nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations were employed in concert with chromatography to provide insight into the effect of urea on protein-ligand interactions in multimodal (MM) chromatography. Chromatographic experiments with a protein library in ion exchange (IEX) and MM systems indicated that, while urea had a significant effect on protein retention and selectivity for a range of proteins in MM systems, the effects were much less pronounced in IEX. NMR titration experiments carried out with a multimodal ligand, and isotopically enriched human ubiquitin indicated that, while the ligand binding face of ubiquitin remained largely intact in the presence of urea, the strength of binding was decreased. MD simulations were carried out to provide further insight into the effect of urea on MM ligand binding. These results indicated that, while the overall ligand binding face of ubiquitin remained the same, there was a reduction in the occupancy of the MM ligand interaction region along with subtle changes in the residues involved in these interactions. This work demonstrates the effectiveness of urea in enhancing selectivity in MM chromatographic systems and also provides an in-depth analysis of how MM ligand-protein interactions are altered in the presence of this fluid phase modifier.

Modeling of Protein Subcellular Localization in Bacteria

NASA Astrophysics Data System (ADS)

Xu, Xiaohua; Kulkarni, Rahul

2006-03-01

Specific subcellular localization of proteins is a vital component of important bacterial processes: e.g. the Min proteins which regulate cell division in E. coli and Spo0J-Soj system which is critical for sporulation in B. subtilis. We examine how the processes of diffusion and membrane attachment contribute to protein subcellular localization for the above systems. We use previous experimental results to suggest minimal models for these processes. For the minimal models, we derive analytic expressions which provide insight into the processes that determine protein subcellular localization. Finally, we present the results of numerical simulations for the systems studied and make connections to the observed experiemental phenomenology.

Conformational Heterogeneity of Unbound Proteins Enhances Recognition in Protein-Protein Encounters.

PubMed

Pallara, Chiara; Rueda, Manuel; Abagyan, Ruben; Fernández-Recio, Juan

2016-07-12

To understand cellular processes at the molecular level we need to improve our knowledge of protein-protein interactions, from a structural, mechanistic, and energetic point of view. Current theoretical studies and computational docking simulations show that protein dynamics plays a key role in protein association and support the need for including protein flexibility in modeling protein interactions. Assuming the conformational selection binding mechanism, in which the unbound state can sample bound conformers, one possible strategy to include flexibility in docking predictions would be the use of conformational ensembles originated from unbound protein structures. Here we present an exhaustive computational study about the use of precomputed unbound ensembles in the context of protein docking, performed on a set of 124 cases of the Protein-Protein Docking Benchmark 3.0. Conformational ensembles were generated by conformational optimization and refinement with MODELLER and by short molecular dynamics trajectories with AMBER. We identified those conformers providing optimal binding and investigated the role of protein conformational heterogeneity in protein-protein recognition. Our results show that a restricted conformational refinement can generate conformers with better binding properties and improve docking encounters in medium-flexible cases. For more flexible cases, a more extended conformational sampling based on Normal Mode Analysis was proven helpful. We found that successful conformers provide better energetic complementarity to the docking partners, which is compatible with recent views of binding association. In addition to the mechanistic considerations, these findings could be exploited for practical docking predictions of improved efficiency.

Heterogenous ribonucleoprotein A18 (hnRNP A18) promotes tumor growth by increasing protein translation of selected transcripts in cancer cells.

PubMed

Chang, Elizabeth T; Parekh, Palak R; Yang, Qingyuan; Nguyen, Duc M; Carrier, France

2016-03-01

The heterogenous ribonucleoprotein A18 (hnRNP A18) promotes tumor growth by coordinating the translation of selected transcripts associated with proliferation and survival. hnRNP A18 binds to and stabilizes the transcripts of pro-survival genes harboring its RNA signature motif in their 3'UTRs. hnRNP A18 binds to ATR, RPA, TRX, HIF-1α and several protein translation factor mRNAs on polysomes and increases de novo protein translation under cellular stress. Most importantly, down regulation of hnRNP A18 decreases proliferation, invasion and migration in addition to significantly reducing tumor growth in two mouse xenograft models, melanoma and breast cancer. Moreover, tissue microarrays performed on human melanoma, prostate, breast and colon cancer indicate that hnRNP A18 is over expressed in 40 to 60% of these malignant tissue as compared to normal adjacent tissue. Immunohistochemistry data indicate that hnRNP A18 is over expressed in the stroma and hypoxic areas of human tumors. These data thus indicate that hnRNP A18 can promote tumor growth in in vivo models by coordinating the translation of pro-survival transcripts to support the demands of proliferating cells and increase survival under cellular stress. hnRNP A18 therefore represents a new target to selectively inhibit protein translation in tumor cells.

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

PubMed Central

Zheng, Bin; McLean, David C; Lu, Xinghua

2006-01-01

Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text. PMID:16466569

Urinary Protein Profiles in a Rat Model for Diabetic Complications*

PubMed Central

Schlatzer, Daniela M.; Dazard, Jean-Eudes; Dharsee, Moyez; Ewing, Rob M.; Ilchenko, Serguei; Stewart, Ian; Christ, George; Chance, Mark R.

2009-01-01

Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring. PMID:19497846

A probabilistic and continuous model of protein conformational space for template-free modeling.

PubMed

Zhao, Feng; Peng, Jian; Debartolo, Joe; Freed, Karl F; Sosnick, Tobin R; Xu, Jinbo

2010-06-01

One of the major challenges with protein template-free modeling is an efficient sampling algorithm that can explore a huge conformation space quickly. The popular fragment assembly method constructs a conformation by stringing together short fragments extracted from the Protein Data Base (PDB). The discrete nature of this method may limit generated conformations to a subspace in which the native fold does not belong. Another worry is that a protein with really new fold may contain some fragments not in the PDB. This article presents a probabilistic model of protein conformational space to overcome the above two limitations. This probabilistic model employs directional statistics to model the distribution of backbone angles and 2(nd)-order Conditional Random Fields (CRFs) to describe sequence-angle relationship. Using this probabilistic model, we can sample protein conformations in a continuous space, as opposed to the widely used fragment assembly and lattice model methods that work in a discrete space. We show that when coupled with a simple energy function, this probabilistic method compares favorably with the fragment assembly method in the blind CASP8 evaluation, especially on alpha or small beta proteins. To our knowledge, this is the first probabilistic method that can search conformations in a continuous space and achieves favorable performance. Our method also generated three-dimensional (3D) models better than template-based methods for a couple of CASP8 hard targets. The method described in this article can also be applied to protein loop modeling, model refinement, and even RNA tertiary structure prediction.

Model Selection Methods for Mixture Dichotomous IRT Models

ERIC Educational Resources Information Center

Li, Feiming; Cohen, Allan S.; Kim, Seock-Ho; Cho, Sun-Joo

2009-01-01

This study examines model selection indices for use with dichotomous mixture item response theory (IRT) models. Five indices are considered: Akaike's information coefficient (AIC), Bayesian information coefficient (BIC), deviance information coefficient (DIC), pseudo-Bayes factor (PsBF), and posterior predictive model checks (PPMC). The five…

Bioinorganic Chemical Modeling of Dioxygen-Activating Copper Proteins.

ERIC Educational Resources Information Center

Karlin, Kenneth D.; Gultneh, Yilma

1985-01-01

Discusses studies done in modeling the copper centers in the proteins hemocyanin (a dioxygen carrier), tyrosinase, and dopamine beta-hydroxylase. Copper proteins, model approach in copper bioinorganic chemistry, characterization of reversible oxygen carriers and dioxygen-metal complexes, a copper mono-oxygenase model reaction, and other topics are…

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Selective interaction of AGS3 with G-proteins and the influence of AGS3 on the activation state of G-proteins.

PubMed

Bernard, M L; Peterson, Y K; Chung, P; Jourdan, J; Lanier, S M

2001-01-12

AGS3 (activator of G-protein signaling 3) was isolated in a yeast-based functional screen for receptor-independent activators of heterotrimeric G-proteins. As an initial approach to define the role of AGS3 in mammalian signal processing, we defined the AGS3 subdomains involved in G-protein interaction, its selectivity for G-proteins, and its influence on the activation state of G-protein. Immunoblot analysis with AGS3 antisera indicated expression in rat brain, the neuronal-like cell lines PC12 and NG108-15, as well as the smooth muscle cell line DDT(1)-MF2. Immunofluorescence studies and confocal imaging indicated that AGS3 was predominantly cytoplasmic and enriched in microdomains of the cell. AGS3 coimmunoprecipitated with Galpha(i3) from cell and tissue lysates, indicating that a subpopulation of AGS3 and Galpha(i) exist as a complex in the cell. The coimmunoprecipitation of AGS3 and Galpha(i) was dependent upon the conformation of Galpha(i3) (GDP GTPgammaS (guanosine 5'-3-O-(thio)triphosphate)). The regions of AGS3 that bound Galpha(i) were localized to four amino acid repeats (G-protein regulatory motif (GPR)) in the carboxyl terminus (Pro(463)-Ser(650)), each of which were capable of binding Galpha(i). AGS3-GPR domains selectively interacted with Galpha(i) in tissue and cell lysates and with purified Galpha(i)/Galpha(t). Subsequent experiments with purified Galpha(i2) and Galpha(i3) indicated that the carboxyl-terminal region containing the four GPR motifs actually bound more than one Galpha(i) subunit at the same time. The AGS3-GPR domains effectively competed with Gbetagamma for binding to Galpha(t(GDP)) and blocked GTPgammaS binding to Galpha(i1). AGS3 and related proteins provide unexpected mechanisms for coordination of G-protein signaling pathways.

Selective association of a fragment of the knob protein with spectrin, actin and the red cell membrane.

PubMed

Kilejian, A; Rashid, M A; Aikawa, M; Aji, T; Yang, Y F

1991-02-01

The knob protein of Plasmodium falciparum is essential for the formation of knob-like protrusions on the host erythrocyte membrane. A functional domain of the knob protein was identified. This peptide formed stable complexes with the two major red cell skeletal proteins, spectrin and actin. When introduced into resealed normal erythrocytes, the peptide associated selectively with the cytoplasmic surface of the membrane and formed knob-like electron dense deposits. Knobs are thought to play an important role in the immunopathology of P. falciparum infections. Our findings provide a first step towards understanding the molecular basis for selective membrane changes at knobs.