Engineering A-kinase Anchoring Protein (AKAP)-selective Regulatory Subunits of Protein Kinase A (PKA) through Structure-based Phage Selection*

PubMed Central

Gold, Matthew G.; Fowler, Douglas M.; Means, Christopher K.; Pawson, Catherine T.; Stephany, Jason J.; Langeberg, Lorene K.; Fields, Stanley; Scott, John D.

2013-01-01

PKA is retained within distinct subcellular environments by the association of its regulatory type II (RII) subunits with A-kinase anchoring proteins (AKAPs). Conventional reagents that universally disrupt PKA anchoring are patterned after a conserved AKAP motif. We introduce a phage selection procedure that exploits high-resolution structural information to engineer RII mutants that are selective for a particular AKAP. Selective RII (RSelect) sequences were obtained for eight AKAPs following competitive selection screening. Biochemical and cell-based experiments validated the efficacy of RSelect proteins for AKAP2 and AKAP18. These engineered proteins represent a new class of reagents that can be used to dissect the contributions of different AKAP-targeted pools of PKA. Molecular modeling and high-throughput sequencing analyses revealed the molecular basis of AKAP-selective interactions and shed new light on native RII-AKAP interactions. We propose that this structure-directed evolution strategy might be generally applicable for the investigation of other protein interaction surfaces. PMID:23625929

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.

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

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.

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.

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

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

Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS).

PubMed

Lu, Yuzhen; Du, Changwen; Yu, Changbing; Zhou, Jianmin

2014-08-01

Fast and non-destructive determination of rapeseed protein content carries significant implications in rapeseed production. This study presented the first attempt of using Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) to quantify protein content of rapeseed. The full-spectrum model was first built using partial least squares (PLS). Interval selection methods including interval partial least squares (iPLS), synergy interval partial least squares (siPLS), backward elimination interval partial least squares (biPLS) and dynamic backward elimination interval partial least squares (dyn-biPLS) were then employed to select the relevant band or band combination for PLS modeling. The full-spectrum PLS model achieved an ratio of prediction to deviation (RPD) of 2.047. In comparison, all interval selection methods produced better results than full-spectrum modeling. siPLS achieved the best predictive accuracy with an RPD of 3.215 when the spectrum was sectioned into 25 intervals, and two intervals (1198-1335 and 1614-1753 cm(-1) ) were selected. iPLS excelled biPLS and dyn-biPLS, and dyn-biPLS performed slightly better than biPLS. FTIR-PAS was verified as a promising analytical tool to quantify rapeseed protein content. Interval selection could extract the relevant individual band or synergy band associated with the sample constituent of interest, and then improve the prediction accuracy of the full-spectrum model. © 2013 Society of Chemical Industry.

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

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.

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.

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.

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.

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

Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection.

PubMed

Liu, Liang; Cai, Yudong; Lu, Wencong; Feng, Kaiyan; Peng, Chunrong; Niu, Bing

2009-03-06

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.

The tangled bank of amino acids.

PubMed

Goldstein, Richard A; Pollock, David D

2016-07-01

The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Rational Design of in Vivo Tau Tangle-Selective Near-Infrared Fluorophores: Expanding the BODIPY Universe.

PubMed

Verwilst, Peter; Kim, Hye-Ri; Seo, Jinho; Sohn, Nak-Won; Cha, Seung-Yun; Kim, Yeongmin; Maeng, Sungho; Shin, Jung-Won; Kwak, Jong Hwan; Kang, Chulhun; Kim, Jong Seung

2017-09-27

The elucidation of the cause of Alzheimer's disease remains one of the greatest questions in neurodegenerative research. The lack of highly reliable low-cost sensors to study the structural changes in key proteins during the progression of the disease is a contributing factor to this lack of insight. In the current work, we describe the rational design and synthesis of two fluorescent BODIPY-based probes, named Tau 1 and Tau 2. The probes were evaluated on the molecular surface formed by a fibril of the PHF6 ( 306 VQIVYK 311 ) tau fragment using molecular docking studies to provide a potential molecular model to rationalize the selectivity of the new probes as compared to a homologous Aβ-selective probe. The probes were synthesized in a few steps from commercially available starting products and could thus prove to be highly cost-effective. We demonstrated the excellent photophysical properties of the dyes, such as a large Stokes shift and emission in the near-infrared window of the electromagnetic spectrum. The probes demonstrated a high selectivity for self-assembled microtubule-associated protein tau (Tau protein), in both solution and cell-based experiments. Moreover, the administration to an acute murine model of tauopathy clearly revealed the staining of self-assembled hyperphosphorylated tau protein in pathologically relevant hippocampal brain regions. Tau 1 demonstrated efficient blood-brain barrier penetrability and demonstrated a clear selectivity for tau tangles over Aβ plaques, as well as the capacity for in vivo imaging in a transgenic mouse model. The current work could open up avenues for the cost-effective monitoring of the tau protein aggregation state in animal models as well as tissue staining. Furthermore, these fluorophores could serve as the basis for the development of clinically relevant sensors, for example based on PET imaging.

Conserved thioredoxin fold is present in Pisum sativum L. sieve element occlusion-1 protein

PubMed Central

Umate, Pavan; Tuteja, Renu

2010-01-01

Homology-based three-dimensional model for Pisum sativum sieve element occlusion 1 (Ps.SEO1) (forisomes) protein was constructed. A stretch of amino acids (residues 320 to 456) which is well conserved in all known members of forisomes proteins was used to model the 3D structure of Ps.SEO1. The structural prediction was done using Protein Homology/analogY Recognition Engine (PHYRE) web server. Based on studies of local sequence alignment, the thioredoxin-fold containing protein [Structural Classification of Proteins (SCOP) code d1o73a_], a member of the glutathione peroxidase family was selected as a template for modeling the spatial structure of Ps.SEO1. Selection was based on comparison of primary sequence, higher match quality and alignment accuracy. Motif 1 (EVF) is conserved in Ps.SEO1, Vicia faba (Vf.For1) and Medicago truncatula (MT.SEO3); motif 2 (KKED) is well conserved across all forisomes proteins and motif 3 (IGYIGNP) is conserved in Ps.SEO1 and Vf.For1. PMID:20404566

NegGOA: negative GO annotations selection using ontology structure.

PubMed

Fu, Guangyuan; Wang, Jun; Yang, Bo; Yu, Guoxian

2016-10-01

Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space. In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods. The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa gxyu@swu.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Data Mining of Macromolecular Structures.

PubMed

van Beusekom, Bart; Perrakis, Anastassis; Joosten, Robbie P

2016-01-01

The use of macromolecular structures is widespread for a variety of applications, from teaching protein structure principles all the way to ligand optimization in drug development. Applying data mining techniques on these experimentally determined structures requires a highly uniform, standardized structural data source. The Protein Data Bank (PDB) has evolved over the years toward becoming the standard resource for macromolecular structures. However, the process selecting the data most suitable for specific applications is still very much based on personal preferences and understanding of the experimental techniques used to obtain these models. In this chapter, we will first explain the challenges with data standardization, annotation, and uniformity in the PDB entries determined by X-ray crystallography. We then discuss the specific effect that crystallographic data quality and model optimization methods have on structural models and how validation tools can be used to make informed choices. We also discuss specific advantages of using the PDB_REDO databank as a resource for structural data. Finally, we will provide guidelines on how to select the most suitable protein structure models for detailed analysis and how to select a set of structure models suitable for data mining.

Inferring Selection on Amino Acid Preference in Protein Domains

PubMed Central

Durbin, Richard

2009-01-01

Models that explicitly account for the effect of selection on new mutations have been proposed to account for “codon bias” or the excess of “preferred” codons that results from selection for translational efficiency and/or accuracy. In principle, such models can be applied to any mutation that results in a preferred allele, but in most cases, the fitness effect of a specific mutation cannot be predicted. Here we show that it is possible to assign preferred and unpreferred states to amino acid changing mutations that occur in protein domains. We propose that mutations that lead to more common amino acids (at a given position in a domain) can be considered “preferred alleles” just as are synonymous mutations leading to codons for more abundant tRNAs. We use genome-scale polymorphism data to show that alleles for preferred amino acids in protein domains occur at higher frequencies in the population, as has been shown for preferred codons. We show that this effect is quantitative, such that there is a correlation between the shift in frequency of preferred alleles and the predicted fitness effect. As expected, we also observe a reduction in the numbers of polymorphisms and substitutions at more important positions in domains, consistent with stronger selection at those positions. We examine the derived allele frequency distribution and polymorphism to divergence ratios of preferred and unpreferred differences and find evidence for both negative and positive selections acting to maintain protein domains in the human population. Finally, we analyze a model for selection on amino acid preferences in protein domains and find that it is consistent with the quantitative effects that we observe. PMID:19095755

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/.

PRINT: A Protein Bioconjugation Method with Exquisite N-terminal Specificity

PubMed Central

Sur, Surojit; Qiao, Yuan; Fries, Anja; O’Meally, Robert N.; Cole, Robert N.; Kinzler, Kenneth W.; Vogelstein, Bert; Zhou, Shibin

2015-01-01

Chemical conjugation is commonly used to enhance the pharmacokinetics, biodistribution, and potency of protein therapeutics, but often leads to non-specific modification or loss of bioactivity. Here, we present a simple, versatile and widely applicable method that allows exquisite N-terminal specific modification of proteins. Combining reversible side-chain blocking and protease mediated cleavage of a commonly used HIS tag appended to a protein, we generate with high yield and purity exquisitely site specific and selective bio-conjugates of TNF-α by using amine reactive NHS ester chemistry. We confirm the N terminal selectivity and specificity using mass spectral analyses and show near complete retention of the biological activity of our model protein both in vitro and in vivo murine models. We believe that this methodology would be applicable to a variety of potentially therapeutic proteins and the specificity afforded by this technique would allow for rapid generation of novel biologics. PMID:26678960

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.

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

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

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.

CalFitter: a web server for analysis of protein thermal denaturation data.

PubMed

Mazurenko, Stanislav; Stourac, Jan; Kunka, Antonin; Nedeljkovic, Sava; Bednar, David; Prokop, Zbynek; Damborsky, Jiri

2018-05-14

Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

The tangled bank of amino acids

PubMed Central

Pollock, David D.

2016-01-01

Abstract The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. PMID:27028523

Collective choice in ants: the role of protein and carbohydrates ratios.

PubMed

Arganda, S; Nicolis, S C; Perochain, A; Péchabadens, C; Latil, G; Dussutour, A

2014-10-01

In a foraging context, social insects make collective decisions from individuals responding to local information. When faced with foods varying in quality, ants are known to be able to select the best food source using pheromone trails. Until now, studies investigating collective decisions have focused on single nutrients, mostly carbohydrates. In the environment, the foods available are a complex mixture and are composed of various nutrients, available in different forms. In this paper, we explore the effect of protein to carbohydrate ratio on ants' ability to detect and choose between foods with different protein characteristics (free amino acids or whole proteins). In a two-choice set up, Argentine ants Linepithema humile were presented with two artificial foods containing either whole protein or amino acids in two different dietary conditions: high protein food or high carbohydrate food. At the collective level, when ants were faced with high carbohydrate foods, they did not show a preference between free amino acids or whole proteins, while a preference for free amino acids emerged when choosing between high protein foods. At the individual level, the probability of feeding was higher for high carbohydrates food and for foods containing free amino acids. Two mathematical models were developed to evaluate the importance of feeding probability in collective food selection. A first model in which a forager deposits pheromone only after feeding, and a second model in which a forager always deposits pheromone, but with greater intensity after feeding. Both models were able to predict free amino acid selection, however the second one was better able to reproduce the experimental results suggesting that modulating trail strength according to feeding probability is likely the mechanism explaining amino acid preference at a collective level in Argentine ants. Copyright © 2014 Elsevier Ltd. All rights reserved.

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) and compare it to a new local consensus-based approach. Improved model selection is obtained by using a composite scoring function operating on single models in order to enrich higher quality models which are subsequently used to calculate the structural consensus. The performance of consensus-based methods such as QMEANclust highly depends on the composition and quality of the model ensemble to be analysed. Therefore, performance estimates for consensus methods based on large meta-datasets (e.g. CASP) might overrate their applicability in more realistic modelling situations with smaller sets of models based on individual methods.

Identification of Genes Involved in Breast Cancer Metastasis by Integrating Protein-Protein Interaction Information with Expression Data.

PubMed

Tian, Xin; Xin, Mingyuan; Luo, Jian; Liu, Mingyao; Jiang, Zhenran

2017-02-01

The selection of relevant genes for breast cancer metastasis is critical for the treatment and prognosis of cancer patients. Although much effort has been devoted to the gene selection procedures by use of different statistical analysis methods or computational techniques, the interpretation of the variables in the resulting survival models has been limited so far. This article proposes a new Random Forest (RF)-based algorithm to identify important variables highly related with breast cancer metastasis, which is based on the important scores of two variable selection algorithms, including the mean decrease Gini (MDG) criteria of Random Forest and the GeneRank algorithm with protein-protein interaction (PPI) information. The new gene selection algorithm can be called PPIRF. The improved prediction accuracy fully illustrated the reliability and high interpretability of gene list selected by the PPIRF approach.

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

Comparative genomics study for the identification of drug and vaccine targets in Staphylococcus aureus: MurA ligase enzyme as a proposed candidate.

PubMed

Ghosh, Soma; Prava, Jyoti; Samal, Himanshu Bhusan; Suar, Mrutyunjay; Mahapatra, Rajani Kanta

2014-06-01

Now-a-days increasing emergence of antibiotic-resistant pathogenic microorganisms is one of the biggest challenges for management of disease. In the present study comparative genomics, metabolic pathways analysis and additional parameters were defined for the identification of 94 non-homologous essential proteins in Staphylococcus aureus genome. Further study prioritized 19 proteins as vaccine candidates where as druggability study reports 34 proteins suitable as drug targets. Enzymes from peptidoglycan biosynthesis, folate biosynthesis were identified as candidates for drug development. Furthermore, bacterial secretory proteins and few hypothetical proteins identified in our analysis fulfill the criteria of vaccine candidates. As a case study, we built a homology model of one of the potential drug target, MurA ligase, using MODELLER (9v12) software. The model has been further selected for in silico docking study with inhibitors from the DrugBank database. Results from this study could facilitate selection of proteins for entry into drug design and vaccine production pipelines. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein.

PubMed

Gebreyesus, G; Lund, M S; Janss, L; Poulsen, N A; Larsen, L B; Bovenhuis, H; Buitenhuis, A J

2016-04-01

Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, β-CN, κ-CN, α-lactalbumin, and β-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for β-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

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

Expression-Linked Patterns of Codon Usage, Amino Acid Frequency, and Protein Length in the Basally Branching Arthropod Parasteatoda tepidariorum

PubMed Central

Whittle, Carrie A.; Extavour, Cassandra G.

2016-01-01

Abstract Spiders belong to the Chelicerata, the most basally branching arthropod subphylum. The common house spider, Parasteatoda tepidariorum, is an emerging model and provides a valuable system to address key questions in molecular evolution in an arthropod system that is distinct from traditionally studied insects. Here, we provide evidence suggesting that codon usage, amino acid frequency, and protein lengths are each influenced by expression-mediated selection in P. tepidariorum. First, highly expressed genes exhibited preferential usage of T3 codons in this spider, suggestive of selection. Second, genes with elevated transcription favored amino acids with low or intermediate size/complexity (S/C) scores (glycine and alanine) and disfavored those with large S/C scores (such as cysteine), consistent with the minimization of biosynthesis costs of abundant proteins. Third, we observed a negative correlation between expression level and coding sequence length. Together, we conclude that protein-coding genes exhibit signals of expression-related selection in this emerging, noninsect, arthropod model. PMID:27017527

Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

NASA Astrophysics Data System (ADS)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

Selectivity Mechanism of ATP-Competitive Inhibitors for PKB and PKA.

PubMed

Wu, Ke; Pang, Jingzhi; Song, Dong; Zhu, Ying; Wu, Congwen; Shao, Tianqu; Chen, Haifeng

2015-07-01

Protein kinase B (PKB) acts as a central node on the PI3K kinase pathway. Constitutive activation and overexpression of PKB have been identified to involve in various cancers. However, protein kinase A (PKA) sharing high homology with PKB is essential for metabolic regulation. Therefore, specific targeting on PKB is crucial strategy in drug design and development for antitumor. Here, we had revealed the selectivity mechanism for PKB inhibitors with molecular dynamics simulation and 3D-QSAR methods. Selective inhibitors of PKB could form more hydrogen bonds and hydrophobic contacts with PKB than those with PKA. This could explain that selective inhibitor M128 is more potent to PKB than to PKA. Then, 3D-QSAR models were constructed for these selective inhibitors and evaluated by test set compounds. 3D-QSAR model comparison of PKB inhibitors and PKA inhibitors reveals possible methods to improve the selectivity of inhibitors. These models can be used to design new chemical entities and make quantitative prediction of the specific selective inhibitors before resorting to in vitro and in vivo experiment. © 2014 John Wiley & Sons A/S.

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.

Inference of epistatic effects in a key mitochondrial protein

NASA Astrophysics Data System (ADS)

Nelson, Erik D.; Grishin, Nick V.

2018-06-01

We use Potts model inference to predict pair epistatic effects in a key mitochondrial protein—cytochrome c oxidase subunit 2—for ray-finned fishes. We examine the effect of phylogenetic correlations on our predictions using a simple exact fitness model, and we find that, although epistatic effects are underpredicted, they maintain a roughly linear relationship to their true (model) values. After accounting for this correction, epistatic effects in the protein are still relatively weak, leading to fitness valleys of depth 2 N s ≃-5 in compensatory double mutants. Interestingly, positive epistasis is more pronounced than negative epistasis, and the strongest positive effects capture nearly all sites subject to positive selection in fishes, similar to virus proteins evolving under selection pressure in the context of drug therapy.

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

PubMed Central

Pons, Jean-Luc; Labesse, Gilles

2009-01-01

@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/ PMID:19443448

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

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.

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

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.

Gi- and Gs-coupled GPCRs show different modes of G-protein binding.

PubMed

Van Eps, Ned; Altenbach, Christian; Caro, Lydia N; Latorraca, Naomi R; Hollingsworth, Scott A; Dror, Ron O; Ernst, Oliver P; Hubbell, Wayne L

2018-03-06

More than two decades ago, the activation mechanism for the membrane-bound photoreceptor and prototypical G protein-coupled receptor (GPCR) rhodopsin was uncovered. Upon light-induced changes in ligand-receptor interaction, movement of specific transmembrane helices within the receptor opens a crevice at the cytoplasmic surface, allowing for coupling of heterotrimeric guanine nucleotide-binding proteins (G proteins). The general features of this activation mechanism are conserved across the GPCR superfamily. Nevertheless, GPCRs have selectivity for distinct G-protein family members, but the mechanism of selectivity remains elusive. Structures of GPCRs in complex with the stimulatory G protein, G s , and an accessory nanobody to stabilize the complex have been reported, providing information on the intermolecular interactions. However, to reveal the structural selectivity filters, it will be necessary to determine GPCR-G protein structures involving other G-protein subtypes. In addition, it is important to obtain structures in the absence of a nanobody that may influence the structure. Here, we present a model for a rhodopsin-G protein complex derived from intermolecular distance constraints between the activated receptor and the inhibitory G protein, G i , using electron paramagnetic resonance spectroscopy and spin-labeling methodologies. Molecular dynamics simulations demonstrated the overall stability of the modeled complex. In the rhodopsin-G i complex, G i engages rhodopsin in a manner distinct from previous GPCR-G s structures, providing insight into specificity determinants. Copyright © 2018 the Author(s). Published by PNAS.

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.

Enhanced thermostability of methyl parathion hydrolase from Ochrobactrum sp. M231 by rational engineering of a glycine to proline mutation.

PubMed

Tian, Jian; Wang, Ping; Gao, Shan; Chu, Xiaoyu; Wu, Ningfeng; Fan, Yunliu

2010-12-01

Protein thermostability can be increased by some glycine to proline mutations in a target protein. However, not all glycine to proline mutations can improve protein thermostability, and this method is suitable only at carefully selected mutation sites that can accommodate structural stabilization. In this study, homology modeling and molecular dynamics simulations were used to select appropriate glycine to proline mutations to improve protein thermostability, and the effect of the selected mutations was proved by the experiments. The structure of methyl parathion hydrolase (MPH) from Ochrobactrum sp. M231 (Ochr-MPH) was constructed by homology modeling, and molecular dynamics simulations were performed on the modeled structure. A profile of the root mean square fluctuations of Ochr-MPH was calculated at the nanosecond timescale, and an eight-amino acid loop region (residues 186-193) was identified as having high conformational fluctuation. The two glycines nearest to this region were selected as mutation targets that might affect protein flexibility in the vicinity. The structures and conformational fluctuations of two single mutants (G194P and G198P) and one double mutant (G194P/G198P) were modeled and analyzed using molecular dynamics simulations. The results predicted that the mutant G194P had the decreased conformational fluctuation in the loop region and might increase the thermostability of Ochr-MPH. The thermostability and kinetic behavior of the wild-type and three mutant enzymes were measured. The results were consistent with the computational predictions, and the mutant G194P was found to have higher thermostability than the wild-type enzyme. © 2010 The Authors Journal compilation © 2010 FEBS.

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

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

PubMed

Steindl, Theodora M; Crump, Carolyn E; Hayden, Frederick G; Langer, Thierry

2005-10-06

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

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.

Assessing the druggability of protein-protein interactions by a supervised machine-learning method.

PubMed

Sugaya, Nobuyoshi; Ikeda, Kazuyoshi

2009-08-25

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM). To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways. Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.

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).

Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition.

PubMed

Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Möller, Steffen; Hartmann, Enno; Kalies, Kai-Uwe; Suganthan, P N; Martinetz, Thomas

2010-12-01

Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail. This work proposes a model for the sorting of proteins that are involved in apoptosis, allowing us to both the prediction of their subcellular locations as well as the molecular properties that contributed to it. We report a novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physicochemical properties. GA is used for selecting a near-optimal subset of informative features that is most relevant for the classification. Jackknife cross-validation is applied to test the predictive capability of the proposed method on 317 apoptosis proteins. Our method achieved 85.80% accuracy using all 119 features and 89.91% accuracy for 25 features selected by GA. Our models were examined by a test dataset of 98 apoptosis proteins and obtained an overall accuracy of 90.34%. The results show that the proposed approach is promising; it is able to select small subsets of features and still improves the classification accuracy. Our model can contribute to the understanding of programmed cell death and drug discovery. The software and dataset are available at http://www.inb.uni-luebeck.de/tools-demos/apoptosis/GASVM.

A Comprehensive Study of Molecular Evolution at the Self-Incompatibility Locus of Rosaceae.

PubMed

Ashkani, Jahanshah; Rees, D J G

2016-03-01

The family Rosaceae includes a range of important fruit trees, most of which have the S-RNase-based self-incompatibility (SI). Several models have been developed to explain how pollen (SLF) and pistil (S-RNase) components of the S-locus interact. It was discovered in 2010 that additional SLF proteins are involved in pollen specificity, and a Collaborative Non-Self Recognition model has been proposed for SI in Solanaceae; however, the validity of such model remains to be elucidated for other species. The results of this study support the divergent evolution of the S-locus genes from two Rosaceae subfamilies, Prunoideae/Amygdaloideae and Maloideae, The difference identified in the selective pressures between the two lineages provides evidence for positive selection at specific sites in both the S-RNase and the SLF proteins. The evolutionary findings of this study support the role of multiple SLF proteins leading to a Collaborative Non-Self Recognition model for SI in the Maloideae. Furthermore, the identification of the sites responsible for SI specificity determination and the mapping of these sites onto the modelled tertiary structure of ancestor proteins provide useful information for rational functional redesign and protein engineering for the future engineering of new functional alleles providing increased diversity in the SI system in the Maloideae.

Rosetta comparative modeling for library design: Engineering alternative inducer specificity in a transcription factor.

PubMed

Jha, Ramesh K; Chakraborti, Subhendu; Kern, Theresa L; Fox, David T; Strauss, Charlie E M

2015-07-01

Structure-based rational mutagenesis for engineering protein functionality has been limited by the scarcity and difficulty of obtaining crystal structures of desired proteins. On the other hand, when high-throughput selection is possible, directed evolution-based approaches for gaining protein functionalities have been random and fortuitous with limited rationalization. We combine comparative modeling of dimer structures, ab initio loop reconstruction, and ligand docking to select positions for mutagenesis to create a library focused on the ligand-contacting residues. The rationally reduced library requirement enabled conservative control of the substitutions by oligonucleotide synthesis and bounding its size within practical transformation efficiencies (∼ 10(7) variants). This rational approach was successfully applied on an inducer-binding domain of an Acinetobacter transcription factor (TF), pobR, which shows high specificity for natural effector molecule, 4-hydroxy benzoate (4HB), but no native response to 3,4-dihydroxy benzoate (34DHB). Selection for mutants with high transcriptional induction by 34DHB was carried out at the single-cell level under flow cytometry (via green fluorescent protein expression under the control of pobR promoter). Critically, this selection protocol allows both selection for induction and rejection of constitutively active mutants. In addition to gain-of-function for 34DHB induction, the selected mutants also showed enhanced sensitivity and response for 4HB (native inducer) while no sensitivity was observed for a non-targeted but chemically similar molecule, 2-hydroxy benzoate (2HB). This is unique application of the Rosetta modeling protocols for library design to engineer a TF. Our approach extends applicability of the Rosetta redesign protocol into regimes without a priori precision structural information. © 2015 Wiley Periodicals, Inc.

Ubiquitin dynamics in complexes reveal molecular recognition mechanisms beyond induced fit and conformational selection.

PubMed

Peters, Jan H; de Groot, Bert L

2012-01-01

Protein-protein interactions play an important role in all biological processes. However, the principles underlying these interactions are only beginning to be understood. Ubiquitin is a small signalling protein that is covalently attached to different proteins to mark them for degradation, regulate transport and other functions. As such, it interacts with and is recognised by a multitude of other proteins. We have conducted molecular dynamics simulations of ubiquitin in complex with 11 different binding partners on a microsecond timescale and compared them with ensembles of unbound ubiquitin to investigate the principles of their interaction and determine the influence of complex formation on the dynamic properties of this protein. Along the main mode of fluctuation of ubiquitin, binding in most cases reduces the conformational space available to ubiquitin to a subspace of that covered by unbound ubiquitin. This behaviour can be well explained using the model of conformational selection. For lower amplitude collective modes, a spectrum of zero to almost complete coverage of bound by unbound ensembles was observed. The significant differences between bound and unbound structures are exclusively situated at the binding interface. Overall, the findings correspond neither to a complete conformational selection nor induced fit scenario. Instead, we introduce a model of conformational restriction, extension and shift, which describes the full range of observed effects.

A Modular Approach To Study Protein Adsorption on Surface Modified Hydroxyapatite.

PubMed

Ozhukil Kollath, Vinayaraj; Van den Broeck, Freya; Fehér, Krisztina; Martins, José C; Luyten, Jan; Traina, Karl; Mullens, Steven; Cloots, Rudi

2015-07-13

Biocompatible inorganic nano- and microcarriers can be suitable candidates for protein delivery. This study demonstrates facile methods of functionalization by using nanoscale linker molecules to change the protein adsorption capacity of hydroxyapatite (HA) powder. The adsorption capacity of bovine serum albumin as a model protein has been studied with respect to the surface modifications. The selected linker molecules (lysine, arginine, and phosphoserine) can influence the adsorption capacity by changing the electrostatic nature of the HA surface. Qualitative and quantitative analyses of linker-molecule interactions with the HA surface have been performed by using NMR spectroscopy, zeta-potential measurements, X-ray photoelectron spectroscopy, and thermogravimetric analyses. Additionally, correlations to theoretical isotherm models have been calculated with respect to Langmuir and Freundlich isotherms. Lysine and arginine increased the protein adsorption, whereas phosphoserine reduced the protein adsorption. The results show that the adsorption capacity can be controlled with different functionalization, depending on the protein-carrier selections under consideration. The scientific knowledge acquired from this study can be applied in various biotechnological applications that involve biomolecule-inorganic material interfaces. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

N'-benzylidene-benzohydrazides as novel and selective tau-PHF ligands.

PubMed

Taghavi, Ali; Nasir, Samir; Pickhardt, Marcus; Heyny-von Haussen, Roland; Mall, Gerhard; Mandelkow, Eckhard; Mandelkow, Eva-Maria; Schmidt, Boris

2011-01-01

The structure activity relationship of N'-benzylidene-benzohydrazide (NBB) binding to tau and paired helical filament (PHF) proteins as well as amyloid-β₁₋₄₂ fibrils indicate differential selectivity for these protein aggregates. The ability of the compounds to stain neurofibrillary tangles and senile plaques isolated from human AD brain was investigated histochemically. These studies resulted in several tau-PHF and amyloid-β₁₋₄₂ fibril selective ligands respectively. Supported by these results, we rationalized a model for the design of selective ligands for tau, PHF, and amyloid-β₁₋₄₂ fibrils.

Structure-bias relationships for fenoterol stereoisomers in six molecular and cellular assays at the β2-adrenoceptor.

PubMed

Reinartz, Michael T; Kälble, Solveig; Littmann, Timo; Ozawa, Takeaki; Dove, Stefan; Kaever, Volkhard; Wainer, Irving W; Seifert, Roland

2015-01-01

Functional selectivity is well established as an underlying concept of ligand-specific signaling via G protein-coupled receptors (GPCRs). Functionally, selective drugs could show greater therapeutic efficacy and fewer adverse effects. Dual coupling of the β2-adrenoceptor (β2AR) triggers a signal transduction via Gsα and Giα proteins. Here, we examined 12 fenoterol stereoisomers in six molecular and cellular assays. Using β2AR-Gsα and β2AR-Giα fusion proteins, (R,S')- and (S,S')-isomers of 4'-methoxy-1-naphthyl-fenoterol were identified as biased ligands with preference for Gs. G protein-independent signaling via β-arrestin-2 was disfavored by these ligands. Isolated human neutrophils constituted an ex vivo model of β2AR signaling and demonstrated functional selectivity through the dissociation of cAMP accumulation and the inhibition of formyl peptide-stimulated production of reactive oxygen species. Ligand bias was calculated using an operational model of agonism and revealed that the fenoterol scaffold constitutes a promising lead structure for the development of Gs-biased β2AR agonists.

[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.

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.

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.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

PubMed

Li, Yaohang; Rata, Ionel; Chiu, See-wing; Jakobsson, Eric

2010-07-20

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

PubMed Central

2010-01-01

Background Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. Results We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods. Conclusions By integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set. PMID:20642859

Biological Chemistry and Functionality of Protein Sulfenic Acids and Related Thiol Modifications

PubMed Central

Devarie-Baez, Nelmi O.; Silva Lopez, Elsa I.; Furdui, Cristina M.

2016-01-01

Selective modification of proteins at cysteine residues by reactive oxygen, nitrogen or sulfur species formed under physiological and pathological states is emerging as a critical regulator of protein activity impacting cellular function. This review focuses primarily on protein sulfenylation (-SOH), a metastable reversible modification connecting reduced cysteine thiols to many products of cysteine oxidation. An overview is first provided on the chemistry principles underlining synthesis, stability and reactivity of sulfenic acids in model compounds and proteins, followed by a brief description of analytical methods currently employed to characterize these oxidative species. The following chapters present a selection of redox-regulated proteins for which the -SOH formation was experimentally confirmed and linked to protein function. These chapters are organized based on the participation of these proteins in the regulation of signaling, metabolism and epigenetics. The last chapter discusses the therapeutic implications of altered redox microenvironment and protein oxidation in disease. PMID:26340608

Detecting Coevolution in and among Protein Domains

PubMed Central

Yeang, Chen-Hsiang; Haussler, David

2007-01-01

Correlated changes of nucleic or amino acids have provided strong information about the structures and interactions of molecules. Despite the rich literature in coevolutionary sequence analysis, previous methods often have to trade off between generality, simplicity, phylogenetic information, and specific knowledge about interactions. Furthermore, despite the evidence of coevolution in selected protein families, a comprehensive screening of coevolution among all protein domains is still lacking. We propose an augmented continuous-time Markov process model for sequence coevolution. The model can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules. We employ this model to large-scale screenings on the entire protein domain database (Pfam). Strikingly, with 0.1 trillion tests executed, the majority of the inferred coevolving protein domains are functionally related, and the coevolving amino acid residues are spatially coupled. Moreover, many of the coevolving positions are located at functionally important sites of proteins/protein complexes, such as the subunit linkers of superoxide dismutase, the tRNA binding sites of ribosomes, the DNA binding region of RNA polymerase, and the active and ligand binding sites of various enzymes. The results suggest sequence coevolution manifests structural and functional constraints of proteins. The intricate relations between sequence coevolution and various selective constraints are worth pursuing at a deeper level. PMID:17983264

Affimer proteins are versatile and renewable affinity reagents

PubMed Central

Tiede, Christian; Bedford, Robert; Heseltine, Sophie J; Smith, Gina; Wijetunga, Imeshi; Ross, Rebecca; AlQallaf, Danah; Roberts, Ashley PE; Balls, Alexander; Curd, Alistair; Hughes, Ruth E; Martin, Heather; Needham, Sarah R; Zanetti-Domingues, Laura C; Sadigh, Yashar; Peacock, Thomas P; Tang, Anna A; Gibson, Naomi; Kyle, Hannah; Platt, Geoffrey W; Ingram, Nicola; Taylor, Thomas; Coletta, Louise P; Manfield, Iain; Knowles, Margaret; Bell, Sandra; Esteves, Filomena; Maqbool, Azhar; Prasad, Raj K; Drinkhill, Mark; Bon, Robin S; Patel, Vikesh; Goodchild, Sarah A; Martin-Fernandez, Marisa; Owens, Ray J; Nettleship, Joanne E; Webb, Michael E; Harrison, Michael; Lippiat, Jonathan D; Ponnambalam, Sreenivasan; Peckham, Michelle; Smith, Alastair; Ferrigno, Paul Ko; Johnson, Matt; McPherson, Michael J; Tomlinson, Darren Charles

2017-01-01

Molecular recognition reagents are key tools for understanding biological processes and are used universally by scientists to study protein expression, localisation and interactions. Antibodies remain the most widely used of such reagents and many show excellent performance, although some are poorly characterised or have stability or batch variability issues, supporting the use of alternative binding proteins as complementary reagents for many applications. Here we report on the use of Affimer proteins as research reagents. We selected 12 diverse molecular targets for Affimer selection to exemplify their use in common molecular and cellular applications including the (a) selection against various target molecules; (b) modulation of protein function in vitro and in vivo; (c) labelling of tumour antigens in mouse models; and (d) use in affinity fluorescence and super-resolution microscopy. This work shows that Affimer proteins, as is the case for other alternative binding scaffolds, represent complementary affinity reagents to antibodies for various molecular and cell biology applications. DOI: http://dx.doi.org/10.7554/eLife.24903.001 PMID:28654419

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.

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

PubMed Central

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

2017-01-01

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

Complex patterns of divergence among green-sensitive (RH2a) African cichlid opsins revealed by Clade model analyses

PubMed Central

2012-01-01

Background Gene duplications play an important role in the evolution of functional protein diversity. Some models of duplicate gene evolution predict complex forms of paralog divergence; orthologous proteins may diverge as well, further complicating patterns of divergence among and within gene families. Consequently, studying the link between protein sequence evolution and duplication requires the use of flexible substitution models that can accommodate multiple shifts in selection across a phylogeny. Here, we employed a variety of codon substitution models, primarily Clade models, to explore how selective constraint evolved following the duplication of a green-sensitive (RH2a) visual pigment protein (opsin) in African cichlids. Past studies have linked opsin divergence to ecological and sexual divergence within the African cichlid adaptive radiation. Furthermore, biochemical and regulatory differences between the RH2aα and RH2aβ paralogs have been documented. It thus seems likely that selection varies in complex ways throughout this gene family. Results Clade model analysis of African cichlid RH2a opsins revealed a large increase in the nonsynonymous-to-synonymous substitution rate ratio (ω) following the duplication, as well as an even larger increase, one consistent with positive selection, for Lake Tanganyikan cichlid RH2aβ opsins. Analysis using the popular Branch-site models, by contrast, revealed no such alteration of constraint. Several amino acid sites known to influence spectral and non-spectral aspects of opsin biochemistry were found to be evolving divergently, suggesting that orthologous RH2a opsins may vary in terms of spectral sensitivity and response kinetics. Divergence appears to be occurring despite intronic gene conversion among the tandemly-arranged duplicates. Conclusions Our findings indicate that variation in selective constraint is associated with both gene duplication and divergence among orthologs in African cichlid RH2a opsins. At least some of this variation may reflect an adaptive response to differences in light environment. Interestingly, these patterns only became apparent through the use of Clade models, not through the use of the more widely employed Branch-site models; we suggest that this difference stems from the increased flexibility associated with Clade models. Our results thus bear both on studies of cichlid visual system evolution and on studies of gene family evolution in general. PMID:23078361

Models of Protocellular Structure, Function and Evolution

NASA Technical Reports Server (NTRS)

New, Michael H.; Pohorille, Andrew; Szostak, Jack W.; Keefe, Tony; Lanyi, Janos K.

2001-01-01

In the absence of any record of protocells, the most direct way to test our understanding of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard Medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids in which a small number of functional molecules are selected from a large, random population of candidates. The selected molecules are next vastly multiplied using the polymerase chain reaction. A mutagenic approach, in which the sequences of selected molecules are randomly altered, can yield further improvements in performance or alterations of specificities. Unfortunately, the catalytic potential of nucleic acids is rather limited. Proteins are more catalytically capable but cannot be directly amplified. In the new technique, this problem is circumvented by covalently linking each protein of the initial, diverse, pool to the RNA sequence that codes for it. Then, selection is performed on the proteins, but the nucleic acids are replicated. Additional information is contained in the original extended abstract.

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.

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.

Understanding protein evolution: from protein physics to Darwinian selection.

PubMed

Zeldovich, Konstantin B; Shakhnovich, Eugene I

2008-01-01

Efforts in whole-genome sequencing and structural proteomics start to provide a global view of the protein universe, the set of existing protein structures and sequences. However, approaches based on the selection of individual sequences have not been entirely successful at the quantitative description of the distribution of structures and sequences in the protein universe because evolutionary pressure acts on the entire organism, rather than on a particular molecule. In parallel to this line of study, studies in population genetics and phenomenological molecular evolution established a mathematical framework to describe the changes in genome sequences in populations of organisms over time. Here, we review both microscopic (physics-based) and macroscopic (organism-level) models of protein-sequence evolution and demonstrate that bridging the two scales provides the most complete description of the protein universe starting from clearly defined, testable, and physiologically relevant assumptions.

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

PubMed Central

Fischer, Axel W.; Bordignon, Enrica; Bleicken, Stephanie; García-Sáez, Ana J.; Jeschke, Gunnar; Meiler, Jens

2016-01-01

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively. PMID:27129417

A novel feature ranking method for prediction of cancer stages using proteomics data

PubMed Central

Saghapour, Ehsan; Sehhati, Mohammadreza

2017-01-01

Proteomic analysis of cancers' stages has provided new opportunities for the development of novel, highly sensitive diagnostic tools which helps early detection of cancer. This paper introduces a new feature ranking approach called FRMT. FRMT is based on the Technique for Order of Preference by Similarity to Ideal Solution method (TOPSIS) which select the most discriminative proteins from proteomics data for cancer staging. In this approach, outcomes of 10 feature selection techniques were combined by TOPSIS method, to select the final discriminative proteins from seven different proteomic databases of protein expression profiles. In the proposed workflow, feature selection methods and protein expressions have been considered as criteria and alternatives in TOPSIS, respectively. The proposed method is tested on seven various classifier models in a 10-fold cross validation procedure that repeated 30 times on the seven cancer datasets. The obtained results proved the higher stability and superior classification performance of method in comparison with other methods, and it is less sensitive to the applied classifier. Moreover, the final introduced proteins are informative and have the potential for application in the real medical practice. PMID:28934234

Modeling the inhibition of quadruple mutant Plasmodium falciparum dihydrofolate reductase by pyrimethamine derivatives

NASA Astrophysics Data System (ADS)

Fogel, Gary B.; Cheung, Mars; Pittman, Eric; Hecht, David

2008-01-01

Modeling studies were performed on known inhibitors of the quadruple mutant Plasmodium falciparum dihydrofolate reductase (DHFR). GOLD was used to dock 32 pyrimethamine derivatives into the active site of DHFR obtained from the x-ray crystal structure 1J3K.pdb. Several scoring functions were evaluated and the Molegro Protein-Ligand Interaction Score was determined to have one of the best correlation to experimental p K i . In conjunction with Protein-Ligand Interaction scores, predicted binding modes and key protein-ligand interactions were evaluated and analyzed in order to develop criteria for selecting compounds having a greater chance of activity versus resistant strains of Plasmodium falciparum. This methodology will be used in future studies for selection of compounds for focused screening libraries.

Analysis of Cytoskeletal and Motility Proteins in the Sea Urchin Genome Assembly

PubMed Central

RL, Morris; MP, Hoffman; RA, Obar; SS, McCafferty; IR, Gibbons; AD, Leone; J, Cool; EL, Allgood; AM, Musante; KM, Judkins; BJ, Rossetti; AP, Rawson; DR, Burgess

2007-01-01

The sea urchin embryo is a classical model system for studying the role of the cytoskeleton in such events as fertilization, mitosis, cleavage, cell migration and gastrulation. We have conducted an analysis of gene models derived from the Strongylocentrotus purpuratus genome assembly and have gathered strong evidence for the existence of multiple gene families encoding cytoskeletal proteins and their regulators in sea urchin. While many cytoskeletal genes have been cloned from sea urchin with sequences already existing in public databases, genome analysis reveals a significantly higher degree of diversity within certain gene families. Furthermore, genes are described corresponding to homologs of cytoskeletal proteins not previously documented in sea urchins. To illustrate the varying degree of sequence diversity that exists within cytoskeletal gene families, we conducted an analysis of genes encoding actins, specific actin-binding proteins, myosins, tubulins, kinesins, dyneins, specific microtubule-associated proteins, and intermediate filaments. We conducted ontological analysis of select genes to better understand the relatedness of urchin cytoskeletal genes to those of other deuterostomes. We analyzed developmental expression (EST) data to confirm the existence of select gene models and to understand their differential expression during various stages of early development. PMID:17027957

Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.

PubMed

Rodrigue, Nicolas; Lartillot, Nicolas

2017-01-01

Codon substitution models have traditionally attempted to uncover signatures of adaptation within protein-coding genes by contrasting the rates of synonymous and non-synonymous substitutions. Another modeling approach, known as the mutation-selection framework, attempts to explicitly account for selective patterns at the amino acid level, with some approaches allowing for heterogeneity in these patterns across codon sites. Under such a model, substitutions at a given position occur at the neutral or nearly neutral rate when they are synonymous, or when they correspond to replacements between amino acids of similar fitness; substitutions from high to low (low to high) fitness amino acids have comparatively low (high) rates. Here, we study the use of such a mutation-selection framework as a null model for the detection of adaptation. Following previous works in this direction, we include a deviation parameter that has the effect of capturing the surplus, or deficit, in non-synonymous rates, relative to what would be expected under a mutation-selection modeling framework that includes a Dirichlet process approach to account for across-codon-site variation in amino acid fitness profiles. We use simulations, along with a few real data sets, to study the behavior of the approach, and find it to have good power with a low false-positive rate. Altogether, we emphasize the potential of recent mutation-selection models in the detection of adaptation, calling for further model refinements as well as large-scale applications. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

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

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.

The Role of Flexibility and Conformational Selection in the Binding Promiscuity of PDZ Domains

PubMed Central

Münz, Márton; Hein, Jotun; Biggin, Philip C.

2012-01-01

In molecular recognition, it is often the case that ligand binding is coupled to conformational change in one or both of the binding partners. Two hypotheses describe the limiting cases involved; the first is the induced fit and the second is the conformational selection model. The conformational selection model requires that the protein adopts conformations that are similar to the ligand-bound conformation in the absence of ligand, whilst the induced-fit model predicts that the ligand-bound conformation of the protein is only accessible when the ligand is actually bound. The flexibility of the apo protein clearly plays a major role in these interpretations. For many proteins involved in signaling pathways there is the added complication that they are often promiscuous in that they are capable of binding to different ligand partners. The relationship between protein flexibility and promiscuity is an area of active research and is perhaps best exemplified by the PDZ domain family of proteins. In this study we use molecular dynamics simulations to examine the relationship between flexibility and promiscuity in five PDZ domains: the human Dvl2 (Dishevelled-2) PDZ domain, the human Erbin PDZ domain, the PDZ1 domain of InaD (inactivation no after-potential D protein) from fruit fly, the PDZ7 domain of GRIP1 (glutamate receptor interacting protein 1) from rat and the PDZ2 domain of PTP-BL (protein tyrosine phosphatase) from mouse. We show that despite their high structural similarity, the PDZ binding sites have significantly different dynamics. Importantly, the degree of binding pocket flexibility was found to be closely related to the various characteristics of peptide binding specificity and promiscuity of the five PDZ domains. Our findings suggest that the intrinsic motions of the apo structures play a key role in distinguishing functional properties of different PDZ domains and allow us to make predictions that can be experimentally tested. PMID:23133356

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness

NASA Astrophysics Data System (ADS)

Noirel, Josselin; Simonson, Thomas

2008-11-01

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate μ and the population size N, the biological population can evolve purely randomly (μN ≪1) or it can evolve in such a way as to select for sequences of higher mutational robustness (μN ≫1). The stringency of the selection depends not only on the product μN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Neutral evolution of proteins: The superfunnel in sequence space and its relation to mutational robustness.

PubMed

Noirel, Josselin; Simonson, Thomas

2008-11-14

Following Kimura's neutral theory of molecular evolution [M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983) (reprinted in 1986)], it has become common to assume that the vast majority of viable mutations of a gene confer little or no functional advantage. Yet, in silico models of protein evolution have shown that mutational robustness of sequences could be selected for, even in the context of neutral evolution. The evolution of a biological population can be seen as a diffusion on the network of viable sequences. This network is called a "neutral network." Depending on the mutation rate mu and the population size N, the biological population can evolve purely randomly (muN<1) or it can evolve in such a way as to select for sequences of higher mutational robustness (muN>1). The stringency of the selection depends not only on the product muN but also on the exact topology of the neutral network, the special arrangement of which was named "superfunnel." Even though the relation between mutation rate, population size, and selection was thoroughly investigated, a study of the salient topological features of the superfunnel that could affect the strength of the selection was wanting. This question is addressed in this study. We use two different models of proteins: on lattice and off lattice. We compare neutral networks computed using these models to random networks. From this, we identify two important factors of the topology that determine the stringency of the selection for mutationally robust sequences. First, the presence of highly connected nodes ("hubs") in the network increases the selection for mutationally robust sequences. Second, the stringency of the selection increases when the correlation between a sequence's mutational robustness and its neighbors' increases. The latter finding relates a global characteristic of the neutral network to a local one, which is attainable through experiments or molecular modeling.

Determinants of cation transport selectivity: Equilibrium binding and transport kinetics

PubMed Central

2015-01-01

The crystal structures of channels and transporters reveal the chemical nature of ion-binding sites and, thereby, constrain mechanistic models for their transport processes. However, these structures, in and of themselves, do not reveal equilibrium selectivity or transport preferences, which can be discerned only from various functional assays. In this Review, I explore the relationship between cation transport protein structures, equilibrium binding measurements, and ion transport selectivity. The primary focus is on K+-selective channels and nonselective cation channels because they have been extensively studied both functionally and structurally, but the principles discussed are relevant to other transport proteins and molecules. PMID:26078056

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

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 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

A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Li, Li-Na; Ma, Chang-Ming; Chang, Ming; Zhang, Ren-Cheng

2017-12-01

A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard-Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

Development and antigenic characterization of three recombinant proteins with potential for Glässer's disease prevention.

PubMed

Li, Miao; Li, Chunling; Song, Shuai; Kang, Huahua; Yang, Dongxia; Li, Guoqing

2016-04-27

Haemophilus parasuis is the causative agent of Glässer's disease, which causes high morbidity and mortality in piglets, leading to severe economic losses. The lack of a commercial vaccine against a broad spectrum of strains has limited the disease control. H. parasuis outer membrane proteins (OMPs) are potentially essential components for vaccine formulation. In this study, seven putative OMPs were selected from the annotated H. parasuis serovar 5 genome; they were predicted by bioinformatics and annotated as potential virulence-related factors. These proteins were cloned, expressed, and purified as His-tagged proteins. Antigenicity of the candidate proteins was assessed using Western blotting with convalescent sera against H. parasuis serovar 5. The immunogenicity of the seven OMPs was assessed in a guinea pig model. Except VacJ, all the other six recombinant proteins elicited a detectable antibody response. Antisera against four of the selected proteins effectively killed the bacteria in vitro. Three proteins (Omp26, VacJ, and HAPS_0742) were found to confer significant protection against challenge with a lethal dose of H. parasuis in a guinea pig model. The results suggest that these three proteins have a strong potential to be vaccine candidates against Glässer's disease. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy.

PubMed

Sutherland, Jeffrey J; Nandigam, Ravi K; Erickson, Jon A; Vieth, Michal

2007-01-01

Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.

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.

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.

Positive selection on human gamete-recognition genes

PubMed Central

Stover, Daryn A.; Guerra, Vanessa; Mozaffari, Sahar V.; Ober, Carole; Mugal, Carina F.; Kaj, Ingemar

2018-01-01

Coevolution of genes that encode interacting proteins expressed on the surfaces of sperm and eggs can lead to variation in reproductive compatibility between mates and reproductive isolation between members of different species. Previous studies in mice and other mammals have focused in particular on evidence for positive or diversifying selection that shapes the evolution of genes that encode sperm-binding proteins expressed in the egg coat or zona pellucida (ZP). By fitting phylogenetic models of codon evolution to data from the 1000 Genomes Project, we identified candidate sites evolving under diversifying selection in the human genes ZP3 and ZP2. We also identified one candidate site under positive selection in C4BPA, which encodes a repetitive protein similar to the mouse protein ZP3R that is expressed in the sperm head and binds to the ZP at fertilization. Results from several additional analyses that applied population genetic models to the same data were consistent with the hypothesis of selection on those candidate sites leading to coevolution of sperm- and egg-expressed genes. By contrast, we found no candidate sites under selection in a fourth gene (ZP1) that encodes an egg coat structural protein not directly involved in sperm binding. Finally, we found that two of the candidate sites (in C4BPA and ZP2) were correlated with variation in family size and birth rate among Hutterite couples, and those two candidate sites were also in linkage disequilibrium in the same Hutterite study population. All of these lines of evidence are consistent with predictions from a previously proposed hypothesis of balancing selection on epistatic interactions between C4BPA and ZP3 at fertilization that lead to the evolution of co-adapted allele pairs. Such patterns also suggest specific molecular traits that may be associated with both natural reproductive variation and clinical infertility. PMID:29340252

Rapidly evolving zona pellucida domain proteins are a major component of the vitelline envelope of abalone eggs

PubMed Central

Aagaard, Jan E.; Yi, Xianhua; MacCoss, Michael J.; Swanson, Willie J.

2006-01-01

Proteins harboring a zona pellucida (ZP) domain are prominent components of vertebrate egg coats. Although less well characterized, the egg coat of the non-vertebrate marine gastropod abalone (Haliotis spp.) is also known to contain a ZP domain protein, raising the possibility of a common molecular basis of metazoan egg coat structures. Egg coat proteins from vertebrate as well as non-vertebrate taxa have been shown to evolve under positive selection. Studied most extensively in the abalone system, coevolution between adaptively diverging egg coat and sperm proteins may contribute to the rapid development of reproductive isolation. Thus, identifying the pattern of evolution among egg coat proteins is important in understanding the role these genes may play in the speciation process. The purpose of the present study is to characterize the constituent proteins of the egg coat [vitelline envelope (VE)] of abalone eggs and to provide preliminary evidence regarding how selection has acted on VE proteins during abalone evolution. A proteomic approach is used to match tandem mass spectra of peptides from purified VE proteins with abalone ovary EST sequences, identifying 9 of 10 ZP domain proteins as components of the VE. Maximum likelihood models of codon evolution suggest positive selection has acted among a subset of amino acids for 6 of these genes. This work provides further evidence of the prominence of ZP proteins as constituents of the egg coat, as well as the prominent role of positive selection in diversification of these reproductive proteins. PMID:17085584

Food allergy animal models: an overview.

PubMed

Helm, Ricki M

2002-05-01

Specific food allergy is characterized by sensitization to innocuous food proteins with production of allergen-specific IgE that binds to receptors on basophils and mast cells. Upon recurrent exposure to the same allergen, an allergic response is induced by mediator release following cross-linking of cell-bound allergen-specific IgE. The determination of what makes an innocuous food protein an allergen in predisposed individuals is unknown; however, mechanistic and protein allergen predictive models are being actively investigated in a number of animal models. Currently, there is no animal model that will actively profile known food allergens, predict the allergic potential of novel food proteins, or demonstrate clinically the human food allergic sensitization/allergic response. Animal models under investigation include mice, rats, the guinea pig, atopic dog, and neonatal swine. These models are being assessed for production of IgE, clinical responses to re-exposure, and a ranking of food allergens (based on potency) including a nonfood allergen protein source. A selection of animal models actively being investigated that will contribute to our understanding of what makes a protein an allergen and future predictive models for assessing the allergenicity of novel proteins is presented in this review.

The genetic incorporation of p-azidomethyl-l-phenylalanine into proteins in yeast.

PubMed

Supekova, Lubica; Zambaldo, Claudio; Choi, Seihyun; Lim, Reyna; Luo, Xiaozhou; Kazane, Stephanie A; Young, Travis S; Schultz, Peter G

2018-05-15

The noncanonical amino acid p-azidomethyl-l-phenylalanine can be genetically incorporated into proteins in bacteria, and has been used both as a spectroscopic probe and for the selective modification of proteins by alkynes using click chemistry. Here we report identification of Escherichia coli tyrosyl tRNA synthetase mutants that allow incorporation of p-azidomethyl-l-phenylalanine into proteins in yeast. When expressed together with the cognate E. coli tRNA CUA Tyr , the new mutant tyrosyl tRNA synthetases directed robust incorporation of p-azidomethyl-l-phenylalanine into a model protein, human superoxide dismutase, in response to the UAG amber nonsense codon. Mass spectrometry analysis of purified superoxide dismutase proteins confirmed the efficient site-specific incorporation of p-azidomethyl-l-phenylalanine. This work provides an additional tool for the selective modification of proteins in eukaryotic cells. Copyright © 2018 Elsevier Ltd. All rights reserved.

Invasion of host cells by malaria parasites: a tale of two protein families.

PubMed

Iyer, Jayasree; Grüner, Anne Charlotte; Rénia, Laurent; Snounou, Georges; Preiser, Peter R

2007-07-01

Malaria parasites are obligate intracellular parasites whose invasive stages select and invade the unique host cell in which they can develop with exquisite specificity and efficacy. Most studies aimed at elucidating the molecules and the mechanisms implicated in the selection and invasion processes have been conducted on the merozoite, the stage that invades erythrocytes to perpetuate the pathological cycles of parasite multiplication in the blood. Bioinformatic analysis has helped identify the members of two parasite protein families, the reticulocyte-binding protein homologues (RBL) and erythrocyte binding like (EBL), in recently sequenced genomes of different Plasmodium species. In this article we review data from classical studies and gene disruption experiments that are helping to illuminate the role of these proteins in the selection-invasion processes. The manner in which subsets of proteins from each of the families act in concert suggests a model to explain the ability of the parasites to use alternate pathways of invasion. Future perspectives and implications are discussed.

Neutrality and evolvability of designed protein sequences

NASA Astrophysics Data System (ADS)

Bhattacherjee, Arnab; Biswas, Parbati

2010-07-01

The effect of foldability on protein’s evolvability is analyzed by a two-prong approach consisting of a self-consistent mean-field theory and Monte Carlo simulations. Theory and simulation models representing protein sequences with binary patterning of amino acid residues compatible with a particular foldability criteria are used. This generalized foldability criterion is derived using the high temperature cumulant expansion approximating the free energy of folding. The effect of cumulative point mutations on these designed proteins is studied under neutral condition. The robustness, protein’s ability to tolerate random point mutations is determined with a selective pressure of stability (ΔΔG) for the theory designed sequences, which are found to be more robust than that of Monte Carlo and mean-field-biased Monte Carlo generated sequences. The results show that this foldability criterion selects viable protein sequences more effectively compared to the Monte Carlo method, which has a marked effect on how the selective pressure shapes the evolutionary sequence space. These observations may impact de novo sequence design and its applications in protein engineering.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

PubMed Central

2016-01-01

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

Stochastic Protein Multimerization, Cooperativity and Fitness

NASA Astrophysics Data System (ADS)

Hagner, Kyle; Setayeshgar, Sima; Lynch, Michael

Many proteins assemble into multimeric structures that can vary greatly among phylogenetic lineages. As protein-protein interactions (PPI) require productive encounters among subunits, these structural variations are related in part to variation in cellular protein abundance. The protein abundance in turn depends on the intrinsic rates of production and decay of mRNA and protein molecules, as well as rates of cell growth and division. We present a stochastic model for prediction of the multimeric state of a protein as a function of these processes and the free energy associated with binding interfaces. We demonstrate favorable agreement between the model and a wide class of proteins using E. coli proteome data. As such, this platform, which links protein abundance, PPI and quaternary structure in growing and dividing cells can be extended to evolutionary models for the emergence and diversification of multimeric proteins. We investigate cooperativity - a ubiquitous functional property of multimeric proteins - as a possible selective force driving multimerization, demonstrating a reduction in the cost of protein production relative to the overall proteome energy budget that can be tied to fitness.

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 knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.

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.

[Molecular organization of glutamate-sensitive chemoexcitable membranes of nerve cells. Function of glutamate-binding proteins of the central nervous system when incorporated into liposomes].

PubMed

Besedin, V I; Kuznetsov, A S; Dambinova, S A

1985-03-01

The functioning of the glutamate-binding protein of rat brain cortex synaptic membranes was studied by its incorporation into liposomes. The optimal conditions for the receptor protein incorporation were established and the kinetics of 22Na+ and 86Rb+ incorporation into the liposomes in the presence of L-glutamate were analyzed. Modelling of the CNS glutamate receptor functions was found to be dependent on the lipid composition and amount of the incorporated membrane protein. The selective transport of 22Na+ into the liposomes was stimulated in the presence of 10(-4) M glutamate. Addition of monoclonal antibodies against glutamate-binding proteins blocked the incorporation of Na+ into the liposomes. The experimental results are suggestive of the nativity of the liposome-incorporated membrane protein, which is capable of binding glutamate and regulating selective transport of Na+. It was assumed that the glutamate receptor macromolecule represents an integral complex made up of several low molecular weight subunits of glucoprotein nature that form a selective ionic channel.

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

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.

Molecular evolution and thermal adaptation

NASA Astrophysics Data System (ADS)

Chen, Peiqiu

2011-12-01

In this thesis, we address problems in molecular evolution, thermal adaptation, and the kinetics of adaptation of bacteria and viruses to elevated environmental temperatures. We use a nearly neutral fitness model where the replication speed of an organism is proportional to the copy number of folded proteins. Our model reproduces the distribution of stabilities of natural proteins in excellent agreement with experiment. We find that species with high mutation rates tend to have less stable proteins compared to species with low mutation rate. We found that a broad distribution of protein stabilities observed in the model and in experiment is the key determinant of thermal response for viruses and bacteria. Our results explain most of the earlier experimental observations: striking asymmetry of thermal response curves, the absence of evolutionary trade-off which was expected but not found in experiments, correlation between denaturation temperature for several protein families and the Optimal Growth Temperature (OGT) of their carrier organisms, and proximity of bacterial or viral OGTs to their evolutionary temperatures. Our theory quantitatively and with high accuracy described thermal response curves for 35 bacterial species. The model also addresses the key to adaptation is in weak-link genes (WLG), which encode least thermodynamically stable essential proteins in the proteome. We observe, as in experiment, a two-stage adaptation process. The first stage is a Luria-Delbruck type of selection, whereby rare WLG alleles, whose proteins are more stable than WLG proteins of the majority of the population (either due to standing genetic variation or due to an early acquired mutation), rapidly rise to fixation. The second stage constitutes subsequent slow accumulation of mutations in an adapted population. As adaptation progresses, selection regime changes from positive to neutral: Selection coefficient of beneficial mutations scales as a negative power of number of generations. Diversity plays an important role in thermal adaptation: While monoclonal strains adapt via acquisition and rapid fixation of new early mutations, wild population adapt via standing genetic variations, and they are more robust against thermal shocks due to greater diversity within the initial population.

RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite.

PubMed

Fleishman, Sarel J; Leaver-Fay, Andrew; Corn, Jacob E; Strauch, Eva-Maria; Khare, Sagar D; Koga, Nobuyasu; Ashworth, Justin; Murphy, Paul; Richter, Florian; Lemmon, Gordon; Meiler, Jens; Baker, David

2011-01-01

Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

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

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

Ergodicity and model quality in template-restrained canonical and temperature/Hamiltonian replica exchange coarse-grained molecular dynamics simulations of proteins.

PubMed

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

2017-12-05

Molecular simulations restrained to single or multiple templates are commonly used in protein-structure modeling. However, the restraints introduce additional barriers, thus impairing the ergodicity of simulations, which can affect the quality of the resulting models. In this work, the effect of restraint types and simulation schemes on ergodicity and model quality was investigated by performing template-restrained canonical molecular dynamics (MD), multiplexed replica-exchange molecular dynamics, and Hamiltonian replica exchange molecular dynamics (HREMD) simulations with the coarse-grained UNRES force field on nine selected proteins, with pseudo-harmonic log-Gaussian (unbounded) or Lorentzian (bounded) restraint functions. The best ergodicity was exhibited by HREMD. It has been found that non-ergodicity does not affect model quality if good templates are used to generate restraints. However, when poor-quality restraints not covering the entire protein are used, the improved ergodicity of HREMD can lead to significantly improved protein models. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Theory of prokaryotic genome evolution.

PubMed

Sela, Itamar; Wolf, Yuri I; Koonin, Eugene V

2016-10-11

Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences.

IRaPPA: Information retrieval based integration of biophysical models for protein assembly selection

PubMed Central

Moal, Iain H.; Barradas-Bautista, Didier; Jiménez-García, Brian; Torchala, Mieczyslaw; van der Velde, Arjan; Vreven, Thom; Weng, Zhiping; Bates, Paul A.; Fernández-Recio, Juan

2018-01-01

Motivation In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability IRaPPA has been implemented in the SwarmDock server (http://bmm.crick.ac.uk/~SwarmDock/), pyDock server (http://life.bsc.es/pid/pydockrescoring/) and ZDOCK server (http://zdock.umassmed.edu/), with code available on request. PMID:28200016

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

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.

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.

Detecting site-specific physicochemical selective pressures: applications to the Class I HLA of the human major histocompatibility complex and the SRK of the plant sporophytic self-incompatibility system.

PubMed

Sainudiin, Raazesh; Wong, Wendy Shuk Wan; Yogeeswaran, Krithika; Nasrallah, June B; Yang, Ziheng; Nielsen, Rasmus

2005-03-01

Models of codon substitution are developed that incorporate physicochemical properties of amino acids. When amino acid sites are inferred to be under positive selection, these models suggest the nature and extent of the physicochemical properties under selection. This is accomplished by first partitioning the codons on the basis of some property of the encoded amino acids. This partition is used to parametrize the rates of property-conserving and property-altering base substitutions at the codon level by means of finite mixtures of Markov models that also account for codon and transition:transversion biases. Here, we apply this method to two positively selected receptors involved in ligand-recognition: the class I alleles of the human major histocompatibility complex (MHC) of known structure and the S-locus receptor kinase (SRK) of the sporophytic self-incompatibility system (SSI) in cruciferous plants (Brassicaceae), whose structure is unknown. Through likelihood ratio tests we demonstrate that at some sites, the positively selected MHC and SRK proteins are under physicochemical selective pressures to alter polarity, volume, polarity and/or volume, and charge to various extents. An empirical Bayes approach is used to identify sites that may be important for ligand recognition in these proteins.

Fuzzy Clustering-Based Modeling of Surface Interactions and Emulsions of Selected Whey Protein Concentrate Combined to i-Carrageenan and Gum Arabic Solutions

USDA-ARS?s Scientific Manuscript database

Gums and proteins are valuable ingredients with a wide spectrum of applications. Surface properties (surface tension, interfacial tension, emulsion activity index “EAI” and emulsion stability index “ESI”) of 4% whey protein concentrate (WPC) in a combination with '- carrageenan (0.05%, 0.1%, and 0.5...

γS-Crystallin Proteins from the Antarctic Nototheniid Toothfish: A Model System for Investigating Differential Resistance to Chemical and Thermal Denaturation

PubMed Central

2015-01-01

The γS1- and γS2-crystallins, structural eye lens proteins from the Antarctic toothfish (Dissostichus mawsoni), are homologues of the human lens protein γS-crystallin. Although γS1 has the higher thermal stability of the two, it is more susceptible to chemical denaturation by urea. The lower thermodynamic stability of both toothfish crystallins relative to human γS-crystallin is consistent with the current picture of how proteins from organisms endemic to perennially cold environments have achieved low-temperature functionality via greater structural flexibility. In some respects, the sequences of γS1- and γS2-crystallin are typical of psychrophilic proteins; however, their amino acid compositions also reflect their selection for a high refractive index increment. Like their counterparts in the human lens and those of mesophilic fish, both toothfish crystallins are relatively enriched in aromatic residues and methionine and exiguous in aliphatic residues. The sometimes contradictory requirements of selection for cold tolerance and high refractive index make the toothfish crystallins an excellent model system for further investigation of the biophysical properties of structural proteins. PMID:25372016

Partitioning of Organic Ions to Muscle Protein: Experimental Data, Modeling, and Implications for in Vivo Distribution of Organic Ions.

PubMed

Henneberger, Luise; Goss, Kai-Uwe; Endo, Satoshi

2016-07-05

The in vivo partitioning behavior of ionogenic organic chemicals (IOCs) is of paramount importance for their toxicokinetics and bioaccumulation. Among other proteins, structural proteins including muscle proteins could be an important sorption phase for IOCs, because of their high quantity in the human and other animals' body and their polar nature. Binding data for IOCs to structural proteins are, however, severely limited. Therefore, in this study muscle protein-water partition coefficients (KMP/w) of 51 systematically selected organic anions and cations were determined experimentally. A comparison of the measured KMP/w with bovine serum albumin (BSA)-water partition coefficients showed that anionic chemicals sorb more strongly to BSA than to muscle protein (by up to 3.5 orders of magnitude), while cations sorb similarly to both proteins. Sorption isotherms of selected IOCs to muscle protein are linear (i.e., KMP/w is concentration independent), and KMP/w is only marginally influenced by pH value and salt concentration. Using the obtained data set of KMP/w a polyparameter linear free energy relationship (PP-LFER) model was established. The derived equation fits the data well (R(2) = 0.89, RMSE = 0.29). Finally, it was demonstrated that the in vitro measured KMP/w values of this study have the potential to be used to evaluate tissue-plasma partitioning of IOCs in vivo.

Pattern Recognition of Adsorbing HP Lattice Proteins

NASA Astrophysics Data System (ADS)

Wilson, Matthew S.; Shi, Guangjie; Wüst, Thomas; Landau, David P.; Schmid, Friederike

2015-03-01

Protein adsorption is relevant in fields ranging from medicine to industry, and the qualitative behavior exhibited by course-grained models could shed insight for further research in such fields. Our study on the selective adsorption of lattice proteins utilizes the Wang-Landau algorithm to simulate the Hydrophobic-Polar (H-P) model with an efficient set of Monte Carlo moves. Each substrate is modeled as a square pattern of 9 lattice sites which attract either H or P monomers, and are located on an otherwise neutral surface. The fully enumerated set of 102 unique surfaces is simulated with each protein sequence. A collection of 27-monomer sequences is used- each of which is non-degenerate and protein-like. Thermodynamic quantities such as the specific heat and free energy are calculated from the density of states, and are used to investigate the adsorption of lattice proteins on patterned substrates. Research supported by NSF.

Investigation of a protein complex network

NASA Astrophysics Data System (ADS)

Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

2004-09-01

The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

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.

Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics.

PubMed

Kumar, Avishek; Campitelli, Paul; Thorpe, M F; Ozkan, S Banu

2015-12-01

The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled. © 2015 Wiley Periodicals, Inc.

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.

Quantitative Targeted Absolute Proteomics (QTAP)-based Pharmacoproteomics: The Importance of International Collaboration.

PubMed

Terasaki, Tetsuya

2017-01-01

Proteins such as membrane transporters, enzymes, receptors and channels play key roles in drug absorption, distribution, metabolism, and elimination, and also influence efficacy and the likelihood of adverse reactions. Therefore, if we can quantify the activities of these molecules, it may be possible to predict the behavior of candidate drugs in humans in disease states; such methodology would be extremely helpful for efficient drug development. We have developed an in silico method to select appropriate peptides within amino acid sequences in order to quantify targeted proteins by LC-MS/MS in selected reaction monitoring (SRM) mode. We have applied this method for the quantification of functional proteins in order to validate various in vitro and in vivo models. We found fairly good correlation between protein amounts and the enzymatic activities of microsomal cytochrome P450 (CYP) isoforms and uridine 5'-diphospho-glucuronosyltransferase (UGT) in human liver, as well as between protein amounts and the transport activities of multiple transporters in human lung cells. These results suggest that protein quantification can be useful in predicting activity. We have applied this approach to evaluate the usefulness and limitations of an immortalized human brain capillary endothelial cell line (D3 cells) and a P-glycoprotein humanized (hMDR1) mouse model by comparing the amounts of functional proteins in the models with those in isolated capillaries from human brain. In order to obtain sufficient human tissue specimens for further studies leading to clinical applications, we believe that international collaboration will be crucial.

Jwalk and MNXL Web Server: Model Validation using Restraints from Crosslinking Mass Spectrometry.

PubMed

Bullock, J M A; Thalassinos, K; Topf, M

2018-05-07

Crosslinking Mass Spectrometry generates restraints that can be used to model proteins and protein complexes. Previously, we have developed two methods, to help users achieve better modelling performance from their crosslinking restraints: Jwalk, to estimate solvent accessible distances between crosslinked residues and MNXL, to assess the quality of the models based on these distances. Here we present the Jwalk and MNXL webservers, which streamline the process of validating monomeric protein models using restraints from crosslinks. We demonstrate this by using the MNXL server to filter models made of varying quality, selecting the most native-like. The webserver and source code are freely available from jwalk.ismb.lon.ac.uk and mnxl.ismb.lon.ac.uk. m.topf@cryst.bbk.ac.uk, j.bullock@cryst.bbk.ac.uk.

Detecting Selection on Protein Stability through Statistical Mechanical Models of Folding and Evolution

PubMed Central

Bastolla, Ugo

2014-01-01

The properties of biomolecules depend both on physics and on the evolutionary process that formed them. These two points of view produce a powerful synergism. Physics sets the stage and the constraints that molecular evolution has to obey, and evolutionary theory helps in rationalizing the physical properties of biomolecules, including protein folding thermodynamics. To complete the parallelism, protein thermodynamics is founded on the statistical mechanics in the space of protein structures, and molecular evolution can be viewed as statistical mechanics in the space of protein sequences. In this review, we will integrate both points of view, applying them to detecting selection on the stability of the folded state of proteins. We will start discussing positive design, which strengthens the stability of the folded against the unfolded state of proteins. Positive design justifies why statistical potentials for protein folding can be obtained from the frequencies of structural motifs. Stability against unfolding is easier to achieve for longer proteins. On the contrary, negative design, which consists in destabilizing frequently formed misfolded conformations, is more difficult to achieve for longer proteins. The folding rate can be enhanced by strengthening short-range native interactions, but this requirement contrasts with negative design, and evolution has to trade-off between them. Finally, selection can accelerate functional movements by favoring low frequency normal modes of the dynamics of the native state that strongly correlate with the functional conformation change. PMID:24970217

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.

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.

Functional redundancy and/or ongoing pseudogenization among F-box protein genes expressed in Arabidopsis male gametophyte.

PubMed

Ikram, Sobia; Durandet, Monique; Vesa, Simona; Pereira, Serge; Guerche, Philippe; Bonhomme, Sandrine

2014-06-01

F-box protein genes family is one of the largest gene families in plants, with almost 700 predicted genes in the model plant Arabidopsis. F-box proteins are key components of the ubiquitin proteasome system that allows targeted protein degradation. Transcriptome analyses indicate that half of these F-box protein genes are found expressed in microspore and/or pollen, i.e., during male gametogenesis. To assess the role of F-box protein genes during this crucial developmental step, we selected 34 F-box protein genes recorded as highly and specifically expressed in pollen and isolated corresponding insertion mutants. We checked the expression level of each selected gene by RT-PCR and confirmed pollen expression for 25 genes, but specific expression for only 10 of the 34 F-box protein genes. In addition, we tested the expression level of selected F-box protein genes in 24 mutant lines and showed that 11 of them were null mutants. Transmission analysis of the mutations to the progeny showed that none of the single mutations was gametophytic lethal. These unaffected transmission efficiencies suggested leaky mutations or functional redundancy among F-box protein genes. Cytological observation of the gametophytes in the mutants confirmed these results. Combinations of mutations in F-box protein genes from the same subfamily did not lead to transmission defect either, further highlighting functional redundancy and/or a high proportion of pseudogenes among these F-box protein genes.

Solubilization of a membrane protein by combinatorial supercharging.

PubMed

Hajduczki, Agnes; Majumdar, Sudipta; Fricke, Marie; Brown, Isola A M; Weiss, Gregory A

2011-04-15

Hydrophobic and aggregation-prone, membrane proteins often prove too insoluble for conventional in vitro biochemical studies. To engineer soluble variants of human caveolin-1, a phage-displayed library of caveolin variants targeted the hydrophobic intramembrane domain with substitutions to charged residues. Anti-selections for insolubility removed hydrophobic variants, and positive selections for binding to the known caveolin ligand HIV gp41 isolated functional, folded variants. Assays with several caveolin binding partners demonstrated the successful folding and functionality by a solubilized, full-length caveolin variant selected from the library. This caveolin variant allowed assay of the direct interaction between caveolin and cavin. Clustered along one face of a putative helix, the solubilizing mutations suggest a structural model for the intramembrane domain of caveolin. The approach provides a potentially general method for solubilization and engineering of membrane-associated proteins by phage display.

Exploiting a novel conformational switch to control innate immunity mediated by complement protein C3a.

PubMed

Lohman, Rink-Jan; Hamidon, Johan K; Reid, Robert C; Rowley, Jessica A; Yau, Mei-Kwan; Halili, Maria A; Nielsen, Daniel S; Lim, Junxian; Wu, Kai-Chen; Loh, Zhixuan; Do, Anh; Suen, Jacky Y; Iyer, Abishek; Fairlie, David P

2017-08-24

Complement C3a is an important protein in innate and adaptive immunity, but its specific roles in vivo remain uncertain because C3a degrades rapidly to form the C3a-desArg protein, which does not bind to the C3a receptor and is indistinguishable from C3a using antibodies. Here we develop the most potent, stable and highly selective small molecule modulators of C3a receptor, using a heterocyclic hinge to switch between agonist and antagonist ligand conformations. This enables characterization of C3 areceptor-selective pro- vs. anti-inflammatory actions in human mast cells and macrophages, and in rats. A C3a receptor-selective agonist induces acute rat paw inflammation by first degranulating mast cells before activating macrophages and neutrophils. An orally administered C3a receptor-selective antagonist inhibits mast cell degranulation, thereby blocking recruitment and activation of macrophages and neutrophils, expression of inflammatory mediators and inflammation in a rat paw edema model. These novel tools reveal the mechanism of C3a-induced inflammation and provide new insights to complement-based medicines.Complement C3a is an important protein in innate and adaptive immunity, but its roles in vivo are unclear. Here the authors develop novel chemical agonists and antagonists for the C3a receptor, and show that they modulate mast cell degranulation and inflammation in a rat paw edema model.

Proteomic profiling of epileptogenesis in a rat model: Focus on cell stress, extracellular matrix and angiogenesis.

PubMed

Keck, Michael; van Dijk, Roelof Maarten; Deeg, Cornelia A; Kistler, Katharina; Walker, Andreas; von Rüden, Eva-Lotta; Russmann, Vera; Hauck, Stefanie M; Potschka, Heidrun

2018-04-01

Information about epileptogenesis-associated changes in protein expression patterns is of particular interest for future selection of target and biomarker candidates. Bioinformatic analysis of proteomic data sets can increase our knowledge about molecular alterations characterizing the different phases of epilepsy development following an initial epileptogenic insult. Here, we report findings from a focused analysis of proteomic data obtained for the hippocampus and parahippocampal cortex samples collected during the early post-insult phase, latency phase, and chronic phase of a rat model of epileptogenesis. The study focused on proteins functionally associated with cell stress, cell death, extracellular matrix (ECM) remodeling, cell-ECM interaction, cell-cell interaction, angiogenesis, and blood-brain barrier function. The analysis revealed prominent pathway enrichment providing information about the complex expression alterations of the respective protein groups. In the hippocampus, the number of differentially expressed proteins declined over time during the course of epileptogenesis. In contrast, a peak in the regulation of proteins linked with cell stress and death as well as ECM and cell-cell interaction became evident at later phases during epileptogenesis in the parahippocampal cortex. The data sets provide valuable information about the time course of protein expression patterns during epileptogenesis for a series of proteins. Moreover, the findings provide comprehensive novel information about expression alterations of proteins that have not been discussed yet in the context of epileptogenesis. These for instance include different members of the lamin protein family as well as the fermitin family member 2 (FERMT2). Induction of FERMT2 and other selected proteins, CD18 (ITGB2), CD44 and Nucleolin were confirmed by immunohistochemistry. Taken together, focused bioinformatic analysis of the proteomic data sets completes our knowledge about molecular alterations linked with cell death and cellular plasticity during epileptogenesis. The analysis provided can guide future selection of target and biomarker candidates. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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-pathogen interactions. PMID:28183813

Prion-like Nanofibrils of Small Molecules (PriSM) Selectively Inhibit Cancer Cells by Impeding Cytoskeleton Dynamics*

PubMed Central

Kuang, Yi; Long, Marcus J. C.; Zhou, Jie; Shi, Junfeng; Gao, Yuan; Xu, Chen; Hedstrom, Lizbeth; Xu, Bing

2014-01-01

Emerging evidence reveals that prion-like structures play important roles to maintain the well-being of cells. Although self-assembly of small molecules also affords prion-like nanofibrils (PriSM), little is known about the functions and mechanisms of PriSM. Previous works demonstrated that PriSM formed by a dipeptide derivative selectively inhibiting the growth of glioblastoma cells over neuronal cells and effectively inhibiting xenograft tumor in animal models. Here we examine the protein targets, the internalization, and the cytotoxicity pathway of the PriSM. The results show that the PriSM selectively accumulate in cancer cells via macropinocytosis to impede the dynamics of cytoskeletal filaments via promiscuous interactions with cytoskeletal proteins, thus inducing apoptosis. Intriguingly, Tau proteins are able to alleviate the effect of the PriSM, thus protecting neuronal cells. This work illustrates PriSM as a new paradigm for developing polypharmacological agents that promiscuously interact with multiple proteins yet result in a primary phenotype, such as cancer inhibition PMID:25157102

PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine.

PubMed

Hayat, Maqsood; Tahir, Muhammad

2015-08-01

Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins.

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

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.

A study of quantum mechanical probabilities in the classical Hodgkin-Huxley model.

PubMed

Moradi, N; Scholkmann, F; Salari, V

2015-03-01

The Hodgkin-Huxley (HH) model is a powerful model to explain different aspects of spike generation in excitable cells. However, the HH model was proposed in 1952 when the real structure of the ion channel was unknown. It is now common knowledge that in many ion-channel proteins the flow of ions through the pore is governed by a gate, comprising a so-called "selectivity filter" inside the ion channel, which can be controlled by electrical interactions. The selectivity filter (SF) is believed to be responsible for the selection and fast conduction of particular ions across the membrane of an excitable cell. Other (generally larger) parts of the molecule such as the pore-domain gate control the access of ions to the channel protein. In fact, two types of gates are considered here for ion channels: the "external gate", which is the voltage sensitive gate, and the "internal gate" which is the selectivity filter gate (SFG). Some quantum effects are expected in the SFG due to its small dimensions, which may play an important role in the operation of an ion channel. Here, we examine parameters in a generalized model of HH to see whether any parameter affects the spike generation. Our results indicate that the previously suggested semi-quantum-classical equation proposed by Bernroider and Summhammer (BS) agrees strongly with the HH equation under different conditions and may even provide a better explanation in some cases. We conclude that the BS model can refine the classical HH model substantially.

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.

Model for Codon Position Bias in RNA Editing

NASA Astrophysics Data System (ADS)

Liu, Tsunglin; Bundschuh, Ralf

2005-08-01

RNA editing can be crucial for the expression of genetic information via inserting, deleting, or substituting a few nucleotides at specific positions in an RNA sequence. Within coding regions in an RNA sequence, editing usually occurs with a certain bias in choosing the positions of the editing sites. In the mitochondrial genes of Physarum polycephalum, many more editing events have been observed at the third codon position than at the first and second, while in some plant mitochondria the second codon position dominates. Here we propose an evolutionary model that explains this bias as the basis of selection at the protein level. The model predicts a distribution of the three positions rather close to the experimental observation in Physarum. This suggests that the codon position bias in Physarum is mainly a consequence of selection at the protein level.

A model for codon position bias in RNA editing

NASA Astrophysics Data System (ADS)

Bundschuh, Ralf; Liu, Tsunglin

2006-03-01

RNA editing can be crucial for the expression of genetic information via inserting, deleting, or substituting a few nucleotides at specific positions in an RNA sequence. Within coding regions in an RNA sequence, editing usually occurs with a certain bias in choosing the positions of the editing sites. In the mitochondrial genes of Physarum polycephalum, many more editing events have been observed at the third codon position than at the first and second, while in some plant mitochondria the second codon position dominates. Here we propose an evolutionary model that explains this bias as the basis of selection at the protein level. The model predicts a distribution of the three positions rather close to the experimental observation in Physarum. This suggests that the codon position bias in Physarum is mainly a consequence of selection at the protein level.

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

DEVELOPMENT OF SULFHYDRYL-REACTIVE SILICA FOR PROTEIN IMMOBILIZATION IN HIGH-PERFORMANCE AFFINITY CHROMATOGRAPHY

PubMed Central

Mallik, Rangan; Wa, Chunling; Hage, David S.

2008-01-01

Two techniques were developed for the immobilization of proteins and other ligands to silica through sulfhydryl groups. These methods made use of maleimide-activated silica (the SMCC method) or iodoacetyl-activated silica (the SIA method). The resulting supports were tested for use in high-performance affinity chromatography by employing human serum albumin (HSA) as a model protein. Studies with normal and iodoacetamide-modified HSA indicated that these methods had a high selectivity for sulfhydryl groups on this protein, which accounted for the coupling of 77–81% of this protein to maleimide- or iodacetyl-activated silica. These supports were also evaluated in terms of their total protein content, binding capacity, specific activity, non-specific binding, stability and chiral selectivity for several test solutes. HSA columns prepared using maleimide-activated silica gave the best overall results for these properties when compared to HSA that had been immobilized to silica through the Schiff base method (i.e., an amine-based coupling technique). A key advantage of the supports developed in this work is that they offer the potential of giving greater site-selective immobilization and ligand activity than amine-based coupling methods. These features make these supports attractive in the development of protein columns for such applications as the study of biological interactions and chiral separations. PMID:17297940

A novel Minimalist Cell-Free MHC Class II Antigen Processing System Identifies Immunodominant Epitopes

PubMed Central

Hartman, Isamu Z.; Kim, AeRyon; Cotter, Robert J.; Walter, Kimberly; Dalai, Sarat K.; Boronina, Tatiana; Griffith, Wendell; Schwenk, Robert; Lanar, David E.; Krzych, Urszula; Cole, Robert N.; Sadegh-Nasseri, Scheherazade

2010-01-01

Immunodominance is defined as restricted responsiveness of T cells to a few selected epitopes from complex antigens. Strategies currently used for elucidating CD4+ T cell epitopes are inadequate. To understand the mechanism of epitope selection for helper T cells, we established a cell-free antigen processing system composed of defined proteins: MHC class II, cathepsins, and HLA-DM. Our minimalist system successfully identified the physiologically selected immunodominant epitopes of model antigens, HA1 from influenza virus (A/Texas/1/77) and type II collagen. When applied for de novo epitope identification to a malaria antigen, or HA1 from H5N1 virus (Avian Flu), the system selected a single epitope from each protein that were confirmed to be immunodominant by their capacity to activate CD4+ T cells in HLA-DR1 positive human volunteers or transgenic mice immunized with the corresponding proteins. Thus, we provide a powerful new tool for the identification of physiologically relevant helper T cell epitopes from antigens. PMID:21037588

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.

Direct Correlation between Motile Behavior and Protein Abundance in Single Cells

PubMed Central

Gillet, Sébastien; Frankel, Nicholas W.; Weibel, Douglas B.

2016-01-01

Understanding how stochastic molecular fluctuations affect cell behavior requires the quantification of both behavior and protein numbers in the same cells. Here, we combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behavior. We characterized the distribution of non-genetic phenotypic diversity in Escherichia coli motility, which affects single-cell exploration. By expressing fluorescently tagged chemotaxis proteins (CheR and CheB) at different levels, we quantitatively mapped motile phenotype (tumble bias) to protein numbers using thousands of single-cell measurements. Our results disagreed with established models until we incorporated the role of CheB in receptor deamidation and the slow fluctuations in receptor methylation. Beyond refining models, our central finding is that changes in numbers of CheR and CheB affect the population mean tumble bias and its variance independently. Therefore, it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression of CheR and CheB while keeping their ratio constant, which, as shown in previous studies, confers functional robustness to the system. Since genetic control of protein expression is heritable, our results suggest that non-genetic diversity in motile behavior is selectable, supporting earlier hypotheses that such diversity confers a selective advantage. PMID:27599206

Diet and the anti-inflammatory effect of heat shock proteins.

PubMed

van Eden, Willem

2015-01-01

Stress proteins or heat shock proteins (HSPs) have a critical role in gut health and immune regulation. They have a functional significance as molecular chaperones for cell skeleton proteins and intercellular tight junction proteins. Herewith HSPs ensure gut epithelium integrity and effective intestinal barrier function. In addition, stress protein molecules such as HSP70 are a target for anti-inflammatory regulatory T cells (Tregs). Inflamed sites in the body feature inflammatory-stress induced enhanced levels of HSPs, which enable the immune system to target Tregs selectively to sites of inflammation. We have shown in experimental models of inflammatory diseases that both microbial HSP and endogenous (self) HSP molecules are capable of inducing the expansion of disease suppressive Tregs. Since the gut associated lymphoid tissue (GALT) is well poised towards the induction of regulation and tolerance, we set out to promote HSP expression and induction of Tregs in the gut lymphoid tissues by the oral administration of HSP co-inducing compounds. For the identification, selection and characterization of such compounds we have developed assay systems, such as reporter cell-lines, HSP specific T cell hybridomas and a transgenic mouse model (expression a HSP specific T cell receptor). The introduction of HSP coinducers into the diet constitutes a novel food based preventive or possibly even therapeutic approach in inflammatory diseases.

Effect of Ca2+ on the promiscuous target-protein binding of calmodulin

PubMed Central

Westerlund, Annie M.

2018-01-01

Calmodulin (CaM) is a calcium sensing protein that regulates the function of a large number of proteins, thus playing a crucial part in many cell signaling pathways. CaM has the ability to bind more than 300 different target peptides in a Ca2+-dependent manner, mainly through the exposure of hydrophobic residues. How CaM can bind a large number of targets while retaining some selectivity is a fascinating open question. Here, we explore the mechanism of CaM selective promiscuity for selected target proteins. Analyzing enhanced sampling molecular dynamics simulations of Ca2+-bound and Ca2+-free CaM via spectral clustering has allowed us to identify distinct conformational states, characterized by interhelical angles, secondary structure determinants and the solvent exposure of specific residues. We searched for indicators of conformational selection by mapping solvent exposure of residues in these conformational states to contacts in structures of CaM/target peptide complexes. We thereby identified CaM states involved in various binding classes arranged along a depth binding gradient. Binding Ca2+ modifies the accessible hydrophobic surface of the two lobes and allows for deeper binding. Apo CaM indeed shows shallow binding involving predominantly polar and charged residues. Furthermore, binding to the C-terminal lobe of CaM appears selective and involves specific conformational states that can facilitate deep binding to target proteins, while binding to the N-terminal lobe appears to happen through a more flexible mechanism. Thus the long-ranged electrostatic interactions of the charged residues of the N-terminal lobe of CaM may initiate binding, while the short-ranged interactions of hydrophobic residues in the C-terminal lobe of CaM may account for selectivity. This work furthers our understanding of the mechanism of CaM binding and selectivity to different target proteins and paves the way towards a comprehensive model of CaM selectivity. PMID:29614072

Effect of Ca2+ on the promiscuous target-protein binding of calmodulin.

PubMed

Westerlund, Annie M; Delemotte, Lucie

2018-04-01

Calmodulin (CaM) is a calcium sensing protein that regulates the function of a large number of proteins, thus playing a crucial part in many cell signaling pathways. CaM has the ability to bind more than 300 different target peptides in a Ca2+-dependent manner, mainly through the exposure of hydrophobic residues. How CaM can bind a large number of targets while retaining some selectivity is a fascinating open question. Here, we explore the mechanism of CaM selective promiscuity for selected target proteins. Analyzing enhanced sampling molecular dynamics simulations of Ca2+-bound and Ca2+-free CaM via spectral clustering has allowed us to identify distinct conformational states, characterized by interhelical angles, secondary structure determinants and the solvent exposure of specific residues. We searched for indicators of conformational selection by mapping solvent exposure of residues in these conformational states to contacts in structures of CaM/target peptide complexes. We thereby identified CaM states involved in various binding classes arranged along a depth binding gradient. Binding Ca2+ modifies the accessible hydrophobic surface of the two lobes and allows for deeper binding. Apo CaM indeed shows shallow binding involving predominantly polar and charged residues. Furthermore, binding to the C-terminal lobe of CaM appears selective and involves specific conformational states that can facilitate deep binding to target proteins, while binding to the N-terminal lobe appears to happen through a more flexible mechanism. Thus the long-ranged electrostatic interactions of the charged residues of the N-terminal lobe of CaM may initiate binding, while the short-ranged interactions of hydrophobic residues in the C-terminal lobe of CaM may account for selectivity. This work furthers our understanding of the mechanism of CaM binding and selectivity to different target proteins and paves the way towards a comprehensive model of CaM selectivity.

Integrative functional analyses using rainbow trout selected for tolerance to plant diets reveal nutrigenomic signatures for soy utilization without the concurrence of enteritis.

PubMed

Abernathy, Jason; Brezas, Andreas; Snekvik, Kevin R; Hardy, Ronald W; Overturf, Ken

2017-01-01

Finding suitable alternative protein sources for diets of carnivorous fish species remains a major concern for sustainable aquaculture. Through genetic selection, we created a strain of rainbow trout that outperforms parental lines in utilizing an all-plant protein diet and does not develop enteritis in the distal intestine, as is typical with salmonids on long-term plant protein-based feeds. By incorporating this strain into functional analyses, we set out to determine which genes are critical to plant protein utilization in the absence of gut inflammation. After a 12-week feeding trial with our selected strain and a control trout strain fed either a fishmeal-based diet or an all-plant protein diet, high-throughput RNA sequencing was completed on both liver and muscle tissues. Differential gene expression analyses, weighted correlation network analyses and further functional characterization were performed. A strain-by-diet design revealed differential expression ranging from a few dozen to over one thousand genes among the various comparisons and tissues. Major gene ontology groups identified between comparisons included those encompassing central, intermediary and foreign molecule metabolism, associated biosynthetic pathways as well as immunity. A systems approach indicated that genes involved in purine metabolism were highly perturbed. Systems analysis among the tissues tested further suggests the interplay between selection for growth, dietary utilization and protein tolerance may also have implications for nonspecific immunity. By combining data from differential gene expression and co-expression networks using selected trout, along with ontology and pathway analyses, a set of 63 candidate genes for plant diet tolerance was found. Risk loci in human inflammatory bowel diseases were also found in our datasets, indicating rainbow trout selected for plant-diet tolerance may have added utility as a potential biomedical model.

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.

A Three-protein Charge Zipper Stabilizes a Complex Modulating Bacterial Gene Silencing*

PubMed Central

Cordeiro, Tiago N.; García, Jesús; Bernadó, Pau; Millet, Oscar; Pons, Miquel

2015-01-01

The Hha/YmoA nucleoid-associated proteins help selectively silence horizontally acquired genetic material, including pathogenicity and antibiotic resistance genes and their maintenance in the absence of selective pressure. Members of the Hha family contribute to gene silencing by binding to the N-terminal dimerization domain of H-NS and modifying its selectivity. Hha-like proteins and the H-NS N-terminal domain are unusually rich in charged residues, and their interaction is mostly electrostatic-driven but, nonetheless, highly selective. The NMR-based structural model of the complex between Hha/YmoA and the H-NS N-terminal dimerization domain reveals that the origin of the selectivity is the formation of a three-protein charge zipper with interdigitated complementary charged residues from Hha and the two units of the H-NS dimer. The free form of YmoA shows collective microsecond-millisecond dynamics that can by measured by NMR relaxation dispersion experiments and shows a linear dependence with the salt concentration. The number of residues sensing the collective dynamics and the population of the minor form increased in the presence of H-NS. Additionally, a single residue mutation in YmoA (D43N) abolished H-NS binding and the dynamics of the apo-form, suggesting the dynamics and binding are functionally related. PMID:26085102

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

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease.

PubMed

Bresso, Emmanuel; Togawa, Roberto; Hammond-Kosack, Kim; Urban, Martin; Maigret, Bernard; Martins, Natalia Florencio

2016-12-15

Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

The Spinal Antinociceptive Effects of Endomorphins in Rats: Behavioral and G Protein Functional Studies

PubMed Central

Xie, Hong; Woods, James H.; Traynor, John R.; Ko, Mei-Chuan

2008-01-01

BACKGROUND Endomorphin-1 and endomorphin-2 are endogenous peptides that are highly selective for μ-opioid receptors. However, studies of their functional efficacy and selectivity are controversial. In this study, we systematically compared the effects of intrathecal (i.t.) administration of endomorphin-1 and -2 on nociception assays and G protein activation with those of [d-Ala2,N-Me-Phe4,Gly5-ol]-enkephalin (DAMGO), a highly effective peptidic μ-opioid receptor agonist. METHODS Male Sprague-Dawley rats were used. Acute and inflammatory pain models were used to compare the duration and magnitude of antinociception. Agonist-stimulated [35S]GTPγS binding was used to observe the functional activity at the level of the receptor-G protein in both spinal cord and thalamic membranes. In addition, antagonists selective for each receptor type were used to verify the functional selectivity of endomorphins in the rat spinal cord. RESULTS After i.t. administration, endomorphin-1 and -2 produced less antinociceptive effects than DAMGO in the model of acute pain. Concentration–response curves for DAMGO-, endomorphin-1-, and endomorphin-2-stimulated [35S]GTPγS binding revealed that both endomorphin-1 and -2 produced less G protein activation (i.e., approximately 50%–60%) than DAMGO did in the membranes of spinal cord and thalamus. In addition, i.t. endomorphin-induced antinociception was blocked by μ-opioid receptor selective dose of naltrexone (P < 0.05), but not by δ- and κ-opioid receptor antagonists, naltrindole and nor-binaltorphimine (P > 0.05). CONCLUSIONS Endomorphins are partial agonists for G protein activation at spinal and thalamic μ-opioid receptors. Both in vivo and in vitro measurements together suggest that DAMGO is more effective than endomorphins. Spinal endomorphins’ antinociceptive efficacy may range between 53% and 84% depending on the intensity and modality of the nociceptive stimulus. PMID:18499626

Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong

2017-01-01

Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Protein Loop Structure Prediction Using Conformational Space Annealing.

PubMed

Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

2017-05-22

We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.

Benchmarking protein classification algorithms via supervised cross-validation.

PubMed

Kertész-Farkas, Attila; Dhir, Somdutta; Sonego, Paolo; Pacurar, Mircea; Netoteia, Sergiu; Nijveen, Harm; Kuzniar, Arnold; Leunissen, Jack A M; Kocsor, András; Pongor, Sándor

2008-04-24

Development and testing of protein classification algorithms are hampered by the fact that the protein universe is characterized by groups vastly different in the number of members, in average protein size, similarity within group, etc. Datasets based on traditional cross-validation (k-fold, leave-one-out, etc.) may not give reliable estimates on how an algorithm will generalize to novel, distantly related subtypes of the known protein classes. Supervised cross-validation, i.e., selection of test and train sets according to the known subtypes within a database has been successfully used earlier in conjunction with the SCOP database. Our goal was to extend this principle to other databases and to design standardized benchmark datasets for protein classification. Hierarchical classification trees of protein categories provide a simple and general framework for designing supervised cross-validation strategies for protein classification. Benchmark datasets can be designed at various levels of the concept hierarchy using a simple graph-theoretic distance. A combination of supervised and random sampling was selected to construct reduced size model datasets, suitable for algorithm comparison. Over 3000 new classification tasks were added to our recently established protein classification benchmark collection that currently includes protein sequence (including protein domains and entire proteins), protein structure and reading frame DNA sequence data. We carried out an extensive evaluation based on various machine-learning algorithms such as nearest neighbor, support vector machines, artificial neural networks, random forests and logistic regression, used in conjunction with comparison algorithms, BLAST, Smith-Waterman, Needleman-Wunsch, as well as 3D comparison methods DALI and PRIDE. The resulting datasets provide lower, and in our opinion more realistic estimates of the classifier performance than do random cross-validation schemes. A combination of supervised and random sampling was used to construct model datasets, suitable for algorithm comparison.

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

PubMed Central

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

2015-01-01

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

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

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

Mapping of ligand-binding cavities in proteins.

PubMed

Andersson, C David; Chen, Brian Y; Linusson, Anna

2010-05-01

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs. 2009 Wiley-Liss, Inc.

Mapping of Ligand-Binding Cavities in Proteins

PubMed Central

Andersson, C. David; Chen, Brian Y.; Linusson, Anna

2010-01-01

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterise and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity and charge). This approach can provide valuable information on the similarities, and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterisation and mapping of “orphan structures”, selection of protein structures for docking studies in structure-based design and identification of proteins for selectivity screens in drug design programs. PMID:20034113

Protein classification using modified n-grams and skip-grams.

PubMed

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

Structure refinement of membrane proteins via molecular dynamics simulations.

PubMed

Dutagaci, Bercem; Heo, Lim; Feig, Michael

2018-07-01

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models. © 2018 Wiley Periodicals, Inc.

Accounting for observed small angle X-ray scattering profile in the protein-protein docking server ClusPro.

PubMed

Xia, Bing; Mamonov, Artem; Leysen, Seppe; Allen, Karen N; Strelkov, Sergei V; Paschalidis, Ioannis Ch; Vajda, Sandor; Kozakov, Dima

2015-07-30

The protein-protein docking server ClusPro is used by thousands of laboratories, and models built by the server have been reported in over 300 publications. Although the structures generated by the docking include near-native ones for many proteins, selecting the best model is difficult due to the uncertainty in scoring. Small angle X-ray scattering (SAXS) is an experimental technique for obtaining low resolution structural information in solution. While not sufficient on its own to uniquely predict complex structures, accounting for SAXS data improves the ranking of models and facilitates the identification of the most accurate structure. Although SAXS profiles are currently available only for a small number of complexes, due to its simplicity the method is becoming increasingly popular. Since combining docking with SAXS experiments will provide a viable strategy for fairly high-throughput determination of protein complex structures, the option of using SAXS restraints is added to the ClusPro server. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

ESI-MS studies of the reactions of novel platinum(II) complexes containing O,O'-chelated acetylacetonate and sulfur ligands with selected model proteins.

PubMed

Marzo, Tiziano; De Pascali, Sandra A; Gabbiani, Chiara; Fanizzi, Francesco P; Messori, Luigi; Pratesi, Alessandro

2017-08-01

A group of mixed-ligand Pt(II) complexes bearing acetylacetonate and sulphur ligands were recently developed in the University of Lecce as a new class of prospective anticancer agents that manifested promising pharma-cological properties in preliminary in vitro and in vivo tests. Though modelled on the basis of cisplatin, these Pt(II) complexes turned out to exhibit a profoundly distinct mode of action as they were found to act mainly on non-genomic targets rather than on DNA. Accordingly, we have explored here their reactions with two representative model proteins through an established ESI-MS procedure with the aim to describe their general interaction mechanism with protein targets. A pronounced reactivity with the tested proteins was indeed documented; the nature of the resulting metallodrug-protein interactions could be characterised in depth in the various cases. Preferential binding to protein targets compared to DNA is supported by independent ICP-OES measurements. The implications of these findings are discussed.

Systems modeling accurately predicts responses to genotoxic agents and their synergism with BCL-2 inhibitors in triple negative breast cancer cells.

PubMed

Lucantoni, Federico; Lindner, Andreas U; O'Donovan, Norma; Düssmann, Heiko; Prehn, Jochen H M

2018-01-19

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer which accounts for 15-20% of this disease and is currently treated with genotoxic chemotherapy. The BCL2 (B-cell lymphoma 2) family of proteins controls the process of mitochondrial outer membrane permeabilization (MOMP), which is required for the activation of the mitochondrial apoptosis pathway in response to genotoxic agents. We previously developed a deterministic systems model of BCL2 protein interactions, DR_MOMP that calculates the sensitivity of cells to undergo mitochondrial apoptosis. Here we determined whether DR_MOMP predicts responses of TNBC cells to genotoxic agents and the re-sensitization of resistant cells by BCL2 inhibitors. Using absolute protein levels of BAX, BAK, BCL2, BCL(X)L and MCL1 as input for DR_MOMP, we found a strong correlation between model predictions and responses of a panel of TNBC cells to 24 and 48 h cisplatin (R 2  = 0.96 and 0.95, respectively) and paclitaxel treatments (R 2  = 0.94 and 0.95, respectively). This outperformed single protein correlations (best performer BCL(X)L with R 2 of 0.69 and 0.50 for cisplatin and paclitaxel treatments, respectively) and BCL2 proteins ratio (R 2 of 0.50 for cisplatin and 0.49 for paclitaxel). Next we performed synergy studies using the BCL2 selective antagonist Venetoclax /ABT199, the BCL(X)L selective antagonist WEHI-539, or the MCL1 selective antagonist A-1210477 in combination with cisplatin. In silico predictions by DR_MOMP revealed substantial differences in treatment responses of BCL(X)L, BCL2 or MCL1 inhibitors combinations with cisplatin that were successfully validated in cell lines. Our findings provide evidence that DR_MOMP predicts responses of TNBC cells to genotoxic therapy, and can aid in the choice of the optimal BCL2 protein antagonist for combination treatments of resistant cells.

Impact of additives on the formation of protein aggregates and viscosity in concentrated protein solutions.

PubMed

Bauer, Katharina Christin; Suhm, Susanna; Wöll, Anna Katharina; Hubbuch, Jürgen

2017-01-10

In concentrated protein solutions attractive protein interactions may not only cause the formation of undesired aggregates but also of gel-like networks with elevated viscosity. To guarantee stable biopharmaceutical processes and safe formulations, both phenomenons have to be avoided as these may hinder regular processing steps. This work screens the impact of additives on both phase behavior and viscosity of concentrated protein solutions. For this purpose, additives known for stabilizing proteins in solution or modulating the dynamic viscosity were selected. These additives were PEG 300, PEG 1000, glycerol, glycine, NaCl and ArgHCl. Concentrated lysozyme and glucose oxidase solutions at pH 3 and 9 served as model systems. Fourier-transformed-infrared spectroscopy was chosen to determine the conformational stability of selected protein samples. Influencing protein interactions, the impact of additives was strongly dependent on pH. Of all additives investigated, glycine was the only one that maintained protein conformational and colloidal stability while decreasing the dynamic viscosity. Low concentrations of NaCl showed the same effect, but increasing concentrations resulted in visible protein aggregation. Copyright © 2016 Elsevier B.V. All rights reserved.

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 accuracy of predictions when computational resources are limited.

Soft Wall Ion Channel in Continuum Representation with Application to Modeling Ion Currents in α-Hemolysin

PubMed Central

Simakov, Nikolay A.

2010-01-01

A soft repulsion (SR) model of short range interactions between mobile ions and protein atoms is introduced in the framework of continuum representation of the protein and solvent. The Poisson-Nernst-Plank (PNP) theory of ion transport through biological channels is modified to incorporate this soft wall protein model. Two sets of SR parameters are introduced: the first is parameterized for all essential amino acid residues using all atom molecular dynamic simulations; the second is a truncated Lennard – Jones potential. We have further designed an energy based algorithm for the determination of the ion accessible volume, which is appropriate for a particular system discretization. The effects of these models of short-range interaction were tested by computing current-voltage characteristics of the α-hemolysin channel. The introduced SR potentials significantly improve prediction of channel selectivity. In addition, we studied the effect of choice of some space-dependent diffusion coefficient distributions on the predicted current-voltage properties. We conclude that the diffusion coefficient distributions largely affect total currents and have little effect on rectifications, selectivity or reversal potential. The PNP-SR algorithm is implemented in a new efficient parallel Poisson, Poisson-Boltzman and PNP equation solver, also incorporated in a graphical molecular modeling package HARLEM. PMID:21028776

Constrained Maximum Likelihood Estimation of Relative Abundances of Protein Conformation in a Heterogeneous Mixture from Small Angle X-Ray Scattering Intensity Measurements

PubMed Central

Onuk, A. Emre; Akcakaya, Murat; Bardhan, Jaydeep P.; Erdogmus, Deniz; Brooks, Dana H.; Makowski, Lee

2015-01-01

In this paper, we describe a model for maximum likelihood estimation (MLE) of the relative abundances of different conformations of a protein in a heterogeneous mixture from small angle X-ray scattering (SAXS) intensities. To consider cases where the solution includes intermediate or unknown conformations, we develop a subset selection method based on k-means clustering and the Cramér-Rao bound on the mixture coefficient estimation error to find a sparse basis set that represents the space spanned by the measured SAXS intensities of the known conformations of a protein. Then, using the selected basis set and the assumptions on the model for the intensity measurements, we show that the MLE model can be expressed as a constrained convex optimization problem. Employing the adenylate kinase (ADK) protein and its known conformations as an example, and using Monte Carlo simulations, we demonstrate the performance of the proposed estimation scheme. Here, although we use 45 crystallographically determined experimental structures and we could generate many more using, for instance, molecular dynamics calculations, the clustering technique indicates that the data cannot support the determination of relative abundances for more than 5 conformations. The estimation of this maximum number of conformations is intrinsic to the methodology we have used here. PMID:26924916

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.

Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024

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.

The making of the minibody: an engineered beta-protein for the display of conformationally constrained peptides.

PubMed

Tramontano, A; Bianchi, E; Venturini, S; Martin, F; Pessi, A; Sollazzo, M

1994-03-01

Conformationally constraining selectable peptides onto a suitable scaffold that enables their conformation to be predicted or readily determined by experimental techniques would considerably boost the drug discovery process by reducing the gap between the discovery of a peptide lead and the design of a peptidomimetic with a more desirable pharmacological profile. With this in mind, we designed the minibody, a 61-residue beta-protein aimed at retaining some desirable features of immunoglobulin variable domains, such as tolerance to sequence variability in selected regions of the protein and predictability of the main chain conformation of the same regions, based on the 'canonical structures' model. To test the ability of the minibody scaffold to support functional sites we also designed a metal binding version of the protein by suitably choosing the sequences of its loops. The minibody was produced both by chemical synthesis and expression in E. coli and characterized by size exclusion chromatography, UV CD (circular dichroism) spectroscopy and metal binding activity. All our data supported the model, but a more detailed structural characterization of the molecule was impaired by its low solubility. We were able to overcome this problem both by further mutagenesis of the framework and by addition of a solubilizing motif. The minibody is being used to select constrained human IL-6 peptidic ligands from a library displayed on the surface of the f1 bacteriophage.

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin.

PubMed

Fuchs, Julian E; Huber, Roland G; Waldner, Birgit J; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm "dynamics govern specificity" might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

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.

ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

PubMed Central

Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G.; Gelly, Jean-Christophe

2016-01-01

Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation —with Protein Blocks—, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the ‘Hard’ category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/. PMID:27319297

ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.

PubMed

Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G; Gelly, Jean-Christophe

2016-06-20

Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation -with Protein Blocks-, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/.

In vivo screen of genetically conserved Streptococcus pneumoniae proteins for protective immunogenicity.

PubMed

Anderson, Richard J; Guru, Siradanahalli; Weeratna, Risini; Makinen, Shawn; Falconer, Derek J; Sheppard, Neil C; Lang, Susanne; Chang, Bingsheng; Goenaga, Anne-Laure; Green, Bruce A; Merson, James R; Gracheck, Stephen J; Eyles, Jim E

2016-12-07

We evaluated 52 different E. coli expressed pneumococcal proteins as immunogens in a BALB/c mouse model of S. pneumoniae lung infection. Proteins were selected based on genetic conservation across disease-causing serotypes and bioinformatic prediction of antibody binding to the target antigen. Seven proteins induced protective responses, in terms of reduced lung burdens of the serotype 3 pneumococci. Three of the protective proteins were histidine triad protein family members (PhtB, PhtD and PhtE). Four other proteins, all bearing LPXTG linkage domains, also had activity in this model (PrtA, NanA, PavB and Eng). PrtA, NanA and Eng were also protective in a CBA/N mouse model of lethal pneumococcal infection. Despite data inferring widespread genomic conservation, flow-cytometer based antisera binding studies confirmed variable levels of antigen expression across a panel of pneumococcal serotypes. Finally, BALB/c mice were immunized and intranasally challenged with a viulent serotype 8 strain, to help understand the breadth of protection. Those mouse studies reaffirmed the effectiveness of the histidine triad protein grouping and a single LPXTG protein, PrtA. Copyright © 2016 Elsevier Ltd. All rights reserved.

Duplicate Abalone Egg Coat Proteins Bind Sperm Lysin Similarly, but Evolve Oppositely, Consistent with Molecular Mimicry at Fertilization

PubMed Central

Aagaard, Jan E.; Springer, Stevan A.; Soelberg, Scott D.; Swanson, Willie J.

2013-01-01

Sperm and egg proteins constitute a remarkable paradigm in evolutionary biology: despite their fundamental role in mediating fertilization (suggesting stasis), some of these molecules are among the most rapidly evolving ones known, and their divergence can lead to reproductive isolation. Because of strong selection to maintain function among interbreeding individuals, interacting fertilization proteins should also exhibit a strong signal of correlated divergence among closely related species. We use evidence of such molecular co-evolution to target biochemical studies of fertilization in North Pacific abalone (Haliotis spp.), a model system of reproductive protein evolution. We test the evolutionary rates (d N/d S) of abalone sperm lysin and two duplicated egg coat proteins (VERL and VEZP14), and find a signal of co-evolution specific to ZP-N, a putative sperm binding motif previously identified by homology modeling. Positively selected residues in VERL and VEZP14 occur on the same face of the structural model, suggesting a common mode of interaction with sperm lysin. We test this computational prediction biochemically, confirming that the ZP-N motif is sufficient to bind lysin and that the affinities of VERL and VEZP14 are comparable. However, we also find that on phylogenetic lineages where lysin and VERL evolve rapidly, VEZP14 evolves slowly, and vice versa. We describe a model of sexual conflict that can recreate this pattern of anti-correlated evolution by assuming that VEZP14 acts as a VERL mimic, reducing the intensity of sexual conflict and slowing the co-evolution of lysin and VERL. PMID:23408913

Cry1A(b)16 toxin from Bacillus thuringiensis: Theoretical refinement of three-dimensional structure and prediction of peptides as molecular markers for detection of genetically modified organisms.

PubMed

Plácido, Alexandra; Coelho, Andreia; Abreu Nascimento, Lucas; Gomes Vasconcelos, Andreanne; Fátima Barroso, Maria; Ramos-Jesus, Joilson; Costa, Vladimir; das Chagas Alves Lima, Francisco; Delerue-Matos, Cristina; Martins Ramos, Ricardo; Marani, Mariela M; Roberto de Souza de Almeida Leite, José

2017-07-01

Transgenic maize produced by the insertion of the Cry transgene into its genome became the second most cultivated crop worldwide. Cry gene from Bacillus thuringiensis kurstaki expresses protein derivatives of crystalline endotoxins which confer insect resistance onto the maize crop. Mandatory labeling of processed food containing or made by genetically modified organisms is in force in many countries, so, it is very urgent to develop fast and practical methods for GMO identification, for example, biosensors. In the absence of an available empirical structure of Cry1A(b)16 protein, a theoretical model was effectively generated, in this work, by homology modeling and molecular dynamics simulations based on two available homologous protein structures. Molecular dynamics simulations were carried out to refine the selected model, and an analysis of its global structure was performed. The refined models of Cry1A(b)16 showed a standard fold and structural characteristics similar to those seen in Bacillus thuringiensis Cry1A(a) insecticidal toxin and Bacillus thuringiensis serovar kurstaki Cry1A(c) toxin. After in silico analysis of Cry1A(b)16, two immunoreactive candidate peptides were selected and specific polyclonal antibodies were produced resulting in antibody-peptide interaction. Biosensing devices are expected to be developed for detection of the Cry1A(b) protein as a marker of transgenic maize in food. Proteins 2017; 85:1248-1257. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Phenolic promiscuity in the cell nucleus--epigallocatechingallate (EGCG) and theaflavin-3,3'-digallate from green and black tea bind to model cell nuclear structures including histone proteins, double stranded DNA and telomeric quadruplex DNA.

PubMed

Mikutis, Gediminas; Karaköse, Hande; Jaiswal, Rakesh; LeGresley, Adam; Islam, Tuhidul; Fernandez-Lahore, Marcelo; Kuhnert, Nikolai

2013-02-01

Flavanols from tea have been reported to accumulate in the cell nucleus in considerable concentrations. The nature of this phenomenon, which could provide novel approaches in understanding the well-known beneficial health effects of tea phenols, is investigated in this contribution. The interaction between epigallocatechin gallate (EGCG) from green tea and a selection of theaflavins from black tea with selected cell nuclear structures such as model histone proteins, double stranded DNA and quadruplex DNA was investigated using mass spectrometry, Circular Dichroism spectroscopy and fluorescent assays. The selected polyphenols were shown to display affinity to all of the selected cell nuclear structures, thereby demonstrating a degree of unexpected molecular promiscuity. Most interestingly theaflavin-digallate was shown to display the highest affinity to quadruplex DNA reported for any naturally occurring molecule reported so far. This finding has immediate implications in rationalising the chemopreventive effect of the tea beverage against cancer and possibly the role of tea phenolics as "life span essentials".

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.

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

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.

Select α-arrestins control cell-surface abundance of the mammalian Kir2.1 potassium channel in a yeast model.

PubMed

Hager, Natalie A; Krasowski, Collin J; Mackie, Timothy D; Kolb, Alexander R; Needham, Patrick G; Augustine, Andrew A; Dempsey, Alison; Szent-Gyorgyi, Christopher; Bruchez, Marcel P; Bain, Daniel J; Kwiatkowski, Adam V; O'Donnell, Allyson F; Brodsky, Jeffrey L

2018-05-21

Protein composition at the plasma membrane is tightly regulated, with rapid protein internalization and selective targeting to the cell surface occurring in response to environmental changes. For example, ion channels are dynamically relocalized to or from the plasma membrane in response to physiological alterations, allowing cells and organisms to maintain osmotic and salt homeostasis. To identify additional factors that regulate the selective trafficking of a specific ion channel, we used a yeast model for a mammalian potassium channel, the K+ inwardly rectifying channel Kir2.1. Kir2.1 maintains potassium homeostasis in heart muscle cells, and Kir2.1 defects lead to human disease. By examining the ability of Kir2.1 to rescue the growth of yeast cells lacking endogenous potassium channels, we discovered that specific α-arrestins regulate Kir2.1 localization. Specifically, we found that the Ldb19/Art1, Aly1/Art6, and Aly2/Art3 α-arrestin adaptor proteins promote Kir2.1 trafficking to the cell surface, increase Kir2.1 activity at the plasma membrane, and raise intracellular potassium levels. To better quantify the intracellular and cell-surface populations of Kir2.1, we created fluorescence-activating protein fusions and for the first time used this technique to measure the cell-surface residency of a plasma membrane protein in yeast. Our experiments revealed that two α-arrestin effectors also control Kir2.1 localization. In particular, both the Rsp5 ubiquitin ligase and the protein phosphatase calcineurin facilitated the α-arrestin-mediated trafficking of Kir2.1. Together, our findings implicate α-arrestins in regulating an additional class of plasma membrane proteins and establish a new tool for dissecting the trafficking itinerary of any membrane protein in yeast. Published under license by The American Society for Biochemistry and Molecular Biology, Inc.

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.

A Large Complement of the Predicted Arabidopsis ARM Repeat Proteins Are Members of the U-Box E3 Ubiquitin Ligase Family1[w

PubMed Central

Mudgil, Yashwanti; Shiu, Shin-Han; Stone, Sophia L.; Salt, Jennifer N.; Goring, Daphne R.

2004-01-01

The Arabidopsis genome was searched to identify predicted proteins containing armadillo (ARM) repeats, a motif known to mediate protein-protein interactions in a number of different animal proteins. Using domain database predictions and models generated in this study, 108 Arabidopsis proteins were identified that contained a minimum of two ARM repeats with the majority of proteins containing four to eight ARM repeats. Clustering analysis showed that the 108 predicted Arabidopsis ARM repeat proteins could be divided into multiple groups with wide differences in their domain compositions and organizations. Interestingly, 41 of the 108 Arabidopsis ARM repeat proteins contained a U-box, a motif present in a family of E3 ligases, and these proteins represented the largest class of Arabidopsis ARM repeat proteins. In 14 of these U-box/ARM repeat proteins, there was also a novel conserved domain identified in the N-terminal region. Based on the phylogenetic tree, representative U-box/ARM repeat proteins were selected for further study. RNA-blot analyses revealed that these U-box/ARM proteins are expressed in a variety of tissues in Arabidopsis. In addition, the selected U-box/ARM proteins were found to be functional E3 ubiquitin ligases. Thus, these U-box/ARM proteins represent a new family of E3 ligases in Arabidopsis. PMID:14657406

A large complement of the predicted Arabidopsis ARM repeat proteins are members of the U-box E3 ubiquitin ligase family.

PubMed

Mudgil, Yashwanti; Shiu, Shin-Han; Stone, Sophia L; Salt, Jennifer N; Goring, Daphne R

2004-01-01

The Arabidopsis genome was searched to identify predicted proteins containing armadillo (ARM) repeats, a motif known to mediate protein-protein interactions in a number of different animal proteins. Using domain database predictions and models generated in this study, 108 Arabidopsis proteins were identified that contained a minimum of two ARM repeats with the majority of proteins containing four to eight ARM repeats. Clustering analysis showed that the 108 predicted Arabidopsis ARM repeat proteins could be divided into multiple groups with wide differences in their domain compositions and organizations. Interestingly, 41 of the 108 Arabidopsis ARM repeat proteins contained a U-box, a motif present in a family of E3 ligases, and these proteins represented the largest class of Arabidopsis ARM repeat proteins. In 14 of these U-box/ARM repeat proteins, there was also a novel conserved domain identified in the N-terminal region. Based on the phylogenetic tree, representative U-box/ARM repeat proteins were selected for further study. RNA-blot analyses revealed that these U-box/ARM proteins are expressed in a variety of tissues in Arabidopsis. In addition, the selected U-box/ARM proteins were found to be functional E3 ubiquitin ligases. Thus, these U-box/ARM proteins represent a new family of E3 ligases in Arabidopsis.

Fold-Unfold Transitions in the Selectivity and Mechanism of Action of the N-Terminal Fragment of the Bactericidal/Permeability-Increasing Protein (rBPI21)

PubMed Central

Domingues, Marco M.; Lopes, Sílvia C.D.N.; Santos, Nuno C.; Quintas, Alexandre; Castanho, Miguel A.R.B.

2009-01-01

Septic or endotoxic shock is a common cause of death in hospital intensive care units. In the last decade numerous antimicrobial peptides and proteins have been tested in the search for an efficient drug to treat this lethal disease. Now in phase III clinical trials, rBPI21, a recombinant N-terminal fragment of the bactericidal/permeability-increasing protein (BPI), is a promising drug to reduce lesions caused by meningococcal sepsis. We correlated structural and stability data with functional information of rBPI21 bound to both model systems of eukaryotic and bacterial membranes. On interaction with membranes, rBPI21 loses its conformational stability, as studied by circular dichroism. This interaction of rBPI21 at membrane level was higher in the presence of negatively charged phospholipid relatively to neutral ones, with higher partition coefficients (Kp), suggesting a preference for bacterial membranes over mammalian membranes. rBPI21 binding to membranes is reinforced when its disulfide bond is broken due to conformational changes of the protein. This interaction is followed by liposome aggregation due to unfolding, which ensures protein aggregation, and interfacial localization of rBPI21 in membranes, as studied by extensive quenching by acrylamide and 5-deoxylstearic acid and not by 16-deoxylstearic acid. An uncommon model of the selectivity and mechanism of action is proposed, where membrane induces unfolding of the antimicrobial protein, rBPI21. The unfolding ensures protein aggregation, established by protein-protein interaction at membrane surface or between adjacent membranes covered by the unfolded protein. This protein aggregation step may lead to membrane perturbation. PMID:19186136

Cystic Fibrosis Heterozygote Resistance to Cholera Toxin in the Cystic Fibrosis Mouse Model

NASA Astrophysics Data System (ADS)

Gabriel, Sherif E.; Brigman, Kristen N.; Koller, Beverly H.; Boucher, Richard C.; Stutts, M. Jackson

1994-10-01

The effect of the number of cystic fibrosis (CF) alleles on cholera toxin (CT)-induced intestinal secretion was examined in the CF mouse model. CF mice that expressed no CF transmembrane conductance regulator (CFTR) protein did not secrete fluid in response to CT. Heterozygotes expressed 50 percent of the normal amount of CFTR protein in the intestinal epithelium and secreted 50 percent of the normal fluid and chloride ion in response to CT. This correlation between CFTR protein and CT-induced chloride ion and fluid secretion suggests that CF heterozygotes might possess a selective advantage of resistance to cholera.

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.

The effect of geometrical presentation of multimodal cation-exchange ligands on selective recognition of hydrophobic regions on protein surfaces.

PubMed

Woo, James; Parimal, Siddharth; Brown, Matthew R; Heden, Ryan; Cramer, Steven M

2015-09-18

The effects of spatial organization of hydrophobic and charged moieties on multimodal (MM) cation-exchange ligands were examined by studying protein retention behavior on two commercial chromatographic media, Capto™ MMC and Nuvia™ cPrime™. Proteins with extended regions of surface-exposed aliphatic residues were found to have enhanced retention on the Capto MMC system as compared to the Nuvia cPrime resin. The results further indicated that while the Nuvia cPrime ligand had a strong preference for interactions with aromatic groups, the Capto MMC ligand appeared to interact with both aliphatic and aromatic clusters on the protein surfaces. These observations were formalized into a new set of protein surface property descriptors, which quantified the local distribution of electrostatic and hydrophobic potentials as well as distinguishing between aromatic and aliphatic properties. Using these descriptors, high-performing quantitative structure-activity relationship (QSAR) models (R(2)>0.88) were generated for both the Capto MMC and Nuvia cPrime datasets at pH 5 and pH 6. Descriptors of electrostatic properties were generally common across the four models; however both Capto MMC models included descriptors that quantified regions of aliphatic-based hydrophobicity in addition to aromatic descriptors. Retention was generally reduced by lowering the ligand densities on both MM resins. Notably, elution order was largely unaffected by the change in surface density, but smaller and more aliphatic proteins tended to be more affected by this drop in ligand density. This suggests that modulating the exposure, shape and density of the hydrophobic moieties in multimodal chromatographic systems can alter the preference for surface exposed aliphatic or aromatic residues, thus providing an additional dimension for modulating the selectivity of MM protein separation systems. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.

PubMed

Calus, M P L; de Haas, Y; Veerkamp, R F

2013-10-01

Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction models are especially important in small data sets. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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, inaccuracies in the description of imidazole ring flip rotamerism were identified.

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-pathogen interactions. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Prion-like nanofibrils of small molecules (PriSM) selectively inhibit cancer cells by impeding cytoskeleton dynamics.

PubMed

Kuang, Yi; Long, Marcus J C; Zhou, Jie; Shi, Junfeng; Gao, Yuan; Xu, Chen; Hedstrom, Lizbeth; Xu, Bing

2014-10-17

Emerging evidence reveals that prion-like structures play important roles to maintain the well-being of cells. Although self-assembly of small molecules also affords prion-like nanofibrils (PriSM), little is known about the functions and mechanisms of PriSM. Previous works demonstrated that PriSM formed by a dipeptide derivative selectively inhibiting the growth of glioblastoma cells over neuronal cells and effectively inhibiting xenograft tumor in animal models. Here we examine the protein targets, the internalization, and the cytotoxicity pathway of the PriSM. The results show that the PriSM selectively accumulate in cancer cells via macropinocytosis to impede the dynamics of cytoskeletal filaments via promiscuous interactions with cytoskeletal proteins, thus inducing apoptosis. Intriguingly, Tau proteins are able to alleviate the effect of the PriSM, thus protecting neuronal cells. This work illustrates PriSM as a new paradigm for developing polypharmacological agents that promiscuously interact with multiple proteins yet result in a primary phenotype, such as cancer inhibition. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

Structure based re-design of the binding specificity of anti-apoptotic Bcl-xL

PubMed Central

Chen, T. Scott; Palacios, Hector; Keating, Amy E.

2012-01-01

Many native proteins are multi-specific and interact with numerous partners, which can confound analysis of their functions. Protein design provides a potential route to generating synthetic variants of native proteins with more selective binding profiles. Re-designed proteins could be used as research tools, diagnostics or therapeutics. In this work, we used a library screening approach to re-engineer the multi-specific anti-apoptotic protein Bcl-xL to remove its interactions with many of its binding partners, making it a high affinity and selective binder of the BH3 region of pro-apoptotic protein Bad. To overcome the enormity of the potential Bcl-xL sequence space, we developed and applied a computational/experimental framework that used protein structure information to generate focused combinatorial libraries. Sequence features were identified using structure-based modeling, and an optimization algorithm based on integer programming was used to select degenerate codons that maximally covered these features. A constraint on library size was used to ensure thorough sampling. Using yeast surface display to screen a designed library of Bcl-xL variants, we successfully identified a protein with ~1,000-fold improvement in binding specificity for the BH3 region of Bad over the BH3 region of Bim. Although negative design was targeted only against the BH3 region of Bim, the best re-designed protein was globally specific against binding to 10 other peptides corresponding to native BH3 motifs. Our design framework demonstrates an efficient route to highly specific protein binders and may readily be adapted for application to other design problems. PMID:23154169

Novel fusion proteins for the antigen-specific staining and elimination of B cell receptor-positive cell populations demonstrated by a tetanus toxoid fragment C (TTC) model antigen.

PubMed

Klose, Diana; Saunders, Ute; Barth, Stefan; Fischer, Rainer; Jacobi, Annett Marita; Nachreiner, Thomas

2016-02-17

In an earlier study we developed a unique strategy allowing us to specifically eliminate antigen-specific murine B cells via their distinct B cell receptors using a new class of fusion proteins. In the present work we elaborated our idea to demonstrate the feasibility of specifically addressing and eliminating human memory B cells. The present study reveals efficient adaptation of the general approach to selectively target and eradicate human memory B cells. In order to demonstrate the feasibility we engineered a fusion protein following the principle of recombinant immunotoxins by combining a model antigen (tetanus toxoid fragment C, TTC) for B cell receptor targeting and a truncated version of Pseudomonas aeruginosa exotoxin A (ETA') to induce apoptosis after cellular uptake. The TTC-ETA' fusion protein not only selectively bound to a TTC-reactive murine B cell hybridoma cell line in vitro but also to freshly isolated human memory B cells from immunized donors ex vivo. Specific toxicity was confirmed on an antigen-specific population of human CD27(+) memory B cells. This protein engineering strategy can be used as a generalized platform approach for the construction of therapeutic fusion proteins with disease-relevant antigens as B cell receptor-binding domains, offering a promising approach for the specific depletion of autoreactive B-lymphocytes in B cell-driven autoimmune diseases.

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

PubMed

Moal, Iain H; Barradas-Bautista, Didier; Jiménez-García, Brian; Torchala, Mieczyslaw; van der Velde, Arjan; Vreven, Thom; Weng, Zhiping; Bates, Paul A; Fernández-Recio, Juan

2017-06-15

In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. moal@ebi.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics.

PubMed

Xu, Xiu-Qin; Leow, Chon K; Lu, Xin; Zhang, Xuegong; Liu, Jun S; Wong, Wing-Hung; Asperger, Arndt; Deininger, Sören; Eastwood Leung, Hon-Chiu

2004-10-01

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.

A New F131V Mutation in Chlamydomonas Phytoene Desaturase Locates a Cluster of Norflurazon Resistance Mutations near the FAD-Binding Site in 3D Protein Models

PubMed Central

Suarez, Julio V.; Banks, Stephen; Thomas, Paul G.; Day, Anil

2014-01-01

The green alga Chlamydomonas reinhardtii provides a tractable genetic model to study herbicide mode of action using forward genetics. The herbicide norflurazon inhibits phytoene desaturase, which is required for carotenoid synthesis. Locating amino acid substitutions in mutant phytoene desaturases conferring norflurazon resistance provides a genetic approach to map the herbicide binding site. We isolated a UV-induced mutant able to grow in very high concentrations of norflurazon (150 µM). The phytoene desaturase gene in the mutant strain contained the first resistance mutation to be localised to the dinucleotide-binding Rossmann-likedomain. A highly conserved phenylalanine amino acid at position 131 of the 564 amino acid precursor protein was changed to a valine in the mutant protein. F131, and two other amino acids whose substitution confers norflurazon resistance in homologous phytoene desaturase proteins, map to distant regions in the primary sequence of the C. reinhardtii protein (V472, L505) but in tertiary models these residues cluster together to a region close to the predicted FAD binding site. The mutant gene allowed direct 5 µM norflurazon based selection of transformants, which were tolerant to other bleaching herbicides including fluridone, flurtamone, and diflufenican but were more sensitive to beflubutamid than wild type cells. Norflurazon resistance and beflubutamid sensitivity allow either positive or negative selection against transformants expressing the mutant phytoene desaturase gene. PMID:24936791

A Method to Find Longevity-Selected Positions in the Mammalian Proteome

PubMed Central

Semeiks, Jeremy; Grishin, Nick V.

2012-01-01

Evolutionary theory suggests that the force of natural selection decreases with age. To explore the extent to which this prediction directly affects protein structure and function, we used multiple regression to find longevity-selected positions, defined as the columns of a sequence alignment conserved in long-lived but not short-lived mammal species. We analyzed 7,590 orthologous protein families in 33 mammalian species, accounting for body mass, phylogeny, and species-specific mutation rate. Overall, we found that the number of longevity-selected positions in the mammalian proteome is much higher than would be expected by chance. Further, these positions are enriched in domains of several proteins that interact with one another in inflammation and other aging-related processes, as well as in organismal development. We present as an example the kinase domain of anti-Müllerian hormone type-2 receptor (AMHR2). AMHR2 inhibits ovarian follicle recruitment and growth, and a homology model of the kinase domain shows that its longevity-selected positions cluster near a SNP associated with delayed human menopause. Distinct from its canonical role in development, this region of AMHR2 may function to regulate the protein’s activity in a lifespan-specific manner. PMID:22701678

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.

Estimation of daily protein intake based on spot urine urea nitrogen concentration in chronic kidney disease patients.

PubMed

Kanno, Hiroko; Kanda, Eiichiro; Sato, Asako; Sakamoto, Kaori; Kanno, Yoshihiko

2016-04-01

Determination of daily protein intake in the management of chronic kidney disease (CKD) requires precision. Inaccuracies in recording dietary intake occur, and estimation from total urea excretion presents hurdles owing to the difficulty of collecting whole urine for 24 h. Spot urine has been used for measuring daily sodium intake and urinary protein excretion. In this cross-sectional study, we investigated whether urea nitrogen (UN) concentration in spot urine can be used to predict daily protein intake instead of the 24-h urine collection in 193 Japanese CKD patients (Stages G1-G5). After patient randomization into 2 datasets for the development and validation of models, bootstrapping was used to develop protein intake estimation models. The parameters for the candidate multivariate regression models were male gender, age, body mass index (BMI), diabetes mellitus, dyslipidemia, proteinuria, estimated glomerular filtration rate, serum albumin level, spot urinary UN and creatinine level, and spot urinary UN/creatinine levels. The final model contained BMI and spot urinary UN level. The final model was selected because of the higher correlation between the predicted and measured protein intakes r = 0.558 (95 % confidence interval 0.400, 0.683), and the smaller distribution of the difference between the measured and predicted protein intakes than those of the other models. The results suggest that UN concentration in spot urine may be used to estimate daily protein intake and that a prediction formula would be useful for nutritional control in CKD patients.

Synergistic Integration of Experimental and Simulation Approaches for the de Novo Design of Silk-Based Materials.

PubMed

Huang, Wenwen; Ebrahimi, Davoud; Dinjaski, Nina; Tarakanova, Anna; Buehler, Markus J; Wong, Joyce Y; Kaplan, David L

2017-04-18

Tailored biomaterials with tunable functional properties are crucial for a variety of task-specific applications ranging from healthcare to sustainable, novel bio-nanodevices. To generate polymeric materials with predictive functional outcomes, exploiting designs from nature while morphing them toward non-natural systems offers an important strategy. Silks are Nature's building blocks and are produced by arthropods for a variety of uses that are essential for their survival. Due to the genetic control of encoded protein sequence, mechanical properties, biocompatibility, and biodegradability, silk proteins have been selected as prototype models to emulate for the tunable designs of biomaterial systems. The bottom up strategy of material design opens important opportunities to create predictive functional outcomes, following the exquisite polymeric templates inspired by silks. Recombinant DNA technology provides a systematic approach to recapitulate, vary, and evaluate the core structure peptide motifs in silks and then biosynthesize silk-based polymers by design. Post-biosynthesis processing allows for another dimension of material design by controlled or assisted assembly. Multiscale modeling, from the theoretical prospective, provides strategies to explore interactions at different length scales, leading to selective material properties. Synergy among experimental and modeling approaches can provide new and more rapid insights into the most appropriate structure-function relationships to pursue while also furthering our understanding in terms of the range of silk-based systems that can be generated. This approach utilizes nature as a blueprint for initial polymer designs with useful functions (e.g., silk fibers) but also employs modeling-guided experiments to expand the initial polymer designs into new domains of functional materials that do not exist in nature. The overall path to these new functional outcomes is greatly accelerated via the integration of modeling with experiment. In this Account, we summarize recent advances in understanding and functionalization of silk-based protein systems, with a focus on the integration of simulation and experiment for biopolymer design. Spider silk was selected as an exemplary protein to address the fundamental challenges in polymer designs, including specific insights into the role of molecular weight, hydrophobic/hydrophilic partitioning, and shear stress for silk fiber formation. To expand current silk designs toward biointerfaces and stimuli responsive materials, peptide modules from other natural proteins were added to silk designs to introduce new functions, exploiting the modular nature of silk proteins and fibrous proteins in general. The integrated approaches explored suggest that protein folding, silk volume fraction, and protein amino acid sequence changes (e.g., mutations) are critical factors for functional biomaterial designs. In summary, the integrated modeling-experimental approach described in this Account suggests a more rationally directed and more rapid method for the design of polymeric materials. It is expected that this combined use of experimental and computational approaches has a broad applicability not only for silk-based systems, but also for other polymer and composite materials.

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.

Automated determination of fibrillar structures by simultaneous model building and fiber diffraction refinement.

PubMed

Potrzebowski, Wojciech; André, Ingemar

2015-07-01

For highly oriented fibrillar molecules, three-dimensional structures can often be determined from X-ray fiber diffraction data. However, because of limited information content, structure determination and validation can be challenging. We demonstrate that automated structure determination of protein fibers can be achieved by guiding the building of macromolecular models with fiber diffraction data. We illustrate the power of our approach by determining the structures of six bacteriophage viruses de novo using fiber diffraction data alone and together with solid-state NMR data. Furthermore, we demonstrate the feasibility of molecular replacement from monomeric and fibrillar templates by solving the structure of a plant virus using homology modeling and protein-protein docking. The generated models explain the experimental data to the same degree as deposited reference structures but with improved structural quality. We also developed a cross-validation method for model selection. The results highlight the power of fiber diffraction data as structural constraints.

[Comparative study of protein markers in serum and bronchoalveolar lavage fluid from patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry].

PubMed

Zhou, Ji-hong; Liu, Guang-nan; Huang, Si-ming; Zhong, Xiao-ning; Su, Hong; Zhou, Yi

2011-04-01

To detect the protein markers in serum and bronchoalveolar lavage fluid (BALF) of the patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) technology, and to explore if they can be used as markers for the diagnosis of lung cancer. SELDI-TOF-MS technology and protein chips weak cation exchange (WCX-2 chip) were used to detect the protein mass spectrum in serum and BALF of 35 patients with lung cancer and 18 cases of benign pulmonary diseases. The different protein markers were analyzed by Biomarker Pattern Software and the initial diagnosis models were set up. The diagnosis models were verified further by blind screen to confirm the efficacy of diagnosis. Five protein peaks in the sera of the patients with lung cancer were significantly higher (P < 0.05). The protein peak with a mass/charge ratio (M/Z) of 5639 was selected to establish the classification tree model. The sensitivity of diagnosis was 80% (28/35) and the specificity was 78% (14/18). The results verified by blind screen showed a sensitivity of 85% (17/20), a specificity of 90% (9/10), a crude accuracy (CA) of 87% (26/30) and Youden's index (γ) of 0.7. Eight protein peaks in the BALF of the patients with lung cancer were significantly higher (P < 0.05). The different protein peaks with M/Z of 7976 and 11 809 respectively were selected to establish the classification tree model. The sensitivity of diagnosis was 86% (30/35) and the specificity was 72% (13/18). The results verified by blind screen showed a sensitivity of 90% (18/20), a specificity of 90% (9/10), a CA of 90% (27/30) and γ of 0.8. There was a complementary role in combination of differential proteins in serum and BALF and the sensitivity, specificity and accuracy of diagnosis for lung cancer were 100% by parallel test. The SELDI-TOF-MS technology can screen out the differential protein markers in serum and BALF of the patients with lung cancer, which show high sensitivity and specificity as tumor markers. The differential proteins in the BALF may be more promising for clinical application.

Defining Aggressive Prostate Cancer Using a 12-Gene Model1

PubMed Central

Riva, Alberto; Kim, Robert; Varambally, Sooryanarayana; He, Le; Kutok, Jeff; Aster, Jonathan C; Tang, Jeffery; Kuefer, Rainer; Hofer, Matthias D; Febbo, Phillip G; Chinnaiyan, Arul M; Rubin, Mark A

2006-01-01

Abstract The critical clinical question in prostate cancer research is: How do we develop means of distinguishing aggressive disease from indolent disease? Using a combination of proteomic and expression array data, we identified a set of 36 genes with concordant dysregulation of protein products that could be evaluated in situ by quantitative immunohistochemistry. Another five prostate cancer biomarkers were included using linear discriminant analysis, we determined that the optimal model used to predict prostate cancer progression consisted of 12 proteins. Using a separate patient population, transcriptional levels of the 12 genes encoding for these proteins predicted prostate-specific antigen failure in 79 men following surgery for clinically localized prostate cancer (P = .0015). This study demonstrates that cross-platform models can lead to predictive models with the possible advantage of being more robust through this selection process. PMID:16533427

Contact Prediction for Beta and Alpha-Beta Proteins Using Integer Linear Optimization and its Impact on the First Principles 3D Structure Prediction Method ASTRO-FOLD

PubMed Central

Rajgaria, R.; Wei, Y.; Floudas, C. A.

2010-01-01

An integer linear optimization model is presented to predict residue contacts in β, α + β, and α/β proteins. The total energy of a protein is expressed as sum of a Cα – Cα distance dependent contact energy contribution and a hydrophobic contribution. The model selects contacts that assign lowest energy to the protein structure while satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the β-sheet alignments. These β-sheet alignments are used as constraints for contacts between residues of β-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of β, α + β, α/β proteins and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 Å and 15.88 Å, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins. PMID:20225257

Structure and Sequence Search on Aptamer-Protein Docking

NASA Astrophysics Data System (ADS)

Xiao, Jiajie; Bonin, Keith; Guthold, Martin; Salsbury, Freddie

2015-03-01

Interactions between proteins and deoxyribonucleic acid (DNA) play a significant role in the living systems, especially through gene regulation. However, short nucleic acids sequences (aptamers) with specific binding affinity to specific proteins exhibit clinical potential as therapeutics. Our capillary and gel electrophoresis selection experiments show that specific sequences of aptamers can be selected that bind specific proteins. Computationally, given the experimentally-determined structure and sequence of a thrombin-binding aptamer, we can successfully dock the aptamer onto thrombin in agreement with experimental structures of the complex. In order to further study the conformational flexibility of this thrombin-binding aptamer and to potentially develop a predictive computational model of aptamer-binding, we use GPU-enabled molecular dynamics simulations to both examine the conformational flexibility of the aptamer in the absence of binding to thrombin, and to determine our ability to fold an aptamer. This study should help further de-novo predictions of aptamer sequences by enabling the study of structural and sequence-dependent effects on aptamer-protein docking specificity.

Kinetic Effects on Self-Assembly and Function of Protein-Polymer Bioconjugates in Thin Films Prepared by Flow Coating.

PubMed

Chang, Dongsook; Huang, Aaron; Olsen, Bradley D

2017-01-01

The self-assembly of nanostructured globular protein arrays in thin films is demonstrated using protein-polymer block copolymers based on a model protein mCherry and the polymer poly(oligoethylene glycol acrylate) (POEGA). Conjugates are flow coated into thin films on a poly(ethylene oxide) grafted Si surface, forming self-assembled cylindrical nanostructures with POEGA domains selectively segregating to the air-film interface. Long-range order and preferential arrangement of parallel cylinders templated by selective surfaces are demonstrated by controlling relative humidity. Long-range order increases with coating speed when the film thicknesses are kept constant, due to reduced nucleation per unit area of drying film. Fluorescence emission spectra of mCherry in films prepared at <25% relative humidity shows a small shift suggesting that proteins are more perturbed at low humidity than high humidity or the solution state. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Probabilistic cross-link analysis and experiment planning for high-throughput elucidation of protein structure.

PubMed

Ye, Xiaoduan; O'Neil, Patrick K; Foster, Adrienne N; Gajda, Michal J; Kosinski, Jan; Kurowski, Michal A; Bujnicki, Janusz M; Friedman, Alan M; Bailey-Kellogg, Chris

2004-12-01

Emerging high-throughput techniques for the characterization of protein and protein-complex structures yield noisy data with sparse information content, placing a significant burden on computation to properly interpret the experimental data. One such technique uses cross-linking (chemical or by cysteine oxidation) to confirm or select among proposed structural models (e.g., from fold recognition, ab initio prediction, or docking) by testing the consistency between cross-linking data and model geometry. This paper develops a probabilistic framework for analyzing the information content in cross-linking experiments, accounting for anticipated experimental error. This framework supports a mechanism for planning experiments to optimize the information gained. We evaluate potential experiment plans using explicit trade-offs among key properties of practical importance: discriminability, coverage, balance, ambiguity, and cost. We devise a greedy algorithm that considers those properties and, from a large number of combinatorial possibilities, rapidly selects sets of experiments expected to discriminate pairs of models efficiently. In an application to residue-specific chemical cross-linking, we demonstrate the ability of our approach to plan experiments effectively involving combinations of cross-linkers and introduced mutations. We also describe an experiment plan for the bacteriophage lambda Tfa chaperone protein in which we plan dicysteine mutants for discriminating threading models by disulfide formation. Preliminary results from a subset of the planned experiments are consistent and demonstrate the practicality of planning. Our methods provide the experimenter with a valuable tool (available from the authors) for understanding and optimizing cross-linking experiments.

Yeast One-Hybrid Gγ Recruitment System for Identification of Protein Lipidation Motifs

PubMed Central

Fukuda, Nobuo; Doi, Motomichi; Honda, Shinya

2013-01-01

Fatty acids and isoprenoids can be covalently attached to a variety of proteins. These lipid modifications regulate protein structure, localization and function. Here, we describe a yeast one-hybrid approach based on the Gγ recruitment system that is useful for identifying sequence motifs those influence lipid modification to recruit proteins to the plasma membrane. Our approach facilitates the isolation of yeast cells expressing lipid-modified proteins via a simple and easy growth selection assay utilizing G-protein signaling that induces diploid formation. In the current study, we selected the N-terminal sequence of Gα subunits as a model case to investigate dual lipid modification, i.e., myristoylation and palmitoylation, a modification that is widely conserved from yeast to higher eukaryotes. Our results suggest that both lipid modifications are required for restoration of G-protein signaling. Although we could not differentiate between myristoylation and palmitoylation, N-terminal position 7 and 8 play some critical role. Moreover, we tested the preference for specific amino-acid residues at position 7 and 8 using library-based screening. This new approach will be useful to explore protein-lipid associations and to determine the corresponding sequence motifs. PMID:23922919

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.

VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy

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

Brothers, Michael C; Nesbitt, Anna E; Hallock, Michael J

2011-01-01

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., (13)C-(13)C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library ofmore » 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (-0.75) commensurate to the control (-0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.« less

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin

PubMed Central

Fuchs, Julian E.; Huber, Roland G.; Waldner, Birgit J.; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R.

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design. PMID:26496636

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.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

PubMed

Brügemann, K; Gernand, E; von Borstel, U U; König, S

2011-08-01

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Structure-Guided Screening for Functionally Selective D2 Dopamine Receptor Ligands from a Virtual Chemical Library.

PubMed

Männel, Barbara; Jaiteh, Mariama; Zeifman, Alexey; Randakova, Alena; Möller, Dorothee; Hübner, Harald; Gmeiner, Peter; Carlsson, Jens

2017-10-20

Functionally selective ligands stabilize conformations of G protein-coupled receptors (GPCRs) that induce a preference for signaling via a subset of the intracellular pathways activated by the endogenous agonists. The possibility to fine-tune the functional activity of a receptor provides opportunities to develop drugs that selectively signal via pathways associated with a therapeutic effect and avoid those causing side effects. Animal studies have indicated that ligands displaying functional selectivity at the D 2 dopamine receptor (D 2 R) could be safer and more efficacious drugs against neuropsychiatric diseases. In this work, computational design of functionally selective D 2 R ligands was explored using structure-based virtual screening. Molecular docking of known functionally selective ligands to a D 2 R homology model indicated that such compounds were anchored by interactions with the orthosteric site and extended into a common secondary pocket. A tailored virtual library with close to 13 000 compounds bearing 2,3-dichlorophenylpiperazine, a privileged orthosteric scaffold, connected to diverse chemical moieties via a linker was docked to the D 2 R model. Eighteen top-ranked compounds that occupied both the orthosteric and allosteric site were synthesized, leading to the discovery of 16 partial agonists. A majority of the ligands had comparable maximum effects in the G protein and β-arrestin recruitment assays, but a subset displayed preference for a single pathway. In particular, compound 4 stimulated β-arrestin recruitment (EC 50 = 320 nM, E max = 16%) but had no detectable G protein signaling. The use of structure-based screening and virtual libraries to discover GPCR ligands with tailored functional properties will be discussed.

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.

Improving the baking quality of bread wheat by genomic selection in early generations.

PubMed

Michel, Sebastian; Kummer, Christian; Gallee, Martin; Hellinger, Jakob; Ametz, Christian; Akgöl, Batuhan; Epure, Doru; Güngör, Huseyin; Löschenberger, Franziska; Buerstmayr, Hermann

2018-02-01

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

Mitogenome Sequencing in the Genus Camelus Reveals Evidence for Purifying Selection and Long-term Divergence between Wild and Domestic Bactrian Camels.

PubMed

Mohandesan, Elmira; Fitak, Robert R; Corander, Jukka; Yadamsuren, Adiya; Chuluunbat, Battsetseg; Abdelhadi, Omer; Raziq, Abdul; Nagy, Peter; Stalder, Gabrielle; Walzer, Chris; Faye, Bernard; Burger, Pamela A

2017-08-30

The genus Camelus is an interesting model to study adaptive evolution in the mitochondrial genome, as the three extant Old World camel species inhabit hot and low-altitude as well as cold and high-altitude deserts. We sequenced 24 camel mitogenomes and combined them with three previously published sequences to study the role of natural selection under different environmental pressure, and to advance our understanding of the evolutionary history of the genus Camelus. We confirmed the heterogeneity of divergence across different components of the electron transport system. Lineage-specific analysis of mitochondrial protein evolution revealed a significant effect of purifying selection in the concatenated protein-coding genes in domestic Bactrian camels. The estimated dN/dS < 1 in the concatenated protein-coding genes suggested purifying selection as driving force for shaping mitogenome diversity in camels. Additional analyses of the functional divergence in amino acid changes between species-specific lineages indicated fixed substitutions in various genes, with radical effects on the physicochemical properties of the protein products. The evolutionary time estimates revealed a divergence between domestic and wild Bactrian camels around 1.1 [0.58-1.8] million years ago (mya). This has major implications for the conservation and management of the critically endangered wild species, Camelus ferus.

Biophysics of protein evolution and evolutionary protein biophysics

PubMed Central

Sikosek, Tobias; Chan, Hue Sun

2014-01-01

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution. PMID:25165599

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

The Influence of HIV on the Evolution of Mycobacterium tuberculosis

PubMed Central

Brites, Daniela; Stucki, David; Evans, Joanna C.; Seldon, Ronnett; Heekes, Alexa; Mulder, Nicola; Nicol, Mark; Oni, Tolu; Mizrahi, Valerie; Warner, Digby F.; Parkhill, Julian; Gagneux, Sebastien; Martin, Darren P.; Wilkinson, Robert J.

2017-01-01

Abstract HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates. PMID:28369607

A comparative hidden Markov model analysis pipeline identifies proteins characteristic of cereal-infecting fungi

PubMed Central

2013-01-01

Background Fungal pathogens cause devastating losses in economically important cereal crops by utilising pathogen proteins to infect host plants. Secreted pathogen proteins are referred to as effectors and have thus far been identified by selecting small, cysteine-rich peptides from the secretome despite increasing evidence that not all effectors share these attributes. Results We take advantage of the availability of sequenced fungal genomes and present an unbiased method for finding putative pathogen proteins and secreted effectors in a query genome via comparative hidden Markov model analyses followed by unsupervised protein clustering. Our method returns experimentally validated fungal effectors in Stagonospora nodorum and Fusarium oxysporum as well as the N-terminal Y/F/WxC-motif from the barley powdery mildew pathogen. Application to the cereal pathogen Fusarium graminearum reveals a secreted phosphorylcholine phosphatase that is characteristic of hemibiotrophic and necrotrophic cereal pathogens and shares an ancient selection process with bacterial plant pathogens. Three F. graminearum protein clusters are found with an enriched secretion signal. One of these putative effector clusters contains proteins that share a [SG]-P-C-[KR]-P sequence motif in the N-terminal and show features not commonly associated with fungal effectors. This motif is conserved in secreted pathogenic Fusarium proteins and a prime candidate for functional testing. Conclusions Our pipeline has successfully uncovered conservation patterns, putative effectors and motifs of fungal pathogens that would have been overlooked by existing approaches that identify effectors as small, secreted, cysteine-rich peptides. It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms. PMID:24252298

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

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing*

PubMed Central

Manes, Nathan P.; Angermann, Bastian R.; Koppenol-Raab, Marijke; An, Eunkyung; Sjoelund, Virginie H.; Sun, Jing; Ishii, Masaru; Germain, Ronald N.; Meier-Schellersheim, Martin; Nita-Lazar, Aleksandra

2015-01-01

Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)1 regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight. PMID:26199343

Integrative functional analyses using rainbow trout selected for tolerance to plant diets reveal nutrigenomic signatures for soy utilization without the concurrence of enteritis

PubMed Central

Brezas, Andreas; Snekvik, Kevin R.; Hardy, Ronald W.; Overturf, Ken

2017-01-01

Finding suitable alternative protein sources for diets of carnivorous fish species remains a major concern for sustainable aquaculture. Through genetic selection, we created a strain of rainbow trout that outperforms parental lines in utilizing an all-plant protein diet and does not develop enteritis in the distal intestine, as is typical with salmonids on long-term plant protein-based feeds. By incorporating this strain into functional analyses, we set out to determine which genes are critical to plant protein utilization in the absence of gut inflammation. After a 12-week feeding trial with our selected strain and a control trout strain fed either a fishmeal-based diet or an all-plant protein diet, high-throughput RNA sequencing was completed on both liver and muscle tissues. Differential gene expression analyses, weighted correlation network analyses and further functional characterization were performed. A strain-by-diet design revealed differential expression ranging from a few dozen to over one thousand genes among the various comparisons and tissues. Major gene ontology groups identified between comparisons included those encompassing central, intermediary and foreign molecule metabolism, associated biosynthetic pathways as well as immunity. A systems approach indicated that genes involved in purine metabolism were highly perturbed. Systems analysis among the tissues tested further suggests the interplay between selection for growth, dietary utilization and protein tolerance may also have implications for nonspecific immunity. By combining data from differential gene expression and co-expression networks using selected trout, along with ontology and pathway analyses, a set of 63 candidate genes for plant diet tolerance was found. Risk loci in human inflammatory bowel diseases were also found in our datasets, indicating rainbow trout selected for plant-diet tolerance may have added utility as a potential biomedical model. PMID:28723948

Calcium-dependent interaction of monomeric S100P protein with serum albumin.

PubMed

Kazakov, Alexei S; Shevelyova, Marina P; Ismailov, Ramis G; Permyakova, Maria E; Litus, Ekaterina A; Permyakov, Eugene A; Permyakov, Sergei E

2018-03-01

S100 proteins are multifunctional (intra/extra)cellular mostly dimeric calcium-binding proteins engaged into numerous diseases. We have found that monomeric recombinant human S100P protein interacts with intact human serum albumin (HSA) in excess of calcium ions with equilibrium dissociation constant of 25-50nM, as evidenced by surface plasmon resonance spectroscopy and fluorescent titration by HSA of S100P labelled by fluorescein isothiocyanate. Calcium removal or S100P dimerization abolish the S100P-HSA interaction. The interaction is selective, since S100P does not bind bovine serum albumin and monomeric human S100B lacks interaction with HSA. In vitro glycation of HSA disables its binding to S100P. The revealed selective and highly specific conformation-dependent interaction between S100P and HSA shows that functional properties of monomeric and dimeric forms of S100 proteins are different, and raises concerns on validity of cell-based assays and animal models used for studies of (patho)physiological roles of extracellular S100 proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts

PubMed Central

Sikosek, Tobias; Bornberg-Bauer, Erich; Chan, Hue Sun

2012-01-01

Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed. PMID:23028272

Targeted Quantification of Isoforms of a Thylakoid-Bound Protein: MRM Method Development.

PubMed

Bru-Martínez, Roque; Martínez-Márquez, Ascensión; Morante-Carriel, Jaime; Sellés-Marchart, Susana; Martínez-Esteso, María José; Pineda-Lucas, José Luis; Luque, Ignacio

2018-01-01

Targeted mass spectrometric methods such as selected/multiple reaction monitoring (SRM/MRM) have found intense application in protein detection and quantification which competes with classical immunoaffinity techniques. It provides a universal procedure to develop a fast, highly specific, sensitive, accurate, and cheap methodology for targeted detection and quantification of proteins based on the direct analysis of their surrogate peptides typically generated by tryptic digestion. This methodology can be advantageously applied in the field of plant proteomics and particularly for non-model species since immunoreagents are scarcely available. Here, we describe the issues to take into consideration in order to develop a MRM method to detect and quantify isoforms of the thylakoid-bound protein polyphenol oxidase from the non-model and database underrepresented species Eriobotrya japonica Lindl.

Nuclear magnetic resonance-based model of a TF1/HmU-DNA complex.

PubMed

Silva, M V; Pasternack, L B; Kearns, D R

1997-12-15

Transcription factor 1 (TF1), a type II DNA-binding protein encoded by the Bacillus subtilis bacteriophage SPO1, has the capacity for sequence-selective DNA binding and a preference for 5-hydroxymethyl-2'-deoxyuridine (HmU)-containing DNA. In NMR studies of the TF1/HmU-DNA complex, intermolecular NOEs indicate that the flexible beta-ribbon and C-terminal alpha-helix are involved in the DNA-binding site of TF1, placing it in the beta-sheet category of DNA-binding proteins proposed to bind by wrapping two beta-ribbon "arms" around the DNA. Intermolecular and intramolecular NOEs were used to generate an energy-minimized model of the protein-DNA complex in which both DNA bending and protein structure changes are evident.

Amino acid Alphabet Size in Protein Evolution Experiments: Better to Search a Small library Thoroughly or a Large Library Sparsely?

PubMed Central

Muñoz, Enrique

2015-01-01

We compare the results obtained from searching a smaller library thoroughly versus searching a more diverse, larger library sparsely. We study protein evolution with reduced amino acid alphabets, by simulating directed evolution experiments at three different alphabet sizes: 20, 5 and 2. We employ a physical model for evolution, the generalized NK model, that has proved successful in modeling protein evolution, antibody evolution, and T cell selection. We find that antibodies with higher affinity are found by searching a library with a larger alphabet sparsely than by searching a smaller library thoroughly, even with well-designed reduced libraries. We find ranked amino acid usage frequencies in agreement with observations of the CDR-H3 variable region of human antibodies. PMID:18375453

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.

Enhanced plastic deformations of nanofibrillated cellulose film by adsorbed moisture and protein-mediated interactions.

PubMed

Malho, Jani-Markus; Ouellet-Plamondon, Claudiane; Rüggeberg, Markus; Laaksonen, Päivi; Ikkala, Olli; Burgert, Ingo; Linder, Markus B

2015-01-12

Biological composites are typically based on an adhesive matrix that interlocks rigid reinforcing elements in fiber composite or brick-and-mortar assemblies. In nature, the adhesive matrix is often made up of proteins, which are also interesting model systems, as they are unique among polymers in that we know how to engineer their structures with atomic detail and to select protein elements for specific interactions with other components. Here we studied how fusion proteins that consist of cellulose binding proteins linked to proteins that show a natural tendency to form multimer complexes act as an adhesive matrix in combination with nanofibrillated cellulose. We found that the fusion proteins are retained with the cellulose and that the proteins mainly affect the plastic yield behavior of the cellulose material as a function of water content. Interestingly, the proteins increased the moisture absorption of the composite, but the well-known plastifying effect of water was clearly decreased. The work helps to understand the functional basis of nanocellulose composites as materials and aims toward building model systems for molecular biomimetic materials.

Heat capacity changes in carbohydrates and protein-carbohydrate complexes.

PubMed

Chavelas, Eneas A; García-Hernández, Enrique

2009-05-13

Carbohydrates are crucial for living cells, playing myriads of functional roles that range from being structural or energy-storage devices to molecular labels that, through non-covalent interaction with proteins, impart exquisite selectivity in processes such as molecular trafficking and cellular recognition. The molecular bases that govern the recognition between carbohydrates and proteins have not been fully understood yet. In the present study, we have obtained a surface-area-based model for the formation heat capacity of protein-carbohydrate complexes, which includes separate terms for the contributions of the two molecular types. The carbohydrate model, which was calibrated using carbohydrate dissolution data, indicates that the heat capacity contribution of a given group surface depends on its position in the saccharide molecule, a picture that is consistent with previous experimental and theoretical studies showing that the high abundance of hydroxy groups in carbohydrates yields particular solvation properties. This model was used to estimate the carbohydrate's contribution in the formation of a protein-carbohydrate complex, which in turn was used to obtain the heat capacity change associated with the protein's binding site. The model is able to account for protein-carbohydrate complexes that cannot be explained using a previous model that only considered the overall contribution of polar and apolar groups, while allowing a more detailed dissection of the elementary contributions that give rise to the formation heat capacity effects of these adducts.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Transition-metal chromophore as a new, sensitive spectroscopic tag for proteins. Selective covalent labeling of histidine residues in cytochromes c with chloro(2,2':6',2''-terpyridine)platinum(II) chloride

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

Ratilla, E.M.A.; Brothers, H.M. II; Kostic, N.M.

1987-07-22

Reactivity and selectivity of Pt(trpy)Cl/sup +/ toward proteins are studied with cytochromes c from horse and tuna as examples. The new transition-metal reagent is specific for histidine residues at pH 5. The reaction, facile one-step displacement of the Cl/sup -/ ligand by imidazole, produces good yield. The binding sites, His 26 and His 33 in the horse protein and His 26 in the tuna protein, are identified by UV-vis spectrophotometry and by peptide-mapping experiments. Model complexes with imidazole, histidine, histidine derivatives, and histidine-containing peptides are prepared and characterized. The covalently attached Pt(trpy)/sup 2 +/ labels allow easy separation of themore » protein derivatives by cation-exchange chromatography. The labels do not perturb the conformation and reduction potential of cytochrome c, as shown by UV-vis spectrophotometry, cyclic voltammetry, differential-pulse voltammetry, EPR spectroscopy, and /sup 1/H NMR spectroscopy. The selectivity of Pt(trpy)Cl/sup +/ is entirely opposite from that of PtCl/sub 4//sup 2 -/ although both of them are platinum(II)-chloro complexes. Owing to an interplay between the steric and electronic effects of the terpyridyl ligand, the new reagent is unreactive toward methionine (a thio ether) and cystine (a disulfide), which are otherwise highly nucleophilic ligands, but very reactive toward imidazole, which is otherwise a relatively weak ligand. Unusual and useful selectivity of preformed transition-metal complexes toward proteins evidently can be achieved by a judicious choice of ancillary ligands.« less

Phenotype selection reveals coevolution of muscle glycogen and protein and PTEN as a gate keeper for the accretion of muscle mass in adult female mice.

PubMed

Sawitzky, Mandy; Zeissler, Anja; Langhammer, Martina; Bielohuby, Maximilian; Stock, Peggy; Hammon, Harald M; Görs, Solvig; Metges, Cornelia C; Stoehr, Barbara J M; Bidlingmaier, Martin; Fromm-Dornieden, Carolin; Baumgartner, Bernhard G; Christ, Bruno; Brenig, Bertram; Binder, Gerhard; Metzger, Friedrich; Renne, Ulla; Hoeflich, Andreas

2012-01-01

We have investigated molecular mechanisms for muscle mass accretion in a non-inbred mouse model (DU6P mice) characterized by extreme muscle mass. This extreme muscle mass was developed during 138 generations of phenotype selection for high protein content. Due to the repeated trait selection a complex setting of different mechanisms was expected to be enriched during the selection experiment. In muscle from 29-week female DU6P mice we have identified robust increases of protein kinase B activation (AKT, Ser-473, up to 2-fold) if compared to 11- and 54-week DU6P mice or controls. While a number of accepted effectors of AKT activation, including IGF-I, IGF-II, insulin/IGF-receptor, myostatin or integrin-linked kinase (ILK), were not correlated with this increase, phosphatase and tensin homologue deleted on chromosome 10 (PTEN) was down-regulated in 29-week female DU6P mice. In addition, higher levels of PTEN phosphorylation were found identifying a second mechanism of PTEN inhibition. Inhibition of PTEN and activation of AKT correlated with specific activation of p70S6 kinase and ribosomal protein S6, reduced phosphorylation of eukaryotic initiation factor 2α (eIF2α) and higher rates of protein synthesis in 29-week female DU6P mice. On the other hand, AKT activation also translated into specific inactivation of glycogen synthase kinase 3ß (GSK3ß) and an increase of muscular glycogen. In muscles from 29-week female DU6P mice a significant increase of protein/DNA was identified, which was not due to a reduction of protein breakdown or to specific increases of translation initiation. Instead our data support the conclusion that a higher rate of protein translation is contributing to the higher muscle mass in mid-aged female DU6P mice. Our results further reveal coevolution of high protein and high glycogen content during the selection experiment and identify PTEN as gate keeper for muscle mass in mid-aged female DU6P mice.

Phenotype Selection Reveals Coevolution of Muscle Glycogen and Protein and PTEN as a Gate Keeper for the Accretion of Muscle Mass in Adult Female Mice

PubMed Central

Sawitzky, Mandy; Zeissler, Anja; Langhammer, Martina; Bielohuby, Maximilian; Stock, Peggy; Hammon, Harald M.; Görs, Solvig; Metges, Cornelia C.; Stoehr, Barbara J. M.; Bidlingmaier, Martin; Fromm-Dornieden, Carolin; Baumgartner, Bernhard G.; Christ, Bruno; Brenig, Bertram; Binder, Gerhard; Metzger, Friedrich; Renne, Ulla; Hoeflich, Andreas

2012-01-01

We have investigated molecular mechanisms for muscle mass accretion in a non-inbred mouse model (DU6P mice) characterized by extreme muscle mass. This extreme muscle mass was developed during 138 generations of phenotype selection for high protein content. Due to the repeated trait selection a complex setting of different mechanisms was expected to be enriched during the selection experiment. In muscle from 29-week female DU6P mice we have identified robust increases of protein kinase B activation (AKT, Ser-473, up to 2-fold) if compared to 11- and 54-week DU6P mice or controls. While a number of accepted effectors of AKT activation, including IGF-I, IGF-II, insulin/IGF-receptor, myostatin or integrin-linked kinase (ILK), were not correlated with this increase, phosphatase and tensin homologue deleted on chromosome 10 (PTEN) was down-regulated in 29-week female DU6P mice. In addition, higher levels of PTEN phosphorylation were found identifying a second mechanism of PTEN inhibition. Inhibition of PTEN and activation of AKT correlated with specific activation of p70S6 kinase and ribosomal protein S6, reduced phosphorylation of eukaryotic initiation factor 2α (eIF2α) and higher rates of protein synthesis in 29-week female DU6P mice. On the other hand, AKT activation also translated into specific inactivation of glycogen synthase kinase 3ß (GSK3ß) and an increase of muscular glycogen. In muscles from 29-week female DU6P mice a significant increase of protein/DNA was identified, which was not due to a reduction of protein breakdown or to specific increases of translation initiation. Instead our data support the conclusion that a higher rate of protein translation is contributing to the higher muscle mass in mid-aged female DU6P mice. Our results further reveal coevolution of high protein and high glycogen content during the selection experiment and identify PTEN as gate keeper for muscle mass in mid-aged female DU6P mice. PMID:22768110

Proteolysis targeting peptide (PROTAP) strategy for protein ubiquitination and degradation.

PubMed

Zheng, Jing; Tan, Chunyan; Xue, Pengcheng; Cao, Jiakun; Liu, Feng; Tan, Ying; Jiang, Yuyang

2016-02-19

Ubiquitination proteasome pathway (UPP) is the most important and selective way to degrade proteins in vivo. Here, a novel proteolysis targeting peptide (PROTAP) strategy, composed of a target protein binding peptide, a linker and a ubiquitin E3 ligase recognition peptide, was designed to recruit both target protein and E3 ligase and then induce polyubiquitination and degradation of the target protein through UPP. In our study, the PROTAP strategy was proved to be a general method with high specificity using Bcl-xL protein as model target in vitro and in cells, which indicates that the strategy has great potential for in vivo application. Copyright © 2016 Elsevier Inc. All rights reserved.

The coming of age of chaperone-mediated autophagy.

PubMed

Kaushik, Susmita; Cuervo, Ana Maria

2018-06-01

Chaperone-mediated autophagy (CMA) was the first studied process that indicated that degradation of intracellular components by the lysosome can be selective - a concept that is now well accepted for other forms of autophagy. Lysosomes can degrade cellular cytosol in a nonspecific manner but can also discriminate what to target for degradation with the involvement of a degradation tag, a chaperone and a sophisticated mechanism to make the selected proteins cross the lysosomal membrane through a dedicated translocation complex. Recent studies modulating CMA activity in vivo using transgenic mouse models have demonstrated that selectivity confers on CMA the ability to participate in the regulation of multiple cellular functions. Timely degradation of specific cellular proteins by CMA modulates, for example, glucose and lipid metabolism, DNA repair, cellular reprograming and the cellular response to stress. These findings expand the physiological relevance of CMA beyond its originally identified role in protein quality control and reveal that CMA failure with age may aggravate diseases, such as ageing-associated neurodegeneration and cancer.

Customization of copolymers to optimize selectivity and yield in polymer-driven antibody purification processes.

PubMed

Capito, Florian; Skudas, Romas; Stanislawski, Bernd; Kolmar, Harald

2013-01-01

This manuscript describes customization of copolymers to be used for polymer-driven protein purification in bioprocessing. To understand how copolymer customization can be used for fine-tuning, precipitation behavior was analyzed for five target antibodies (mAbs) and BSA as model impurity protein, at ionic strength similar to undiluted cell culture fluid. In contrast to the use of standardized homopolymers, customized copolymers, composed of 2-acrylamido-2-methylpropane sulfonic acid (AMPS) and 4-(acryloylamino)benzoic acid (ABZ), exhibited antibody precipitation yields exceeding 90%. Additionally, copolymer average molecular weight (Mw ) was varied and its influence on precipitation yield and contaminant coprecipitation was investigated. Results revealed copolymer composition as the major driving force for precipitation selectivity, which was also dependent on protein hydrophobicity. By adjusting ABZ content and Mw of the precipitant for each of the mAbs, conditions were found that allowed for high precipitation yield and selectivity. These findings may open up new avenues for using polymers in antibody purification processes. © 2013 American Institute of Chemical Engineers.

Comprehensive 3D-modeling of allergenic proteins and amino acid composition of potential conformational IgE epitopes

PubMed Central

Oezguen, Numan; Zhou, Bin; Negi, Surendra S.; Ivanciuc, Ovidiu; Schein, Catherine H.; Labesse, Gilles; Braun, Werner

2008-01-01

Similarities in sequences and 3D structures of allergenic proteins provide vital clues to identify clinically relevant IgE cross-reactivities. However, experimental 3D structures are available in the Protein Data Bank for only 5% (45/829) of all allergens catalogued in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP). Here, an automated procedure was used to prepare 3D-models of all allergens where there was no experimentally determined 3D structure or high identity (95%) to another protein of known 3D structure. After a final selection by quality criteria, 433 reliable 3D models were retained and are available from our SDAP Website. The new 3D models extensively enhance our knowledge of allergen structures. As an example of their use, experimentally derived “continuous IgE epitopes” were mapped on 3 experimentally determined structures and 13 of our 3D-models of allergenic proteins. Large portions of these continuous sequences are not entirely on the surface and therefore cannot interact with IgE or other proteins. Only the surface exposed residues are constituents of “conformational IgE epitopes” which are not in all cases continuous in sequence. The surface exposed parts of the experimental determined continuous IgE epitopes showed a distinct statistical distribution as compared to their presence in typical protein-protein interfaces. The amino acids Ala, Ser, Asn, Gly and particularly Lys have a high propensity to occur in IgE binding sites. The 3D-models will facilitate further analysis of the common properties of IgE binding sites of allergenic proteins. PMID:18621419

Recognition of the murine coronavirus genomic RNA packaging signal depends on the second RNA-binding domain of the nucleocapsid protein.

PubMed

Kuo, Lili; Koetzner, Cheri A; Hurst, Kelley R; Masters, Paul S

2014-04-01

The coronavirus nucleocapsid (N) protein forms a helical ribonucleoprotein with the viral positive-strand RNA genome and binds to the principal constituent of the virion envelope, the membrane (M) protein, to facilitate assembly and budding. Besides these structural roles, N protein associates with a component of the replicase-transcriptase complex, nonstructural protein 3, at a critical early stage of infection. N protein has also been proposed to participate in the replication and selective packaging of genomic RNA and the transcription and translation of subgenomic mRNA. Coronavirus N proteins contain two structurally distinct RNA-binding domains, an unusual characteristic among RNA viruses. To probe the functions of these domains in the N protein of the model coronavirus mouse hepatitis virus (MHV), we constructed mutants in which each RNA-binding domain was replaced by its counterpart from the N protein of severe acute respiratory syndrome coronavirus (SARS-CoV). Mapping of revertants of the resulting chimeric viruses provided evidence for extensive intramolecular interactions between the two RNA-binding domains. Through analysis of viral RNA that was packaged into virions we identified the second of the two RNA-binding domains as a principal determinant of MHV packaging signal recognition. As expected, the interaction of N protein with M protein was not affected in either of the chimeric viruses. Moreover, the SARS-CoV N substitutions did not alter the fidelity of leader-body junction formation during subgenomic mRNA synthesis. These results more clearly delineate the functions of N protein and establish a basis for further exploration of the mechanism of genomic RNA packaging. This work describes the interactions of the two RNA-binding domains of the nucleocapsid protein of a model coronavirus, mouse hepatitis virus. The main finding is that the second of the two domains plays an essential role in recognizing the RNA structure that allows the selective packaging of genomic RNA into assembled virions.

Predicting the Accuracy of Protein–Ligand Docking on Homology Models

PubMed Central

BORDOGNA, ANNALISA; PANDINI, ALESSANDRO; BONATI, LAURA

2011-01-01

Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics. PMID:20607693

Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands.

PubMed

Rodríguez, David; Brea, José; Loza, María Isabel; Carlsson, Jens

2014-08-05

The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Autophagy selectivity through receptor clustering

NASA Astrophysics Data System (ADS)

Rutenberg, Andrew; Brown, Aidan

Substrate selectivity in autophagy requires an all-or-none cellular response. We focus on peroxisomes, for which autophagy receptor proteins NBR1 and p62 are well characterized. Using computational models, we explore the hypothesis that physical clustering of autophagy receptor proteins on the peroxisome surface provides an appropriate all-or-none response. We find that larger peroxisomes nucleate NBR1 clusters first, and lose them due to competitive coarsening last, resulting in significant size-selectivity. We then consider a secondary hypothesis that p62 inhibits NBR1 cluster formation. We find that p62 inhibition enhances size-selectivity enough that, even if there is no change of the pexophagy rate, the volume of remaining peroxisomes can significantly decrease. We find that enhanced ubiquitin levels suppress size-selectivity, and that this effect is more pronounced for individual peroxisomes. Sufficient ubiquitin allows receptor clusters to form on even the smallest peroxisomes. We conclude that NBR1 cluster formation provides a viable physical mechanism for all-or-none substrate selectivity in pexophagy. We predict that cluster formation is associated with significant size-selectivity. Now at Simon Fraser University.

A novel multimodal chromatography based single step purification process for efficient manufacturing of an E. coli based biotherapeutic protein product.

PubMed

Bhambure, Rahul; Gupta, Darpan; Rathore, Anurag S

2013-11-01

Methionine oxidized, reduced and fMet forms of a native recombinant protein product are often the critical product variants which are associated with proteins expressed as bacterial inclusion bodies in E. coli. Such product variants differ from native protein in their structural and functional aspects, and may lead to loss of biological activity and immunogenic response in patients. This investigation focuses on evaluation of multimodal chromatography for selective removal of these product variants using recombinant human granulocyte colony stimulating factor (GCSF) as the model protein. Unique selectivity in separation of closely related product variants was obtained using combined pH and salt based elution gradients in hydrophobic charge induction chromatography. Simultaneous removal of process related impurities was also achieved in flow-through leading to single step purification process for the GCSF. Results indicate that the product recovery of up to 90.0% can be obtained with purity levels of greater than 99.0%. Binding the target protein at pH

Molecular modeling of the AhR structure and interactions can shed light on ligand-dependent activation and transformation mechanisms.

PubMed

Bonati, Laura; Corrada, Dario; Tagliabue, Sara Giani; Motta, Stefano

2017-02-01

Molecular modeling has given important contributions to elucidation of the main stages in the AhR signal transduction pathway. Despite the lack of experimentally determined structures of the AhR functional domains, information derived from homologous systems has been exploited for modeling their structure and interactions. Homology models of the AhR PASB domain have provided information on the binding cavity and contributed to elucidate species-specific differences in ligand binding. Molecular Docking simulations of the ligand binding process have given insights into differences in binding of diverse agonists, antagonists, and selective AhR modulators, and their application to virtual screening of large databases of compounds have allowed identification of novel AhR ligands. Recently available structural information on protein-protein and protein-DNA complexes of other bHLH-PAS systems has opened the way for modeling the AhR:ARNT dimer structure and investigating the mechanisms of AhR transformation and DNA binding. Future research directions should include simulation of the protein dynamics to obtain a more reliable description of intermolecular interactions involved in signal transmission.

Transduced Tat-DJ-1 Protein Protects against Oxidative Stress-Induced SH-SY5Y Cell Death and Parkinson Disease in a Mouse Model

PubMed Central

Jeong, Hoon Jae; Kim, Dae Won; Woo, Su Jung; Kim, Hye Ri; Kim, So Mi; Jo, Hyo Sang; Park, Meeyoung; Kim, Duk-Soo; Kwon, Oh-Shin; Hwang, In Koo; Han, Kyu Hyung; Park, Jinseu; Eum, Won Sik; Choi, Soo Young

2012-01-01

Parkinson’s disease (PD) is a well known neurodegenerative disorder characterized by selective loss of dopaminergic neurons in the substantia nigra pars compact (SN). Although the exact mechanism remains unclear, oxidative stress plays a critical role in the pathogenesis of PD. DJ-1 is a multifunctional protein, a potent antioxidant and chaperone, the loss of function of which is linked to the autosomal recessive early onset of PD. Therefore, we investigated the protective effects of DJ-1 protein against SH-SY5Y cells and in a PD mouse model using a cell permeable Tat-DJ-1 protein. Tat-DJ-1 protein rapidly transduced into the cells and showed a protective effect on 6-hydroxydopamine (6-OHDA)-induced neuronal cell death by reducing the reactive oxygen species (ROS). In addition, we found that Tat-DJ-1 protein protects against dopaminergic neuronal cell death in 1-methyl-4-phenyl-1,2,3,6,-tetrahydropyridine (MPTP)-induced PD mouse models. These results suggest that Tat-DJ-1 protein provides a potential therapeutic strategy for against ROS related human diseases including PD. PMID:22526393

UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics.

PubMed

Czaplewski, Cezary; Karczynska, Agnieszka; Sieradzan, Adam K; Liwo, Adam

2018-04-30

A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarse-grained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user. The UNRES server can be freely accessed at http://unres-server.chem.ug.edu.pl.

Phage display for the discovery of hydroxyapatite-associated peptides.

PubMed

Jin, Hyo-Eon; Chung, Woo-Jae; Lee, Seung-Wuk

2013-01-01

In nature, proteins play a critical role in the biomineralization process. Understanding how different peptide or protein sequences selectively interact with the target crystal is of great importance. Identifying such protein structures is one of the critical steps in verifying the molecular mechanisms of biomineralization. One of the promising ways to obtain such information for a particular crystal surface is to screen combinatorial peptide libraries in a high-throughput manner. Among the many combinatorial library screening procedures, phage display is a powerful method to isolate such proteins and peptides. In this chapter, we will describe our established methods to perform phage display with inorganic crystal surfaces. Specifically, we will use hydroxyapatite as a model system for discovery of apatite-associated proteins in bone or tooth biomineralization studies. This model approach can be generalized to other desired crystal surfaces using the same experimental design principles with a little modification of the procedures. © 2013 Elsevier Inc. All rights reserved.

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

PubMed Central

Sinha, Siddharth; Goyal, Sukriti; Somvanshi, Pallavi; Grover, Abhinav

2017-01-01

Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r2) value of 0.6774, cross-validated correlation coefficient (q2) of 0.6157 and co-relation coefficient for external test set (pred_r2) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds. PMID:28119557

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach.

PubMed

Sinha, Siddharth; Goyal, Sukriti; Somvanshi, Pallavi; Grover, Abhinav

2016-01-01

Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient ( r 2 ) value of 0.6774, cross-validated correlation coefficient ( q 2 ) of 0.6157 and co-relation coefficient for external test set ( pred _ r 2 ) of 0.7570. A high F -test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of -10.097 and -9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties*

PubMed Central

Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

2013-01-01

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions. PMID:23422585

Structural model of the hUbA1-UbcH10 quaternary complex: in silico and experimental analysis of the protein-protein interactions between E1, E2 and ubiquitin.

PubMed

Correale, Stefania; de Paola, Ivan; Morgillo, Carmine Marco; Federico, Antonella; Zaccaro, Laura; Pallante, Pierlorenzo; Galeone, Aldo; Fusco, Alfredo; Pedone, Emilia; Luque, F Javier; Catalanotti, Bruno

2014-01-01

UbcH10 is a component of the Ubiquitin Conjugation Enzymes (Ubc; E2) involved in the ubiquitination cascade controlling the cell cycle progression, whereby ubiquitin, activated by E1, is transferred through E2 to the target protein with the involvement of E3 enzymes. In this work we propose the first three dimensional model of the tetrameric complex formed by the human UbA1 (E1), two ubiquitin molecules and UbcH10 (E2), leading to the transthiolation reaction. The 3D model was built up by using an experimentally guided incremental docking strategy that combined homology modeling, protein-protein docking and refinement by means of molecular dynamics simulations. The structural features of the in silico model allowed us to identify the regions that mediate the recognition between the interacting proteins, revealing the active role of the ubiquitin crosslinked to E1 in the complex formation. Finally, the role of these regions involved in the E1-E2 binding was validated by designing short peptides that specifically interfere with the binding of UbcH10, thus supporting the reliability of the proposed model and representing valuable scaffolds for the design of peptidomimetic compounds that can bind selectively to Ubcs and inhibit the ubiquitylation process in pathological disorders.

Selective isolation of hydrophobin SC3 by solid-phase extraction with polytetrafluoroethylene microparticles and subsequent mass spectrometric analysis.

PubMed

Kupčík, Rudolf; Zelená, Miroslava; Řehulka, Pavel; Bílková, Zuzana; Česlová, Lenka

2016-02-01

Hydrophobins are small proteins that play a role in a number of processes during the filamentous fungi growth and development. These proteins are characterized by the self-assembly of their molecules into an amphipathic membrane at hydrophilic-hydrophobic interfaces. Isolation and purification of hydrophobins generally present a challenge in their analysis. Hydrophobin SC3 from Schizophyllum commune was selected as a representative of class I hydrophobins in this work. A novel procedure for selective and effective isolation of hydrophobin SC3 based on solid-phase extraction with polytetrafluoroethylene microparticles loaded in a small self-made microcolumn is reported. The tailored binding of hydrophobins to polytetrafluoroethylene followed by harsh elution conditions resulted in a highly specific isolation of hydrophobin SC3 from the model mixture of ten proteins. The presented isolation protocol can have a positive impact on the analysis and utilization of these proteins including all class I hydrophobins. Hydrophobin SC3 was further subjected to reduction of its highly stable disulfide bonds and to chymotryptic digestion followed by mass spectrometric analysis. The isolation and digestion protocols presented in this work make the analysis of these highly hydrophobic and compact proteins possible. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Autophagy-linked FYVE protein (Alfy) promotes autophagic removal of misfolded proteins involved in amyotrophic lateral sclerosis (ALS).

PubMed

Han, Huihui; Wei, Wanyi; Duan, Weisong; Guo, Yansu; Li, Yi; Wang, Jie; Bi, Yue; Li, Chunyan

2015-03-01

Autophagy-linked FYVE (Alfy) is a protein implicated in the selective degradation of aggregated proteins. In our present study, we found that Alfy was recruited into the aggregated G93A-SOD1 in transgenic mice with amyotrophic lateral sclerosis (ALS). We demonstrated that Alfy overexpression could decrease the expression of mutant proteins via the autophagosome-lysosome pathway, and thereby, the toxicity of mutant proteins was reduced. The clearance of the mutant proteins in NSC34 cells was significantly inhibited in an Alfy knockdown cellular model. We therefore deduced that Alfy translocalization likely is involved in the pathogenesis of ALS. Alfy may be developed into a useful target for ALS therapy.

Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).

PubMed

Kamath, Padmaja; Fernandez, Alberto; Giralt, Francesc; Rallo, Robert

2015-01-01

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R(2)LOO=0.76 and R(2)LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

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.

Structure-function insights of membrane and soluble proteins revealed by electron crystallography.

PubMed

Dreaden, Tina M; Devarajan, Bharanidharan; Barry, Bridgette A; Schmidt-Krey, Ingeborg

2013-01-01

Electron crystallography is emerging as an important method in solving protein structures. While it has found extensive applications in the understanding of membrane protein structure and function at a wide range of resolutions, from revealing oligomeric arrangements to atomic models, electron crystallography has also provided invaluable information on the soluble α/β-tubulin which could not be obtained by any other method to date. Examples of critical insights from selected structures of membrane proteins as well as α/β-tubulin are described here, demonstrating the vast potential of electron crystallography that is first beginning to unfold.

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.

Targeted delivery of siRNA into breast cancer cells via phage fusion proteins.

PubMed

Bedi, Deepa; Gillespie, James W; Petrenko, Vasily A; Ebner, Andreas; Leitner, Michael; Hinterdorfer, Peter; Petrenko, Valery A

2013-02-04

Nucleic acids, including antisense oligonucleotides, small interfering RNA (siRNA), aptamers, and rybozymes, emerged as versatile therapeutics due to their ability to interfere in a well-planned manner with the flow of genetic information from DNA to protein. However, a systemic use of NAs is hindered by their instability in physiological liquids and inability of intracellular accumulation in the site of action. We first evaluated the potential of cancer specific phage fusion proteins as targeting ligands that provide encapsulation, protection, and navigation of siRNA to the target cell. The tumor-specific proteins were isolated from phages that were affinity selected from a landscape phage library against target breast cancer cells. It was found that fusion phage coat protein fpVIII displaying cancer-targeting peptides can effectively encapsulate siRNAs and deliver them into the cells leading to specific silencing of the model gene GAPDH. Complexes of siRNA and phage protein form nanoparticles (nanophages), which were characterized by atomic force microscopy and ELISA, and their stability was demonstrated by resistance of encapsulated siRNA to degradation by serum nucleases. The phage protein/siRNA complexes can make a new type of highly selective, stable, active, and physiologically acceptable cancer nanomedicine.

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

ERIC Educational Resources Information Center

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

2007-01-01

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…

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.

Physiologically based pharmacokinetic and pharmacodynamic modeling of an antagonist (SM-406/AT-406) of multiple inhibitor of apoptosis proteins (IAPs) in a mouse xenograft model of human breast cancer.

PubMed

Zhang, Tao; Li, Yanyan; Zou, Peng; Yu, Jing-yu; McEachern, Donna; Wang, Shaomeng; Sun, Duxin

2013-09-01

The inhibitors of apoptosis proteins (IAPs) are a class of key apoptosis regulators overexpressed or dysregulated in cancer. SM-406/AT-406 is a potent and selective small molecule mimetic of Smac that antagonizes the inhibitor of apoptosis proteins (IAPs). A physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) model was developed to predict the tissue concentration-time profiles of SM-406, the related onco-protein levels in tumor, and the tumor growth inhibition in a mouse model bearing human breast cancer xenograft. In the whole body physiologically based pharmacokinetic (PBPK) model for pharmacokinetics characterization, a well stirred (perfusion rate-limited) model was used to describe SM-406 pharmacokinetics in the lung, heart, kidney, intestine, liver and spleen, and a diffusion rate-limited (permeability limited) model was used for tumor. Pharmacodynamic (PD) models were developed to correlate the SM-406 concentration in tumor to the cIAP1 degradation, pro-caspase 8 decrease, CL-PARP accumulation and tumor growth inhibition. The PBPK-PD model well described the experimental pharmacokinetic data, the pharmacodynamic biomarker responses and tumor growth. This model may be helpful to predict tumor and plasma SM-406 concentrations in the clinic. Copyright © 2013 John Wiley & Sons, Ltd.

A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.

PubMed

Habibi, Narjeskhatoon; Mohd Hashim, Siti Z; Norouzi, Alireza; Samian, Mohammed Razip

2014-05-08

Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods. This paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system. The models are investigated and compared regarding the datasets used, features, feature selection methods, machine learning techniques and accuracy of prediction. A discussion on the models is provided at the end. This study aims to investigate extensively the machine learning based methods to predict recombinant protein solubility, so as to offer a general as well as a detailed understanding for researches in the field. Some of the models present acceptable prediction performances and convenient user interfaces. These models can be considered as valuable tools to predict recombinant protein overexpression results before performing real laboratory experiments, thus saving labour, time and cost.

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.

Computational-based structural, functional and phylogenetic analysis of Enterobacter phytases.

PubMed

Pramanik, Krishnendu; Kundu, Shreyasi; Banerjee, Sandipan; Ghosh, Pallab Kumar; Maiti, Tushar Kanti

2018-06-01

Myo-inositol hexakisphosphate phosphohydrolases (i.e., phytases) are known to be a very important enzyme responsible for solubilization of insoluble phosphates. In the present study, Enterobacter phytases have characterized by different phylogenetic, structural and functional parameters using some standard bio-computational tools. Results showed that majority of the Enterobacter phytases are acidic in nature as most of the isoelectric points were under 7.0. The aliphatic indices predicted for the selected proteins were below 40 indicating their thermostable nature. The average molecular weight of the proteins was 48 kDa. The lower values of GRAVY of the said proteins implied that they have better interactions with water. Secondary structure prediction revealed that alpha-helical content was highest among the other forms such as sheets, coils, etc. Moreover, the predicted 3D structure of Enterobacter phytases divulged that the proteins consisted of four monomeric polypeptide chains i.e., it was a tetrameric protein. The predicted tertiary model of E. aerogenes (A0A0M3HCJ2) was deposited in Protein Model Database (Acc. No.: PM0080561) for further utilization after a thorough quality check from QMEAN and SAVES server. Functional analysis supported their classification as histidine acid phosphatases. Besides, multiple sequence alignment revealed that "DG-DP-LG" was the most highly conserved residues within the Enterobacter phytases. Thus, the present study will be useful in selecting suitable phytase-producing microbe exclusively for using in the animal food industry as a food additive.

Systemic GLIPR1-ΔTM protein as a novel therapeutic approach for prostate cancer.

PubMed

Karantanos, Theodoros; Tanimoto, Ryuta; Edamura, Kohei; Hirayama, Takahiro; Yang, Guang; Golstov, Alexei A; Wang, Jianxiang; Kurosaka, Shinji; Park, Sanghee; Thompson, Timothy C

2014-04-15

GLIPR1 is a p53 target gene known to be downregulated in prostate cancer, and increased endogenous GLIPR1 expression has been associated with increased production of reactive oxygen species, increased apoptosis, decreased c-Myc protein levels and increased cell cycle arrest. Recently, we found that upregulation of GLIPR1 in prostate cancer cells increases mitotic catastrophe through interaction with heat shock cognate protein 70 (Hsc70) and downregulation of Aurora kinase A and TPX2. In this study, we evaluated the mechanisms of recombinant GLIPR1 protein (glioma pathogenesis-related protein 1-transmembrane domain deleted [GLIPR1-ΔTM]) uptake by prostate cancer cells and the efficacy of systemic GLIPR1-ΔTM administration in a prostate cancer xenograft mouse model. GLIPR1-ΔTM was selectively internalized by prostate cancer cells, leading to increased apoptosis through reactive oxygen species production and to decreased c-Myc protein levels. Interestingly, GLIPR1-ΔTM was internalized through clathrin-mediated endocytosis in association with Hsc70. Systemic administration of GLIPR1-ΔTM significantly inhibited VCaP xenograft growth. GLIPR1-ΔTM showed no evidence of toxicity following elimination from mouse models 8 hr after injection. Our results demonstrate that GLIPR1-ΔTM is selectively endocytosed by prostate cancer cells, leading to increased reactive oxygen species production and apoptosis, and that systemic GLIPR1-ΔTM significantly inhibits growth of VCaP xenografts without substantial toxicity. © 2013 UICC.

Use of specific peptide biomarkers for quantitative confirmation of hidden allergenic peanut proteins Ara h 2 and Ara h 3/4 for food control by liquid chromatography-tandem mass spectrometry.

PubMed

Careri, M; Costa, A; Elviri, L; Lagos, J-B; Mangia, A; Terenghi, M; Cereti, A; Garoffo, L Perono

2007-11-01

A liquid chromatography-electrospray-tandem mass spectrometry (LC-ESI-MS-MS) method based on the detection of biomarker peptides from allergenic proteins was devised for confirming and quantifying peanut allergens in foods. Peptides obtained from tryptic digestion of Ara h 2 and Ara h 3/4 proteins were identified and characterized by LC-MS and LC-MS-MS with a quadrupole-time of flight mass analyzer. Four peptides were chosen and investigated as biomarkers taking into account their selectivity, the absence of missed cleavages, the uniform distribution in the Ara h 2 and Ara h 3/4 protein isoforms together with their spectral features under ESI-MS-MS conditions, and good repeatability of LC retention time. Because of the different expression levels, the selection of two different allergenic proteins was proved to be useful in the identification and univocal confirmation of the presence of peanuts in foodstuffs. Using rice crisp and chocolate-based snacks as model food matrix, an LC-MS-MS method with triple quadrupole mass analyzer allowed good detection limits to be obtained for Ara h 2 (5 microg protein g(-1) matrix) and Ara h 3/4 (1 microg protein g(-1) matrix). Linearity of the method was established in the 10-200 microg g(-1) range of peanut proteins in the food matrix investigated. Method selectivity was demonstrated by analyzing tree nuts (almonds, pecan nuts, hazelnuts, walnuts) and food ingredients such as milk, soy beans, chocolate, cornflakes, and rice crisp.

QconCATs: design and expression of concatenated protein standards for multiplexed protein quantification.

PubMed

Simpson, Deborah M; Beynon, Robert J

2012-09-01

Systems biology requires knowledge of the absolute amounts of proteins in order to model biological processes and simulate the effects of changes in specific model parameters. Quantification concatamers (QconCATs) are established as a method to provide multiplexed absolute peptide standards for a set of target proteins in isotope dilution standard experiments. Two or more quantotypic peptides representing each of the target proteins are concatenated into a designer gene that is metabolically labelled with stable isotopes in Escherichia coli or other cellular or cell-free systems. Co-digestion of a known amount of QconCAT with the target proteins generates a set of labelled reference peptide standards for the unlabelled analyte counterparts, and by using an appropriate mass spectrometry platform, comparison of the intensities of the peptide ratios delivers absolute quantification of the encoded peptides and in turn the target proteins for which they are surrogates. In this review, we discuss the criteria and difficulties associated with surrogate peptide selection and provide examples in the design of QconCATs for quantification of the proteins of the nuclear factor κB pathway.

Tumor-selective proteotoxicity of verteporfin inhibits colon cancer progression independently of YAP1#

PubMed Central

Zhang, Huabing; Ramakrishnan, Sadeesh K.; Triner, Daniel; Centofanti, Brook; Maitra, Dhiman; Győrffy, Balázs; Sebolt-Leopold, Judith S.; Dame, Michael K.; Varani, James; Brenner, Dean E.; Fearon, Eric R.; Omary, M. Bishr; Shah, Yatrik M.

2016-01-01

Yes-associated protein 1 (YAP1) is a transcriptional coactivator in the Hippo signaling pathway. Increased YAP1- activity promotes the growth of tumors, including that of colorectal cancer (CRC). Verteporfin, a drug that enhances phototherapy to treat neovascular macular degeneration, is an inhibitor of YAP1. Here, we found that verteporfin inhibited tumor growth independently of its effects on YAP1 or the related protein TAZ in genetic or chemical-induced mouse models of CRC, in patient-derived xenografts and in enteroid models of CRC. Instead, verteporfin exhibited in vivo selectivity for killing tumor cells in part by impairing the global clearance of high molecular weight oligomerized proteins, particularly p62 (a sequestrome involved in autophagy) and STAT3 (a transcription factor). Verteporfin inhibited cytokine-induced STAT3 activity and cell proliferation and reduced the viabilty of cultured CRC cells. Although verteporfin accumulated to a greater extent in normal cells than in tumor cells in vivo, experiments with cultured cells indicated that the normal cells efficiently cleared verteporfin-induced protein oligomers through autophagic and proteasomal pathways. Culturing CRC cells in hypoxic or nutrient-deprived conditions (modeling a typical CRC microenvironment) impaired the clearance of protein oligomers and resulted in cell death; whereas culturing cells in normoxic or glucose-replete conditions protected cell viability and proliferation in the presence of verteporfin. Furthermore, verteporfin suppressed the proliferation of other cancer cell lines even in the absence of YAP1, suggesting that verteporfin may be effective against multiple types of solid cancers. PMID:26443705

Charges drive selection of specific antibodies by phage display.

PubMed

Persson, Helena; Persson, Jonas; Danielsson, Lena; Ohlin, Mats

2010-02-28

Phage display technology has emerged as a leading approach to select proteins with improved properties for many different types of applications. The selection typically selects not only for improved binding properties but also for other factors such as efficiency of protein production and folding in Escherichia coli, the host in which the proteins and the phage are produced. Furthermore, the selection methodology is likely to influence the character of retrieved variants. We have now defined the extent whereby the charge of the displayed proteins influence the selection process, resulting in an increased average positive charge among selected proteins in comparison to the proteins that are harbored in the library before selection. Implications of and possible routes to minimize this effect are discussed. 2009 Elsevier B.V. All rights reserved.

VoroMQA: Assessment of protein structure quality using interatomic contact areas.

PubMed

Olechnovič, Kliment; Venclovas, Česlovas

2017-06-01

In the absence of experimentally determined protein structure many biological questions can be addressed using computational structural models. However, the utility of protein structural models depends on their quality. Therefore, the estimation of the quality of predicted structures is an important problem. One of the approaches to this problem is the use of knowledge-based statistical potentials. Such methods typically rely on the statistics of distances and angles of residue-residue or atom-atom interactions collected from experimentally determined structures. Here, we present VoroMQA (Voronoi tessellation-based Model Quality Assessment), a new method for the estimation of protein structure quality. Our method combines the idea of statistical potentials with the use of interatomic contact areas instead of distances. Contact areas, derived using Voronoi tessellation of protein structure, are used to describe and seamlessly integrate both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. VoroMQA produces scores at atomic, residue, and global levels, all in the fixed range from 0 to 1. The method was tested on the CASP data and compared to several other single-model quality assessment methods. VoroMQA showed strong performance in the recognition of the native structure and in the structural model selection tests, thus demonstrating the efficacy of interatomic contact areas in estimating protein structure quality. The software implementation of VoroMQA is freely available as a standalone application and as a web server at http://bioinformatics.lt/software/voromqa. Proteins 2017; 85:1131-1145. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

The effect of fenoterol stereochemistry on the β2 adrenergic receptor system: ligand directed chiral recognition

PubMed Central

Jozwiak, Krzysztof; Plazinska, Anita; Toll, Lawrence; Jimenez, Lucita; Woo, Anthony Yiu-Ho; Xiao, Rui-Ping; Wainer, Irving W.

2011-01-01

The β2 adrenergic receptor (β2-AR) is a model system for studying the ligand recognition process in G-protein coupled receptors. Fenoterol (FEN) is a β2-AR selective agonist that has two centers of chirality and exists as four stereoisomers. Radioligand binding studies determined that stereochemistry greatly influences the binding affinity. Subsequent Van’t Hoff analysis shows very different thermodynamics of binding depending on the stereoconfiguration of the molecule. The binding of (S,x’)-isomers is almost entirely enthalpy controlled whereas binding of (R,x’)-isomers is purely entropy driven. Stereochemistry of FEN molecule also affects the coupling of the receptor to different G proteins. In a rat cardiomyocyte contractility model, (R,R’)-FEN was shown to selectively activate Gs protein signaling while the (S,R’)- isomer activated both Gi and Gs protein. The overall data demonstrate that the chirality at the two chiral centers of the FEN molecule influences the magnitude of binding affinity, thermodynamics of local interactions within the binding site and the global mechanism of β2-AR activation. Differences in thermodynamic parameters and non-uniform G-protein coupling suggest a mechanism of chiral recognition in which observed enantioselectivities arise from the interaction of the (R,x’)-FEN stereoisomers with a different receptor conformation than the one with which the (S,x’)-isomer interacts. PMID:21618615

Effect of fenoterol stereochemistry on the β2 adrenergic receptor system: ligand-directed chiral recognition.

PubMed

Jozwiak, Krzysztof; Plazinska, Anita; Toll, Lawrence; Jimenez, Lucita; Woo, Anthony Yiu-Ho; Xiao, Rui-Ping; Wainer, Irving W

2011-01-01

The β(2) adrenergic receptor (β(2)-AR) is a model system for studying the ligand recognition process in G protein-coupled receptors. Fenoterol (FEN) is a β(2)-AR selective agonist that has two centers of chirality and exists as four stereoisomers. Radioligand binding studies determined that stereochemistry greatly influences the binding affinity. Subsequent Van't Hoff analysis shows very different thermodynamics of binding depending on the stereoconfiguration of the molecule. The binding of (S,x')-isomers is almost entirely enthalpy controlled whereas binding of (R,x')-isomers is purely entropy driven. Stereochemistry of FEN molecule also affects the coupling of the receptor to different G proteins. In a rat cardiomyocyte contractility model, (R,R')-FEN was shown to selectively activate G(s) protein signaling while the (S,R')-isomer activated both G(i) and G(s) protein. The overall data demonstrate that the chirality at the two chiral centers of the FEN molecule influences the magnitude of binding affinity, thermodynamics of local interactions within the binding site, and the global mechanism of β(2)-AR activation. Differences in thermodynamic parameters and nonuniform G-protein coupling suggest a mechanism of chiral recognition in which observed enantioselectivities arise from the interaction of the (R,x')-FEN stereoisomers with a different receptor conformation than the one with which the (S,x')-isomer interacts. Copyright © 2011 Wiley-Liss, Inc.

Genome-Wide Motif Statistics are Shaped by DNA Binding Proteins over Evolutionary Time Scales

NASA Astrophysics Data System (ADS)

Qian, Long; Kussell, Edo

2016-10-01

The composition of a genome with respect to all possible short DNA motifs impacts the ability of DNA binding proteins to locate and bind their target sites. Since nonfunctional DNA binding can be detrimental to cellular functions and ultimately to organismal fitness, organisms could benefit from reducing the number of nonfunctional DNA binding sites genome wide. Using in vitro measurements of binding affinities for a large collection of DNA binding proteins, in multiple species, we detect a significant global avoidance of weak binding sites in genomes. We demonstrate that the underlying evolutionary process leaves a distinct genomic hallmark in that similar words have correlated frequencies, a signal that we detect in all species across domains of life. We consider the possibility that natural selection against weak binding sites contributes to this process, and using an evolutionary model we show that the strength of selection needed to maintain global word compositions is on the order of point mutation rates. Likewise, we show that evolutionary mechanisms based on interference of protein-DNA binding with replication and mutational repair processes could yield similar results and operate with similar rates. On the basis of these modeling and bioinformatic results, we conclude that genome-wide word compositions have been molded by DNA binding proteins acting through tiny evolutionary steps over time scales spanning millions of generations.

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.

Prioritizing and modelling of putative drug target proteins of Candida albicans by systems biology approach.

PubMed

Ismail, Tariq; Fatima, Nighat; Muhammad, Syed Aun; Zaidi, Syed Saoud; Rehman, Nisar; Hussain, Izhar; Tariq, Najam Us Sahr; Amirzada, Imran; Mannan, Abdul

2018-01-01

Candida albicans (Candida albicans) is one of the major sources of nosocomial infections in humans which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to treat affectively due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring 5090 completely annotated genes of Candida albicans from available databases which were categorized into essential and non-essential genes. The result indicated that 9% of proteins were essential and could become potential candidates for intervention which might result in pathogen eradication. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome was made as a reference to minimize the side effects. It was seen that 14% of Candida albicans proteins were evolutionary related to the human proteins while 86% are non-human homologs. In the next step of compatible drug target selections, the non-human homologs were sequentially compared to the human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of the essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems indicated that 80% of them are cytoplasmic, 10% are mitochondrial and the remaining 10% are associated with the cell wall. The role of these non-human and non-gut flora putative target proteins in Candida albicans biological pathways was studied. Due to their integrated and critical role in Candida albicans replication cycle, four proteins were selected for molecular modeling. For drug designing and development, four high quality and reliable protein models with more than 70% sequence identity were constructed. These proteins are used for the docking studies of the known and new ligands (unpublished data). Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against the infections they cause.

Prediction of beta-turns from amino acid sequences using the residue-coupled model.

PubMed

Guruprasad, K; Shukla, S

2003-04-01

We evaluated the prediction of beta-turns from amino acid sequences using the residue-coupled model with an enlarged representative protein data set selected from the Protein Data Bank. Our results show that the probability values derived from a data set comprising 425 protein chains yielded an overall beta-turn prediction accuracy 68.74%, compared with 94.7% reported earlier on a data set of 30 proteins using the same method. However, we noted that the overall beta-turn prediction accuracy using probability values derived from the 30-protein data set reduces to 40.74% when tested on the data set comprising 425 protein chains. In contrast, using probability values derived from the 425 data set used in this analysis, the overall beta-turn prediction accuracy yielded consistent results when tested on either the 30-protein data set (64.62%) used earlier or a more recent representative data set comprising 619 protein chains (64.66%) or on a jackknife data set comprising 476 representative protein chains (63.38%). We therefore recommend the use of probability values derived from the 425 representative protein chains data set reported here, which gives more realistic and consistent predictions of beta-turns from amino acid sequences.

"Plate cherry picking": a novel semi-sequential screening paradigm for cheaper, faster, information-rich compound selection.

PubMed

Crisman, Thomas J; Jenkins, Jeremy L; Parker, Christian N; Hill, W Adam G; Bender, Andreas; Deng, Zhan; Nettles, James H; Davies, John W; Glick, Meir

2007-04-01

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC(50) < 10 microM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming "classic" sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.

Adaptation of model proteins from cold to hot environments involves continuous and small adjustments of average parameters related to amino acid composition.

PubMed

De Vendittis, Emmanuele; Castellano, Immacolata; Cotugno, Roberta; Ruocco, Maria Rosaria; Raimo, Gennaro; Masullo, Mariorosario

2008-01-07

The growth temperature adaptation of six model proteins has been studied in 42 microorganisms belonging to eubacterial and archaeal kingdoms, covering optimum growth temperatures from 7 to 103 degrees C. The selected proteins include three elongation factors involved in translation, the enzymes glyceraldehyde-3-phosphate dehydrogenase and superoxide dismutase, the cell division protein FtsZ. The common strategy of protein adaptation from cold to hot environments implies the occurrence of small changes in the amino acid composition, without altering the overall structure of the macromolecule. These continuous adjustments were investigated through parameters related to the amino acid composition of each protein. The average value per residue of mass, volume and accessible surface area allowed an evaluation of the usage of bulky residues, whereas the average hydrophobicity reflected that of hydrophobic residues. The specific proportion of bulky and hydrophobic residues in each protein almost linearly increased with the temperature of the host microorganism. This finding agrees with the structural and functional properties exhibited by proteins in differently adapted sources, thus explaining the great compactness or the high flexibility exhibited by (hyper)thermophilic or psychrophilic proteins, respectively. Indeed, heat-adapted proteins incline toward the usage of heavier-size and more hydrophobic residues with respect to mesophiles, whereas the cold-adapted macromolecules show the opposite behavior with a certain preference for smaller-size and less hydrophobic residues. An investigation on the different increase of bulky residues along with the growth temperature observed in the six model proteins suggests the relevance of the possible different role and/or structure organization played by protein domains. The significance of the linear correlations between growth temperature and parameters related to the amino acid composition improved when the analysis was collectively carried out on all model proteins.

Molecular recognition of a model globular protein apomyoglobin by synthetic receptor cyclodextrin: effect of fluorescence modification of the protein and cavity size of the receptor in the interaction.

PubMed

Saha, Ranajay; Rakshit, Surajit; Pal, Samir Kumar

2013-11-01

Labelling of proteins with some extrinsic probe is unavoidable in molecular biology research. Particularly, spectroscopic studies in the optical region require fluorescence modification of native proteins by attaching polycyclic aromatic fluoroprobe with the proteins under investigation. Our present study aims to address the consequence of the attachment of a fluoroprobe at the protein surface in the molecular recognition of the protein by selectively small model receptor. A spectroscopic study involving apomyoglobin (Apo-Mb) and cyclodextrin (CyD) of various cavity sizes as model globular protein and synthetic receptors, respectively, using steady-state and picosecond-resolved techniques, is detailed here. A study involving Förster resonance energy transfer, between intrinsic amino acid tryptophan (donor) and N, N-dimethyl naphthalene moiety of the extrinsic dansyl probes at the surface of Apo-Mb, precisely monitor changes in donor acceptor distance as a consequence of interaction of the protein with CyD having different cavity sizes (β and γ variety). Molecular modelling studies on the interaction of tryptophan and dansyl probe with β-CyD is reported here and found to be consistent with the experimental observations. In order to investigate structural aspects of the interacting protein, we have used circular dichroism spectroscopy. Temperature-dependent circular dichroism studies explore the change in the secondary structure of Apo-Mb in association with CyD, before and after fluorescence modification of the protein. Overall, the study well exemplifies approaches to protein recognition by CyD as a synthetic receptor and offers a cautionary note on the use of hydrophobic fluorescent labels for proteins in biochemical studies involving recognition of molecules. Copyright © 2013 John Wiley & Sons, Ltd.

Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2009-01-01

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus Cα contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models. PMID:18004783

A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.

PubMed

Pagan, Rafael F; Massey, Steven E

2014-02-01

Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."

Directed evolution induces tributyrin hydrolysis in a virulence factor of Xylella fastidiosa using a duplicated gene as a template.

PubMed

Gouran, Hossein; Chakraborty, Sandeep; Rao, Basuthkar J; Asgeirsson, Bjarni; Dandekar, Abhaya

2014-01-01

Duplication of genes is one of the preferred ways for natural selection to add advantageous functionality to the genome without having to reinvent the wheel with respect to catalytic efficiency and protein stability. The duplicated secretory virulence factors of Xylella fastidiosa (LesA, LesB and LesC), implicated in Pierce's disease of grape and citrus variegated chlorosis of citrus species, epitomizes the positive selection pressures exerted on advantageous genes in such pathogens. A deeper insight into the evolution of these lipases/esterases is essential to develop resistance mechanisms in transgenic plants. Directed evolution, an attempt to accelerate the evolutionary steps in the laboratory, is inherently simple when targeted for loss of function. A bigger challenge is to specify mutations that endow a new function, such as a lost functionality in a duplicated gene. Previously, we have proposed a method for enumerating candidates for mutations intended to transfer the functionality of one protein into another related protein based on the spatial and electrostatic properties of the active site residues (DECAAF). In the current work, we present in vivo validation of DECAAF by inducing tributyrin hydrolysis in LesB based on the active site similarity to LesA. The structures of these proteins have been modeled using RaptorX based on the closely related LipA protein from Xanthomonas oryzae. These mutations replicate the spatial and electrostatic conformation of LesA in the modeled structure of the mutant LesB as well, providing in silico validation before proceeding to the laborious in vivo work. Such focused mutations allows one to dissect the relevance of the duplicated genes in finer detail as compared to gene knockouts, since they do not interfere with other moonlighting functions, protein expression levels or protein-protein interaction.

Directed evolution induces tributyrin hydrolysis in a virulence factor of Xylella fastidiosa using a duplicated gene as a template

PubMed Central

Rao, Basuthkar J.; Asgeirsson, Bjarni; Dandekar, Abhaya

2014-01-01

Duplication of genes is one of the preferred ways for natural selection to add advantageous functionality to the genome without having to reinvent the wheel with respect to catalytic efficiency and protein stability. The duplicated secretory virulence factors of Xylella fastidiosa (LesA, LesB and LesC), implicated in Pierce's disease of grape and citrus variegated chlorosis of citrus species, epitomizes the positive selection pressures exerted on advantageous genes in such pathogens. A deeper insight into the evolution of these lipases/esterases is essential to develop resistance mechanisms in transgenic plants. Directed evolution, an attempt to accelerate the evolutionary steps in the laboratory, is inherently simple when targeted for loss of function. A bigger challenge is to specify mutations that endow a new function, such as a lost functionality in a duplicated gene. Previously, we have proposed a method for enumerating candidates for mutations intended to transfer the functionality of one protein into another related protein based on the spatial and electrostatic properties of the active site residues (DECAAF). In the current work, we present in vivo validation of DECAAF by inducing tributyrin hydrolysis in LesB based on the active site similarity to LesA. The structures of these proteins have been modeled using RaptorX based on the closely related LipA protein from Xanthomonas oryzae. These mutations replicate the spatial and electrostatic conformation of LesA in the modeled structure of the mutant LesB as well, providing in silico validation before proceeding to the laborious in vivo work. Such focused mutations allows one to dissect the relevance of the duplicated genes in finer detail as compared to gene knockouts, since they do not interfere with other moonlighting functions, protein expression levels or protein-protein interaction. PMID:25717364

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.

Domain wise docking analyses of the modular chitin binding protein CBP50 from Bacillus thuringiensis serovar konkukian S4.

PubMed

Sehar, Ujala; Mehmood, Muhammad Aamer; Hussain, Khadim; Nawaz, Salman; Nadeem, Shahid; Siddique, Muhammad Hussnain; Nadeem, Habibullah; Gull, Munazza; Ahmad, Niaz; Sohail, Iqra; Gill, Saba Shahid; Majeed, Summera

2013-01-01

This paper presents an in silico characterization of the chitin binding protein CBP50 from B. thuringiensis serovar konkukian S4 through homology modeling and molecular docking. The CBP50 has shown a modular structure containing an N-terminal CBM33 domain, two consecutive fibronectin-III (Fn-III) like domains and a C-terminal CBM5 domain. The protein presented a unique modular structure which could not be modeled using ordinary procedures. So, domain wise modeling using MODELLER and docking analyses using Autodock Vina were performed. The best conformation for each domain was selected using standard procedure. It was revealed that four amino acid residues Glu-71, Ser-74, Glu-76 and Gln-90 from N-terminal domain are involved in protein-substrate interaction. Similarly, amino acid residues Trp-20, Asn-21, Ser-23 and Val-30 of Fn-III like domains and Glu-15, Ala-17, Ser-18 and Leu-35 of C-terminal domain were involved in substrate binding. Site-directed mutagenesis of these proposed amino acid residues in future will elucidate the key amino acids involved in chitin binding activity of CBP50 protein.

GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure.

PubMed

Baek, Minkyung; Park, Taeyong; Heo, Lim; Park, Chiwook; Seok, Chaok

2017-07-03

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

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.

Evaluation of Trichodysplasia Spinulosa-Associated Polyomavirus Capsid Protein as a New Carrier for Construction of Chimeric Virus-Like Particles Harboring Foreign Epitopes

PubMed Central

Gedvilaite, Alma; Kucinskaite-Kodze, Indre; Lasickiene, Rita; Timinskas, Albertas; Vaitiekaite, Ausra; Ziogiene, Danguole; Zvirbliene, Aurelija

2015-01-01

Recombinant virus-like particles (VLPs) represent a promising tool for protein engineering. Recently, trichodysplasia spinulosa-associated polyomavirus (TSPyV) viral protein 1 (VP1) was efficiently produced in yeast expression system and shown to self-assemble to VLPs. In the current study, TSPyV VP1 protein was exploited as a carrier for construction of chimeric VLPs harboring selected B and T cell-specific epitopes and evaluated in comparison to hamster polyomavirus VP1 protein. Chimeric VLPs with inserted either hepatitis B virus preS1 epitope DPAFR or a universal T cell-specific epitope AKFVAAWTLKAAA were produced in yeast Saccharomyces cerevisiae. Target epitopes were incorporated either at the HI or BC loop of the VP1 protein. The insertion sites were selected based on molecular models of TSPyV VP1 protein. The surface exposure of the insert positions was confirmed using a collection of monoclonal antibodies raised against the intact TSPyV VP1 protein. All generated chimeric proteins were capable to self-assemble to VLPs, which induced a strong immune response in mice. The chimeric VLPs also activated dendritic cells and T cells as demonstrated by analysis of cell surface markers and cytokine production profiles in spleen cell cultures. In conclusion, TSPyV VP1 protein represents a new potential carrier for construction of chimeric VLPs harboring target epitopes. PMID:26230706

Proteins feel more than they see: fine-tuning of binding affinity by properties of the non-interacting surface.

PubMed

Kastritis, Panagiotis L; Rodrigues, João P G L M; Folkers, Gert E; Boelens, Rolf; Bonvin, Alexandre M J J

2014-07-15

Protein-protein complexes orchestrate most cellular processes such as transcription, signal transduction and apoptosis. The factors governing their affinity remain elusive however, especially when it comes to describing dissociation rates (koff). Here we demonstrate that, next to direct contributions from the interface, the non-interacting surface (NIS) also plays an important role in binding affinity, especially polar and charged residues. Their percentage on the NIS is conserved over orthologous complexes indicating an evolutionary selection pressure. Their effect on binding affinity can be explained by long-range electrostatic contributions and surface-solvent interactions that are known to determine the local frustration of the protein complex surface. Including these in a simple model significantly improves the affinity prediction of protein complexes from structural models. The impact of mutations outside the interacting surface on binding affinity is supported by experimental alanine scanning mutagenesis data. These results enable the development of more sophisticated and integrated biophysical models of binding affinity and open new directions in experimental control and modulation of biomolecular interactions. Copyright © 2014. Published by Elsevier Ltd.

Stereochemical studies of the monocyclic agouti-related protein (103-122) Arg-Phe-Phe residues: conversion of a melanocortin-4 receptor antagonist into an agonist and results in the discovery of a potent and selective melanocortin-1 agonist.

PubMed

Joseph, Christine G; Wang, Xiang S; Scott, Joseph W; Bauzo, Rayna M; Xiang, Zhimin; Richards, Nigel G; Haskell-Luevano, Carrie

2004-12-30

The agouti-related protein (AGRP) is an endogenous antagonist of the centrally expressed melanocortin receptors. The melanocortin-4 receptor (MC4R) is involved in energy homeostasis, food intake, sexual function, and obesity. The endogenous hAGRP protein is 132 amino acids in length, possesses five disulfide bridges at the C-terminus of the molecule, and is expressed in the hypothalamus of the brain. We have previously reported that a monocyclic hAGRP(103-122) peptide is an antagonist at the melanocortin receptors expressed in the brain. Stereochemical inversion from the endogenous l- to d-isomers of single or multiple amino acid modifications in this monocyclic truncated hAGRP sequence resulted in molecules that are converted from melanocortin receptor antagonists into melanocortin receptor agonists. The Asp-Pro-Ala-Ala-Thr-Ala-Tyr-cyclo[Cys-Arg-DPhe-DPhe-Asn-Ala-Phe-Cys]-Tyr-Ala-Arg-Lys-Leu peptide resulted in a 60 nM melanocortin-1 receptor agonist that is 100-fold selective versus the mMC4R, 1000-fold selective versus the mMC3R, and ca. 180-fold selective versus the mMC5R. In attempts to identify putative ligand-receptor interactions that may be participating in the agonist induced stimulation of the MC4R, selected ligands were docked into a homology molecular model of the mMC4R. These modeling studies have putatively identified hAGRP ligand DArg111-mMC4RAsn115 (TM3) and the hAGRP DPhe113-mMC4RPhe176 (TM4) interactions as important for agonist activity.

Choosing non-redundant representative subsets of protein sequence data sets using submodular optimization.

PubMed

Libbrecht, Maxwell W; Bilmes, Jeffrey A; Noble, William Stafford

2018-04-01

Selecting a non-redundant representative subset of sequences is a common step in many bioinformatics workflows, such as the creation of non-redundant training sets for sequence and structural models or selection of "operational taxonomic units" from metagenomics data. Previous methods for this task, such as CD-HIT, PISCES, and UCLUST, apply a heuristic threshold-based algorithm that has no theoretical guarantees. We propose a new approach based on submodular optimization. Submodular optimization, a discrete analogue to continuous convex optimization, has been used with great success for other representative set selection problems. We demonstrate that the submodular optimization approach results in representative protein sequence subsets with greater structural diversity than sets chosen by existing methods, using as a gold standard the SCOPe library of protein domain structures. In this setting, submodular optimization consistently yields protein sequence subsets that include more SCOPe domain families than sets of the same size selected by competing approaches. We also show how the optimization framework allows us to design a mixture objective function that performs well for both large and small representative sets. The framework we describe is the best possible in polynomial time (under some assumptions), and it is flexible and intuitive because it applies a suite of generic methods to optimize one of a variety of objective functions. © 2018 Wiley Periodicals, Inc.

HotSpot Wizard 3.0: web server for automated design of mutations and smart libraries based on sequence input information.

PubMed

Sumbalova, Lenka; Stourac, Jan; Martinek, Tomas; Bednar, David; Damborsky, Jiri

2018-05-23

HotSpot Wizard is a web server used for the automated identification of hotspots in semi-rational protein design to give improved protein stability, catalytic activity, substrate specificity and enantioselectivity. Since there are three orders of magnitude fewer protein structures than sequences in bioinformatic databases, the major limitation to the usability of previous versions was the requirement for the protein structure to be a compulsory input for the calculation. HotSpot Wizard 3.0 now accepts the protein sequence as input data. The protein structure for the query sequence is obtained either from eight repositories of homology models or is modeled using Modeller and I-Tasser. The quality of the models is then evaluated using three quality assessment tools-WHAT_CHECK, PROCHECK and MolProbity. During follow-up analyses, the system automatically warns the users whenever they attempt to redesign poorly predicted parts of their homology models. The second main limitation of HotSpot Wizard's predictions is that it identifies suitable positions for mutagenesis, but does not provide any reliable advice on particular substitutions. A new module for the estimation of thermodynamic stabilities using the Rosetta and FoldX suites has been introduced which prevents destabilizing mutations among pre-selected variants entering experimental testing. HotSpot Wizard is freely available at http://loschmidt.chemi.muni.cz/hotspotwizard.

Characterization of complex systems using the design of experiments approach: transient protein expression in tobacco as a case study.

PubMed

Buyel, Johannes Felix; Fischer, Rainer

2014-01-31

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio.

PubMed

Ryu, Do-Yeal; Rahman, Md Saidur; Pang, Myung-Geol

2017-09-06

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases.

Determination of Highly Sensitive Biological Cell Model Systems to Screen BPA-Related Health Hazards Using Pathway Studio

PubMed Central

Ryu, Do-Yeal

2017-01-01

Bisphenol-A (BPA) is a ubiquitous endocrine-disrupting chemical. Recently, many issues have arisen surrounding the disease pathogenesis of BPA. Therefore, several studies have been conducted to investigate the proteomic biomarkers of BPA that are associated with disease processes. However, studies on identifying highly sensitive biological cell model systems in determining BPA health risk are lacking. Here, we determined suitable cell model systems and potential biomarkers for predicting BPA-mediated disease using the bioinformatics tool Pathway Studio. We compiled known BPA-mediated diseases in humans, which were categorized into five major types. Subsequently, we investigated the differentially expressed proteins following BPA exposure in several cell types, and analyzed the efficacy of altered proteins to investigate their associations with BPA-mediated diseases. Our results demonstrated that colon cancer cells (SW480), mammary gland, and Sertoli cells were highly sensitive biological model systems, because of the efficacy of predicting the majority of BPA-mediated diseases. We selected glucose-6-phosphate dehydrogenase (G6PD), cytochrome b-c1 complex subunit 1 (UQCRC1), and voltage-dependent anion-selective channel protein 2 (VDAC2) as highly sensitive biomarkers to predict BPA-mediated diseases. Furthermore, we summarized proteomic studies in spermatozoa following BPA exposure, which have recently been considered as another suitable cell type for predicting BPA-mediated diseases. PMID:28878155

Role of electrostatic interactions during protein ultrafiltration.

PubMed

Rohani, Mahsa M; Zydney, Andrew L

2010-10-15

A number of studies over the last decade have clearly demonstrated the importance of electrostatic interactions on the transport of charged proteins through semipermeable ultrafiltration membranes. This paper provides a review of recent developments in this field with a focus on the role of both protein and membrane charge on the rate of protein transport. Experimental results are analyzed using available theoretical models developed from the solution of the Poisson-Boltzmann equation for the partitioning of a charged particle into a charged pore. The potential of exploiting these electrostatic interactions for selective protein separations and for the development of ultrafiltration membranes with enhanced performance characteristics is also examined. Copyright © 2010 Elsevier B.V. All rights reserved.

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

Fragment-Based Drug Discovery of Potent Protein Kinase C Iota Inhibitors.

PubMed

Kwiatkowski, Jacek; Liu, Boping; Tee, Doris Hui Ying; Chen, Guoying; Ahmad, Nur Huda Binte; Wong, Yun Xuan; Poh, Zhi Ying; Ang, Shi Hua; Tan, Eldwin Sum Wai; Ong, Esther Hq; Nurul Dinie; Poulsen, Anders; Pendharkar, Vishal; Sangthongpitag, Kanda; Lee, May Ann; Sepramaniam, Sugunavathi; Ho, Soo Yei; Cherian, Joseph; Hill, Jeffrey; Keller, Thomas H; Hung, Alvin W

2018-05-24

Protein kinase C iota (PKC-ι) is an atypical kinase implicated in the promotion of different cancer types. A biochemical screen of a fragment library has identified several hits from which an azaindole-based scaffold was chosen for optimization. Driven by a structure-activity relationship and supported by molecular modeling, a weakly bound fragment was systematically grown into a potent and selective inhibitor against PKC-ι.

Defining an essence of structure determining residue contacts in proteins.

PubMed

Sathyapriya, R; Duarte, Jose M; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-12-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this "structural essence" has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts-such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed "cone-peeling" that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 A Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This "structural essence" opens new avenues in the fields of structure prediction, empirical potentials and docking.

Defining an Essence of Structure Determining Residue Contacts in Proteins

PubMed Central

Sathyapriya, R.; Duarte, Jose M.; Stehr, Henning; Filippis, Ioannis; Lappe, Michael

2009-01-01

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this “structural essence” has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts—such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed “cone-peeling” that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 Å Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This “structural essence” opens new avenues in the fields of structure prediction, empirical potentials and docking. PMID:19997489

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

PubMed

Khoury, George A; Smadbeck, James; Kieslich, Chris A; Koskosidis, Alexandra J; Guzman, Yannis A; Tamamis, Phanourios; Floudas, Christodoulos A

2017-06-01

Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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.

Development of a stress response therapy targeting aggressive prostate cancer.

PubMed

Nguyen, Hao G; Conn, Crystal S; Kye, Yae; Xue, Lingru; Forester, Craig M; Cowan, Janet E; Hsieh, Andrew C; Cunningham, John T; Truillet, Charles; Tameire, Feven; Evans, Michael J; Evans, Christopher P; Yang, Joy C; Hann, Byron; Koumenis, Constantinos; Walter, Peter; Carroll, Peter R; Ruggero, Davide

2018-05-02

Oncogenic lesions up-regulate bioenergetically demanding cellular processes, such as protein synthesis, to drive cancer cell growth and continued proliferation. However, the hijacking of these key processes by oncogenic pathways imposes onerous cell stress that must be mitigated by adaptive responses for cell survival. The mechanism by which these adaptive responses are established, their functional consequences for tumor development, and their implications for therapeutic interventions remain largely unknown. Using murine and humanized models of prostate cancer (PCa), we show that one of the three branches of the unfolded protein response is selectively activated in advanced PCa. This adaptive response activates the phosphorylation of the eukaryotic initiation factor 2-α (P-eIF2α) to reset global protein synthesis to a level that fosters aggressive tumor development and is a marker of poor patient survival upon the acquisition of multiple oncogenic lesions. Using patient-derived xenograft models and an inhibitor of P-eIF2α activity, ISRIB, our data show that targeting this adaptive brake for protein synthesis selectively triggers cytotoxicity against aggressive metastatic PCa, a disease for which presently there is no cure. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Generation of a Homozygous Transgenic Rat Strain Stably Expressing a Calcium Sensor Protein for Direct Examination of Calcium Signaling.

PubMed

Szebényi, Kornélia; Füredi, András; Kolacsek, Orsolya; Pergel, Enikő; Bősze, Zsuzsanna; Bender, Balázs; Vajdovich, Péter; Tóvári, József; Homolya, László; Szakács, Gergely; Héja, László; Enyedi, Ágnes; Sarkadi, Balázs; Apáti, Ágota; Orbán, Tamás I

2015-08-03

In drug discovery, prediction of selectivity and toxicity require the evaluation of cellular calcium homeostasis. The rat is a preferred laboratory animal for pharmacology and toxicology studies, while currently no calcium indicator protein expressing rat model is available. We established a transgenic rat strain stably expressing the GCaMP2 fluorescent calcium sensor by a transposon-based methodology. Zygotes were co-injected with mRNA of transposase and a CAG-GCaMP2 expressing construct, and animals with one transgene copy were pre-selected by measuring fluorescence in blood cells. A homozygous rat strain was generated with high sensor protein expression in the heart, kidney, liver, and blood cells. No pathological alterations were found in these animals, and fluorescence measurements in cardiac tissue slices and primary cultures demonstrated the applicability of this system for studying calcium signaling. We show here that the GCaMP2 expressing rat cardiomyocytes allow the prediction of cardiotoxic drug side-effects, and provide evidence for the role of Na(+)/Ca(2+) exchanger and its beneficial pharmacological modulation in cardiac reperfusion. Our data indicate that drug-induced alterations and pathological processes can be followed by using this rat model, suggesting that transgenic rats expressing a calcium-sensitive protein provide a valuable system for pharmacological and toxicological studies.

Discovery and preclinical pharmacology of a selective ATP-competitive Akt inhibitor (GDC-0068) for the treatment of human tumors.

PubMed

Blake, James F; Xu, Rui; Bencsik, Josef R; Xiao, Dengming; Kallan, Nicholas C; Schlachter, Stephen; Mitchell, Ian S; Spencer, Keith L; Banka, Anna L; Wallace, Eli M; Gloor, Susan L; Martinson, Matthew; Woessner, Richard D; Vigers, Guy P A; Brandhuber, Barbara J; Liang, Jun; Safina, Brian S; Li, Jun; Zhang, Birong; Chabot, Christine; Do, Steven; Lee, Leslie; Oeh, Jason; Sampath, Deepak; Lee, Brian B; Lin, Kui; Liederer, Bianca M; Skelton, Nicholas J

2012-09-27

The discovery and optimization of a series of 6,7-dihydro-5H-cyclopenta[d]pyrimidine compounds that are ATP-competitive, selective inhibitors of protein kinase B/Akt is reported. The initial design and optimization was guided by the use of X-ray structures of inhibitors in complex with Akt1 and the closely related protein kinase A. The resulting compounds demonstrate potent inhibition of all three Akt isoforms in biochemical assays and poor inhibition of other members of the cAMP-dependent protein kinase/protein kinase G/protein kinase C extended family and block the phosphorylation of multiple downstream targets of Akt in human cancer cell lines. Biological studies with one such compound, 28 (GDC-0068), demonstrate good oral exposure resulting in dose-dependent pharmacodynamic effects on downstream biomarkers and a robust antitumor response in xenograft models in which the phosphatidylinositol 3-kinase-Akt-mammalian target of rapamycin pathway is activated. 28 is currently being evaluated in human clinical trials for the treatment of cancer.

Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.

PubMed

Carugo, Oliviero; Djinović-Carugo, Kristina

2016-01-01

It is often necessary to build subsets of the Protein Data Bank to extract structural trends and average values. For this purpose it is mandatory that the subsets are non-redundant and of high quality. The first problem can be solved relatively easily at the sequence level or at the structural level. The second, on the contrary, needs special attention. It is not sufficient, in fact, to consider the crystallographic resolution and other feature must be taken into account: the absence of strings of residues from the electron density maps and from the files deposited in the Protein Data Bank; the B-factor values; the appropriate validation of the structural models; the quality of the electron density maps, which is not uniform; and the temperature of the diffraction experiments. More stringent criteria produce smaller subsets, which can be enlarged with more tolerant selection criteria. The incessant growth of the Protein Data Bank and especially of the number of high-resolution structures is allowing the use of more stringent selection criteria, with a consequent improvement of the quality of the subsets of the Protein Data Bank.

Genotypic gain with simultaneous selection of production, nutrition, and culinary traits in cowpea crosses and backcrosses using mixed models.

PubMed

Oliveira, D G; Rocha, M M; Damasceno-Silva, K J; Sá, F V; Lima, L R L; Resende, M D V

2017-08-17

The aim of this study was to estimate the genotypic gain with simultaneous selection of production, nutrition, and culinary traits in cowpea crosses and backcrosses and to compare different selection indexes. Eleven cowpea populations were evaluated in a randomized complete block design with four replications. Fourteen traits were evaluated, and the following parameters were estimated: genotypic variation coefficient, genotypic determination coefficient, experimental quality indicator and selection reliability, estimated genotypic values ​​- BLUE, genotypic correlation coefficient among traits, and genotypic gain with simultaneous selection of all traits. The genotypic gain was estimated based on tree selection indexes: classical, multiplicative, and the sum of ranks. The genotypic variation coefficient was higher than the environmental variation coefficient for the number of days to start flowering, plant type, the weight of one hundred grains, grain index, and protein concentration. The majority of the traits presented genotypic determination coefficient from medium to high magnitude. The identification of increases in the production components is associated with decreases in protein concentration, and the increase in precocity leads to decreases in protein concentration and cooking time. The index based on the sum of ranks was the best alternative for simultaneous selection of traits in the cowpea segregating populations resulting from the crosses and backcrosses evaluated, with emphasis on the F 4 BC 12 , F 4 C 21 , and F 4 C 12 populations, which had the highest genotypic gains.

Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries.

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

Kay, B. K.; Castagnoli, L.; Biosciences Division

This unit describes the process and analysis of affinity selecting bacteriophage M13 from libraries displaying combinatorial peptides fused to either a minor or major capsid protein. Direct affinity selection uses target protein bound to a microtiter plate followed by purification of selected phage by ELISA. Alternatively, there is a bead-based affinity selection method. These methods allow one to readily isolate peptide ligands that bind to a protein target of interest and use the consensus sequence to search proteomic databases for putative interacting proteins.

Covering complete proteomes with X-ray structures: A current snapshot

DOE PAGES

Mizianty, Marcin J.; Fan, Xiao; Yan, Jing; ...

2014-10-23

Structural genomics programs have developed and applied structure-determination pipelines to a wide range of protein targets, facilitating the visualization of macromolecular interactions and the understanding of their molecular and biochemical functions. The fundamental question of whether three-dimensional structures of all proteins and all functional annotations can be determined using X-ray crystallography is investigated. A first-of-its-kind large-scale analysis of crystallization propensity for all proteins encoded in 1953 fully sequenced genomes was performed. It is shown that current X-ray crystallographic knowhow combined with homology modeling can provide structures for 25% of modeling families (protein clusters for which structural models can be obtainedmore » through homology modeling), with at least one structural model produced for each Gene Ontology functional annotation. The coverage varies between superkingdoms, with 19% for eukaryotes, 35% for bacteria and 49% for archaea, and with those of viruses following the coverage values of their hosts. It is shown that the crystallization propensities of proteomes from the taxonomic superkingdoms are distinct. The use of knowledge-based target selection is shown to substantially increase the ability to produce X-ray structures. It is demonstrated that the human proteome has one of the highest attainable coverage values among eukaryotes, and GPCR membrane proteins suitable for X-ray structure determination were determined.« less

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.

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 receptor with adenosine derivative compounds. Finally, we show that our model can qualitatively describe the temporal dynamics of this competing G protein activation. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Potential Role for Drosophila Mucins in Development and Physiology

PubMed Central

Syed, Zulfeqhar A.; Härd, Torleif; Uv, Anne; van Dijk-Härd, Iris F.

2008-01-01

Vital vertebrate organs are protected from the external environment by a barrier that to a large extent consists of mucins. These proteins are characterized by poorly conserved repeated sequences that are rich in prolines and potentially glycosylated threonines and serines (PTS). We have now used the characteristics of the PTS repeat domain to identify Drosophila mucins in a simple bioinformatics approach. Searching the predicted protein database for proteins with at least 4 repeats and a high ST content, more than 30 mucin-like proteins were identified, ranging from 300–23000 amino acids in length. We find that Drosophila mucins are present at all stages of the fly life cycle, and that their transcripts localize to selective organs analogous to sites of vertebrate mucin expression. The results could allow for addressing basic questions about human mucin-related diseases in this model system. Additionally, many of the mucins are expressed in selective tissues during embryogenesis, thus revealing new potential functions for mucins as apical matrix components during organ morphogenesis. PMID:18725942

Selective complexation of K+ and Na+ in simple polarizable ion-ligating systems.

PubMed

Bostick, David L; Brooks, Charles L

2010-09-29

An influx of experimental and theoretical studies of ion transport protein structure has inspired efforts to understand underlying determinants of ionic selectivity. Design principles for selective ion binding can be effectively isolated and interrogated using simplified models composed of a single ion surrounded by a set of ion-ligating molecular species. While quantum mechanical treatments of such systems naturally incorporate electronic degrees of freedom, their computational overhead typically prohibits thorough dynamic sampling of configurational space and, thus, requires approximations when determining ion-selective free energy. As an alternative, we employ dynamical simulations with a polarizable force field to probe the structure and K(+)/Na(+) selectivity in simple models composed of one central K(+)/Na(+) ion surrounded by 0-8 identical model compounds: N-methylacetamide, formamide, or water. In the absence of external restraints, these models represent gas-phase clusters displaying relaxed coordination structures with low coordination number. Such systems display Na(+) selectivity when composed of more than ∼3 organic carbonyl-containing compounds and always display K(+) selectivity when composed of water molecules. Upon imposing restraints that solely enforce specific coordination numbers, we find all models are K(+)-selective when ∼7-8-fold ion coordination is achieved. However, when models composed of the organic compounds provide ∼4-6-fold coordination, they retain their Na(+) selectivity. From these trends, design principles emerge that are of basic importance in the behavior of K(+) channel selectivity filters and suggest a basis not only for K(+) selectivity but also for modulation of block and closure by smaller ions.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches.

PubMed

Sabatino, Manuela; Rotili, Dante; Patsilinakos, Alexandros; Forgione, Mariantonietta; Tomaselli, Daniela; Alby, Fréderic; Arimondo, Paola B; Mai, Antonello; Ragno, Rino

2018-03-01

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

NASA Astrophysics Data System (ADS)

Sabatino, Manuela; Rotili, Dante; Patsilinakos, Alexandros; Forgione, Mariantonietta; Tomaselli, Daniela; Alby, Fréderic; Arimondo, Paola B.; Mai, Antonello; Ragno, Rino

2018-03-01

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

FRODOCK 2.0: fast protein-protein docking server.

PubMed

Ramírez-Aportela, Erney; López-Blanco, José Ramón; Chacón, Pablo

2016-08-01

The prediction of protein-protein complexes from the structures of unbound components is a challenging and powerful strategy to decipher the mechanism of many essential biological processes. We present a user-friendly protein-protein docking server based on an improved version of FRODOCK that includes a complementary knowledge-based potential. The web interface provides a very effective tool to explore and select protein-protein models and interactively screen them against experimental distance constraints. The competitive success rates and efficiency achieved allow the retrieval of reliable potential protein-protein binding conformations that can be further refined with more computationally demanding strategies. The server is free and open to all users with no login requirement at http://frodock.chaconlab.org pablo@chaconlab.org Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Determinants of BH3 Binding Specificity for Mcl-1 versus Bcl-x[subscript L

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

Dutta, Sanjib; Gullá, Stefano; Chen, T. Scott

2010-06-25

Interactions among Bcl-2 family proteins are important for regulating apoptosis. Prosurvival members of the family interact with proapoptotic BH3 (Bcl-2-homology-3)-only members, inhibiting execution of cell death through the mitochondrial pathway. Structurally, this interaction is mediated by binding of the {alpha}-helical BH3 region of the proapoptotic proteins to a conserved hydrophobic groove on the prosurvival proteins. Native BH3-only proteins exhibit selectivity in binding prosurvival members, as do small molecules that block these interactions. Understanding the sequence and structural basis of interaction specificity in this family is important, as it may allow the prediction of new Bcl-2 family associations and/or the designmore » of new classes of selective inhibitors to serve as reagents or therapeutics. In this work, we used two complementary techniques - yeast surface display screening from combinatorial peptide libraries and SPOT peptide array analysis - to elucidate specificity determinants for binding to Bcl-x{sub L} versus Mcl-1, two prominent prosurvival proteins. We screened a randomized library and identified BH3 peptides that bound to either Mcl-1 or Bcl-x{sub L} selectively or to both with high affinity. The peptides competed with native ligands for binding into the conserved hydrophobic groove, as illustrated in detail by a crystal structure of a specific peptide bound to Mcl-1. Mcl-1-selective peptides from the screen were highly specific for binding Mcl-1 in preference to Bcl-x{sub L}, Bcl-2, Bcl-w, and Bfl-1, whereas Bcl-x{sub L}-selective peptides showed some cross-interaction with related proteins Bcl-2 and Bcl-w. Mutational analyses using SPOT arrays revealed the effects of 170 point mutations made in the background of a peptide derived from the BH3 region of Bim, and a simple predictive model constructed using these data explained much of the specificity observed in our Mcl-1 versus Bcl-x{sub L} binders.« less

Determinants of BH3 binding specificity for Mcl-1 vs. Bcl-xL

PubMed Central

Dutta, Sanjib; Gullá, Stefano; Chen, T. Scott; Fire, Emiko; Grant, Robert A.; Keating, Amy E.

2010-01-01

Interactions among Bcl-2 family proteins are important for regulating apoptosis. Pro-survival members of the family interact with pro-apoptotic BH3-only members, inhibiting execution of cell death through the mitochondrial pathway. Structurally, this interaction is mediated by binding of the alpha-helical BH3 region of the pro-apoptotic proteins to a conserved hydrophobic groove on the pro-survival proteins. Native BH3-only proteins exhibit selectivity in binding pro-survival members, as do small molecules that block these interactions. Understanding the sequence and structural basis of interaction specificity in this family is important, as it may allow the prediction of new Bcl-2 family associations and/or the design of new classes of selective inhibitors to serve as reagents or therapeutics. In this work we used two complementary techniques, yeast surface display screening from combinatorial peptide libraries and SPOT peptide array analysis, to elucidate specificity determinants for binding to Bcl-xL vs. Mcl-1, two prominent pro-survival proteins. We screened a randomized library and identified BH3 peptides that bound to either Mcl-1 or Bcl-xL selectively, or to both with high affinity. The peptides competed with native ligands for binding into the conserved hydrophobic groove, as illustrated in detail by a crystal structure of a specific peptide bound to Mcl-1. Mcl-1 selective peptides from the screen were highly specific for binding Mcl-1 in preference to Bcl-xL, Bcl-2, Bcl-w and Bfl-1, whereas Bcl-xL selective peptides showed some cross-interaction with related proteins Bcl-2 and Bcl-w. Mutational analyses using SPOT arrays revealed the effects of 170 point mutations made in the background of a peptide derived from the BH3 region of Bim, and a simple predictive model constructed using these data explained much of the specificity observed in our Mcl-1 vs. Bcl-xL binders. PMID:20363230

Identification of Human N-Myristoylated Proteins from Human Complementary DNA Resources by Cell-Free and Cellular Metabolic Labeling Analyses.

PubMed

Takamitsu, Emi; Otsuka, Motoaki; Haebara, Tatsuki; Yano, Manami; Matsuzaki, Kanako; Kobuchi, Hirotsugu; Moriya, Koko; Utsumi, Toshihiko

2015-01-01

To identify physiologically important human N-myristoylated proteins, 90 cDNA clones predicted to encode human N-myristoylated proteins were selected from a human cDNA resource (4,369 Kazusa ORFeome project human cDNA clones) by two bioinformatic N-myristoylation prediction systems, NMT-The MYR Predictor and Myristoylator. After database searches to exclude known human N-myristoylated proteins, 37 cDNA clones were selected as potential human N-myristoylated proteins. The susceptibility of these cDNA clones to protein N-myristoylation was first evaluated using fusion proteins in which the N-terminal ten amino acid residues were fused to an epitope-tagged model protein. Then, protein N-myristoylation of the gene products of full-length cDNAs was evaluated by metabolic labeling experiments both in an insect cell-free protein synthesis system and in transfected human cells. As a result, the products of 13 cDNA clones (FBXL7, PPM1B, SAMM50, PLEKHN, AIFM3, C22orf42, STK32A, FAM131C, DRICH1, MCC1, HID1, P2RX5, STK32B) were found to be human N-myristoylated proteins. Analysis of the role of protein N-myristoylation on the intracellular localization of SAMM50, a mitochondrial outer membrane protein, revealed that protein N-myristoylation was required for proper targeting of SAMM50 to mitochondria. Thus, the strategy used in this study is useful for the identification of physiologically important human N-myristoylated proteins from human cDNA resources.

Identification of Human N-Myristoylated Proteins from Human Complementary DNA Resources by Cell-Free and Cellular Metabolic Labeling Analyses

PubMed Central

Takamitsu, Emi; Otsuka, Motoaki; Haebara, Tatsuki; Yano, Manami; Matsuzaki, Kanako; Kobuchi, Hirotsugu; Moriya, Koko; Utsumi, Toshihiko

2015-01-01

To identify physiologically important human N-myristoylated proteins, 90 cDNA clones predicted to encode human N-myristoylated proteins were selected from a human cDNA resource (4,369 Kazusa ORFeome project human cDNA clones) by two bioinformatic N-myristoylation prediction systems, NMT-The MYR Predictor and Myristoylator. After database searches to exclude known human N-myristoylated proteins, 37 cDNA clones were selected as potential human N-myristoylated proteins. The susceptibility of these cDNA clones to protein N-myristoylation was first evaluated using fusion proteins in which the N-terminal ten amino acid residues were fused to an epitope-tagged model protein. Then, protein N-myristoylation of the gene products of full-length cDNAs was evaluated by metabolic labeling experiments both in an insect cell-free protein synthesis system and in transfected human cells. As a result, the products of 13 cDNA clones (FBXL7, PPM1B, SAMM50, PLEKHN, AIFM3, C22orf42, STK32A, FAM131C, DRICH1, MCC1, HID1, P2RX5, STK32B) were found to be human N-myristoylated proteins. Analysis of the role of protein N-myristoylation on the intracellular localization of SAMM50, a mitochondrial outer membrane protein, revealed that protein N-myristoylation was required for proper targeting of SAMM50 to mitochondria. Thus, the strategy used in this study is useful for the identification of physiologically important human N-myristoylated proteins from human cDNA resources. PMID:26308446

In Vitro Comparison of Adipokine Export Signals.

PubMed

Sharafi, Parisa; Kocaefe, Y Çetin

2016-01-01

Mammalian cells are widely used for recombinant protein production in research and biotechnology. Utilization of export signals significantly facilitates production and purification processes. 35 years after the discovery of the mammalian export machinery, there still are obscurities regarding the efficiency of the export signals. The aim of this study was the comparative evaluation of the efficiency of selected export signals using adipocytes as a cell model. Adipocytes have a large capacity for protein secretion including several enzymes, adipokines, and other signaling molecules, providing a valid system for a quantitative evaluation. Constructs that expressed N-terminal fusion export signals were generated to express Enhanced Green Fluorescence Protein (EGFP) as a reporter for quantitative and qualitative evaluation. Furthermore, fluorescent microscopy was used to trace the intracellular traffic of the reporter. The export efficiency of six selected proteins secreted from adipocytes was evaluated. Quantitative comparison of intracellular and exported fractions of the recombinant constructs demonstrated a similar efficiency among the studied sequences with minor variations. The export signal of Retinol Binding Protein (RBP4) exhibited the highest efficiency. This study presents the first quantitative data showing variations among export signals, in adipocytes which will help optimization of recombinant protein distribution.

Proteomic analysis of mouse islets after multiple low-dose streptozotocin injection.

PubMed

Xie, Xiaolei; Li, Shuai; Liu, Siyu; Lu, Yan; Shen, Pingping; Ji, Jianguo

2008-02-01

The islets of Langerhans are scattered throughout the pancreas and play a major role in the control of metabolic fuel homeostasis. To get a better understanding of the mechanisms underlying type 1 diabetes mellitus, we have generated a mouse model by injections of multiple low-dose streptozotocin. The islets in the mouse pancreas were handpicked and proteins from the islets were then isolated and separated by two-dimensional gel electrophoresis. Seven proteins were found to be altered significantly at expression level. Among the seven proteins, four were up-regulated and three were down-regulated in diabetic mice as compared with controls. These proteins were successfully identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry and the changes of selected protein expression were further validated by quantitative real time PCR and Western blotting. Voltage-dependent anion-selective channel protein 1 and peroxiredoxin-4 were found for the first time to be associated with type 1 diabetes mellitus in mouse islets in the current study. These results suggest that glucose transport, beta cell proliferation/death, and oxidative stress play important roles in maintaining the balance of glucose level. Our study also provides novel insight into the mechanism of type 1 diabetes mellitus.

Binding Pathway of Opiates to μ-Opioid Receptors Revealed by Machine Learning

NASA Astrophysics Data System (ADS)

Barati Farimani, Amir; Feinberg, Evan; Pande, Vijay

2018-02-01

Many important analgesics relieve pain by binding to the $\\mu$-Opioid Receptor ($\\mu$OR), which makes the $\\mu$OR among the most clinically relevant proteins of the G Protein Coupled Receptor (GPCR) family. Despite previous studies on the activation pathways of the GPCRs, the mechanism of opiate binding and the selectivity of $\\mu$OR are largely unknown. We performed extensive molecular dynamics (MD) simulation and analysis to find the selective allosteric binding sites of the $\\mu$OR and the path opiates take to bind to the orthosteric site. In this study, we predicted that the allosteric site is responsible for the attraction and selection of opiates. Using Markov state models and machine learning, we traced the pathway of opiates in binding to the orthosteric site, the main binding pocket. Our results have important implications in designing novel analgesics.

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.

Prevention of Pulmonary Fibrosis via Trichostatin A (TSA) in Bleomycin Induced Rats.

PubMed

Ye, Qing; Li, Yanqin; Jiang, Handong; Xiong, Jianfei; Xu, Jiabo; Qin, Hui; Liu, Bin

2014-10-20

To investigate the effects of non selective histone deacetylase inhibitors Trichostatin A (TSA)on bleomycin-induced pulmonary fibrosis. To investigate the effects of non selective histone deacetylase inhibitors Trichostatin A ( TSA ) on HDAC2, p-SMAD2, HDAC2 mRNA, SMAD2mRNA in pulmonary fibrosis rats and investigate impossible mechanism. 46 SPF level male SD rats were randomly divided into four groups: ten for normal control group, fourteen for model control group I, twelve for model control group II and ten for treatment group. Rat pulmonary fibrosis was induced by bleomycin(5mg/kg) via single intratracheal perfusion in the two model control groups and treatment group. Normal control mice were instilled with a corresponding volume of 0.9% saline intratracheally. Treatment group was treated by the dilution of TSA 2mg/kg DMSO 60ul and0.9% saline 1.2ml intraperitoneal injection from the next day ,once a day for three days. Model control group II was treated by the dilution of DMSO 60ul and0.9% saline 1.2ml intraperitoneal injection from the next day once a day for three days. Model control group I and normal control group were treated by 0.9% saline 1.2ml intraperitoneal injection from the next day once a day for three days. All the animals were sacrificed on the 21 day after modeling. The pathological changes were observed by hematoxylin and eosin(HE)stain and masson trichrome stain. The expression of HDAC2 mRNA,SMAD2 mRNA were measured by real-time PCR. The protein level of HDAC2 and p-SMAD2 in serum was measured by Western blot. The pulmonary fibrosis in treatment group were significantly alleviated compared to the two model control groups (P<0.05). Real-time PCR showed that the treatment group had lower expression of lung tissue HDAC2 mRNA than the two model control groups and normal control group (P<0.05). The expression of lung tissue SMAD2 mRNA increased in the two model control groups and treatment group (P<0.05),but there were no significant differences among the three groups(P>0.05). Western blot indicated that the protein level of HDAC2 and p-SMAD2 in serum increased in the two model control groups compared with normal control group(P<0.05).But treatment group had lower protein level of HDAC2 (P<0.05) and no significant difference in the protein level of p-SMAD2 compared to the two model control groups (P>0.05). Non selective histone deacetylase inhibitors of Trichostatin A (TSA) can reduce the bleomycin induced pulmonary fibrosis in rats. TSA attenuates pulmonary fibrosis and it can inhibit HDAC2 expression at the gene and protein level. Bleomycin induced fibrosis has the relationship with p-SMAD2 in gene and protein levels, but TSA inhibit bleomycin-induced lung fibrosis effect with no relation with SMAD2 phosphorylation pathways.

Comparative Proteomic Analysis of Two Uveitis Models in Lewis Rats.

PubMed

Pepple, Kathryn L; Rotkis, Lauren; Wilson, Leslie; Sandt, Angela; Van Gelder, Russell N

2015-12-01

Inflammation generates changes in the protein constituents of the aqueous humor. Proteins that change in multiple models of uveitis may be good biomarkers of disease or targets for therapeutic intervention. The present study was conducted to identify differentially-expressed proteins in the inflamed aqueous humor. Two models of uveitis were induced in Lewis rats: experimental autoimmune uveitis (EAU) and primed mycobacterial uveitis (PMU). Differential gel electrophoresis was used to compare naïve and inflamed aqueous humor. Differentially-expressed proteins were separated by using 2-D gel electrophoresis and excised for identification with matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF). Expression of select proteins was verified by Western blot analysis in both the aqueous and vitreous. The inflamed aqueous from both models demonstrated an increase in total protein concentration when compared to naïve aqueous. Calprotectin, a heterodimer of S100A8 and S100A9, was increased in the aqueous in both PMU and EAU. In the vitreous, S100A8 and S100A9 were preferentially elevated in PMU. Apolipoprotein E was elevated in the aqueous of both uveitis models but was preferentially elevated in EAU. Beta-B2-crystallin levels decreased in the aqueous and vitreous of EAU but not PMU. The proinflammatory molecules S100A8 and S100A9 were elevated in both models of uveitis but may play a more significant role in PMU than EAU. The neuroprotective protein β-B2-crystallin was found to decline in EAU. Therapies to modulate these proteins in vivo may be good targets in the treatment of ocular inflammation.

Comparative Proteomic Analysis of Two Uveitis Models in Lewis Rats

PubMed Central

Pepple, Kathryn L.; Rotkis, Lauren; Wilson, Leslie; Sandt, Angela; Van Gelder, Russell N.

2015-01-01

Purpose Inflammation generates changes in the protein constituents of the aqueous humor. Proteins that change in multiple models of uveitis may be good biomarkers of disease or targets for therapeutic intervention. The present study was conducted to identify differentially-expressed proteins in the inflamed aqueous humor. Methods Two models of uveitis were induced in Lewis rats: experimental autoimmune uveitis (EAU) and primed mycobacterial uveitis (PMU). Differential gel electrophoresis was used to compare naïve and inflamed aqueous humor. Differentially-expressed proteins were separated by using 2-D gel electrophoresis and excised for identification with matrix-assisted laser desorption/ionization–time of flight (MALDI-TOF). Expression of select proteins was verified by Western blot analysis in both the aqueous and vitreous. Results The inflamed aqueous from both models demonstrated an increase in total protein concentration when compared to naïve aqueous. Calprotectin, a heterodimer of S100A8 and S100A9, was increased in the aqueous in both PMU and EAU. In the vitreous, S100A8 and S100A9 were preferentially elevated in PMU. Apolipoprotein E was elevated in the aqueous of both uveitis models but was preferentially elevated in EAU. Beta-B2–crystallin levels decreased in the aqueous and vitreous of EAU but not PMU. Conclusions The proinflammatory molecules S100A8 and S100A9 were elevated in both models of uveitis but may play a more significant role in PMU than EAU. The neuroprotective protein β-B2–crystallin was found to decline in EAU. Therapies to modulate these proteins in vivo may be good targets in the treatment of ocular inflammation. PMID:26747776

IMPACT_S: integrated multiprogram platform to analyze and combine tests of selection.

PubMed

Maldonado, Emanuel; Sunagar, Kartik; Almeida, Daniela; Vasconcelos, Vitor; Antunes, Agostinho

2014-01-01

Among the major goals of research in evolutionary biology are the identification of genes targeted by natural selection and understanding how various regimes of evolution affect the fitness of an organism. In particular, adaptive evolution enables organisms to adapt to changing ecological factors such as diet, temperature, habitat, predatory pressures and prey abundance. An integrative approach is crucial for the identification of non-synonymous mutations that introduce radical changes in protein biochemistry and thus in turn influence the structure and function of proteins. Performing such analyses manually is often a time-consuming process, due to the large number of statistical files generated from multiple approaches, especially when assessing numerous taxa and/or large datasets. We present IMPACT_S, an easy-to-use Graphical User Interface (GUI) software, which rapidly and effectively integrates, filters and combines results from three widely used programs for assessing the influence of selection: Codeml (PAML package), Datamonkey and TreeSAAP. It enables the identification and tabulation of sites detected by these programs as evolving under the influence of positive, neutral and/or negative selection in protein-coding genes. IMPACT_S further facilitates the automatic mapping of these sites onto the three-dimensional structures of proteins. Other useful tools incorporated in IMPACT_S include Jmol, Archaeopteryx, Gnuplot, PhyML, a built-in Swiss-Model interface and a PDB downloader. The relevance and functionality of IMPACT_S is shown through a case study on the toxicoferan-reptilian Cysteine-rich Secretory Proteins (CRiSPs). IMPACT_S is a platform-independent software released under GPLv3 license, freely available online from http://impact-s.sourceforge.net.

Discriminatory bio-adhesion over nano-patterned polymer brushes

NASA Astrophysics Data System (ADS)

Gon, Saugata

Surfaces functionalized with bio-molecular targeting agents are conventionally used for highly-specific protein and cell adhesion. This thesis explores an alternative approach: Small non-biological adhesive elements are placed on a surface randomly, with the rest of the surface rendered repulsive towards biomolecules and cells. While the adhesive elements themselves, for instance in solution, typically exhibit no selectivity for various compounds within an analyte suspension, selective adhesion of targeted objects or molecules results from their placement on the repulsive surface. The mechanism of selectivity relies on recognition of length scales of the surface distribution of adhesive elements relative to species in the analyte solution, along with the competition between attractions and repulsions between various species in the suspension and different parts of the collecting surface. The resulting binding selectivity can be exquisitely sharp; however, complex mixtures generally require the use of multiple surfaces to isolate the various species: Different components will be adhered, sharply, with changes in collector composition. The key feature of these surface designs is their lack of reliance on biomolecular fragments for specificity, focusing entirely on physicochemical principles at the lengthscales from 1 - 100 nm. This, along with a lack of formal patterning, provides the advantages of simplicity and cost effectiveness. This PhD thesis demonstrates these principles using a system in which cationic poly-L-lysine (PLL) patches (10 nm) are deposited randomly on a silica substrate and the remaining surface is passivated with a bio-compatible PEG brush. TIRF microscopy revealed that the patches were randomly arranged, not clustered. By precisely controlling the number of patches per unit area, the interfaces provide sharp selectivity for adhesion of proteins and bacterial cells. For instance, it was found that a critical density of patches (on the order of 1000/mum 2) was required for fibrinogen adsorption while a greater density comprised the adhesion threshold for albumin. Surface compositions between these two thresholds discriminated binding of the two proteins. The binding behavior of the two proteins from a mixture was well anticipated by the single- protein binding behaviors of the individual proteins. The mechanism for protein capture was shown to be multivalent: protein adhesion always occurred for averages spacings of the adhesive patches smaller than the dimensions of the protein of interest. For some backfill brush architectures, the spacing between the patches at the threshold for protein capture clearly corresponded to the major dimension of the target protein. For more dense PEG brush backfills however, larger adhesion thresholds were observed, corresponding to greater numbers of patches involved with the adhesion of each protein molecule. . The thesis demonstrates the tuning of the position of the adhesion thresholds, using fibrinogen as a model protein, using variations in brush properties and ionic strength. The directions of the trends indicate that the brushes do indeed exert steric repulsions toward the proteins while the attractions are electrostatic in nature. The surfaces also demonstrated sharp adhesion thresholds for S. Aureus bacteria, at smaller concentrations of adhesive surfaces elements than those needed for the protein capture. The results suggest that bacteria may be captured while proteins are rejected from these surfaces, and there may be potential to discriminate different bacterial types. Such discrimination from protein-containing bacterial suspensions was investigated briefly in this thesis using S. Aureus and fibrinogen as a model mixture. However, due to binding of fibrinogen to the bacterial surface, the separation did not succeed. It is still expected, however, that these surfaces could be used to selectively capture bacteria in the presence of non-interacting proteins. The interaction of these brushes with two different cationic species PLL and lysozyme were studied. The thesis documents rapid and complete brush displacement by PLL, highlighting a major limitation of using such brushes in some applications. Also unanticipated, lysozyme, a small cationic protein, was found to adhere to the brushes in increasing amounts with the PEG content of the brush. This finding contradicts current understanding of protein-brush interactions that suggests increases in interfacial PEG content increase biocompatibility.

The Chemical Basis for the Origin of the Genetic Code and the Process of Protein Synthesis

NASA Technical Reports Server (NTRS)

Lacey, James C., Jr.

1990-01-01

A model for the origin of protein synthesis. The essential features of the model are that 5'-AMP and perhaps other monoribonucleotides can serve as catalysts for the selective synthesis of L-based peptides. A unique set of characteristics of 5'-AMP is responsible for the selective catalysts and these characteristics are described in detail. The model involves the formation of diesters as intermediates and selectivity for use of the L-isomer occurs principally at the step of forming the diester. However, in the formation of acetyl phenylalanine-AMP monoester there is a selectivity for esterification by the D-isomer. Data showing this selectivity is presented. This selectivity for D-isomer disappears after the first step. The identity was confirmed of all four of possible diesters of acetyl-D- and -L phenylaline with 5'-AMP by nuclear magnetic resonance (NMR). The data using flourescence and NMR show the Trp ring can associate with the adenine ring more strongly when the D-isomer is in the 2' position than it can when in the 3' position. These same data also suggest a molecular mechanisim for the faster esterificaton of 5'-AMP by acetyl-D-phenylaline. Some new data is also presented on the possible structure of the 2' isomer of acetyl-D-tryptophan-AMP monoester. The HPLC elution times of all four possible acetyl diphenylalanine esters of 5'-AMP were studied, these peptidyl esters will be products in the studies of peptide formation on the ribose of 5'-AMP. Other studies were on the rate of synthesis and the identity of the product when producing 3'Ac-Phe-2'tBOC-Phe-AMP diester. HPLC purification and identification of this product were accomplished.

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

Tryptophan tags and de novo designed complementary affinity ligands for the expression and purification of recombinant proteins.

PubMed

Pina, Ana Sofia; Carvalho, Sara; Dias, Ana Margarida G C; Guilherme, Márcia; Pereira, Alice S; Caraça, Luciana T; Coroadinha, Ana Sofia; Lowe, Christopher R; Roque, A Cecília A

2016-11-11

A common strategy for the production and purification of recombinant proteins is to fuse a tag to the protein terminal residues and employ a "tag-specific" ligand for fusion protein capture and purification. In this work, we explored the effect of two tryptophan-based tags, NWNWNW and WFWFWF, on the expression and purification of Green Fluorescence Protein (GFP) used as a model fusion protein. The titers obtained with the expression of these fusion proteins in soluble form were 0.11mgml -1 and 0.48mgml -1 for WFWFWF and NWNWNW, respectively. A combinatorial library comprising 64 ligands based on the Ugi reaction was prepared and screened for binding GFP-tagged and non-tagged proteins. Complementary ligands A2C2 and A3C1 were selected for the effective capture of NWNWNW and WFWFWF tagged proteins, respectively, in soluble forms. These affinity pairs displayed 10 6 M -1 affinity constants and Qmax values of 19.11±2.60ugg -1 and 79.39ugg -1 for the systems WFWFWF AND NWNWNW, respectively. GFP fused to the WFWFWF affinity tag was also produced as inclusion bodies, and a refolding-on column strategy was explored using the ligand A4C8, selected from the combinatorial library of ligands but in presence of denaturant agents. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of Conflicting Selective Effects on Highly Expressed Genes

PubMed Central

Higgs, Paul G.; Hao, Weilong; Golding, G. Brian

2007-01-01

Many different selective effects on DNA and proteins influence the frequency of codons and amino acids in coding sequences. Selection is often stronger on highly expressed genes. Hence, by comparing high- and low-expression genes it is possible to distinguish the factors that are selected by evolution. It has been proposed that highly expressed genes should (i) preferentially use codons matching abundant tRNAs (translational efficiency), (ii) preferentially use amino acids with low cost of synthesis, (iii) be under stronger selection to maintain the required amino acid content, and (iv) be selected for translational robustness. These effects act simultaneously and can be contradictory. We develop a model that combines these factors, and use Akaike’s Information Criterion for model selection. We consider pairs of paralogues that arose by whole-genome duplication in Saccharmyces cerevisiae. A codon-based model is used that includes asymmetric effects due to selection on highly expressed genes. The largest effect is translational efficiency, which is found to strongly influence synonymous, but not non-synonymous rates. Minimization of the cost of amino acid synthesis is implicated. However, when a more general measure of selection for amino acid usage is used, the cost minimization effect becomes redundant. Small effects that we attribute to selection for translational robustness can be identified as an improvement in the model fit on top of the effects of translational efficiency and amino acid usage. PMID:19430600

CASP10-BCL::Fold efficiently samples topologies of large proteins.

PubMed

Heinze, Sten; Putnam, Daniel K; Fischer, Axel W; Kohlmann, Tim; Weiner, Brian E; Meiler, Jens

2015-03-01

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some β-strand proteins model refinement failed as β-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein. © 2015 Wiley Periodicals, Inc.

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.

Biophysical and structural considerations for protein sequence evolution

PubMed Central

2011-01-01

Background Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field. Results Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS < 1 and gamma-distributed rates across sites. Conclusions Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model. PMID:22171550

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

PubMed

Murrell, Daniel S; Cortes-Ciriano, Isidro; van Westen, Gerard J P; Stott, Ian P; Bender, Andreas; Malliavin, Thérèse E; Glen, Robert C

2015-01-01

In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface.

PubMed

Johnson, David K; Karanicolas, John

2013-01-01

Despite intense interest and considerable effort via high-throughput screening, there are few examples of small molecules that directly inhibit protein-protein interactions. This suggests that many protein interaction surfaces may not be intrinsically "druggable" by small molecules, and elevates in importance the few successful examples as model systems for improving our fundamental understanding of druggability. Here we describe an approach for exploring protein fluctuations enriched in conformations containing surface pockets suitable for small molecule binding. Starting from a set of seven unbound protein structures, we find that the presence of low-energy pocket-containing conformations is indeed a signature of druggable protein interaction sites and that analogous surface pockets are not formed elsewhere on the protein. We further find that ensembles of conformations generated with this biased approach structurally resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Collectively these results suggest that "druggability" is a property encoded on a protein surface through its propensity to form pockets, and inspire a model in which the crude features of the predisposed pocket(s) restrict the range of complementary ligands; additional smaller conformational changes then respond to details of a particular ligand. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches.

PubMed

Gozalbes, Rafael; Mosulén, Silvia; Ortí, Leticia; Rodríguez-Díaz, Jesús; Carbajo, Rodrigo J; Melnyk, Patricia; Pineda-Lucena, Antonio

2013-04-01

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation. Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their KD values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range. Copyright © 2013 Elsevier Ltd. All rights reserved.

Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure-based and Sequence-based Descriptors.

PubMed

Rasti, Behnam; Namazi, Mohsen; Karimi-Jafari, M H; Ghasemi, Jahan B

2017-04-01

Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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 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.

Mitochondrial genetic codes evolve to match amino acid requirements of proteins.

PubMed

Swire, Jonathan; Judson, Olivia P; Burt, Austin

2005-01-01

Mitochondria often use genetic codes different from the standard genetic code. Now that many mitochondrial genomes have been sequenced, these variant codes provide the first opportunity to examine empirically the processes that produce new genetic codes. The key question is: Are codon reassignments the sole result of mutation and genetic drift? Or are they the result of natural selection? Here we present an analysis of 24 phylogenetically independent codon reassignments in mitochondria. Although the mutation-drift hypothesis can explain reassignments from stop to an amino acid, we found that it cannot explain reassignments from one amino acid to another. In particular--and contrary to the predictions of the mutation-drift hypothesis--the codon involved in such a reassignment was not rare in the ancestral genome. Instead, such reassignments appear to take place while the codon is in use at an appreciable frequency. Moreover, the comparison of inferred amino acid usage in the ancestral genome with the neutral expectation shows that the amino acid gaining the codon was selectively favored over the amino acid losing the codon. These results are consistent with a simple model of weak selection on the amino acid composition of proteins in which codon reassignments are selected because they compensate for multiple slightly deleterious mutations throughout the mitochondrial genome. We propose that the selection pressure is for reduced protein synthesis cost: most reassignments give amino acids that are less expensive to synthesize. Taken together, our results strongly suggest that mitochondrial genetic codes evolve to match the amino acid requirements of proteins.

Encrypted Antimicrobial Peptides from Plant Proteins.

PubMed

Ramada, M H S; Brand, G D; Abrão, F Y; Oliveira, M; Filho, J L Cardozo; Galbieri, R; Gramacho, K P; Prates, M V; Bloch, C

2017-10-16

Examples of bioactive peptides derived from internal sequences of proteins are known for decades. The great majority of these findings appear to be fortuitous rather than the result of a deliberate and methodological-based enterprise. In the present work, we describe the identification and the biological activities of novel antimicrobial peptides unveiled as internal fragments of various plant proteins founded on our hypothesis-driven search strategy. All putative encrypted antimicrobial peptides were selected based upon their physicochemical properties that were iteratively selected by an in-house computer program named Kamal. The selected peptides were chemically synthesized and evaluated for their interaction with model membranes. Sixteen of these peptides showed antimicrobial activity against human and/or plant pathogens, some with a wide spectrum of activity presenting similar or superior inhibition efficacy when compared to classical antimicrobial peptides (AMPs). These original and previously unforeseen molecules constitute a broader and undisputable set of evidences produced by our group that illustrate how the intragenic concept is a workable reality and should be carefully explored not only for microbicidal agents but also for many other biological functions.

Metal site occupancy and allosteric switching in bacterial metal sensor proteins.

PubMed

Guerra, Alfredo J; Giedroc, David P

2012-03-15

All prokaryotes encode a panel of metal sensor or metalloregulatory proteins that govern the expression of genes that allows an organism to quickly adapt to toxicity or deprivation of both biologically essential transition metal ions, e.g., Zn, Cu, Fe, and heavy metal pollutants. As such, metal sensor proteins can be considered arbiters of intracellular transition metal bioavailability and thus potentially control the metallation state of the metalloproteins in the cell. Metal sensor proteins are specialized allosteric proteins that regulate transcription as a result direct binding of one or two cognate metal ions, to the exclusion of all others. In most cases, the binding of the cognate metal ion induces a structural change in a protein oligomer that either activates or inhibits operator DNA binding. A quantitative measure of the degree to which a particular metal drives metalloregulation of operator DNA-binding is the allosteric coupling free energy, ΔGc. In this review, we summarize recent work directed toward understanding metal occupancy and metal selectivity of these allosteric switches in selected families of metal sensor proteins and examine the structural origins of ΔGc in the functional context a thermodynamic "set-point" model of intracellular metal homeostasis. Copyright © 2011 Elsevier Inc. All rights reserved.

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

Positive selection sites in tertiary structure of Leguminosae chalcone isomerase 1.

PubMed

Wang, R K; Zhan, S F; Zhao, T J; Zhou, X L; Wang, C E

2015-03-20

Isoflavonoids and the related synthesis enzyme, chalcone isomerase 1 (CHI1), are unique in the Leguminosae, with diverse biological functions. Among the Leguminosae, the soybean is an important oil, protein crop, and model plant. In this study, we aimed to detect the generation pattern of Leguminosae CHI1. Genome-wide sequence analysis of CHI in 3 Leguminosae and 3 other closely related model plants was performed; the expression levels of soybean chalcone isomerases were also analyzed. By comparing positively selected sites and their protein structures, we retrieved the evolution patterns for Leguminosae CHI1. A total of 28 CHI and 7 FAP3 (CHI4) genes were identified and separated into 4 clades: CHI1, CHI2, CHI3, and FAP3. Soybean genes belonging to the same chalcone isomerase subfamily had similar expression patterns. CHI1, the unique chalcone isomerase subfamily in Leguminosae, showed signs of significant positive selection as well as special expression characteristics, indicating an accelerated evolution throughout its divergence. Eight sites were identified as undergoing positive selection with high confidence. When mapped onto the tertiary structure of CHI1, these 8 sites were observed surrounding the enzyme substrate only; some of them connected to the catalytic core of CHI. Thus, we inferred that the generation of Leguminosae CHI1 is dependent on the positively selected amino acids surrounding its catalytic substrate. In other words, the evolution of CHI1 was driven by specific selection or processing conditions within the substrate.

Prescreening of Nicotine Hapten Linkers in Vitro To Select Hapten-Conjugate Vaccine Candidates for Pharmacokinetic Evaluation in Vivo.

PubMed

Arutla, Viswanath; Leal, Joseph; Liu, Xiaowei; Sokalingam, Sriram; Raleigh, Michael; Adaralegbe, Adejimi; Liu, Li; Pentel, Paul R; Hecht, Sidney M; Chang, Yung

2017-05-08

Since the demonstration of nicotine vaccines as a possible therapeutic intervention for the effects of tobacco smoke, extensive effort has been made to enhance nicotine specific immunity. Linker modifications of nicotine haptens have been a focal point for improving the immunogenicity of nicotine, in which the evaluation of these modifications usually relies on in vivo animal models, such as mice, rats or nonhuman primates. Here, we present two in vitro screening strategies to estimate and predict the immunogenic potential of our newly designed nicotine haptens. One utilizes a competition enzyme-linked immunoabsorbent assay (ELISA) to profile the interactions of nicotine haptens or hapten-protein conjugates with nicotine specific antibodies, both polyclonal and monoclonal. Another relies on computational modeling of the interactions between haptens and amino acid residues near the conjugation site of the carrier protein to infer linker-carrier protein conjugation effect on antinicotine antibody response. Using these two in vitro methods, we ranked the haptens with different linkers for their potential as viable vaccine candidates. The ELISA-based hapten ranking was in an agreement with the results obtained by in vivo nicotine pharmacokinetic analysis. A correlation was found between the average binding affinity (IC 50 ) of the haptens to an anti-Nic monoclonal antibody and the average brain nicotine concentration in the immunized mice. The computational modeling of hapten and carrier protein interactions helps exclude conjugates with strong linker-carrier conjugation effects and low in vivo efficacy. The simplicity of these in vitro screening strategies should facilitate the selection and development of more effective nicotine conjugate vaccines. In addition, these data highlight a previously under-appreciated contribution of linkers and hapten-protein conjugations to conjugate vaccine immunogenicity by virtue of their inclusion in the epitope that binds and activates B cells.

Mutation Bias Favors Protein Folding Stability in the Evolution of Small Populations

PubMed Central

Porto, Markus; Bastolla, Ugo

2010-01-01

Mutation bias in prokaryotes varies from extreme adenine and thymine (AT) in obligatory endosymbiotic or parasitic bacteria to extreme guanine and cytosine (GC), for instance in actinobacteria. GC mutation bias deeply influences the folding stability of proteins, making proteins on the average less hydrophobic and therefore less stable with respect to unfolding but also less susceptible to misfolding and aggregation. We study a model where proteins evolve subject to selection for folding stability under given mutation bias, population size, and neutrality. We find a non-neutral regime where, for any given population size, there is an optimal mutation bias that maximizes fitness. Interestingly, this optimal GC usage is small for small populations, large for intermediate populations and around 50% for large populations. This result is robust with respect to the definition of the fitness function and to the protein structures studied. Our model suggests that small populations evolving with small GC usage eventually accumulate a significant selective advantage over populations evolving without this bias. This provides a possible explanation to the observation that most species adopting obligatory intracellular lifestyles with a consequent reduction of effective population size shifted their mutation spectrum towards AT. The model also predicts that large GC usage is optimal for intermediate population size. To test these predictions we estimated the effective population sizes of bacterial species using the optimal codon usage coefficients computed by dos Reis et al. and the synonymous to non-synonymous substitution ratio computed by Daubin and Moran. We found that the population sizes estimated in these ways are significantly smaller for species with small and large GC usage compared to species with no bias, which supports our prediction. PMID:20463869

Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach.

PubMed

Gadhe, Changdev G; Kothandan, Gugan; Cho, Seung Joo

2013-01-01

Chemokine receptor 5 (CCR5) is an important receptor used by human immunodeficiency virus type 1 (HIV-1) to gain viral entry into host cell. In this study, we used a combined approach of comparative modeling, molecular docking, and three dimensional quantitative structure activity relationship (3D-QSAR) analyses to elucidate detailed interaction of CCR5 with their inhibitors. Docking study of the most potent inhibitor from a series of compounds was done to derive the bioactive conformation. Parameters such as random selection, rational selection, different charges and grid spacing were utilized in the model development to check their performance on the model predictivity. Final comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were chosen based on the rational selection method, Gasteiger-Hückel charges and a grid spacing of 0.5 Å. Rational model for CoMFA (q(2) = 0.722, r(2) = 0.884, Q(2) = 0.669) and CoMSIA (q(2) = 0.712, r(2) = 0.825, Q(2) = 0.522) was obtained with good statistics. Mapping of contour maps onto CCR5 interface led us to better understand of the ligand-protein interaction. Docking analysis revealed that the Glu283 is crucial for interaction. Two new amino acid residues, Tyr89 and Thr167 were identified as important in ligand-protein interaction. No site directed mutagenesis studies on these residues have been reported.

Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning

PubMed Central

Bacik, John-Paul; Wrenbeck, Emily E.; Michalczyk, Ryszard; Whitehead, Timothy A.

2017-01-01

Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We assayed a TEM-1 beta-lactamase variant and levoglucosan kinase (LGK) using yeast surface display (YSD) screening and a twin-arginine translocation pathway selection. We then compared these scans with published experimental fitness landscapes. Results from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme. Five percent to 10% of all single missense mutations improve solubility, matching theoretical predictions of global protein stability. For a given solubility-enhancing mutation, the probability that it would retain wild-type fitness was correlated with evolutionary conservation and distance to active site, and anticorrelated with contact number. Hybrid classification models were developed that could predict solubility-enhancing mutations that maintain wild-type fitness with an accuracy of 90%. The downside of using such classification models is the removal of rare mutations that improve both fitness and solubility. To reveal the biophysical basis of enhanced protein solubility and function, we determined the crystallographic structure of one such LGK mutant. Beyond fundamental insights into trade-offs between stability and activity, these results have potential biotechnological applications. PMID:28196882

A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

PubMed Central

Apgar, James; Ross, Mary; Zuo, Xiao; Dohle, Sarah; Sturtevant, Derek; Shen, Binzhang; de la Vega, Humberto; Lessard, Philip; Lazar, Gabor; Raab, R. Michael

2012-01-01

Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch. PMID:22649521

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

PubMed Central

Frame, Nicholas M.; Gursky, Olga

2016-01-01

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

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

Enzymes with lid-gated active sites must operate by an induced fit mechanism instead of conformational selection

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

Sullivan, Sarah M.; Holyoak, Todd

2008-09-17

The induced fit and conformational selection/population shift models are two extreme cases of a continuum aimed at understanding the mechanism by which the final key-lock or active enzyme conformation is achieved upon formation of the correctly ligated enzyme. Structures of complexes representing the Michaelis and enolate intermediate complexes of the reaction catalyzed by phosphoenolpyruvate carboxykinase provide direct structural evidence for the encounter complex that is intrinsic to the induced fit model and not required by the conformational selection model. In addition, the structural data demonstrate that the conformational selection model is not sufficient to explain the correlation between dynamics andmore » catalysis in phosphoenolpyruvate carboxykinase and other enzymes in which the transition between the uninduced and the induced conformations occludes the active site from the solvent. The structural data are consistent with a model in that the energy input from substrate association results in changes in the free energy landscape for the protein, allowing for structural transitions along an induced fit pathway.« less

Enzymes With Lid-Gated Active Sites Must Operate By An Induced Fit Mechanism Instead of Conformational Selection

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

Sullivan, S.M.; Holyoak, T.

2009-05-26

The induced fit and conformational selection/population shift models are two extreme cases of a continuum aimed at understanding the mechanism by which the final key-lock or active enzyme conformation is achieved upon formation of the correctly ligated enzyme. Structures of complexes representing the Michaelis and enolate intermediate complexes of the reaction catalyzed by phosphoenolpyruvate carboxykinase provide direct structural evidence for the encounter complex that is intrinsic to the induced fit model and not required by the conformational selection model. In addition, the structural data demonstrate that the conformational selection model is not sufficient to explain the correlation between dynamics andmore » catalysis in phosphoenolpyruvate carboxykinase and other enzymes in which the transition between the uninduced and the induced conformations occludes the active site from the solvent. The structural data are consistent with a model in that the energy input from substrate association results in changes in the free energy landscape for the protein, allowing for structural transitions along an induced fit pathway.« less

Interdependency of formation and localisation of the Min complex controls symmetric plastid division.

PubMed

Maple, Jodi; Møller, Simon G

2007-10-01

Plastid division represents a fundamental biological process essential for plant development; however, the molecular basis of symmetric plastid division is unclear. AtMinE1 plays a pivotal role in selection of the plastid division site in concert with AtMinD1. AtMinE1 localises to discrete foci in chloroplasts and interacts with AtMinD1, which shows a similar localisation pattern. Here, we investigate the importance of Min protein complex formation during the chloroplast division process. Dissection of the assembly of the Min protein complex and determination of the interdependency of complex assembly and localisation in planta allow us to present a model of the molecular basis of selection of the division site in plastids. Moreover, functional analysis of AtMinE1 in bacteria demonstrates the level of functional conservation and divergence of the plastidic MinE proteins.

Genetically Encoded Molecular Tension Probe for Tracing Protein-Protein Interactions in Mammalian Cells.

PubMed

Kim, Sung Bae; Nishihara, Ryo; Citterio, Daniel; Suzuki, Koji

2016-02-17

Optical imaging of protein-protein interactions (PPIs) facilitates comprehensive elucidation of intracellular molecular events. We demonstrate an optical measure for visualizing molecular tension triggered by any PPI in mammalian cells. Twenty-three kinds of candidate designs were fabricated, in which a full-length artificial luciferase (ALuc) was sandwiched between two model proteins of interest, e.g., FKBP and FRB. One of the designs greatly enhanced the bioluminescence in response to varying concentrations of rapamycin. It is confirmed with negative controls that the elevated bioluminescence is solely motivated from the molecular tension. The probe design was further modified toward eliminating the C-terminal end of ALuc and was found to improve signal-to-background ratios, named "a combinational probe". The utilities were elucidated with detailed substrate selectivity, bioluminescence imaging of live cells, and different PPI models. This study expands capabilities of luciferases as a tool for analyses of molecular dynamics and cell signaling in living subjects.

Direct observation of transcription activator-like effector (TALE) protein dynamics

NASA Astrophysics Data System (ADS)

Cuculis, Luke; Abil, Zhanar; Zhao, Huimin; Schroeder, Charles M.

2014-03-01

In this work, we describe a single molecule assay to probe the site-search dynamics of transcription activator-like effector (TALE) proteins along DNA. In modern genetics, the ability to selectively edit the human genome is an unprecedented development, driven by recent advances in targeted nuclease proteins. Specific gene editing can be accomplished using TALE proteins, which are programmable DNA-binding proteins that can be fused to a nuclease domain. In this way, TALENs are a leading technology that has shown great success in the genomic editing of pluripotent stem cells. A major hurdle facing clinical implementation, however, is the potential for deleterious off-target binding events. For these reasons, a molecular-level understanding of TALE binding and target sequence search on DNA is essential. To this end, we developed a single-molecule fluorescence imaging assay that provides a first-of-its-kind view of the 1-D diffusion of TALE proteins along stretched DNA. Taken together with co-crystal structures of DNA-bound TALEs, our results suggest a rotationally-coupled, major groove tracking model for diffusion. We further report diffusion constants for TALE proteins as a function of salt concentration, consistent with previously described models of 1-D protein diffusion.

Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools.

PubMed

Felgueiras, Juliana; Silva, Joana Vieira; Fardilha, Margarida

2018-01-16

"A man is known by the company he keeps" is a popular expression that perfectly fits proteins. A common approach to characterize the function of a target protein is to identify its interacting partners and thus infer its roles based on the known functions of the interactors. Protein-protein interaction networks (PPINs) have been created for several organisms, including humans, primarily as results of high-throughput screenings, such as yeast two-hybrid (Y2H). Their unequivocal use to understand events underlying human pathophysiology is promising in identifying genes and proteins associated with diseases. Therefore, numerous opportunities have emerged for PPINs as tools for clinical management of diseases: network-based disease classification systems, discovery of biomarkers and identification of therapeutic targets. Despite the great advantages of PPINs, their use is still unrecognised by several researchers who generate high-throughput data to generally characterize interactions in a certain model or to select an interaction to study in detail. We strongly believe that both approaches are not exclusive and that we can use PPINs as a complementary methodology and rich-source of information to the initial study proposal. Here, we suggest a pipeline to deal with Y2H results using bioinformatics tools freely available for academics. Yeast two-hybrid is widely-used to identify protein-protein interactions. Conventionally, the positive clones that result from a yeast two-hybrid screening are sequenced to identify the interactors of the protein of interest (also known as bait protein), and few interactions, thought as potentially relevant for the model in study, are selected for further validation using biochemical methods (e.g. co-immunoprecipitation and co-localization). The huge amount of data that is potentially lost during this conservative approach motivated us to write this tutorial-like review, so that researchers feel encouraged to take advantage of bioinformatics tools to their full potential to analyse protein-protein interactions as a comprehensive network. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling.

PubMed

Gutierrez, Jahir M; Lewis, Nathan E

2015-07-01

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Population mitogenomics provides insights into evolutionary history, source of invasions and diversifying selection in the House Crow (Corvus splendens).

PubMed

Krzemińska, Urszula; Morales, Hernán E; Greening, Chris; Nyári, Árpád S; Wilson, Robyn; Song, Beng Kah; Austin, Christopher M; Sunnucks, Paul; Pavlova, Alexandra; Rahman, Sadequr

2018-04-01

The House Crow (Corvus splendens) is a useful study system for investigating the genetic basis of adaptations underpinning successful range expansion. The species originates from the Indian subcontinent, but has successfully spread through a variety of thermal environments across Asia, Africa and Europe. Here, population mitogenomics was used to investigate the colonisation history and to test for signals of molecular selection on the mitochondrial genome. We sequenced the mitogenomes of 89 House Crows spanning four native and five invasive populations. A Bayesian dated phylogeny, based on the 13 mitochondrial protein-coding genes, supports a mid-Pleistocene (~630,000 years ago) divergence between the most distant genetic lineages. Phylogeographic patterns suggest that northern South Asia is the likely centre of origin for the species. Codon-based analyses of selection and assessments of changes in amino acid properties provide evidence of positive selection on the ND2 and ND5 genes against a background of purifying selection across the mitogenome. Protein homology modelling suggests that four amino acid substitutions inferred to be under positive selection may modulate coupling efficiency and proton translocation mediated by OXPHOS complex I. The identified substitutions are found within native House Crow lineages and ecological niche modelling predicts suitable climatic areas for the establishment of crow populations within the invasive range. Mitogenomic patterns in the invasive range of the species are more strongly associated with introduction history than climate. We speculate that invasions of the House Crow have been facilitated by standing genetic variation that accumulated due to diversifying selection within the native range.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms. Copyright © 2014 Elsevier B.V. All rights reserved.

Pertussis toxin

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

Sekura, R.D.; Moss, J.; Vaughan, M.

1985-01-01

This book contains 13 selections. Some of the titles are: Genetic and Functional Studies of Pertussis Toxin Substrates; Effect of Pertussis Toxin on the Hormonal Responsiveness of Different Tissues; Extracellular Adenylate Cyclase of Bordetella pertussis; and GTP-Regulatory Proteins are Introcellular Messagers: A Model for Hormone Action.

Year 2 Report: Protein Function Prediction Platform

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

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

Landscape phages and their fusion proteins targeted to breast cancer cells

PubMed Central

Fagbohun, Olusegun A.; Bedi, Deepa; Grabchenko, Natalia I.; Deinnocentes, Patricia A.; Bird, Richard C.; Petrenko, Valery A.

2012-01-01

Breast cancer is a leading cause of death among women in the USA. The efficacy of existing anticancer therapeutics can be improved by targeting them through conjugation with ligands binding to cellular receptors. Recently, we developed a novel drug targeting strategy based on the use of pre-selected cancer-specific ‘fusion pVIII proteins’ (fpVIII), as targeting ligands. To study the efficiency of this approach in animal models, we developed a panel of breast cancer cell-binding phages as a source of targeted fpVIIIs. Two landscape phage peptide libraries (8-mer f8/8 and 9-mer f8/9) were screened to isolate 132 phage variants that recognize breast carcinoma cells MCF-7 and ZR-75-1 and internalize into the cells. When tested for their interaction with the breast cancer cells in comparison with liver cancer cells HepG2, human mammary cells MCF-10A cells and serum, 16 of the phage probes selectively interacted with the breast cancer cells whereas 32 bound both breast and liver cancer cells. The most prominent cancer-specific phage DMPGTVLP, demonstrating sub-nanomolar Kd in interaction with target cells, was used for affinity chromatography of cellular membrane molecules to reveal its potential binding receptor. The isolated protein was identified by direct sequencing as cellular surface nucleolin. This conclusion was confirmed by inhibition of the phage–cell interaction with nucleolin antibodies. Other prominent phage binders VPTDTDYS, VEEGGYIAA, and DWRGDSMDS demonstrate consensus motifs common to previously identified cancer-specific peptides. Isolated phage proteins exhibit inherent binding specificity towards cancer cells, demonstrating the functional activity of the selected fused peptides. The selected phages, their peptide inserts and intact fusion proteins can serve as promising ligands for the development of targeted nanomedicines and their study in model mice with xenograft of human cells MCF-7 and ZR-75-1. PMID:22490956

Selected HIV-1 Env trimeric formulations act as potent immunogens in a rabbit vaccination model.

PubMed

Heyndrickx, Leo; Stewart-Jones, Guillaume; Jansson, Marianne; Schuitemaker, Hanneke; Bowles, Emma; Buonaguro, Luigi; Grevstad, Berit; Vinner, Lasse; Vereecken, Katleen; Parker, Joe; Ramaswamy, Meghna; Biswas, Priscilla; Vanham, Guido; Scarlatti, Gabriella; Fomsgaard, Anders

2013-01-01

Ten to 30% of HIV-1 infected subjects develop broadly neutralizing antibodies (bNAbs) during chronic infection. We hypothesized that immunizing rabbits with viral envelope glycoproteins (Envs) from these patients may induce bNAbs, when formulated as a trimeric protein and in the presence of an adjuvant. Based on in vitro neutralizing activity in serum, patients with bNAbs were selected for cloning of their HIV-1 Env. Seven stable soluble trimeric gp140 proteins were generated from sequences derived from four adults and two children infected with either clade A or B HIV-1. From one of the clade A Envs both the monomeric and trimeric Env were produced for comparison. Rabbits were immunized with soluble gp120 or trimeric gp140 proteins in combination with the adjuvant dimethyl dioctadecyl ammonium/trehalose dibehenate (CAF01). Env binding in rabbit immune serum was determined using ELISAs based on gp120-IIIB protein. Neutralizing activity of IgG purified from rabbit immune sera was measured with the pseudovirus-TZMbl assay and a PBMC-based neutralization assay for selected experiments. It was initially established that gp140 trimers induce better antibody responses over gp120 monomers and that the adjuvant CAF01 was necessary for such strong responses. Gp140 trimers, based on HIV-1 variants from patients with bNAbs, were able to elicit both gp120IIIB specific IgG and NAbs to Tier 1 viruses of different subtypes. Potency of NAbs closely correlated with titers, and an gp120-binding IgG titer above a threshold of 100,000 was predictive of neutralization capability. Finally, peptide inhibition experiments showed that a large fraction of the neutralizing IgG was directed against the gp120 V3 region. Our results indicate that the strategy of reverse immunology based on selected Env sequences is promising when immunogens are delivered as stabilized trimers in CAF01 adjuvant and that the rabbit is a valuable model for HIV vaccine studies.

Chemical cross-linking of the urease complex from Helicobacter pylori and analysis by Fourier transform ion cyclotron resonance mass spectrometry and molecular modeling

NASA Astrophysics Data System (ADS)

Carlsohn, Elisabet; Ångström, Jonas; Emmett, Mark R.; Marshall, Alan G.; Nilsson, Carol L.

2004-05-01

Chemical cross-linking of proteins is a well-established method for structural mapping of small protein complexes. When combined with mass spectrometry, cross-linking can reveal protein topology and identify contact sites between the peptide surfaces. When applied to surface-exposed proteins from pathogenic organisms, the method can reveal structural details that are useful in vaccine design. In order to investigate the possibilities of applying cross-linking on larger protein complexes, we selected the urease enzyme from Helicobacter pylori as a model. This membrane-associated protein complex consists of two subunits: [alpha] (26.5 kDa) and [beta] (61.7 kDa). Three ([alpha][beta]) heterodimers form a trimeric ([alpha][beta])3 assembly which further associates into a unique dodecameric 1.1 MDa complex composed of four ([alpha][beta])3 units. Cross-linked peptides from trypsin-digested urease complex were analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and molecular modeling. Two potential cross-linked peptides (present in the cross-linked sample but undetectable in [alpha], [beta], and native complex) were assigned. Molecular modeling of urease [alpha][beta] complex and trimeric urease units ([alpha][beta])3 revealed a linkage site between the [alpha]-subunit and the [beta]-subunit, and an internal cross-linkage in the [beta]-subunit.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

PubMed

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

QSAR models for prediction of chromatographic behavior of homologous Fab variants.

PubMed

Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

2017-06-01

While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Strong adsorption of random heteropolymers on protein surfaces

NASA Astrophysics Data System (ADS)

Nguyen, Trung; Qiao, Baofu; Panganiban, Brian; Delre, Christopher; Xu, Ting; Olvera de La Cruz, Monica

Rational design of copolymers for stablizing proteins' functionalities in unfavorable solvents and delivering nanoparticles through organic membranes demands a thorough understanding of how the proteins and colloids are encapsulated by a given type of copolymers. Random heteropolymers (RHPs), a special family of copolymers with random segment order, have long been recognized as a promising coating materials due to their biomimetic behaviors while allowing for much flexibility in the synthesis procedure. Of practical importance is the ability to predict the conditions under which a given family of random heteropolymers would provide optimal encapsulatio. Here we investigate the key factors that govern the adsorption of RHPs on the surface of a model protein. Using coarse-grained molecular simulation we identify the conditions under which the model protein is fully covered by the polymers. We have examined the nanometer-level details of the adsorbed polymer chains and found a clear connection between the surface coverage and adsorption strength, solvent selectivity and the volume fraction of adsorbing monomers. The results in this work set the stage for further investigation on engineering biomimetic RHPs for stabilizing and delivering functional proteins across multiple media.

Detection of microbial invasion of the amniotic cavity by analysis of cervicovaginal proteins in women with preterm labor and intact membranes.

PubMed

Combs, C Andrew; Garite, Thomas J; Lapidus, Jodi A; Lapointe, Jerome P; Gravett, Michael; Rael, Julie; Amon, Erol; Baxter, Jason K; Brady, Kim; Clewell, William; Eddleman, Keith A; Fortunato, Stephen; Franco, Albert; Haas, David M; Heyborne, Kent; Hickok, Durlin E; How, Helen Y; Luthy, David; Miller, Hugh; Nageotte, Michael; Pereira, Leonardo; Porreco, Richard; Robilio, Peter A; Simhan, Hyagriv; Sullivan, Scott A; Trofatter, Kenneth; Westover, Thomas

2015-04-01

Microbial invasion of the amniotic cavity (MIAC) is common in early preterm labor and is associated with maternal and neonatal infectious morbidity. MIAC is usually occult and is reliably detected only with amniocentesis. We sought to develop a noninvasive test to predict MIAC based on protein biomarkers in cervicovaginal fluid (CVF) in a cohort of women with preterm labor (phase 1) and to validate the test in an independent cohort (phase 2). This was a prospective study of women with preterm labor who had amniocentesis to screen for MIAC. MIAC was defined by positive culture and/or 16S ribosomal DNA results. Nine candidate CVF proteins were analyzed by enzyme-linked immunosorbent assay. Logistic regression was used to identify combinations of up to 3 proteins that could accurately classify the phase 1 cohort (N = 108) into those with or without MIAC. The best models, selected by area under the curve (AUC) of the receiver operating characteristic curve in phase 1, included various combinations of interleukin (IL)-6, chemokine (C-X-C motif) ligand 1 (CXCL1), alpha fetoprotein, and insulin-like growth factor binding protein-1. Model performance was then tested in the phase 2 cohort (N = 306). MIAC was present in 15% of cases in phase 1 and 9% in phase 2. A 3-marker CVF model using IL-6 plus CXCL1 plus insulin-like growth factor binding protein-1 had AUC 0.87 in phase 1 and 0.78 in phase 2. Two-marker models using IL-6 plus CXCL1 or alpha fetoprotein plus CXCL1 performed similarly in phase 2 (AUC 0.78 and 0.75, respectively), but were not superior to CVF IL-6 alone (AUC 0.80). A cutoff value of CVF IL-6 ≥463 pg/mL (which had 81% sensitivity in phase 1) predicted MIAC in phase 2 with sensitivity 79%, specificity 78%, positive predictive value 38%, and negative predictive value 97%. High levels of IL-6 in CVF are strongly associated with MIAC. If developed into a bedside test or rapid laboratory assay, cervicovaginal IL-6 might be useful in selecting patients in whom the probability of MIAC is high enough to warrant amniocentesis or transfer to a higher level of care. Such a test might also guide selection of potential subjects for treatment trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.

PubMed

An, Yi; Wang, Jiawei; Li, Chen; Leier, André; Marquez-Lago, Tatiana; Wilksch, Jonathan; Zhang, Yang; Webb, Geoffrey I; Song, Jiangning; Lithgow, Trevor

2018-01-01

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Macronutrient Optimization and Seasonal Diet Mixing in a Large Omnivore, the Grizzly Bear: A Geometric Analysis

PubMed Central

Coogan, Sean C. P.; Raubenheimer, David; Stenhouse, Gordon B.; Nielsen, Scott E.

2014-01-01

Nutrient balance is a strong determinant of animal fitness and demography. It is therefore important to understand how the compositions of available foods relate to required balance of nutrients and habitat suitability for animals in the wild. These relationships are, however, complex, particularly for omnivores that often need to compose balanced diets by combining their intake from diverse nutritionally complementary foods. Here we apply geometric models to understand how the nutritional compositions of foods available to an omnivorous member of the order Carnivora, the grizzly bear (Ursus arctos L.), relate to optimal macronutrient intake, and assess the seasonal nutritional constraints on the study population in west-central Alberta, Canada. The models examined the proportion of macronutrients that bears could consume by mixing their diet from food available in each season, and assessed the extent to which bears could consume the ratio of protein to non-protein energy previously demonstrated using captive bears to optimize mass gain. We found that non-selective feeding on ungulate carcasses provided a non-optimal macronutrient balance with surplus protein relative to fat and carbohydrate, reflecting adaptation to an omnivorous lifestyle, and that optimization through feeding selectively on different tissues of ungulate carcasses is unlikely. Bears were, however, able to dilute protein intake to an optimal ratio by mixing their otherwise high-protein diet with carbohydrate-rich fruit. Some individual food items were close to optimally balanced in protein to non-protein energy (e.g. Hedysarum alpinum roots), which may help explain their dietary prevalence. Ants may be consumed particularly as a source of lipids. Overall, our analysis showed that most food available to bears in the study area were high in protein relative to lipid or carbohydrate, suggesting the lack of non-protein energy limits the fitness (e.g. body size and reproduction) and population density of grizzly bears in this ecosystem. PMID:24841821

Macronutrient optimization and seasonal diet mixing in a large omnivore, the grizzly bear: a geometric analysis.

PubMed

Coogan, Sean C P; Raubenheimer, David; Stenhouse, Gordon B; Nielsen, Scott E

2014-01-01

Nutrient balance is a strong determinant of animal fitness and demography. It is therefore important to understand how the compositions of available foods relate to required balance of nutrients and habitat suitability for animals in the wild. These relationships are, however, complex, particularly for omnivores that often need to compose balanced diets by combining their intake from diverse nutritionally complementary foods. Here we apply geometric models to understand how the nutritional compositions of foods available to an omnivorous member of the order Carnivora, the grizzly bear (Ursus arctos L.), relate to optimal macronutrient intake, and assess the seasonal nutritional constraints on the study population in west-central Alberta, Canada. The models examined the proportion of macronutrients that bears could consume by mixing their diet from food available in each season, and assessed the extent to which bears could consume the ratio of protein to non-protein energy previously demonstrated using captive bears to optimize mass gain. We found that non-selective feeding on ungulate carcasses provided a non-optimal macronutrient balance with surplus protein relative to fat and carbohydrate, reflecting adaptation to an omnivorous lifestyle, and that optimization through feeding selectively on different tissues of ungulate carcasses is unlikely. Bears were, however, able to dilute protein intake to an optimal ratio by mixing their otherwise high-protein diet with carbohydrate-rich fruit. Some individual food items were close to optimally balanced in protein to non-protein energy (e.g. Hedysarum alpinum roots), which may help explain their dietary prevalence. Ants may be consumed particularly as a source of lipids. Overall, our analysis showed that most food available to bears in the study area were high in protein relative to lipid or carbohydrate, suggesting the lack of non-protein energy limits the fitness (e.g. body size and reproduction) and population density of grizzly bears in this ecosystem.

Protective effects of positive lysosomal modulation in Alzheimer's disease transgenic mouse models.

PubMed

Butler, David; Hwang, Jeannie; Estick, Candice; Nishiyama, Akiko; Kumar, Saranya Santhosh; Baveghems, Clive; Young-Oxendine, Hollie B; Wisniewski, Meagan L; Charalambides, Ana; Bahr, Ben A

2011-01-01

Alzheimer's disease (AD) is an age-related neurodegenerative pathology in which defects in proteolytic clearance of amyloid β peptide (Aβ) likely contribute to the progressive nature of the disorder. Lysosomal proteases of the cathepsin family exhibit up-regulation in response to accumulating proteins including Aβ(1-42). Here, the lysosomal modulator Z-Phe-Ala-diazomethylketone (PADK) was used to test whether proteolytic activity can be enhanced to reduce the accumulation events in AD mouse models expressing different levels of Aβ pathology. Systemic PADK injections in APP(SwInd) and APPswe/PS1ΔE9 mice caused 3- to 8-fold increases in cathepsin B protein levels and 3- to 10-fold increases in the enzyme's activity in lysosomal fractions, while neprilysin and insulin-degrading enzyme remained unchanged. Biochemical analyses indicated the modulation predominantly targeted the active mature forms of cathepsin B and markedly changed Rab proteins but not LAMP1, suggesting the involvement of enhanced trafficking. The modulated lysosomal system led to reductions in both Aβ immunostaining as well as Aβ(x-42) sandwich ELISA measures in APP(SwInd) mice of 10-11 months. More extensive Aβ deposition in 20-22-month APPswe/PS1ΔE9 mice was also reduced by PADK. Selective ELISAs found that a corresponding production of the less pathogenic Aβ(1-38) occurs as Aβ(1-42) levels decrease in the mouse models, indicating that PADK treatment leads to Aβ truncation. Associated with Aβ clearance was the elimination of behavioral and synaptic protein deficits evident in the two transgenic models. These findings indicate that pharmacologically-controlled lysosomal modulation reduces Aβ(1-42) accumulation, possibly through intracellular truncation that also influences extracellular deposition, and in turn offsets the defects in synaptic composition and cognitive functions. The selective modulation promotes clearance at different levels of Aβ pathology and provides proof-of-principle for small molecule therapeutic development for AD and possibly other protein accumulation disorders.

Updating the Salivary Gland Transcriptome of Phlebotomus papatasi (Tunisian Strain): The Search for Sand Fly-Secreted Immunogenic Proteins for Humans

PubMed Central

Ben Ahmed, Melika; Zhioua, Elyes; Chelbi, Ifhem; Cherni, Saifedine; Louzir, Hechmi; Ribeiro, José M. C.; Valenzuela, Jesus G.

2012-01-01

Introduction Sand fly saliva plays an important role in both blood feeding and outcome of Leishmania infection. A cellular immune response against a Phlebotomus papatasi salivary protein was shown to protect rodents against Leishmania major infection. In humans, P. papatasi salivary proteins induce a systemic cellular immune response as well as a specific antisaliva humoral immune response, making these salivary proteins attractive targets as markers of exposure for this Leishmania vector. Surprisingly, the repertoire of salivary proteins reported for P. papatasi–a model sand fly for Leishmania-vector-host molecular interactions–is very limited compared with other sand fly species. We hypothesize that a more comprehensive study of the transcripts present in the salivary glands of P. papatasi will provide better knowledge of the repertoire of proteins of this important vector and will aid in selection of potential immunogenic proteins for humans and of those proteins that are highly conserved between different sand fly strains. Methods and Findings A cDNA library from P. papatasi (Tunisian strain) salivary glands was constructed, and randomly selected transcripts were sequenced and analyzed. The most abundant transcripts encoding secreted proteins were identified and compared with previously reported sequences. Importantly, we identified salivary proteins not described before in this sand fly species. Conclusions Comparative analysis between the salivary proteins of P. papatasi from Tunisia and Israel strains shows a high level of identity, suggesting these proteins as potential common targets for markers of vector exposure or inducers of cellular immune responses in humans for different geographic areas. PMID:23139741

Updating the salivary gland transcriptome of Phlebotomus papatasi (Tunisian strain): the search for sand fly-secreted immunogenic proteins for humans.

PubMed

Abdeladhim, Maha; Jochim, Ryan C; Ben Ahmed, Melika; Zhioua, Elyes; Chelbi, Ifhem; Cherni, Saifedine; Louzir, Hechmi; Ribeiro, José M C; Valenzuela, Jesus G

2012-01-01

Sand fly saliva plays an important role in both blood feeding and outcome of Leishmania infection. A cellular immune response against a Phlebotomus papatasi salivary protein was shown to protect rodents against Leishmania major infection. In humans, P. papatasi salivary proteins induce a systemic cellular immune response as well as a specific antisaliva humoral immune response, making these salivary proteins attractive targets as markers of exposure for this Leishmania vector. Surprisingly, the repertoire of salivary proteins reported for P. papatasi-a model sand fly for Leishmania-vector-host molecular interactions-is very limited compared with other sand fly species. We hypothesize that a more comprehensive study of the transcripts present in the salivary glands of P. papatasi will provide better knowledge of the repertoire of proteins of this important vector and will aid in selection of potential immunogenic proteins for humans and of those proteins that are highly conserved between different sand fly strains. A cDNA library from P. papatasi (Tunisian strain) salivary glands was constructed, and randomly selected transcripts were sequenced and analyzed. The most abundant transcripts encoding secreted proteins were identified and compared with previously reported sequences. Importantly, we identified salivary proteins not described before in this sand fly species. Comparative analysis between the salivary proteins of P. papatasi from Tunisia and Israel strains shows a high level of identity, suggesting these proteins as potential common targets for markers of vector exposure or inducers of cellular immune responses in humans for different geographic areas.

Models of the Protocellular Structures, Functions and Evolution

NASA Technical Reports Server (NTRS)

Pohorille, Andrew; New, Michael; Keefe, Anthony; Szostak, Jack W.; Lanyi, Janos F.; DeVincenzi, Donald L. (Technical Monitor)

2000-01-01

In the absence of extinct or extant record of protocells, the most direct way to test our understanding of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids: First, a very large population of candidate molecules is generated using a random synthetic approach. Next, the small numbers of molecules that can accomplish the desired task are selected. These molecules are next vastly multiplied using the polymerase chain reaction. A mutagenic approach, in which the sequences of selected molecules are randomly altered, can yield further improvements in performance or alterations of specificities. Unfortunately, the catalytic potential of nucleic acids is rather limited. Proteins are more catalytically capable but cannot be directly amplified. In the new technique, this problem is circumvented by covalently linking each protein of the initial, diverse, pool to the RNA sequence that codes for it. Then, selection is performed on the proteins, but the nucleic acids are replicated. To date, we have obtained "a proof of concept" by evolving simple, novel proteins capable of selectively binding adenosine tri-phosphate (ATP). Our next goal is to create an enzyme that can phosphorylate amino acids and another to catalyze the formation of peptide bonds in the absence of nucleic acid templates. This latter reaction does not take place in contemporary cells. once developed, these enzymes will be encapsulated in liposomes so that they will function in a simulated cellular environment. To provide a continuous energy supply, usually needed to activate the substrates, an energy transduction complex which generates ATP from adenosine diphosphate, inorganic phosphate and light will be used. This system, consisting of two modern proteins, ATP synthase and bacteriorhodopsin, has already been built and shown to work efficiently. By coupling chemical synthesis to such a system, it will be possible to drive chemical reactions by light if only the substrates for these reactions are supplied.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

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.

Whole-Genome Positive Selection and Habitat-Driven Evolution in a Shallow and a Deep-Sea Urchin

PubMed Central

Oliver, Thomas A.; Garfield, David A.; Manier, Mollie K.; Haygood, Ralph; Wray, Gregory A.; Palumbi, Stephen R.

2010-01-01

Comparisons of genomic sequence between divergent species can provide insight into the action of natural selection across many distinct classes of proteins. Here, we examine the extent of positive selection as a function of tissue-specific and stage-specific gene expression in two closely-related sea urchins, the shallow-water Strongylocentrotus purpuratus and the deep-sea Allocentrotus fragilis, which have diverged greatly in their adult but not larval habitats. Genes that are expressed specifically in adult somatic tissue have significantly higher dN/dS ratios than the genome-wide average, whereas those in larvae are indistinguishable from the genome-wide average. Testis-specific genes have the highest dN/dS values, whereas ovary-specific have the lowest. Branch-site models involving the outgroup S. franciscanus indicate greater selection (ωFG) along the A. fragilis branch than along the S. purpuratus branch. The A. fragilis branch also shows a higher proportion of genes under positive selection, including those involved in skeletal development, endocytosis, and sulfur metabolism. Both lineages are approximately equal in enrichment for positive selection of genes involved in immunity, development, and cell–cell communication. The branch-site models further suggest that adult-specific genes have experienced greater positive selection than those expressed in larvae and that ovary-specific genes are more conserved (i.e., experienced greater negative selection) than those expressed specifically in adult somatic tissues and testis. Our results chart the patterns of protein change that have occurred after habitat divergence in these two species and show that the developmental or functional context in which a gene acts can play an important role in how divergent species adapt to new environments. PMID:20935062

TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

PubMed Central

2014-01-01

Background Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC. Results Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets. Conclusions We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression. PMID:25521548

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments*

PubMed Central

Searle, Brian C.; Egertson, Jarrett D.; Bollinger, James G.; Stergachis, Andrew B.; MacCoss, Michael J.

2015-01-01

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40–85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature. PMID:26100116

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.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

PubMed Central

Chudasama, Vaishali L.; Ovacik, Meric A.; Abernethy, Darrell R.

2015-01-01

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens. PMID:26163548

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

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

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

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

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

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy.

PubMed

Ribeiro, J S; Ferreira, M M C; Salva, T J G

2011-02-15

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein. Copyright © 2010 Elsevier B.V. All rights reserved.

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

Weak cation exchange magnetic beads coupled with matrix-assisted laser desorption ionization-time of flight-mass spectrometry in screening serum protein markers in osteopenia.

PubMed

He, Wei-Tao; Liang, Bo-Cheng; Shi, Zhen-Yu; Li, Xu-Yun; Li, Chun-Wen; Shi, Xiao-Lin

2016-01-01

The present study aimed at investigating the weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of osteopenia from ten postmenopausal women and ten postmenopausal women without osteopenia as control group, to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were collected from postmenopausal women with osteopenia and postmenopausal women with normal bone mass. Proteins were extracted from serum samples by weak cation exchange magnetic beads technology, and mass spectra acquisition was done by MALDI-TOF-MS. The visualization and comparison of data sets, statistical peak evaluation, model recognition, and discovery of biomarker candidates were handled by the proteinchip data analysis system software(ZJU-PDAS). The diagnostic models were established using genetic arithmetic based support vector machine (SVM). The SVM result with the highest Youden Index was selected as the model. Combinatorial Peaks having the highest accuracy in distinguishing different samples were selected as potential biomarker. From the two group serum samples, a total of 133 differential features were selected. Ten features with significant intensity differences were screened. In the pair-wise comparisons, processing of MALDI-TOF spectra resulted in the identification of ten differential features between postmenopausal women with osteopenia and postmenopausal women with normal bone mass. The difference of features by Youden index showed that the highest features had a mass to charge ratio of 1699 and 3038 Da. A diagnosis model was established with these two peaks as the candidate marker, and the specificity of the model is 100 %, the sensitivity was 90 % by leave-one-out cross validation test. The two groups of specimens in SVM results on the scatter plot could be clearly distinguished. The peak with m/z 3038 in the SVM model was suggested as Secretin by TagIdent tool. To provide further validation, the secretin levels in serum were analyzed using enzyme-linked immunosorbent assays that is a competitive inhibition enzyme immunoassay technique for the in vitro quantitative measurement of secretin in human serum.

Modeling synthetic lethality

PubMed Central

Le Meur, Nolwenn; Gentleman, Robert

2008-01-01

Background Synthetic lethality defines a genetic interaction where the combination of mutations in two or more genes leads to cell death. The implications of synthetic lethal screens have been discussed in the context of drug development as synthetic lethal pairs could be used to selectively kill cancer cells, but leave normal cells relatively unharmed. A challenge is to assess genome-wide experimental data and integrate the results to better understand the underlying biological processes. We propose statistical and computational tools that can be used to find relationships between synthetic lethality and cellular organizational units. Results In Saccharomyces cerevisiae, we identified multi-protein complexes and pairs of multi-protein complexes that share an unusually high number of synthetic genetic interactions. As previously predicted, we found that synthetic lethality can arise from subunits of an essential multi-protein complex or between pairs of multi-protein complexes. Finally, using multi-protein complexes allowed us to take into account the pleiotropic nature of the gene products. Conclusions Modeling synthetic lethality using current estimates of the yeast interactome is an efficient approach to disentangle some of the complex molecular interactions that drive a cell. Our model in conjunction with applied statistical methods and computational methods provides new tools to better characterize synthetic genetic interactions. PMID:18789146

Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences

PubMed Central

Groves, Benjamin; Kuchina, Anna; Rosenberg, Alexander B.; Jojic, Nebojsa; Fields, Stanley; Seelig, Georg

2017-01-01

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5′ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5′ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5′ UTRs as well as native S. cerevisiae 5′ UTRs. The model additionally was used to computationally evolve highly active 5′ UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model. PMID:29097404

Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.

PubMed

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L; Hemmatzadeh, Farhid; Ebrahimie, Esmaeil

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

PubMed Central

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L.

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics. PMID:24809455

Implications of Parasites Lacking Plasmodium falciparum Histidine-Rich Protein 2 on Malaria Morbidity and Control When Rapid Diagnostic Tests Are Used for Diagnosis.

PubMed

Gatton, Michelle L; Dunn, Jessica; Chaudhry, Alisha; Ciketic, Sadmir; Cunningham, Jane; Cheng, Qin

2017-04-01

Rapid diagnostic tests (RDTs) are an important tool for malaria diagnosis, with most using antibodies against Plasmodium falciparum histidine-rich protein 2 (PfHRP2). Reports of P. falciparum lacking this protein are increasing, creating a problem for diagnosis of falciparum malaria in locations without quality-assured microscopy. An agent-based stochastic simulation model of P. falciparum transmission was used to investigate the selective pressure exerted on parasite populations by use of RDTs for diagnosis of symptomatic cases. The model considered parasites with normal, reduced, or no PfHRP2, and diagnosis using PfHRP2-only or combination RDTs. Use of PfHRP2-only RDTs in communities where a PfHRP2-negative parasite was introduced during the simulation resulted in transmission of the parasite in >80% of cases, compared with <30% for normal or PfHRP2-reduced parasites. Using PfHRP2-only RDTs in the presence of PfHRP2-negative parasites caused an increase in prevalence, reduced RDT positivity within symptomatic patients but no change in the number of antimalarial treatments due to false-negative RDT results. Diagnosis with PfHRP2/Pf-Plasmodium lactate dehydrogenase combination RDTs did not select for PfHRP2-negative parasites. The use of PfHRP2-only RDTs is sufficient to select P. falciparum parasites lacking this protein, thus posing a significant public health problem, which could be moderated by using PfHRP2/Pf-Plasmodium lactate dehydrogenase combination RDTs. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.

PubMed

Teo, Guoshou; Kim, Sinae; Tsou, Chih-Chiang; Collins, Ben; Gingras, Anne-Claude; Nesvizhskii, Alexey I; Choi, Hyungwon

2015-11-03

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome. The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics. Copyright © 2015 Elsevier B.V. All rights reserved.

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/.

A Score of the Ability of a Three-Dimensional Protein Model to Retrieve Its Own Sequence as a Quantitative Measure of Its Quality and Appropriateness

PubMed Central

Martínez-Castilla, León P.; Rodríguez-Sotres, Rogelio

2010-01-01

Background Despite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel. Principal Findings The approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449–460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function. Conclusion Because the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone. PMID:20830209

Generation of a Homozygous Transgenic Rat Strain Stably Expressing a Calcium Sensor Protein for Direct Examination of Calcium Signaling

PubMed Central

Szebényi, Kornélia; Füredi, András; Kolacsek, Orsolya; Pergel, Enikő; Bősze, Zsuzsanna; Bender, Balázs; Vajdovich, Péter; Tóvári, József; Homolya, László; Szakács, Gergely; Héja, László; Enyedi, Ágnes; Sarkadi, Balázs; Apáti, Ágota; Orbán, Tamás I.

2015-01-01

In drug discovery, prediction of selectivity and toxicity require the evaluation of cellular calcium homeostasis. The rat is a preferred laboratory animal for pharmacology and toxicology studies, while currently no calcium indicator protein expressing rat model is available. We established a transgenic rat strain stably expressing the GCaMP2 fluorescent calcium sensor by a transposon-based methodology. Zygotes were co-injected with mRNA of transposase and a CAG-GCaMP2 expressing construct, and animals with one transgene copy were pre-selected by measuring fluorescence in blood cells. A homozygous rat strain was generated with high sensor protein expression in the heart, kidney, liver, and blood cells. No pathological alterations were found in these animals, and fluorescence measurements in cardiac tissue slices and primary cultures demonstrated the applicability of this system for studying calcium signaling. We show here that the GCaMP2 expressing rat cardiomyocytes allow the prediction of cardiotoxic drug side-effects, and provide evidence for the role of Na+/Ca2+ exchanger and its beneficial pharmacological modulation in cardiac reperfusion. Our data indicate that drug-induced alterations and pathological processes can be followed by using this rat model, suggesting that transgenic rats expressing a calcium-sensitive protein provide a valuable system for pharmacological and toxicological studies. PMID:26234466

Structure and ion channel activity of the human respiratory syncytial virus (hRSV) small hydrophobic protein transmembrane domain

PubMed Central

Gan, Siok Wan; Ng, Lifang; Lin, Xin; Gong, Xiandi; Torres, Jaume

2008-01-01

The small hydrophobic (SH) protein from the human respiratory syncytial virus (hRSV) is a glycoprotein of ∼64 amino acids with one putative α-helical transmembrane domain. Although SH protein is important for viral infectivity, its exact role during viral infection is not clear. Herein, we have studied the secondary structure, orientation, and oligomerization of the transmembrane domain of SH (SH-TM) in the presence of lipid bilayers. Only one oligomer, a pentamer, was observed in PFO-PAGE. Using polarized attenuated total reflection-Fourier transform infrared (PATR-FTIR) spectroscopy, we show that the SH-TM is α-helical. The rotational orientation of SH-TM was determined by site-specific infrared dichroism (SSID) at two consecutive isotopically labeled residues. This orientation is consistent with that of an evolutionary conserved pentameric model obtained from a global search protocol using 13 homologous sequences of RSV. Conductance studies of SH-TM indicate ion channel activity, which is cation selective, and inactive below the predicted pKa of histidine. Thus, our results provide experimental evidence that the transmembrane domain of SH protein forms pentameric α-helical bundles that form cation-selective ion channels in planar lipid bilayers. We provide a model for this pore, which should be useful in mutagenesis studies to elucidate its role during the virus cycle. PMID:18369195

A rapid method for selecting suitable animal species for studying pathogen interactions with plasma protein ligands in vivo.

PubMed

Naudin, Clément; Schumski, Ariane; Salo-Ahen, Outi M H; Herwald, Heiko; Smeds, Emanuel

2017-05-01

Species tropism constitutes a serious problem for developing relevant animal models of infection. Human pathogens can express virulence factors that show specific selectivity to human proteins, while their affinity for orthologs from other species can vary significantly. Suitable animal species must be used to analyse whether virulence factors are potential targets for drug development. We developed an assay that rapidly predicts applicable animal species for studying virulence factors binding plasma proteins. We used two well-characterized Staphylococcus aureus proteins, SSL7 and Efb, to develop an ELISA-based inhibition assay using plasma from different animal species. The interaction between SSL7 and human C5 and the binding of Efb to human fibrinogen and human C3 was studied. Affinity experiments and Western blot analyses were used to validate the assay. Human, monkey and cat plasma interfered with binding of SSL7 to human C5. Binding of Efb to human fibrinogen was blocked in human, monkey, gerbil and pig plasma, while human, monkey, gerbil, rabbit, cat and guinea pig plasma inhibited the binding of Efb to human C3. These results emphasize the importance of choosing correct animal models, and thus, our approach is a rapid and cost-effective method that can be used to prevent unnecessary animal experiments. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Defining the property space for chromatographic ligands from a homologous series of mixed-mode ligands.

PubMed

Woo, James A; Chen, Hong; Snyder, Mark A; Chai, Yiming; Frost, Russell G; Cramer, Steven M

2015-08-14

A homologous ligand library based on the commercially-available Nuvia cPrime ligand was generated to systematically explore various features of a multimodal cation-exchange ligand and to identify structural variants that had significantly altered chromatographic selectivity. Substitution of the polar amide bond with more hydrophobic chemistries was found to enhance retention while remaining hydrophobically-selective for aromatic residues. In contrast, increasing the solvent exposure of the aromatic ring was observed to strengthen the ligand affinity for both types of hydrophobic residues. An optimal linker length between the charged and hydrophobic moieties was also observed to enhance retention, balancing the steric accessibility of the hydrophobic moiety with its ability to interact independently of the charged group. The weak pKa of the carboxylate charge group was found to have a notable impact on protein retention on Nuvia cPrime at lower pH, increasing hydrophobic interactions with the protein. Substituting the charged group with a sulfonic acid allowed this strong MM ligand to retain its electrostatic-dominant character in this lower pH range. pH gradient experiments were also carried out to further elucidate this pH dependent behavior. A single QSAR model was generated using this accumulated experimental data to predict protein retention across a range of multimodal and ion exchange systems. This model could correctly predict the retention of proteins on resins that were not included in the original model and could prove quite powerful as an in silico approach toward designing more effective and differentiated multimodal ligands. Copyright © 2015. Published by Elsevier B.V.

Fractionation of whey proteins with high-capacity superparamagnetic ion-exchangers.

PubMed

Heebøll-Nielsen, Anders; Justesen, Sune F L; Thomas, Owen R T

2004-09-30

In this study we describe the design, preparation and testing of superparamagnetic anion-exchangers, and their use together with cation-exchangers in the fractionation of bovine whey proteins as a model study for high-gradient magnetic fishing. Adsorbents prepared by attachment of trimethyl amine to particles activated in sequential reactions with allyl bromide and N-bromosuccinimide yielded a maximum bovine serum albumin binding capacity of 156 mg g(-1) combined with a dissociation constant of 0.60 microM, whereas ion-exchangers created by linking polyethylene imine through superficial aldehydes bound up to 337 mg g(-1) with a dissociation constant of 0.042 microM. The latter anion-exchanger was selected for studies of whey protein fractionation. In these, crude bovine whey was treated with a superparamagnetic cation-exchanger to adsorb basic protein species, and the supernatant arising from this treatment was then contacted with the anion-exchanger. For both adsorbent classes of ion-exchanger, desorption selectivity was subsequently studied by sequentially increasing the concentration of NaCl in the elution buffer. In the initial cation-exchange step quantitative removal of lactoferrin (LF) and lactoperoxidase (LPO) was achieved with some simultaneous binding of immunoglobulins (Ig). The immunoglobulins were separated from the other two proteins by desorbing with a low concentration of NaCl (< or = 0.4 M), whereas lactoferrin and lactoperoxidase were co-eluted in significantly purer form, e.g. lactoperoxidase was purified 28-fold over the starting material, when the NaCl concentration was increased to 0.4-1 M. The anion-exchanger adsorbed beta-lactoglobulin (beta-LG) selectively allowing separation from the remaining protein.

The anti-cataract molecular mechanism study in selenium cataract rats for baicalin ophthalmic nanoparticles.

PubMed

Li, Nan; Han, Zhenzhen; Li, Lin; Zhang, Bing; Liu, Zhidong; Li, Jiawei

2018-01-01

The objective of this study was to investigate the effects of the solid lipid nanoparticles of baicalin (BA-SLNs) on an experimental cataract model and explore the molecular mechanism combined with bioinformatics analysis. The transparency of lens was observed daily by slit-lamp and photography. Lenticular opacity was graded. Two-dimensional gel electrophoresis (2-DE) was employed to analyze the differential protein expression modes in each group. Proteins of interest were subjected to protein identification by nano-liquid chromatography tandem mass spectrometry (LC-MS/MS). Bioinformatics analysis was performed using the Ingenuity Pathway Analysis (IPA) online software to comprehend the biological implications of the proteins identified by proteomics. At the end of the sodium selenite-induced cataract progression, almost all lenses from the model group developed partial nuclear opacity; however, all lenses were clear and normal in the blank group. There was no significant difference between the BA-SLNs group and the blank group. Many protein spots were differently expressed in 2-DE patterns of total proteins of lenses from each group, and 65 highly different protein spots were selected to be identified between the BA-SLNs group and the model group. A total of 23 proteins were identified, and 12 of which were crystalline proteins. We considered crystalline proteins to play important roles in preserving the normal expression levels of proteins and the transparency of lenses. The general trend in the BA-SLN-treated lenses' data showed that BA-SLNs regulated the protein expression mode of cataract lenses to normal lenses. Our findings suggest that BA-SLNs may be a potential therapeutic agent in treating cataract by regulating protein expression and may also be a strong candidate for future clinical research.

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

PubMed

González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

2012-03-01

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

Multivariate Modeling of Proteins Related to Trapezius Myalgia, a Comparative Study of Female Cleaners with or without Pain

PubMed Central

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia. PMID:24023854

Multivariate modeling of proteins related to trapezius myalgia, a comparative study of female cleaners with or without pain.

PubMed

Hadrevi, Jenny; Ghafouri, Bijar; Larsson, Britt; Gerdle, Björn; Hellström, Fredrik

2013-01-01

The prevalence of chronic trapezius myalgia is high in women with high exposure to awkward working positions, repetitive movements and movements with high precision demands. The mechanisms behind chronic trapezius myalgia are not fully understood. The purpose of this study was to explore the differences in protein content between healthy and myalgic trapezius muscle using proteomics. Muscle biopsies from 12 female cleaners with work-related trapezius myalgia and 12 pain free female cleaners were obtained from the descending part of the trapezius. Proteins were separated with two-dimensional differential gel electrophoresis (2D-DIGE) and selected proteins were identified with mass spectrometry. In order to discriminate the two groups, quantified proteins were fitted to a multivariate analysis: partial least square discriminate analysis. The model separated 28 unique proteins which were related to glycolysis, the tricaboxylic acid cycle, to the contractile apparatus, the cytoskeleton and to acute response proteins. The results suggest altered metabolism, a higher abundance of proteins related to inflammation in myalgic cleaners compared to healthy, and a possible alteration of the contractile apparatus. This explorative proteomic screening of proteins related to chronic pain in the trapezius muscle provides new important aspects of the pathophysiology behind chronic trapezius myalgia.

The role of internal duplication in the evolution of multi-domain proteins.

PubMed

Nacher, J C; Hayashida, M; Akutsu, T

2010-08-01

Many proteins consist of several structural domains. These multi-domain proteins have likely been generated by selective genome growth dynamics during evolution to perform new functions as well as to create structures that fold on a biologically feasible time scale. Domain units frequently evolved through a variety of genetic shuffling mechanisms. Here we examine the protein domain statistics of more than 1000 organisms including eukaryotic, archaeal and bacterial species. The analysis extends earlier findings on asymmetric statistical laws for proteome to a wider variety of species. While proteins are composed of a wide range of domains, displaying a power-law decay, the computation of domain families for each protein reveals an exponential distribution, characterizing a protein universe composed of a thin number of unique families. Structural studies in proteomics have shown that domain repeats, or internal duplicated domains, represent a small but significant fraction of genome. In spite of its importance, this observation has been largely overlooked until recently. We model the evolutionary dynamics of proteome and demonstrate that these distinct distributions are in fact rooted in an internal duplication mechanism. This process generates the contemporary protein structural domain universe, determines its reduced thickness, and tames its growth. These findings have important implications, ranging from protein interaction network modeling to evolutionary studies based on fundamental mechanisms governing genome expansion.

Preparation and recognition of surface molecularly imprinted core-shell microbeads for protein in aqueous solutions

NASA Astrophysics Data System (ADS)

Lu, Yan; Yan, Chang-Ling; Gao, Shu-Yan

2009-04-01

In this paper, a surface molecular imprinting technique was reported for preparing core-shell microbeads of protein imprinting, and bovine hemoglobin or bovine serum albumin were used as model proteins for studying the imprinted core-shell microbeads. 3-Aminophenylboronic acid (APBA) was polymerized onto the surface of polystyrene microbead in the presence of the protein templates to create protein-imprinted core-shell microbeads. The various samples were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and Brunauer-Emmett-Teller (BET) methods. The effect of pH on rebinding of the template hemoglobin, the specific binding and selective recognition were studied for the imprinted microbeads. The results show that the bovine hemoglobin-imprinted core-shell microbeads were successfully created. The shell was a sort of imprinted thin films with porous structure and larger surface areas. The imprinted microbeads have good selectivity for templates and high stability. Due to the recognition sites locating at or closing to the surface, these imprinted microbeads have good property of mass-transport. Unfortunately, the imprint technology was not successfully applied to imprinting bovine serum albumin (BSA).

Genome-Wide Motif Statistics are Shaped by DNA Binding Proteins over Evolutionary Time Scales

NASA Astrophysics Data System (ADS)

Qian, Long; Kussell, Edo

The composition of genomes with respect to short DNA motifs impacts the ability of DNA binding proteins to locate and bind their target sites. Since nonfunctional DNA binding can be detrimental to cellular functions and ultimately to organismal fitness, organisms could benefit from reducing the number of nonfunctional binding sites genome wide. Using in vitro measurements of binding affinities for a large collection of DNA binding proteins, in multiple species, we detect a significant global avoidance of weak binding sites in genomes. The underlying evolutionary process leaves a distinct genomic hallmark in that similar words have correlated frequencies, which we detect in all species across domains of life. We hypothesize that natural selection against weak binding sites contributes to this process, and using an evolutionary model we show that the strength of selection needed to maintain global word compositions is on the order of point mutation rates. Alternative contributions may come from interference of protein-DNA binding with replication and mutational repair processes, which operates with similar rates. We conclude that genome-wide word compositions have been molded by DNA binding proteins through tiny evolutionary steps over timescales spanning millions of generations.

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.

Targeted Identification of Metastasis-associated Cell-surface Sialoglycoproteins in Prostate Cancer*

PubMed Central

Yang, Lifang; Nyalwidhe, Julius O.; Guo, Siqi; Drake, Richard R.; Semmes, O. John

2011-01-01

Covalent attachment of carbohydrates to proteins is one of the most common post-translational modifications. At the cell surface, sugar moieties of glycoproteins contribute to molecular recognition events involved in cancer metastasis. We have combined glycan metabolic labeling with mass spectrometry analysis to identify and characterize metastasis-associated cell surface sialoglycoproteins. Our model system used syngeneic prostate cancer cell lines derived from PC3 (N2, nonmetastatic, and ML2, highly metastatic). The metabolic incorporation of AC4ManNAz and subsequent specific labeling of cell surface sialylation was confirmed by flow cytometry and confocal microscopy. Affinity isolation of the modified sialic-acid containing cell surface proteins via click chemistry was followed by SDS-PAGE separation and liquid chromatography-tandem MS analysis. We identified 324 proteins from N2 and 372 proteins of ML2. Using conservative annotation, 64 proteins (26%) from N2 and 72 proteins (29%) from ML2 were classified as extracellular or membrane-associated glycoproteins. A selective enrichment of sialoglycoproteins was confirmed. When compared with global proteomic analysis of the same cells, the proportion of identified glycoprotein and cell-surface proteins were on average threefold higher using the selective capture approach. Functional clustering of differentially expressed proteins by Ingenuity Pathway Analysis revealed that the vast majority of glycoproteins overexpressed in the metastatic ML2 subline were involved in cell motility, migration, and invasion. Our approach effectively targeted surface sialoglycoproteins and efficiently identified proteins that underlie the metastatic potential of the ML2 cells. PMID:21447706

Targeted identification of metastasis-associated cell-surface sialoglycoproteins in prostate cancer.

PubMed

Yang, Lifang; Nyalwidhe, Julius O; Guo, Siqi; Drake, Richard R; Semmes, O John

2011-06-01

Covalent attachment of carbohydrates to proteins is one of the most common post-translational modifications. At the cell surface, sugar moieties of glycoproteins contribute to molecular recognition events involved in cancer metastasis. We have combined glycan metabolic labeling with mass spectrometry analysis to identify and characterize metastasis-associated cell surface sialoglycoproteins. Our model system used syngeneic prostate cancer cell lines derived from PC3 (N2, nonmetastatic, and ML2, highly metastatic). The metabolic incorporation of AC(4)ManNAz and subsequent specific labeling of cell surface sialylation was confirmed by flow cytometry and confocal microscopy. Affinity isolation of the modified sialic-acid containing cell surface proteins via click chemistry was followed by SDS-PAGE separation and liquid chromatography-tandem MS analysis. We identified 324 proteins from N2 and 372 proteins of ML2. Using conservative annotation, 64 proteins (26%) from N2 and 72 proteins (29%) from ML2 were classified as extracellular or membrane-associated glycoproteins. A selective enrichment of sialoglycoproteins was confirmed. When compared with global proteomic analysis of the same cells, the proportion of identified glycoprotein and cell-surface proteins were on average threefold higher using the selective capture approach. Functional clustering of differentially expressed proteins by Ingenuity Pathway Analysis revealed that the vast majority of glycoproteins overexpressed in the metastatic ML2 subline were involved in cell motility, migration, and invasion. Our approach effectively targeted surface sialoglycoproteins and efficiently identified proteins that underlie the metastatic potential of the ML2 cells.

Contributions of depth filter components to protein adsorption in bioprocessing.

PubMed

Khanal, Ohnmar; Singh, Nripen; Traylor, Steven J; Xu, Xuankuo; Ghose, Sanchayita; Li, Zheng J; Lenhoff, Abraham M

2018-04-16

Depth filtration is widely used in downstream bioprocessing to remove particulate contaminants via depth straining and is therefore applied to harvest clarification and other processing steps. However, depth filtration also removes proteins via adsorption, which can contribute variously to impurity clearance and to reduction in product yield. The adsorption may occur on the different components of the depth filter, that is, filter aid, binder, and cellulose filter. We measured adsorption of several model proteins and therapeutic proteins onto filter aids, cellulose, and commercial depth filters at pH 5-8 and ionic strengths <50 mM and correlated the adsorption data to bulk measured properties such as surface area, morphology, surface charge density, and composition. We also explored the role of each depth filter component in the adsorption of proteins with different net charges, using confocal microscopy. Our findings show that a complete depth filter's maximum adsorptive capacity for proteins can be estimated by its protein monolayer coverage values, which are of order mg/m 2 , depending on the protein size. Furthermore, the extent of adsorption of different proteins appears to depend on the nature of the resin binder and its extent of coating over the depth filter surface, particularly in masking the cation-exchanger-like capacity of the siliceous filter aids. In addition to guiding improved depth filter selection, the findings can be leveraged in inspiring a more intentional selection of components and design of depth filter construction for particular impurity removal targets. © 2018 Wiley Periodicals, Inc.

The bioinformatics of nucleotide sequence coding for proteins requiring metal coenzymes and proteins embedded with metals

NASA Astrophysics Data System (ADS)

Tremberger, G.; Dehipawala, Sunil; Cheung, E.; Holden, T.; Sullivan, R.; Nguyen, A.; Lieberman, D.; Cheung, T.

2015-09-01

All metallo-proteins need post-translation metal incorporation. In fact, the isotope ratio of Fe, Cu, and Zn in physiology and oncology have emerged as an important tool. The nickel containing F430 is the prosthetic group of the enzyme methyl coenzyme M reductase which catalyzes the release of methane in the final step of methano-genesis, a prime energy metabolism candidate for life exploration space mission in the solar system. The 3.5 Gyr early life sulfite reductase as a life switch energy metabolism had Fe-Mo clusters. The nitrogenase for nitrogen fixation 3 billion years ago had Mo. The early life arsenite oxidase needed for anoxygenic photosynthesis energy metabolism 2.8 billion years ago had Mo and Fe. The selection pressure in metal incorporation inside a protein would be quantifiable in terms of the related nucleotide sequence complexity with fractal dimension and entropy values. Simulation model showed that the studied metal-required energy metabolism sequences had at least ten times more selection pressure relatively in comparison to the horizontal transferred sequences in Mealybug, guided by the outcome histogram of the correlation R-sq values. The metal energy metabolism sequence group was compared to the circadian clock KaiC sequence group using magnesium atomic level bond shifting mechanism in the protein, and the simulation model would suggest a much higher selection pressure for the energy life switch sequence group. The possibility of using Kepler 444 as an example of ancient life in Galaxy with the associated exoplanets has been proposed and is further discussed in this report. Examples of arsenic metal bonding shift probed by Synchrotron-based X-ray spectroscopy data and Zn controlled FOXP2 regulated pathways in human and chimp brain studied tissue samples are studied in relationship to the sequence bioinformatics. The analysis results suggest that relatively large metal bonding shift amount is associated with low probability correlation R-sq outcome in the bioinformatics simulation.

New Universal Rules of Eukaryotic Translation Initiation Fidelity

PubMed Central

Zur, Hadas; Tuller, Tamir

2013-01-01

The accepted model of eukaryotic translation initiation begins with the scanning of the transcript by the pre-initiation complex from the 5′end until an ATG codon with a specific nucleotide (nt) context surrounding it is recognized (Kozak rule). According to this model, ATG codons upstream to the beginning of the ORF should affect translation. We perform for the first time, a genome-wide statistical analysis, uncovering a new, more comprehensive and quantitative, set of initiation rules for improving the cost of translation and its efficiency. Analyzing dozens of eukaryotic genomes, we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically, 16–27 codons upstream, but also 5–11 codons downstream of the START ATG, include less ATG codons than expected. We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG. Thus, the efficiency and fidelity of translation initiation is encoded in the 5′UTR as required by the scanning model, but also at the beginning of the ORF. The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF, and may start translation from an alternative initiation start-site. Thus, to prevent the translation of undesired proteins, there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF. With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the Kozak rule alone (e.g. for protein levels r = 0.7 vs. r = 0.31; p<10−12). PMID:23874179

Molecular Chaperone Dysfunction in Neurodegenerative Diseases and Effects of Curcumin

PubMed Central

Frautschy, Sally

2014-01-01

The intra- and extracellular accumulation of misfolded and aggregated amyloid proteins is a common feature in several neurodegenerative diseases, which is thought to play a major role in disease severity and progression. The principal machineries maintaining proteostasis are the ubiquitin proteasomal and lysosomal autophagy systems, where heat shock proteins play a crucial role. Many protein aggregates are degraded by the lysosomes, depending on aggregate size, peptide sequence, and degree of misfolding, while others are selectively tagged for removal by heat shock proteins and degraded by either the proteasome or phagosomes. These systems are compromised in different neurodegenerative diseases. Therefore, developing novel targets and classes of therapeutic drugs, which can reduce aggregates and maintain proteostasis in the brains of neurodegenerative models, is vital. Natural products that can modulate heat shock proteins/proteosomal pathway are considered promising for treating neurodegenerative diseases. Here we discuss the current knowledge on the role of HSPs in protein misfolding diseases and knowledge gained from animal models of Alzheimer's disease, tauopathies, and Huntington's diseases. Further, we discuss the emerging treatment regimens for these diseases using natural products, like curcumin, which can augment expression or function of heat shock proteins in the cell. PMID:25386560

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

Dynamic Redox Regulation of IL-4 Signaling.

PubMed

Dwivedi, Gaurav; Gran, Margaret A; Bagchi, Pritha; Kemp, Melissa L

2015-11-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation.

Dynamic Redox Regulation of IL-4 Signaling

PubMed Central

Dwivedi, Gaurav; Gran, Margaret A.; Bagchi, Pritha; Kemp, Melissa L.

2015-01-01

Quantifying the magnitude and dynamics of protein oxidation during cell signaling is technically challenging. Computational modeling provides tractable, quantitative methods to test hypotheses of redox mechanisms that may be simultaneously operative during signal transduction. The interleukin-4 (IL-4) pathway, which has previously been reported to induce reactive oxygen species and oxidation of PTP1B, may be controlled by several other putative mechanisms of redox regulation; widespread proteomic thiol oxidation observed via 2D redox differential gel electrophoresis upon IL-4 treatment suggests more than one redox-sensitive protein implicated in this pathway. Through computational modeling and a model selection strategy that relied on characteristic STAT6 phosphorylation dynamics of IL-4 signaling, we identified reversible protein tyrosine phosphatase (PTP) oxidation as the primary redox regulatory mechanism in the pathway. A systems-level model of IL-4 signaling was developed that integrates synchronous pan-PTP oxidation with ROS-independent mechanisms. The model quantitatively predicts the dynamics of IL-4 signaling over a broad range of new redox conditions, offers novel hypotheses about regulation of JAK/STAT signaling, and provides a framework for interrogating putative mechanisms involving receptor-initiated oxidation. PMID:26562652

Functional diversification of B MADS-box homeotic regulators of flower development: Adaptive evolution in protein-protein interaction domains after major gene duplication events.

PubMed

Hernández-Hernández, Tania; Martínez-Castilla, León Patricio; Alvarez-Buylla, Elena R

2007-02-01

B-class MADS-box genes have been shown to be the key regulators of petal and stamen specification in several eudicot model species such as Arabidopsis thaliana, Antirrhinum majus, and Petunia hybrida. Orthologs of these genes have been found across angiosperms and gymnosperms, and it is thought that the basic regulatory function of B proteins is conserved in seed plant lineages. The evolution of B genes is characterized by numerous duplications that might represent key elements fostering the functional diversification of duplicates with a deep impact on their role in the evolution of the floral developmental program. To evaluate this, we performed a rigorous statistical analysis with B gene sequences. Using maximum likelihood and Bayesian methods, we estimated molecular substitution rates and determined the selective regimes operating at each residue of B proteins. We implemented tests that rely on phylogenetic hypotheses and codon substitution models to detect significant differences in substitution rates (DSRs) and sites under positive adaptive selection (PS) in specific lineages before and after duplication events. With these methods, we identified several protein residues fixed by PS shortly after the origin of PISTILLATA-like and APETALA3-like lineages in angiosperms and shortly after the origin of the euAP3-like lineage in core eudicots, the 2 main B gene duplications. The residues inferred to have been fixed by positive selection lie mostly within the K domain of the protein, which is key to promote heterodimerization. Additionally, we used a likelihood method that accommodates DSRs among lineages to estimate duplication dates for AP3-PI and euAP3-TM6, calibrating with data from the fossil record. The dates obtained are consistent with angiosperm origins and diversification of core eudicots. Our results strongly suggest that novel multimer formation with other MADS proteins could have been crucial for the functional divergence of B MADS-box genes. We thus propose a mechanism of functional diversification and persistence of gene duplicates by the appearance of novel multimerization capabilities after duplications. Multimer formation in different combinations of regulatory proteins can be a mechanistic basis for the origin of novel regulatory functions and a gene regulatory mechanism for the appearance of morphological innovations.

Biofunctionalized nanofibrous membranes as super separators of protein and enzyme from water.

PubMed

Homaeigohar, Shahin; Dai, Tianhe; Elbahri, Mady

2013-09-15

Here, we report development of a novel biofunctionalized nanofibrous membrane which, despite its macroporous structure, is able to separate even trace amounts (as low as 2mg/L) of biomolecules such as protein and enzyme from water with an optimum efficiency of ~90%. Such an extraordinary protein selectivity at this level of pollutant concentration for a nanofibrous membrane has never been reported. In the current study, poly(acrylonitrile-co-glycidyl methacrylate) (PANGMA) electrospun nanofibers are functionalized by a bovine serum albumin (BSA) protein. This membrane is extraordinarily successful in removal of BSA protein and Candida antarctica Lipase B (Cal-B) enzyme from a water based solution. Despite a negligible non-specific adsorption of both BSA and Cal-B to the PANGMA nanofibrous membrane (8%), the separation efficiency of the biofunctionalized membrane for BSA and Cal-B reaches to 88% and 81%, respectively. The optimum separation efficiency at a trace amount of protein models is due to the water-induced conformational change of the biofunctional agent. The conformational change not only exposes more functional groups available to catch the biomolecules but also leads to swelling of the nanofibers thereby a higher steric hindrance for the solutes. Besides the optimum selectivity, the biofunctionalized membranes are highly wettable thereby highly water permeable. Copyright © 2013 Elsevier Inc. All rights reserved.

A single amino-acid substitution in the Ets domain alters core DNA binding specificity of Ets1 to that of the related transcription factors Elf1 and E74.

PubMed

Bosselut, R; Levin, J; Adjadj, E; Ghysdael, J

1993-11-11

Ets proteins form a family of sequence specific DNA binding proteins which bind DNA through a 85 aminoacids conserved domain, the Ets domain, whose sequence is unrelated to any other characterized DNA binding domain. Unlike all other known Ets proteins, which bind specific DNA sequences centered over either GGAA or GGAT core motifs, E74 and Elf1 selectively bind to GGAA corecontaining sites. Elf1 and E74 differ from other Ets proteins in three residues located in an otherwise highly conserved region of the Ets domain, referred to as conserved region III (CRIII). We show that a restricted selectivity for GGAA core-containing sites could be conferred to Ets1 upon changing a single lysine residue within CRIII to the threonine found in Elf1 and E74 at this position. Conversely, the reciprocal mutation in Elf1 confers to this protein the ability to bind to GGAT core containing EBS. This, together with the fact that mutation of two invariant arginine residues in CRIII abolishes DNA binding, indicates that CRIII plays a key role in Ets domain recognition of the GGAA/T core motif and lead us to discuss a model of Ets proteins--core motif interaction.

A Case-by-Case Evolutionary Analysis of Four Imprinted Retrogenes

PubMed Central

McCole, Ruth B; Loughran, Noeleen B; Chahal, Mandeep; Fernandes, Luis P; Roberts, Roland G; Fraternali, Franca; O'Connell, Mary J; Oakey, Rebecca J

2011-01-01

Retroposition is a widespread phenomenon resulting in the generation of new genes that are initially related to a parent gene via very high coding sequence similarity. We examine the evolutionary fate of four retrogenes generated by such an event; mouse Inpp5f_v2, Mcts2, Nap1l5, and U2af1-rs1. These genes are all subject to the epigenetic phenomenon of parental imprinting. We first provide new data on the age of these retrogene insertions. Using codon-based models of sequence evolution, we show these retrogenes have diverse evolutionary trajectories, including divergence from the parent coding sequence under positive selection pressure, purifying selection pressure maintaining parent-retrogene similarity, and neutral evolution. Examination of the expression pattern of retrogenes shows an atypical, broad pattern across multiple tissues. Protein 3D structure modeling reveals that a positively selected residue in U2af1-rs1, not shared by its parent, may influence protein conformation. Our case-by-case analysis of the evolution of four imprinted retrogenes reveals that this interesting class of imprinted genes, while similar in regulation and sequence characteristics, follow very varied evolutionary paths. PMID:21166792

Microgravity

NASA Image and Video Library

2001-06-06

X-rays diffracted from a well-ordered protein crystal create sharp patterns of scattered light on film. A computer can use these patterns to generate a model of a protein molecule. To analyze the selected crystal, an X-ray crystallographer shines X-rays through the crystal. Unlike a single dental X-ray, which produces a shadow image of a tooth, these X-rays have to be taken many times from different angles to produce a pattern from the scattered light, a map of the intensity of the X-rays after they diffract through the crystal. The X-rays bounce off the electron clouds that form the outer structure of each atom. A flawed crystal will yield a blurry pattern; a well-ordered protein crystal yields a series of sharp diffraction patterns. From these patterns, researchers build an electron density map. With powerful computers and a lot of calculations, scientists can use the electron density patterns to determine the structure of the protein and make a computer-generated model of the structure. The models let researchers improve their understanding of how the protein functions. They also allow scientists to look for receptor sites and active areas that control a protein's function and role in the progress of diseases. From there, pharmaceutical researchers can design molecules that fit the active site, much like a key and lock, so that the protein is locked without affecting the rest of the body. This is called structure-based drug design.

Comparative analysis of amino acid composition in the active site of nirk gene encoding copper-containing nitrite reductase (CuNiR) in bacterial spp.

PubMed

Adhikari, Utpal Kumar; Rahman, M Mizanur

2017-04-01

The nirk gene encoding the copper-containing nitrite reductase (CuNiR), a key catalytic enzyme in the environmental denitrification process that helps to produce nitric oxide from nitrite. The molecular mechanism of denitrification process is definitely complex and in this case a theoretical investigation has been conducted to know the sequence information and amino acid composition of the active site of CuNiR enzyme using various Bioinformatics tools. 10 Fasta formatted sequences were retrieved from the NCBI database and the domain and disordered regions identification and phylogenetic analyses were done on these sequences. The comparative modeling of protein was performed through Modeller 9v14 program and visualized by PyMOL tools. Validated protein models were deposited in the Protein Model Database (PMDB) (PMDB id: PM0080150 to PM0080159). Active sites of nirk encoding CuNiR enzyme were identified by Castp server. The PROCHECK showed significant scores for four protein models in the most favored regions of the Ramachandran plot. Active sites and cavities prediction exhibited that the amino acid, namely Glycine, Alanine, Histidine, Aspartic acid, Glutamic acid, Threonine, and Glutamine were common in four predicted protein models. The present in silico study anticipates that active site analyses result will pave the way for further research on the complex denitrification mechanism of the selected species in the experimental laboratory. Copyright © 2016. Published by Elsevier Ltd.

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids.

PubMed

Hayat, Maqsood; Khan, Asifullah

2013-05-01

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .

Parecoxib Increases Blood Pressure Through Inhibition of Cyclooxygenase-2 Messenger RNA in an Experimental Model.

PubMed

Vértiz-Hernández, Ángel Antonio; Martínez-Morales, Flavio; Valle-Aguilera, Roberto; López-Sánchez, Pedro; Villalobos-Molina, Rafael; Pérez-Urizar, José

2015-01-01

Cyclooxygenase-2 selective inhibitors have been developed to alleviate pain and inflammation; however, the use of a selective cyclooxygenase-2 inhibitor is associated with mild edema, hypertension, and cardiovascular risk. To evaluate, in an experimental model in normotensive rats, the effect of treatment with parecoxib in comparison with diclofenac and aspirin and L-NAME, a non-selective nitric oxide synthetase, on mean arterial blood pressure, and cyclooxygenase-1 and -2 messenger RNA and protein expression in aortic tissue. Rats were treated for seven days with parecoxib (10 mg/kg/day), diclofenac (3.2 mg/kg/day), aspirin (10 mg/kg/day), or L-NAME (10 mg/kg/day). Mean arterial blood pressure was evaluated in rat tail; cyclooxygenase-1 and -2 were evaluated by reverse transcription-polymerase chain reaction and Western blot analysis in aortic tissue. Parecoxib and L-NAME, but not aspirin and diclofenac, increased mean arterial blood pressure by about 50% (p < 0.05) without changes in cardiac frequency. Messenger RNA cyclooxygenase-1 expression in aortic tissue was not modified with any drug (p < 0.05). L-NAME and parecoxib treatment decreased messenger RNA cyclooxygenase-2 and cyclooxygenase-2 (p < 0.05). While cyclooxygenase-1 protein decreased with the three drugs tested but not with L-NAME (p < 0.05), the cyclooxygenase-2 protein decreased only with aspirin and parecoxib (p < 0.05). Parecoxib increases the blood pressure of normotensive rats by the suppression of COX-2 gene expression, which apparently induced cardiovascular control.

Improving threading algorithms for remote homology modeling by combining fragment and template comparisons

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2010-01-01

In this work, we develop a method called FTCOM for assessing the global quality of protein structural models for targets of medium and hard difficulty (remote homology) produced by structure prediction approaches such as threading or ab initio structure prediction. FTCOM requires the Cα coordinates of full length models and assesses model quality based on fragment comparison and a score derived from comparison of the model to top threading templates. On a set of 361 medium/hard targets, FTCOM was applied to and assessed for its ability to improve upon the results from the SP3, SPARKS, PROSPECTOR_3, and PRO-SP3-TASSER threading algorithms. The average TM-score improves by 5%–10% for the first selected model by the new method over models obtained by the original selection procedure in the respective threading methods. Moreover the number of foldable targets (TM-score ≥0.4) increases from least 7.6% for SP3 to 54% for SPARKS. Thus, FTCOM is a promising approach to template selection. PMID:20455261

Tight junctions of the proximal tubule and their channel proteins.

PubMed

Fromm, Michael; Piontek, Jörg; Rosenthal, Rita; Günzel, Dorothee; Krug, Susanne M

2017-08-01

The renal proximal tubule achieves the majority of renal water and solute reabsorption with the help of paracellular channels which lead through the tight junction. The proteins forming such channels in the proximal tubule are claudin-2, claudin-10a, and possibly claudin-17. Claudin-2 forms paracellular channels selective for small cations like Na + and K + . Independently of each other, claudin-10a and claudin-17 form anion-selective channels. The claudins form the paracellular "pore pathway" and are integrated, together with purely sealing claudins and other tight junction proteins, in the belt of tight junction strands surrounding the tubular epithelial cells. In most species, the proximal tubular tight junction consists of only 1-2 (pars convoluta) to 3-5 (pars recta) horizontal strands. Even so, they seal the tubule very effectively against leak passage of nutrients and larger molecules. Remarkably, claudin-2 channels are also permeable to water so that 20-25% of proximal water absorption may occur paracellularly. Although the exact structure of the claudin-2 channel is still unknown, it is clear that Na + and water share the same pore. Already solved claudin crystal structures reveal a characteristic β-sheet, comprising β-strands from both extracellular loops, which is anchored to a left-handed four-transmembrane helix bundle. This allowed homology modeling of channel-forming claudins present in the proximal tubule. The surface of cation- and anion-selective claudins differ in electrostatic potentials in the area of the proposed ion channel, resulting in the opposite charge selectivity of these claudins. Presently, while models of the molecular structure of the claudin-based oligomeric channels have been proposed, its full understanding has only started.

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.

Adaptive Patterns of Mitogenome Evolution Are Associated with the Loss of Shell Scutes in Turtles.

PubMed

Escalona, Tibisay; Weadick, Cameron J; Antunes, Agostinho

2017-10-01

The mitochondrial genome encodes several protein components of the oxidative phosphorylation (OXPHOS) pathway and is critical for aerobic respiration. These proteins have evolved adaptively in many taxa, but linking molecular-level patterns with higher-level attributes (e.g., morphology, physiology) remains a challenge. Turtles are a promising system for exploring mitochondrial genome evolution as different species face distinct respiratory challenges and employ multiple strategies for ensuring efficient respiration. One prominent adaptation to a highly aquatic lifestyle in turtles is the secondary loss of keratenized shell scutes (i.e., soft-shells), which is associated with enhanced swimming ability and, in some species, cutaneous respiration. We used codon models to examine patterns of selection on mitochondrial protein-coding genes along the three turtle lineages that independently evolved soft-shells. We found strong evidence for positive selection along the branches leading to the pig-nosed turtle (Carettochelys insculpta) and the softshells clade (Trionychidae), but only weak evidence for the leatherback (Dermochelys coriacea) branch. Positively selected sites were found to be particularly prevalent in OXPHOS Complex I proteins, especially subunit ND2, along both positively selected lineages, consistent with convergent adaptive evolution. Structural analysis showed that many of the identified sites are within key regions or near residues involved in proton transport, indicating that positive selection may have precipitated substantial changes in mitochondrial function. Overall, our study provides evidence that physiological challenges associated with adaptation to a highly aquatic lifestyle have shaped the evolution of the turtle mitochondrial genome in a lineage-specific manner. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface

PubMed Central

Johnson, David K.; Karanicolas, John

2013-01-01

Despite intense interest and considerable effort via high-throughput screening, there are few examples of small molecules that directly inhibit protein-protein interactions. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our fundamental understanding of druggability. Here we describe an approach for exploring protein fluctuations enriched in conformations containing surface pockets suitable for small molecule binding. Starting from a set of seven unbound protein structures, we find that the presence of low-energy pocket-containing conformations is indeed a signature of druggable protein interaction sites and that analogous surface pockets are not formed elsewhere on the protein. We further find that ensembles of conformations generated with this biased approach structurally resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Collectively these results suggest that “druggability” is a property encoded on a protein surface through its propensity to form pockets, and inspire a model in which the crude features of the predisposed pocket(s) restrict the range of complementary ligands; additional smaller conformational changes then respond to details of a particular ligand. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention. PMID:23505360

Amyloid-β peptide-specific DARPins as a novel class of potential therapeutics for Alzheimer disease.

PubMed

Hanenberg, Michael; McAfoose, Jordan; Kulic, Luka; Welt, Tobias; Wirth, Fabian; Parizek, Petra; Strobel, Lisa; Cattepoel, Susann; Späni, Claudia; Derungs, Rebecca; Maier, Marcel; Plückthun, Andreas; Nitsch, Roger M

2014-09-26

Passive immunization with anti-amyloid-β peptide (Aβ) antibodies is effective in animal models of Alzheimer disease. With the advent of efficient in vitro selection technologies, the novel class of designed ankyrin repeat proteins (DARPins) presents an attractive alternative to the immunoglobulin scaffold. DARPins are small and highly stable proteins with a compact modular architecture ideal for high affinity protein-protein interactions. In this report, we describe the selection, binding profile, and epitope analysis of Aβ-specific DARPins. We further showed their ability to delay Aβ aggregation and prevent Aβ-mediated neurotoxicity in vitro. To demonstrate their therapeutic potential in vivo, mono- and trivalent Aβ-specific DARPins (D23 and 3×D23) were infused intracerebroventricularly into the brains of 11-month-old Tg2576 mice over 4 weeks. Both D23 and 3×D23 treatments were shown to result in improved cognitive performance and reduced soluble Aβ levels. These findings demonstrate the therapeutic potential of Aβ-specific DARPins for the treatment of Alzheimer disease. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

QuickProbs 2: Towards rapid construction of high-quality alignments of large protein families

PubMed Central

Gudyś, Adam; Deorowicz, Sebastian

2017-01-01

The ever-increasing size of sequence databases caused by the development of high throughput sequencing, poses to multiple alignment algorithms one of the greatest challenges yet. As we show, well-established techniques employed for increasing alignment quality, i.e., refinement and consistency, are ineffective when large protein families are investigated. We present QuickProbs 2, an algorithm for multiple sequence alignment. Based on probabilistic models, equipped with novel column-oriented refinement and selective consistency, it offers outstanding accuracy. When analysing hundreds of sequences, Quick-Probs 2 is noticeably better than ClustalΩ and MAFFT, the previous leaders for processing numerous protein families. In the case of smaller sets, for which consistency-based methods are the best performing, QuickProbs 2 is also superior to the competitors. Due to low computational requirements of selective consistency and utilization of massively parallel architectures, presented algorithm has similar execution times to ClustalΩ, and is orders of magnitude faster than full consistency approaches, like MSAProbs or PicXAA. All these make QuickProbs 2 an excellent tool for aligning families ranging from few, to hundreds of proteins. PMID:28139687

Surface Plasmon Resonance Investigations of Bioselective Element Based on the Recombinant Protein A for Immunoglobulin Detection

NASA Astrophysics Data System (ADS)

Bakhmachuk, A.; Gorbatiuk, O.; Rachkov, A.; Dons'koi, B.; Khristosenko, R.; Ushenin, I.; Peshkova, V.; Soldatkin, A.

2017-02-01

The developed surface plasmon resonance (SPR) biosensor based on the recombinant Staphylococcal protein A with an additional cysteine residue (SPA-Cys) used as a biorecognition component showed a good selectivity and sensitivity for the immunoglobulin detection. The developed biosensor with SPA-Cys-based bioselective element can also be used as a first step of immunosensor creation. The successful immobilization of SPA-Cys on the nanolayer gold sensor surface of the SPR spectrometer was performed. The efficiency of blocking nonspecific sorption sites on the sensor surface with milk proteins, gelatin, BSA, and HSA was studied, and a rather high efficiency of using gelatin was confirmed. The SPR biosensor selectively interacted with IgG and did not interact with the control proteins. The linear dependence of the sensor response on the IgG concentration in the range from 2 to 10 μg/ml was shown. Using the calibration curve, the IgG concentration was measured in the model samples. The determined concentrations are in good agreement ( r 2 = 0.97) with the given concentration of IgG.

REEPs Are Membrane Shaping Adapter Proteins That Modulate Specific G Protein-Coupled Receptor Trafficking by Affecting ER Cargo Capacity

PubMed Central

Ho, Vincent K.; Angelotti, Timothy

2013-01-01

Receptor expression enhancing proteins (REEPs) were identified by their ability to enhance cell surface expression of a subset of G protein-coupled receptors (GPCRs), specifically GPCRs that have proven difficult to express in heterologous cell systems. Further analysis revealed that they belong to the Yip (Ypt-interacting protein) family and that some REEP subtypes affect ER structure. Yip family comparisons have established other potential roles for REEPs, including regulation of ER-Golgi transport and processing/neuronal localization of cargo proteins. However, these other potential REEP functions and the mechanism by which they selectively enhance GPCR cell surface expression have not been clarified. By utilizing several REEP family members (REEP1, REEP2, and REEP6) and model GPCRs (α2A and α2C adrenergic receptors), we examined REEP regulation of GPCR plasma membrane expression, intracellular processing, and trafficking. Using a combination of immunolocalization and biochemical methods, we demonstrated that this REEP subset is localized primarily to ER, but not plasma membranes. Single cell analysis demonstrated that these REEPs do not specifically enhance surface expression of all GPCRs, but affect ER cargo capacity of specific GPCRs and thus their surface expression. REEP co-expression with α2 adrenergic receptors (ARs) revealed that this REEP subset interacts with and alter glycosidic processing of α2C, but not α2A ARs, demonstrating selective interaction with cargo proteins. Specifically, these REEPs enhanced expression of and interacted with minimally/non-glycosylated forms of α2C ARs. Most importantly, expression of a mutant REEP1 allele (hereditary spastic paraplegia SPG31) lacking the carboxyl terminus led to loss of this interaction. Thus specific REEP isoforms have additional intracellular functions besides altering ER structure, such as enhancing ER cargo capacity, regulating ER-Golgi processing, and interacting with select cargo proteins. Therefore, some REEPs can be further described as ER membrane shaping adapter proteins. PMID:24098485

Implementation of a parallel protein structure alignment service on cloud.

PubMed

Hung, Che-Lun; Lin, Yaw-Ling

2013-01-01

Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.

Implementation of a Parallel Protein Structure Alignment Service on Cloud

PubMed Central

Hung, Che-Lun; Lin, Yaw-Ling

2013-01-01

Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform. PMID:23671842

Method for voltage-gated protein fractionation

DOEpatents

Hatch, Anson [Tracy, CA; Singh, Anup K [Danville, CA

2012-04-24

We report unique findings on the voltage dependence of protein exclusion from the pores of nanoporous polymer exclusion membranes. The pores are small enough that proteins are excluded from passage with low applied electric fields, but increasing the field enables proteins to pass through. The requisite field necessary for a change in exclusion is protein-specific with a correlation to protein size. The field-dependence of exclusion is important to consider for preconcentration applications. The ability to selectively gate proteins at exclusion membranes is also a promising means for manipulating and characterizing proteins. We show that field-gated exclusion can be used to selectively remove proteins from a mixture, or to selectively trap protein at one exclusion membrane in a series.

Highly Constrained Bicyclic Scaffolds for the Discovery of Protease-Stable Peptides via mRNA Display.

PubMed

Hacker, David E; Hoinka, Jan; Iqbal, Emil S; Przytycka, Teresa M; Hartman, Matthew C T

2017-03-17

Highly constrained peptides such as the knotted peptide natural products are promising medicinal agents because of their impressive biostability and potent activity. Yet, libraries of highly constrained peptides are challenging to prepare. Here, we present a method which utilizes two robust, orthogonal chemical steps to create highly constrained bicyclic peptide libraries. This technology was optimized to be compatible with in vitro selections by mRNA display. We performed side-by-side monocyclic and bicyclic selections against a model protein (streptavidin). Both selections resulted in peptides with mid-nanomolar affinity, and the bicyclic selection yielded a peptide with remarkable protease resistance.

Big domains are novel Ca²+-binding modules: evidences from big domains of Leptospira immunoglobulin-like (Lig) proteins.

PubMed

Raman, Rajeev; Rajanikanth, V; Palaniappan, Raghavan U M; Lin, Yi-Pin; He, Hongxuan; McDonough, Sean P; Sharma, Yogendra; Chang, Yung-Fu

2010-12-29

Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca²+. Leptospiral immunoglobulin-like (Lig) proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big) domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca²+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9(th) (Lig A9) and 10(th) repeats (Lig A10); and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon). All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca²+ with dissociation constants of 2-4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. We demonstrate that the Lig are Ca²+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca²+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca²+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca²+ binding.

Big Domains Are Novel Ca2+-Binding Modules: Evidences from Big Domains of Leptospira Immunoglobulin-Like (Lig) Proteins

PubMed Central

Palaniappan, Raghavan U. M.; Lin, Yi-Pin; He, Hongxuan; McDonough, Sean P.; Sharma, Yogendra; Chang, Yung-Fu

2010-01-01

Background Many bacterial surface exposed proteins mediate the host-pathogen interaction more effectively in the presence of Ca2+. Leptospiral immunoglobulin-like (Lig) proteins, LigA and LigB, are surface exposed proteins containing Bacterial immunoglobulin like (Big) domains. The function of proteins which contain Big fold is not known. Based on the possible similarities of immunoglobulin and βγ-crystallin folds, we here explore the important question whether Ca2+ binds to a Big domains, which would provide a novel functional role of the proteins containing Big fold. Principal Findings We selected six individual Big domains for this study (three from the conserved part of LigA and LigB, denoted as Lig A3, Lig A4, and LigBCon5; two from the variable region of LigA, i.e., 9th (Lig A9) and 10th repeats (Lig A10); and one from the variable region of LigB, i.e., LigBCen2. We have also studied the conserved region covering the three and six repeats (LigBCon1-3 and LigCon). All these proteins bind the calcium-mimic dye Stains-all. All the selected four domains bind Ca2+ with dissociation constants of 2–4 µM. Lig A9 and Lig A10 domains fold well with moderate thermal stability, have β-sheet conformation and form homodimers. Fluorescence spectra of Big domains show a specific doublet (at 317 and 330 nm), probably due to Trp interaction with a Phe residue. Equilibrium unfolding of selected Big domains is similar and follows a two-state model, suggesting the similarity in their fold. Conclusions We demonstrate that the Lig are Ca2+-binding proteins, with Big domains harbouring the binding motif. We conclude that despite differences in sequence, a Big motif binds Ca2+. This work thus sets up a strong possibility for classifying the proteins containing Big domains as a novel family of Ca2+-binding proteins. Since Big domain is a part of many proteins in bacterial kingdom, we suggest a possible function these proteins via Ca2+ binding. PMID:21206924

SELECTIVE CHANGES IN BRAIN PROTEIN KINASE C ISOFORMS FOLLOWING DEVELOPMENTAL EXPOSURE TO A PCB MIXTURE.

EPA Science Inventory

Introduction
Polychlorinated biphenyls (PCBs) offer a unique model to understand the major issues related to complex environmental mixtures. These environmental pollutants are ubiquitous, persistent, bioaccumulate in human body through the food chain, and exist as mixtures of ...

Monocarbonyl Curcumin Analogs: Heterocyclic Pleiotropic Kinase Inhibitors that Mediate Anti-Cancer Properties

PubMed Central

Brown, Andrew; Shi, Qi; Moore, Terry W.; Yoon, Younghyoun; Prussia, Andrew; Maddox, Clinton; Liotta, Dennis C.; Shim*, Hyunsuk; Snyder*, James P.

2014-01-01

Curcumin is a biologically active component of curry powder. A structurally-related class of mimetics possesses similar anti-inflammatory and anticancer properties. Mechanism has been examined by exploring kinase inhibition trends. In a screen of 50 kinases relevant to many forms of cancer, one member of the series (4, EF31) showed ≥85% inhibition for ten of the enzymes at 5 μM, while twenty-two of the proteins were blocked at ≥40%. IC50’s for an expanded set of curcumin analogs established a rank order of potencies, and analyses of IKKβ and AKT2 enzyme kinetics for 4 revealed a mixed inhibition model, ATP competition dominating. Our curcumin mimetics are generally selective for Ser/Thr kinases. Both selectivity and potency trends are compatible with protein sequence comparisons, while modeled kinase binding site geometries deliver a reasonable correlation with mixed inhibition. Overall, these analogs are shown to be pleiotropic inhibitors that operate at multiple points along cell signaling pathways. PMID:23550937