Predicting protein-protein binding sites in membrane proteins

<p>Abstract</p> <p>Background</p> <p>Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.</p> <p>Results</p> <p>We present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.</p> <p>Conclusion</p> <p>Given a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.</p

Individual characteristics and student's engagement in scientific research : a cross-sectional study

Background: In light of the increasing recognition of the importance of physician scientists, and given the association between undergraduate research experiences with future scientific activity, it is important to identify and understand variables related to undergraduate student’s decision to engage in scientific research activities. The present study assessed the influence of individual characteristics, including personality traits and socio-demographic characteristics, on voluntary engagement in scientific research of undergraduate medical students. Methods: For this study, all undergraduate students and alumni of the School of Health Sciences in Minho, Portugal were invited to participate in a survey about voluntary engagement in scientific research activities. Data were available on socio-demographic, personality and university admission variables, as part of an ongoing longitudinal study. A regression model was used to compare (1) engaged with (2) not engaged students. A classification and regression tree model was used to compare students engaged in (3) elective curricular research (4) and extra-curricular research. Results: A total of 466 students (88%) answered the survey. A complete set of data was available for 435 students (83%).Higher scores in admission grade point average and the personality dimensions of “openness to experience” and “conscientiousness” increased chances of engagement. Higher “extraversion” scores had the opposite effect. Male undergraduate students were two times more likely than females to engage in curricular elective scientific research and were also more likely to engage in extra-curricular research activities. Conclusions: This study demonstrated that student’s grade point average and individual characteristics, like gender, openness and consciousness have a unique and statistically significant contribution to student’s involvement in undergraduate scientific research activities.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/ESC/65116/200

Investigation of Atomic Level Patterns in Protein—Small Ligand Interactions

BACKGROUND: Shape complementarity and non-covalent interactions are believed to drive protein-ligand interaction. To date protein-protein, protein-DNA, and protein-RNA interactions were systematically investigated, which is in contrast to interactions with small ligands. We investigate the role of covalent and non-covalent bonds in protein-small ligand interactions using a comprehensive dataset of 2,320 complexes. METHODOLOGY AND PRINCIPAL FINDINGS: We show that protein-ligand interactions are governed by different forces for different ligand types, i.e., protein-organic compound interactions are governed by hydrogen bonds, van der Waals contacts, and covalent bonds; protein-metal ion interactions are dominated by electrostatic force and coordination bonds; protein-anion interactions are established with electrostatic force, hydrogen bonds, and van der Waals contacts; and protein-inorganic cluster interactions are driven by coordination bonds. We extracted several frequently occurring atomic-level patterns concerning these interactions. For instance, 73% of investigated covalent bonds were summarized with just three patterns in which bonds are formed between thiol of Cys and carbon or sulfur atoms of ligands, and nitrogen of Lys and carbon of ligands. Similar patterns were found for the coordination bonds. Hydrogen bonds occur in 67% of protein-organic compound complexes and 66% of them are formed between NH- group of protein residues and oxygen atom of ligands. We quantify relative abundance of specific interaction types and discuss their characteristic features. The extracted protein-organic compound patterns are shown to complement and improve a geometric approach for prediction of binding sites. CONCLUSIONS AND SIGNIFICANCE: We show that for a given type (group) of ligands and type of the interaction force, majority of protein-ligand interactions are repetitive and could be summarized with several simple atomic-level patterns. We summarize and analyze 10 frequently occurring interaction patterns that cover 56% of all considered complexes and we show a practical application for the patterns that concerns interactions with organic compounds

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p

Volume-based solvation models out-perform area-based models in combined studies of wild-type and mutated protein-protein interfaces

<p>Abstract</p> <p>Background</p> <p>Empirical binding models have previously been investigated for the energetics of protein complexation (ΔG models) and for the influence of mutations on complexation (i.e. differences between wild-type and mutant complexes, ΔΔG models). We construct binding models to directly compare these processes, which have generally been studied separately.</p> <p>Results</p> <p>Although reasonable fit models were found for both ΔG and ΔΔG cases, they differ substantially. In a dataset curated for the absence of mainchain rearrangement upon binding, non-polar area burial is a major determinant of ΔG models. However this ΔG model does not fit well to the data for binding differences upon mutation. Burial of non-polar area is weighted down in fitting of ΔΔG models. These calculations were made with no repacking of sidechains upon complexation, and only minimal packing upon mutation. We investigated the consequences of more extensive packing changes with a modified mean-field packing scheme. Rather than emphasising solvent exposure with relatively extended sidechains, rotamers are selected that exhibit maximal packing with protein. This provides solvent accessible areas for proteins that are much closer to those of experimental structures than the more extended sidechain regime. The new packing scheme increases changes in non-polar burial for mutants compared to wild-type proteins, but does not substantially improve agreement between ΔG and ΔΔG binding models.</p> <p>Conclusion</p> <p>We conclude that solvent accessible area, based on modelled mutant structures, is a poor correlate for ΔΔG upon mutation. A simple volume-based, rather than solvent accessibility-based, model is constructed for ΔG and ΔΔG systems. This shows a more consistent behaviour. We discuss the efficacy of volume, as opposed to area, approaches to describe the energetic consequences of mutations at interfaces. This knowledge can be used to develop simple computational screens for binding in comparative modelled interfaces.</p

Mouse HORMAD1 and HORMAD2, two conserved meiotic chromosomal proteins, are depleted from synapsed chromosome axes with the help of TRIP13 AAA-ATPase

Meiotic crossovers are produced when programmed double-strand breaks (DSBs) are repaired by recombination from homologous chromosomes (homologues). In a wide variety of organisms, meiotic HORMA-domain proteins are required to direct DSB repair towards homologues. This inter-homologue bias is required for efficient homology search, homologue alignment, and crossover formation. HORMA-domain proteins are also implicated in other processes related to crossover formation, including DSB formation, inhibition of promiscuous formation of the synaptonemal complex (SC), and the meiotic prophase checkpoint that monitors both DSB processing and SCs. We examined the behavior of two previously uncharacterized meiosis-specific mouse HORMA-domain proteins-HORMAD1 and HORMAD2-in wild-type mice and in mutants defective in DSB processing or SC formation. HORMADs are preferentially associated with unsynapsed chromosome axes throughout meiotic prophase. We observe a strong negative correlation between SC formation and presence of HORMADs on axes, and a positive correlation between the presumptive sites of high checkpoint-kinase ATR activity and hyper-accumulation of HORMADs on axes. HORMADs are not depleted from chromosomes in mutants that lack SCs. In contrast, DSB formation and DSB repair are not absolutely required for depletion of HORMADs from synapsed axes. A simple interpretation of these findings is that SC formation directly or indirectly promotes depletion of HORMADs from chromosome axes. We also find that TRIP13 protein is required for reciprocal distribution of HORMADs and the SYCP1/SC-component along chromosome axes. Similarities in mouse and budding yeast meiosis suggest that TRIP13/Pch2 proteins have a conserved role in establishing mutually exclusive HORMAD-rich and synapsed chromatin domains in both mouse and yeast. Taken together, our observations raise the possibility that involvement of meiotic HORMA-domain proteins in the regulation of homologue interactions is conserved in mammals

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

Mutations in Protein-Binding Hot-Spots on the Hub Protein Smad3 Differentially Affect Its Protein Interactions and Smad3-Regulated Gene Expression

Hub proteins are connected through binding interactions to many other proteins. Smad3, a mediator of signal transduction induced by transforming growth factor beta (TGF-β), serves as a hub protein for over 50 protein-protein interactions. Different cellular responses mediated by Smad3 are the product of cell-type and context dependent Smad3-nucleated protein complexes acting in concert. Our hypothesis is that perturbation of this spectrum of protein complexes by mutation of single protein-binding hot-spots on Smad3 will have distinct consequences on Smad3-mediated responses.We mutated 28 amino acids on the surface of the Smad3 MH2 domain and identified 22 Smad3 variants with reduced binding to subsets of 17 Smad3-binding proteins including Smad4, SARA, Ski, Smurf2 and SIP1. Mutations defective in binding to Smad4, e.g., D408H, or defective in nucleocytoplasmic shuttling, e.g., W406A, were compromised in modulating the expression levels of a Smad3-dependent reporter gene or six endogenous Smad3-responsive genes: Mmp9, IL11, Tnfaip6, Fermt1, Olfm2 and Wnt11. However, the Smad3 mutants Y226A, Y297A, W326A, K341A, and E267A had distinct differences on TGF-β signaling. For example, K341A and Y226A both reduced the Smad3-mediated activation of the reporter gene by ∼50% but K341A only reduced the TGF-β inducibilty of Olfm2 in contrast to Y226A which reduced the TGF-β inducibility of all six endogenous genes as severely as the W406A mutation. E267A had increased protein binding but reduced TGF-β inducibility because it caused higher basal levels of expression. Y297A had increased TGF-β inducibility because it caused lower Smad3-induced basal levels of gene expression.Mutations in protein binding hot-spots on Smad3 reduced the binding to different subsets of interacting proteins and caused a range of quantitative changes in the expression of genes induced by Smad3. This approach should be useful for unraveling which Smad3 protein complexes are critical for specific biological responses