Prediction Of Interaction Sites Research Articles

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as “feedback” to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

In Silico Approaches to Identify Mutagenesis Targets to Probe and Alter Protein-Cofactor and Protein-Protein Functional Relationships.

When performing site-directed mutagenesis experiments to study protein structure-function relationships, ideally one would know the structure of the protein under study. It is also very useful to have structures of multiple related proteins in order to determine whether or not particular amino acid residues are conserved in the structures either in the active site of an enzyme at the surface of a protein or at a putative protein-protein interface. While many protein structures are available in the Protein Data Base (PDB), a structure of the protein of interest may not be available. In the study of reversible and often transient protein-protein interactions it is rare to have a structure of the complex of the two interacting proteins. In this chapter, methods are described for comparing protein structures, generating putative structures of proteins with homology models based on the protein primary sequence, and generating docking models to predict interaction sites between proteins and cofactor-protein interactions. The rationale used to predict mutagenesis targets from these structures and models is also described.

An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data.

RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data. Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-art methods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs. The program RactIPAce along with data used in this work is available at https://github.com/satoken/ractip/releases/tag/v1.0.1 CONTACT: : ykato@rna.med.osaka-u.ac.jp or shingo@i.kyoto-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

Sequence-Based Prediction of Protein-Carbohydrate Binding Sites Using Support Vector Machines.

Carbohydrate-binding proteins play significant roles in many diseases including cancer. Here, we established a machine-learning-based method (called sequence-based prediction of residue-level interaction sites of carbohydrates, SPRINT-CBH) to predict carbohydrate-binding sites in proteins using support vector machines (SVMs). We found that integrating evolution-derived sequence profiles with additional information on sequence and predicted solvent accessible surface area leads to a reasonably accurate, robust, and predictive method, with area under receiver operating characteristic curve (AUC) of 0.78 and 0.77 and Matthew's correlation coefficient of 0.34 and 0.29, respectively for 10-fold cross validation and independent test without balancing binding and nonbinding residues. The quality of the method is further demonstrated by having statistically significantly more binding residues predicted for carbohydrate-binding proteins than presumptive nonbinding proteins in the human proteome, and by the bias of rare alleles toward predicted carbohydrate-binding sites for nonsynonymous mutations from the 1000 genome project. SPRINT-CBH is available as an online server at http://sparks-lab.org/server/SPRINT-CBH .

Lipid interaction sites on channels, transporters and receptors: Recent insights from molecular dynamics simulations

Lipid molecules are able to selectively interact with specific sites on integral membrane proteins, and modulate their structure and function. Identification and characterization of these sites are of importance for our understanding of the molecular basis of membrane protein function and stability, and may facilitate the design of lipid-like drug molecules. Molecular dynamics simulations provide a powerful tool for the identification of these sites, complementing advances in membrane protein structural biology and biophysics. We describe recent notable biomolecular simulation studies which have identified lipid interaction sites on a range of different membrane proteins. The sites identified in these simulation studies agree well with those identified by complementary experimental techniques. This demonstrates the power of the molecular dynamics approach in the prediction and characterization of lipid interaction sites on integral membrane proteins.This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests

Predicting protein–protein interaction (PPI) sites from protein sequences is still a challenge task in computational biology. There exists a severe class imbalance phenomenon in predicting PPI sites, which leads to a decrease in overall performance for traditional statistical machine-learning-based classifiers, such as SVM and random forests. In this study, an ensemble of SVM and sample-weighted random forests (SSWRF) was proposed to deal with class imbalance. An SVM classifier was trained and applied to estimate the weights of training samples. Then, the training samples with estimated weights were utilized to train a sample-weighted random forests (SWRF). In addition, a lower-dimensional feature representation method, which consists of evolutionary conservation, hydrophobic property, solvent accessibility features derived from a target residue and its neighbors, was developed to improve the discriminative capability for PPI sites prediction. The analysis of feature importance shows that the proposed feature representation method is an effective representation for predicting PPI sites. The proposed SSWRF achieved 22.4% and 35.1% in MCC and F-measure, respectively, on independent validation dataset Dtestset72, and achieved 15.2% and 36.5% in MCC and F-measure, respectively, on PDBtestset164. Computational comparisons between existing PPI sites predictors on benchmark datasets demonstrated that the proposed SSWRF is effective for PPI sites prediction and outperforms the state-of-the-art sequence-based method (i.e., LORIS) released most recently. The benchmark datasets used in this study and the source codes of the proposed method are publicly available at http://csbio.njust.edu.cn/bioinf/SSWRF for academic use.

Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case.

BackgroundProtein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.ResultsWe collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9 % recall and up to 25.1 % precision.ConclusionsTo our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0758-y) contains supplementary material, which is available to authorized users.

Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

A Review and Comparative Assessment of Machine Learning Approaches for Interaction Site Prediction in Membrane Proteins

Protein-protein interactions at membranes play an inevitable role in the function of proteins there. However, the task of studying the interactions is of many difficulties in case of the membrane proteins, due to their hydrophobic environment. This is why the use of bioinformatics methods, especially machine learning approaches is essential to understand the membrane proteins' function. However, the machine learning methods for prediction of the interaction sites of membrane proteins are faced with numerous challenges. This paper aims to describe the current state of machine learning applications in inferring the membrane protein interaction sites, and to assess the methods up to now proposed. We have introduced the membrane protein interaction site prediction methods, presented the contribution degrees of parameters at membrane protein interaction interfaces, and compared the performance of methods through several case predictions. Keywords: Bioinformatics, example prediction, membrane proteins, machine learning, performance comparison, protein interaction sites.

Algorithmic approaches to protein-protein interaction site prediction.

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier.

Protein functions through interactions with other proteins and biomolecules and these interactions occur on the so-called interface residues of the protein sequences. Identifying interface residues makes us better understand the biological mechanism of protein interaction. Meanwhile, information about the interface residues contributes to the understanding of metabolic, signal transduction networks and indicates directions in drug designing. In recent years, researchers have focused on developing new computational methods for predicting protein interface residues. Here we creatively used a 181-dimension protein sequence feature vector as input to the Naive Bayes Classifier- (NBC-) based method to predict interaction sites in protein-protein complexes interaction. The prediction of interaction sites in protein interactions is regarded as an amino acid residue binary classification problem by applying NBC with protein sequence features. Independent test results suggested that Naive Bayes Classifier-based method with the protein sequence features as input vectors performed well.

Predicting protein–RNA interaction amino acids using random forest based on submodularity subset selection

Protein–RNA interaction plays a very crucial role in many biological processes, such as protein synthesis, transcription and post-transcription of gene expression and pathogenesis of disease. Especially RNAs always function through binding to proteins. Identification of binding interface region is especially useful for cellular pathways analysis and drug design. In this study, we proposed a novel approach for binding sites identification in proteins, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection. Firstly we extracted local features and global features from protein sequence, such as evolution information and molecule weight. Secondly, the number of non-interaction sites is much more than interaction sites, which leads to a sample imbalance problem, and hence biased machine learning model with preference to non-interaction sites. To better resolve this problem, instead of previous randomly sub-sampling over-represented non-interaction sites, a novel sampling approach based on submodularity subset selection was employed, which can select more representative data subset. Finally random forest were trained on optimally selected training subsets to predict interaction sites. Our result showed that our proposed method is very promising for predicting protein–RNA interaction residues, it achieved an accuracy of 0.863, which is better than other state-of-the-art methods. Furthermore, it also indicated the extracted global features have very strong discriminate ability for identifying interaction residues from random forest feature importance analysis.

Brefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) is predicted to interact with its partner through an ARM-type α-helical structure.

BackgroundBrefeldin A-inhibited guanine nucleotide-exchange protein 3 (BIG3) has been identified recently as a novel regulator of estrogen signalling in breast cancer cells. Despite being a potential target for new breast cancer treatment, its amino acid sequence suggests no association with any well-characterized protein family and provides little clues as to its molecular function. In this paper, we predicted the structure, function and interactions of BIG3 using a range of bioinformatic tools.ResultsHomology search results showed that BIG3 had distinct features from its paralogues, BIG1 and BIG2, with a unique region between the two shared domains, Sec7 and DUF1981. Although BIG3 contains Sec7 domain, the lack of the conserved motif and the critical glutamate residue suggested no potential guaninyl-exchange factor (GEF) activity. Fold recognition tools predicted BIG3 to adopt an α-helical repeat structure similar to that of the armadillo (ARM) family. Using state-of-the-art methods, we predicted interaction sites between BIG3 and its partner PHB2.ConclusionsThe combined results of the structure and interaction prediction led to a novel hypothesis that one of the predicted helices of BIG3 might play an important role in binding to PHB2 and thereby preventing its translocation to the nucleus. This hypothesis has been subsequently verified experimentally.

Sequence-based prediction of protein–protein interaction sites with L1-logreg classifier

Protein–protein interactions are of central importance for virtually every process in a living cell. Information about the interaction sites in proteins improves our understanding of disease mechanisms and can provide the basis for new therapeutic approaches. Since a multitude of unique residue–residue contacts facilitate the interactions, protein–protein interaction sites prediction has become one of the most important and challenging problems of computational biology. Although much progress in this field has been reported, this problem is yet to be satisfactorily solved. Here, a novel method (LORIS: L1-regularized LOgistic Regression based protein–protein Interaction Sites predictor) is proposed, that identifies interaction residues, using sequence features and is implemented via the L1-logreg classifier. Results show that LORIS is not only quite effective, but also, performs better than existing state-of-the art methods. LORIS, available as standalone package, can be useful for facilitating drug-design and targeted mutation related studies, which require a deeper knowledge of protein interactions sites.

PAIRpred: Partner‐specific prediction of interacting residues from sequence and structure

We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/.

Type I pyridoxal 5′-phosphate dependent enzymatic domains embedded within multimodular nonribosomal peptide synthetase and polyketide synthase assembly lines

BackgroundPyridoxal 5′-phosphate (PLP)-dependent enzymes of fold type I, the most studied structural class of the PLP-dependent enzyme superfamily, are known to exist as stand-alone homodimers or homotetramers. These enzymes have been found also embedded in multimodular and multidomain assembly lines involved in the biosynthesis of polyketides (PKS) and nonribosomal peptides (NRPS). The aim of this work is to provide a proteome-wide view of the distribution and characteristics of type I domains covalently integrated in these assemblies in prokaryotes.ResultsAn ad-hoc Hidden Markov profile was calculated using a sequence alignment derived from a multiple structural superposition of distantly related PLP-enzymes of fold type I. The profile was utilized to scan the sequence databank and to collect the proteins containing at least one type I domain linked to a component of an assembly line in bacterial genomes. The domains adjacent to a carrier protein were further investigated. Phylogenetic analysis suggested the presence of four PLP-dependent families: Aminotran_3, Beta_elim_lyase and Pyridoxal_deC, occurring mainly within mixed NRPS/PKS clusters, and Aminotran_1_2 found mainly in PKS clusters. Sequence similarity to the reference PLP enzymes with solved structures ranged from 24 to 42% identity. Homology models were built for each representative type I domain and molecular docking simulations with putative substrates were carried out. Prediction of the protein-protein interaction sites evidenced that the surface regions of the type I domains embedded within multienzyme assemblies were different from those of the self-standing enzymes; these structural features appear to be required for productive interactions with the adjacent domains in a multidomain context.ConclusionsThis work provides a systematic view of the occurrence of type I domain within NRPS and PKS assembly lines and it predicts their structural characteristics using computational methods. Comparison with the corresponding stand-alone enzymes highlighted the common and different traits related to various aspects of their structure-function relationship. Therefore, the results of this work, on one hand contribute to the understanding of the functional and structural diversity of the PLP-dependent type I enzymes and, on the other, pave the way to further studies aimed at their applications in combinatorial biosynthesis.

Glycan Masking of Plasmodium vivax Duffy Binding Protein for Probing Protein Binding Function and Vaccine Development

Glycan masking is an emerging vaccine design strategy to focus antibody responses to specific epitopes, but it has mostly been evaluated on the already heavily glycosylated HIV gp120 envelope glycoprotein. Here this approach was used to investigate the binding interaction of Plasmodium vivax Duffy Binding Protein (PvDBP) and the Duffy Antigen Receptor for Chemokines (DARC) and to evaluate if glycan-masked PvDBPII immunogens would focus the antibody response on key interaction surfaces. Four variants of PVDBPII were generated and probed for function and immunogenicity. Whereas two PvDBPII glycosylation variants with increased glycan surface coverage distant from predicted interaction sites had equivalent binding activity to wild-type protein, one of them elicited slightly better DARC-binding-inhibitory activity than wild-type immunogen. Conversely, the addition of an N-glycosylation site adjacent to a predicted PvDBP interaction site both abolished its interaction with DARC and resulted in weaker inhibitory antibody responses. PvDBP is composed of three subdomains and is thought to function as a dimer; a meta-analysis of published PvDBP mutants and the new DBPII glycosylation variants indicates that critical DARC binding residues are concentrated at the dimer interface and along a relatively flat surface spanning portions of two subdomains. Our findings suggest that DARC-binding-inhibitory antibody epitope(s) lie close to the predicted DARC interaction site, and that addition of N-glycan sites distant from this site may augment inhibitory antibodies. Thus, glycan resurfacing is an attractive and feasible tool to investigate protein structure-function, and glycan-masked PvDBPII immunogens might contribute to P. vivax vaccine development.

PPIcons: identification of protein-protein interaction sites in selected organisms.

The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein–protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively.Electronic supplementary materialThe online version of this article (doi:10.1007/s00894-013-1886-9) contains supplementary material, which is available to authorized users.