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      I-COMS: Interprotein-COrrelated Mutations Server

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          Abstract

          Interprotein contact prediction using multiple sequence alignments (MSAs) is a useful approach to help detect protein–protein interfaces. Different computational methods have been developed in recent years as an approximation to solve this problem. However, as there are discrepancies in the results provided by them, there is still no consensus on which is the best performing methodology. To address this problem, I-COMS (interprotein COrrelated Mutations Server) is presented. I-COMS allows to estimate covariation between residues of different proteins by four different covariation methods. It provides a graphical and interactive output that helps compare results obtained using different methods. I-COMS automatically builds the required MSA for the calculation and produces a rich visualization of either intraprotein and/or interprotein covariating positions in a circos representation. Furthermore, comparison between any two methods is available as well as the overlap between any or all four methodologies. In addition, as a complementary source of information, a matrix visualization of the corresponding scores is made available and the density plot distribution of the inter, intra and inter+intra scores are calculated. Finally, all the results can be downloaded (including MSAs, scores and graphics) for comparison and visualization and/or for further analysis.

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          Most cited references 22

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          Protein 3D Structure Computed from Evolutionary Sequence Variation

          The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.
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            PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.

            The accurate prediction of residue-residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets, the L/5 (i.e. top-L/5 predictions for a protein of length L) precision for long-range contacts (sequence separation >23) was ≥ 0.5, which represents an improvement sufficient to be of significant benefit in protein structure prediction or model quality assessment. The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV.
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              Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era.

              Recently developed methods have shown considerable promise in predicting residue-residue contacts in protein 3D structures using evolutionary covariance information. However, these methods require large numbers of evolutionarily related sequences to robustly assess the extent of residue covariation, and the larger the protein family, the more likely that contact information is unnecessary because a reasonable model can be built based on the structure of a homolog. Here we describe a method that integrates sequence coevolution and structural context information using a pseudolikelihood approach, allowing more accurate contact predictions from fewer homologous sequences. We rigorously assess the utility of predicted contacts for protein structure prediction using large and representative sequence and structure databases from recent structure prediction experiments. We find that contact predictions are likely to be accurate when the number of aligned sequences (with sequence redundancy reduced to 90%) is greater than five times the length of the protein, and that accurate predictions are likely to be useful for structure modeling if the aligned sequences are more similar to the protein of interest than to the closest homolog of known structure. These conditions are currently met by 422 of the protein families collected in the Pfam database.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 July 2015
                01 June 2015
                01 June 2015
                : 43
                : Web Server issue
                : W320-W325
                Affiliations
                Fundación Instituto Leloir. Av. Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +5411 52387500; Fax: +5411 52387501; Email: cmb@ 123456leloir.org.ar
                Article
                10.1093/nar/gkv572
                4489276
                26032772
                © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 6
                Product
                Categories
                Web Server issue
                Custom metadata
                1 July 2015

                Genetics

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