Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

Baldassi, Carlo; Zamparo, Marco; Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

doi:10.1371/journal.pone.0092721

Cited by 143 publications

(194 citation statements)

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“…Our approach is based on pairwise maximum entropy models, which have proved successful at predicting residue contacts between known interaction partners (7,(15)(16)(17)(18)(19). To our knowledge, the important problem of predicting interaction partners among paralogs from sequences has only been addressed by Burger and van Nimwegen (6), who used a Bayesian network method.…”

Section: Discussionmentioning

confidence: 99%

“…The ability to accurately predict interaction partners without training data is surprising; to understand it, we examine the evolution of the model over iterations of the IPA. In a well-trained model, the residue pairs with the largest couplings have been shown to correspond to contacts in the protein complex (7,16,31). Up to iteration ∼ 100 − 150 (with N increment = 6), models starting from random pairings do no better than chance at identifying contacts.…”

Section: Ipamentioning

confidence: 99%

“…In particular, the maximum entropy principle (8) specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment (5). This approach has recently been used with success to determine 3D protein structures from sequences (9)(10)(11), to predict mutational effects (12)(13)(14), and to find residue contacts between known interaction partners (7,(15)(16)(17)(18)(19). Pairwise maximum entropy models have also been used productively in various other fields (20)(21)(22)(23)(24)(25)(26).…”

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Inferring interaction partners from protein sequences

Bitbol

Dwyer

Colwell

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

134

188

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Specific protein−protein interactions are crucial in the cell, both to ensure the formation and stability of multiprotein complexes and to enable signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interaction partners, causing their sequences to be correlated. Here we exploit these correlations to accurately identify, from sequence data alone, which proteins are specific interaction partners. Our general approach, which employs a pairwise maximum entropy model to infer couplings between residues, has been successfully used to predict the 3D structures of proteins from sequences. Thus inspired, we introduce an iterative algorithm to predict specific interaction partners from two protein families whose members are known to interact. We first assess the algorithm's performance on histidine kinases and response regulators from bacterial twocomponent signaling systems. We obtain a striking 0.93 true positive fraction on our complete dataset without any a priori knowledge of interaction partners, and we uncover the origin of this success. We then apply the algorithm to proteins from ATP-binding cassette (ABC) transporter complexes, and obtain accurate predictions in these systems as well. Finally, we present two metrics that accurately distinguish interacting protein families from noninteracting ones, using only sequence data. proteins. For instance, specific protein−protein interactions ensure proper signal transduction in various pathways. Hence, mapping specific protein−protein interactions is central to a systems-level understanding of cells, and has broad applications to areas such as drug targeting. High-throughput experiments have recently elucidated a substantial fraction of protein−protein interactions in a few model organisms (1), but such experiments remain challenging. Meanwhile, there has been an explosion of available sequence data. Can we exploit this abundant new sequence data to identify specific protein−protein interaction partners?Specific interactions between proteins imply evolutionary constraints on the interacting partners. For instance, mutation of a contact residue in one partner generally impairs binding, but may be compensated by a complementary mutation in the other partner. This coevolution of interaction partners results in correlations between their amino acid sequences. Similar correlations exist within single proteins, for example, between amino acids that are in contact in the folded protein. However, the simple fact of a correlation between residues in a multiple sequence alignment is only weakly predictive of a 3D contact (2-4), as correlation can also stem from indirect effects. Fortunately, global statistical models allow direct and indirect interactions to be disentangled (5-7). In particular, the maximum entropy principle (8) specifies the least-structured global statistical model consistent with the one-and two-point statistics of an alignment (5). This approach has recently been used with success to determine 3D ...

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Section: Discussionmentioning

confidence: 99%

Section: Ipamentioning

confidence: 99%

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confidence: 99%

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Inferring interaction partners from protein sequences

Bitbol

Dwyer

Colwell

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

134

188

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“…The basis of the paralog matching procedure is the GaussDCA formulated in ref. 28. Let us assume a matched MSA A of M sequences of length L. The MSA is transformed into an M × 20L-dimensional binary array X by replacing each amino acid with a distinct 20-dimensional vector containing one entry "1" and 19 entries "0"; gaps are represented by zero vectors.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Gueudré

Baldassi

Zamparo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Understanding protein−protein interactions is central to our understanding of almost all complex biological processes. Computational tools exploiting rapidly growing genomic databases to characterize protein−protein interactions are urgently needed. Such methods should connect multiple scales from evolutionary conserved interactions between families of homologous proteins, over the identification of specifically interacting proteins in the case of multiple paralogs inside a species, down to the prediction of residues being in physical contact across interaction interfaces. Statistical inference methods detecting residue−residue coevolution have recently triggered considerable progress in using sequence data for quaternary protein structure prediction; they require, however, large joint alignments of homologous protein pairs known to interact. The generation of such alignments is a complex computational task on its own; application of coevolutionary modeling has, in turn, been restricted to proteins without paralogs, or to bacterial systems with the corresponding coding genes being colocalized in operons. Here we show that the direct coupling analysis of residue coevolution can be extended to connect the different scales, and simultaneously to match interacting paralogs, to identify interprotein residue−residue contacts and to discriminate interacting from noninteracting families in a multiprotein system. Our results extend the potential applications of coevolutionary analysis far beyond cases treatable so far.A lmost all biological processes depend on interacting proteins.Understanding protein−protein interactions is therefore key to our understanding of complex biological systems. In this context, at least two questions are of interest: First, the question "who with whom," i.e., which proteins interact; this concerns the networks connecting specific proteins inside one organism, but alsoin the context of this article-the evolutionary perspective of protein−protein interactions, which are conserved across different species. Their coevolution is at the basis of many modern computational techniques for characterizing protein−protein interactions. The second question is the question "how" proteins interact with each other, in particular, which residues are involved in the interaction interfaces, and which residues are in contact across the interfaces. Such knowledge may provide important mechanistic insight into questions related to interaction specificity or competitive interaction with partially shared interfaces.The experimental identification of protein−protein interactions is an arduous task (for reviews, cf. refs. 1 and 2): High-throughput techniques that aim to identify protein−protein interactions in vivo or in vitro are well documented and include large-scale yeast two-hybrid assays and protein affinity mass spectrometry assays. Such large-scale efforts have revealed useful information but are hampered by high false positive and false negative error rates. Structural approaches based on protein cocrystalli...

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“…for residue contact prediction simply takes the L 2 norm of the 20 × 20-dimensional vector w ij with components w ij (a, b) [3,10,11,31,50],…”

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confidence: 99%

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Vorberg

Seemayer

Soeding

2018

Preprint

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Compensatory mutations between protein residues that are in physical contact with each other can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, high coupling coefficients predict residues contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on boosting contact prediction quality by adding external information, little progress has been made to improve the statistical procedure at the core. In that regard, our lack of understanding of the sources of noise poses a major obstacle. We have developed CCMgen, the first method for simulating protein evolution by providing full control over the generation of realistic synthetic MSAs with pairwise statistical couplings between residue positions. This procedure requires an exact statistical model that reliably reproduces observed alignment statistics. With CCMpredPy we also provide an implementation of persistent contrastive divergence (PCD), a precise inference technique that enables us to learn the required high-quality statistical models. We demonstrate how CCMgen can facilitate the development and testing of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. For that purpose we propose a simple entropy correction (EC) strategy which disentangles the correction for both sources of noise. We find that entropy contributes typically roughly twice as much noise as phylogeny.

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Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

Cited by 143 publications

References 48 publications

Inferring interaction partners from protein sequences

Inferring interaction partners from protein sequences

Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

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