Learning generative models for protein fold families

Balakrishnan, Sivaraman; Kamisetty, Hetunandan; Carbonell, Jaime G.; Lee, Su-In; Langmead, Christopher J.

doi:10.1002/prot.22934

Cited by 327 publications

(413 citation statements)

References 39 publications

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“…Previous studies have developed a number of techniques to do this (Mézard and Mora 2009;Weigt et al 2009;Balakrishnan et al 2011;Cocco and Monasson 2011;Morcos et al 2011;Haq et al 2012;Jones et al 2012;Ekeberg et al 2013;Ferguson et al 2013;Barton et al 2016a). Following Ferguson et al (2013), we estimate the bivariate marginals given a set of fields and couplings by generating sequences through Markov Chain Monte Carlo (MCMC) where the Metropolis criterion for a generated sequence is proportional to the exponentiated Potts Hamiltonian.…”

Section: Model Inferencementioning

confidence: 99%

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

Flynn

Haldane

Torbett

et al. 2016

Preprint

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Understanding the complex mutation patterns that give rise to drug resistant viral strains provides a foundation for developing more effective treatment strategies for HIV/AIDS. Multiple sequence alignments of drug-experienced HIV-1 protease sequences contain networks of many pair correlations which can be used to build a (Potts) Hamiltonian model of these mutation patterns. Using this Hamiltonian model, we translate HIV-1 protease sequence covariation data into quantitative predictions for the probability of observing specific mutation patterns which are in agreement with the observed sequence statistics. We find that the statistical energies of the Potts model are correlated with the fitness of individual proteins containing therapy-associated mutations as estimated by in vitro measurements of protein stability and viral infectivity. We show that the penalty for acquiring primary resistance mutations depends on the epistatic interactions with the sequence background. Primary mutations which lead to drug resistance can become highly advantageous (or entrenched) by the complex mutation patterns which arise in response to drug therapy despite being destabilizing in the wildtype background. Anticipating epistatic effects is important for the design of future protease inhibitor therapies.

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Section: Model Inferencementioning

confidence: 99%

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

Flynn

Haldane

Torbett

et al. 2016

Preprint

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“…The most popular approximation is to maximize the pseudo-likelihood 100 instead of the likelihood, as it can be shown that it converges to the same solution for large numbers of samples and it is fast to compute [2,10,50]. Even though pseudolikelihood maximization gives results of the same quality of predicted residue-residue contacts as those using the 105 full likelihood optimization, several studies unveiled that the pseudo-likelihood model is inaccurate and not able to accurately reproduce the empirical alignment statistics [7,12].…”

mentioning

confidence: 99%

“…The approach that has consistently been found to work best for residue contact prediction is the pseudo-likelihood approximation, in which we replace the likelihood with the pseudolikelihood and maximize the regularized log pseudolikelihood [2,10,50],…”

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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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“…In contrast to more traditional approaches based on homology detection and sequence conservation, contact prediction supported by residue coevolution (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) makes use of sequence variability as an alternative source of information (26). The analysis of residue coevolution has been successfully applied to contact prediction at the interface of protein dimers (27)(28)(29)(30)(31)(32)(33), eventually leading to de novo prediction of protein complexes assisted by coevolution (29,30).…”

mentioning

confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

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

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Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequencebased coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequencebased accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.coevolution | protein-protein interaction | protein complex | homology modeling | contact prediction C ells function as a remarkably synchronized orchestra of finely tuned molecular interactions, and establishing this molecular network has become a major goal of molecular biology. Important methodological and technical advances have led to the identification of a large number of novel protein-protein interactions and to major contributions to our understanding of the functioning of cells and organisms (1, 2). In contrast, and despite relevant advances in EM (3) and crystallography (4), the molecular details of a large number of interactions remain unknown.When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5, 6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target. Templatebased techniques have provided models for a large number of protein complexes with structurally solved homologous complexes (7-10). Unfortunately, proteins involved in homologous protein dimers tend to systema...

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Learning generative models for protein fold families

Cited by 327 publications

References 39 publications

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

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

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

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