PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Jones, David T.; Buchan, Daniel W. A.; Cozzetto, Domenico; Pontil, Massimiliano

doi:10.1093/bioinformatics/btr638

Cited by 747 publications

(912 citation statements)

References 41 publications

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“…Previous work (2)(3)(4) has demonstrated that contacts between residues in the 3D structure of a protein can be predicted with considerable accuracy for large protein families based on the evolutionary covariance observed in multiple sequence alignments. We briefly review the basis for these approaches to motivate the method described in this paper.…”

Section: Resultsmentioning

confidence: 99%

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Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

Kamisetty

Овчинников

Baker

2013

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

642

764

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Significance We develop an improved method for predicting residue–residue contacts in protein structures that achieves higher accuracy than previous methods by integrating structural context and sequence coevolution information. We then determine the conditions under which these predicted contacts are likely to be useful for structure modeling and identify more than 400 protein families where these conditions are currently met.

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Section: Resultsmentioning

confidence: 99%

“…DCA, PSICOV and PLMDCA reweigh sequences in the input multiple sequence alignment to account for redundancy (2,3,6). When making predictions using GREMLIN, we reweigh sequences in a filtered alignment (filtered at 90% sequence identity) instead of using the full alignment.…”

Section: Svmcon We Used Svmcon With the Default Settingsmentioning

confidence: 99%

Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

Kamisetty

Овчинников

Baker

2013

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

642

764

View full text Add to dashboard Cite

show abstract

“…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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“…However, these methods ignore the transitive correlation between residues and thus generate many false positive results. The inverse covariance matrix and pseudo-likelihood maximization were introduced subsequently to eliminate transitivity in methods such as DCA [12], PSICOV [13], plmDCA [14], GREMLIN [15], CCMpred [16], FreeContact [17] and PconsC2 [18]. These methods effectively reduce false positive predictions by globally considering all inter-residue correlations.…”

Section: Author Summarymentioning

confidence: 99%

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Mao

Wang

Zhang

et al. 2017

Preprint

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Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β -β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we introduce a novel ridge-detection-based β -β contact predictor, RDb 2 C, to identify residue pairing in β strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76%at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly β proteins, improvement in the β -β contact prediction can further ameliorate the structural prediction. Availability:All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C. Author summaryDue to the topological complexity, mainly β proteins are challenging targets in protein structure prediction. Knowledge of the pairing between β strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly β proteins. In this work, we developed a novel algorithm to identify the residue pairing in β strands from a predicted residue contact map.This method adopts the ridge detection technique to capture the characteristic pattern of β -β interactions from the map and then utilizes a multi-stage random forest framework to predict β -β contacts at the residue level. According to our tests, our method could effectively improve the prediction of β -β contacts even from a highly noisy contact map. Moreover, the refined β -β contact information could effectively improve the structural modeling of mainly β proteins.

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PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Abstract: The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV.

Cited by 747 publications

References 41 publications

Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era

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

Identification of residue pairing in interacting β-strands from a predicted residue contact map

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