From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analy...

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Autores principales:	Simona Cocco, Remi Monasson, Martin Weigt
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/68c16feebb164cbb9f09f23f2fca9ce6
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spelling	oai:doaj.org-article:68c16feebb164cbb9f09f23f2fca9ce62021-11-18T05:53:40ZFrom principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.1553-734X1553-735810.1371/journal.pcbi.1003176https://doaj.org/article/68c16feebb164cbb9f09f23f2fca9ce62013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23990764/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold.Simona CoccoRemi MonassonMartin WeigtPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 8, p e1003176 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Simona Cocco Remi Monasson Martin Weigt From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
description	Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold.
format	article
author	Simona Cocco Remi Monasson Martin Weigt
author_facet	Simona Cocco Remi Monasson Martin Weigt
author_sort	Simona Cocco
title	From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
title_short	From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
title_full	From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
title_fullStr	From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
title_full_unstemmed	From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
title_sort	from principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/68c16feebb164cbb9f09f23f2fca9ce6
work_keys_str_mv	AT simonacocco fromprincipalcomponenttodirectcouplinganalysisofcoevolutioninproteinsloweigenvaluemodesareneededforstructureprediction AT remimonasson fromprincipalcomponenttodirectcouplinganalysisofcoevolutioninproteinsloweigenvaluemodesareneededforstructureprediction AT martinweigt fromprincipalcomponenttodirectcouplinganalysisofcoevolutioninproteinsloweigenvaluemodesareneededforstructureprediction
_version_	1718424670672257024

From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.

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