Literature DB >> 12691987

Using multiple interdependency to separate functional from phylogenetic correlations in protein alignments.

Elisabeth R M Tillier1, Thomas W H Lui.   

Abstract

MOTIVATION: Multiple sequence alignments of homologous proteins are useful for inferring their phylogenetic history and to reveal functionally important regions in the proteins. Functional constraints may lead to co-variation of two or more amino acids in the sequence, such that a substitution at one site is accompanied by compensatory substitutions at another site. It is not sufficient to find the statistical correlations between sites in the alignment because these may be the result of several undetermined causes. In particular, phylogenetic clustering will lead to many strong correlations.
RESULTS: A procedure is developed to detect statistical correlations stemming from functional interaction by removing the strong phylogenetic signal that leads to the correlations of each site with many others in the sequence. Our method relies upon the accuracy of the alignment but it does not require any assumptions about the phylogeny or the substitution process. The effectiveness of the method was verified using computer simulations and then applied to predict functional interactions between amino acids in the Pfam database of alignments.

Mesh:

Substances:

Year:  2003        PMID: 12691987     DOI: 10.1093/bioinformatics/btg072

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  54 in total

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2.  Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.

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

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Journal:  PLoS Comput Biol       Date:  2018-11-05       Impact factor: 4.475

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Authors:  Andrew J Roger; Laura A Hug
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-06-29       Impact factor: 6.237

5.  A novel method for detecting intramolecular coevolution: adding a further dimension to selective constraints analyses.

Authors:  Mario A Fares; Simon A A Travers
Journal:  Genetics       Date:  2006-03-17       Impact factor: 4.562

6.  Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information.

Authors:  Cristina Marino Buslje; Javier Santos; Jose Maria Delfino; Morten Nielsen
Journal:  Bioinformatics       Date:  2009-03-10       Impact factor: 6.937

7.  The human protein coevolution network.

Authors:  Elisabeth R M Tillier; Robert L Charlebois
Journal:  Genome Res       Date:  2009-08-20       Impact factor: 9.043

8.  Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics.

Authors:  J Gregory Caporaso; Sandra Smit; Brett C Easton; Lawrence Hunter; Gavin A Huttley; Rob Knight
Journal:  BMC Evol Biol       Date:  2008-12-03       Impact factor: 3.260

9.  Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution.

Authors:  Daniel Y Little; Lu Chen
Journal:  PLoS One       Date:  2009-03-10       Impact factor: 3.240

10.  Structural and functional roles of coevolved sites in proteins.

Authors:  Saikat Chakrabarti; Anna R Panchenko
Journal:  PLoS One       Date:  2010-01-06       Impact factor: 3.240

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