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Inference of functional regions in proteins by quantification of evolutionary constraints
Author(s) -
Alexander L. Simon,
Eric A. Stone,
Arend Sidow
Publication year - 2002
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.042692299
Subject(s) - divergence (linguistics) , sequence (biology) , inference , constraint (computer aided design) , stability (learning theory) , molecular clock , function (biology) , biology , protein sequencing , protein structure , computational biology , evolutionary biology , computer science , peptide sequence , mathematics , genetics , phylogenetics , artificial intelligence , gene , machine learning , philosophy , linguistics , biochemistry , geometry
Likelihood estimates of local rates of evolution within proteins reveal that selective constraints on structure and function are quantitatively stable over billions of years of divergence. The stability of constraints produces an intramolecular clock that gives each protein a characteristic pattern of evolutionary rates along its sequence. This pattern allows the identification of constrained regions and, because the rate of evolution is a quantitative measure of the strength of the constraint, of their functional importance. We show that results from such analyses, which require only sequence alignments, are consistent with experimental and mutational data. The methodology has significant predictive power and may be used to guide structure--function studies for any protein represented by a modest number of homologs in sequence databases.

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