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Revealing evolutionary constraints on proteins through sequence analysis.

Author(s): Wang, Shou-Wen; Bitbol, Anne-Florence; Wingreen, Ned

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Abstract: Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.
Publication Date: 24-Apr-2019
Citation: Wang, Shou-Wen, Bitbol, Anne-Florence, Wingreen, Ned S. (2019). Revealing evolutionary constraints on proteins through sequence analysis.. PLoS computational biology, 15 (4), e1007010 - e1007010. doi:10.1371/journal.pcbi.1007010
DOI: doi:10.1371/journal.pcbi.1007010
ISSN: 1553-734X
EISSN: 1553-7358
Pages: e1007010 - e1007010
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: PLoS Computational Biology
Version: Final published version. This is an open access article.

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