A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.

Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural...

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Autores principales:	Julie Baussand, Alessandra Carbone
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/03c328528ed146d98a4e592788d959a5
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spelling	oai:doaj.org-article:03c328528ed146d98a4e592788d959a52021-11-25T05:42:11ZA combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.1553-734X1553-735810.1371/journal.pcbi.1000488https://doaj.org/article/03c328528ed146d98a4e592788d959a52009-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19730672/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.Julie BaussandAlessandra CarbonePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 9, p e1000488 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Julie Baussand Alessandra Carbone A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
description	Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.
format	article
author	Julie Baussand Alessandra Carbone
author_facet	Julie Baussand Alessandra Carbone
author_sort	Julie Baussand
title	A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
title_short	A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
title_full	A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
title_fullStr	A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
title_full_unstemmed	A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
title_sort	combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/03c328528ed146d98a4e592788d959a5
work_keys_str_mv	AT juliebaussand acombinatorialapproachtodetectcoevolvedaminoacidnetworksinproteinfamiliesofvariabledivergence AT alessandracarbone acombinatorialapproachtodetectcoevolvedaminoacidnetworksinproteinfamiliesofvariabledivergence AT juliebaussand combinatorialapproachtodetectcoevolvedaminoacidnetworksinproteinfamiliesofvariabledivergence AT alessandracarbone combinatorialapproachtodetectcoevolvedaminoacidnetworksinproteinfamiliesofvariabledivergence
_version_	1718414499028926464

A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.

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