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

TitleA combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.
Publication TypeJournal Article
Year of Publication2009
AuthorsBaussand, J, Carbone, A
JournalPLoS Comput Biol
Date Published2009 Sep
KeywordsAlgorithms, Amino Acid Sequence, Amino Acids, Binding Sites, Cluster Analysis, Combinatorial Chemistry Techniques, Conserved Sequence, Hemoglobins, Leucine Dehydrogenase, Models, Molecular, Molecular Sequence Data, PDZ Domains, Protein Interaction Mapping, Proteins, Sequence Alignment, Serine Proteases, Substrate Specificity

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.

Alternate JournalPLoS Comput. Biol.
PubMed ID19730672
PubMed Central IDPMC2723916