title: Regional covariation and its application for predicting protein contact patches.
abstract: Correlated mutation analysis (CMA) is an effective approach for predicting functional and structural residue interactions from multiple sequence alignments (MSAs) of proteins. As nearby residues may also play a role in a given functional interaction, we were interested in seeing whether covarying sites were clustered, and whether this could be used to enhance the predictive power of CMA. A large-scale search for coevolving regions within protein domains revealed that if two sites in a MSA covary, then neighboring sites in the alignment also typically covary, resulting in clusters of covarying residues. The program PatchD(http://www.uhnres.utoronto.ca/labs/tillier/) was developed to measure the covariation between disconnected sequence clusters to reveal patch covariation. Patches that exhibit strong covariation identify multiple residues that are generally nearby in the protein structure, suggesting that the detection of covarying patches can be used in conjunction with traditional CMA approaches to reveal functional interaction partners.
authors.affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Yongbai
authors.initials: Y
authors.lastname: Xu
authors.forename: Elisabeth R M
authors.initials: ER
authors.lastname: Tillier
mesh_terms: D000595:Amino Acid Sequence; D001665:Binding Sites; D016000:Cluster Analysis; D017124:Conserved Sequence; D004252:DNA Mutational Analysis / Q000379:methods*; D014644:Genetic Variation; D008957:Models, Genetic; D008958:Models, Molecular; D010802:Phylogeny; D011506:Proteins / Q000737:chemistry* / Q000235:genetics* / Q000378:metabolism; D016415:Sequence Alignment
chemical_list: D011506:Proteins