title: Gleaning structural and functional information from correlations in protein multiple sequence alignments.
abstract: The availability of vast amounts of protein sequence data facilitates detection of subtle statistical correlations due to imposed structural and functional constraints. Recent breakthroughs using Direct Coupling Analysis (DCA) and related approaches have tapped into correlations believed to be due to compensatory mutations. This has yielded some remarkable results, including substantially improved prediction of protein intra- and inter-domain 3D contacts, of membrane and globular protein structures, of substrate binding sites, and of protein conformational heterogeneity. A complementary approach is Bayesian Partitioning with Pattern Selection (BPPS), which partitions related proteins into hierarchically-arranged subgroups based on correlated residue patterns. These correlated patterns are presumably due to structural and functional constraints associated with evolutionary divergence rather than to compensatory mutations. Hence joint application of DCA- and BPPS-based approaches should help sort out the structural and functional constraints contributing to sequence correlations.
authors.affiliation: Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, 801 West Baltimore St., BioPark II, Room 617, Baltimore, MD 21201, United States. Electronic address: aneuwald@som.umaryland.edu.
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Neuwald
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D000074021:Interatrial Block; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment
chemical_list: D011506:Proteins