title: Bayesian models and algorithms for protein β-sheet prediction.
abstract: Prediction of the 3D structure greatly benefits from the information related to secondary structure, solvent accessibility, and nonlocal contacts that stabilize a protein's structure. We address the problem of \beta-sheet prediction defined as the prediction of \beta--strand pairings, interaction types (parallel or antiparallel), and \beta-residue interactions (or contact maps). We introduce a Bayesian approach for proteins with six or less \beta-strands in which we model the conformational features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of \beta-strand organizations. To select the optimum \beta-sheet architecture, we significantly reduce the search space by heuristics that enforce the amino acid pairs with strong interaction potentials. In addition, we find the optimum pairwise alignment between \beta-strands using dynamic programming in which we allow any number of gaps in an alignment to model \beta-bulges more effectively. For proteins with more than six \beta-strands, we first compute \beta-strand pairings using the BetaPro method. Then, we compute gapped alignments of the paired \beta-strands and choose the interaction types and \beta--residue pairings with maximum alignment scores. We performed a 10-fold cross-validation experiment on the BetaSheet916 set and obtained significant improvements in the prediction accuracy.
authors.affiliation: Department of Genome Sciences, University of Washington, Genome Sciences, Box 357456, 1705 NE Pacific St., Seattle, WA 98195-5065, USA. zafer@u.washington.edu
authors.forename: Zafer
authors.initials: Z
authors.lastname: Aydin
authors.forename: Yucel
authors.initials: Y
authors.lastname: Altunbasak
authors.forename: Hakan
authors.initials: H
authors.lastname: Erdogan
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment
chemical_list: D011506:Proteins