title: Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation.
abstract: Proteins with complex, nonlocal beta-sheets are challenging for de novo structure prediction, due in part to the difficulty of efficiently sampling long-range strand pairings. We present a new, multilevel approach to beta-sheet structure prediction that circumvents this difficulty by reformulating structure generation in terms of a folding tree. Nonlocal connections in this tree allow us to explicitly sample alternative beta-strand pairings while simultaneously exploring local conformational space using backbone torsion-space moves. An iterative, energy-biased resampling strategy is used to explore the space of beta-strand pairings; we expect that such a strategy will be generally useful for searching large conformational spaces with a high degree of combinatorial complexity.
authors.affiliation: University of Washington, Seattle, Washington 98195, USA.
authors.forename: Philip
authors.initials: P
authors.lastname: Bradley
authors.forename: David
authors.initials: D
authors.lastname: Baker
mesh_terms: D000818:Animals; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins