title: Protein threading by learning.
abstract: By using techniques borrowed from statistical physics and neural networks, we determine the parameters, associated with a scoring function, that are chosen optimally to ensure complete success in threading tests in a training set of proteins. These parameters provide a quantitative measure of the propensities of amino acids to be buried or exposed and to be in a given secondary structure and are a good starting point for solving both the threading and design problems.
authors.affiliation: Department of Physics, 104 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: I
authors.initials: I
authors.lastname: Chang
authors.forename: M
authors.initials: M
authors.lastname: Cieplak
authors.forename: R I
authors.initials: RI
authors.lastname: Dima
authors.forename: A
authors.initials: A
authors.lastname: Maritan
authors.forename: J R
authors.initials: JR
authors.lastname: Banavar
mesh_terms: D000595:Amino Acid Sequence; D015195:Drug Design; D008956:Models, Chemical; D015233:Models, Statistical; D008969:Molecular Sequence Data; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins