title: Potential for assessing quality of protein structure based on contact number prediction.
abstract: We developed a novel knowledge-based residue environment potential for assessing the quality of protein structures in protein structure prediction. The potential uses the contact number of residues in a protein structure and the absolute contact number of residues predicted from its amino acid sequence using a new prediction method based on a support vector regression (SVR). The contact number of an amino acid residue in a protein structure is defined by the number of residues around a given residue. First, the contact number of each residue is predicted using SVR from an amino acid sequence of a target protein. Then, the potential of the protein structure is calculated from the probability distribution of the native contact numbers corresponding to the predicted ones. The performance of this potential is compared with other score functions using decoy structures to identify both native structure from other structures and near-native structures from nonnative structures. This potential improves not only the ability to identify native structures from other structures but also the ability to discriminate near-native structures from nonnative structures.
authors.affiliation: Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan. tak@bi.a.u-tokyo.ac.jp
authors.forename: Takashi
authors.initials: T
authors.lastname: Ishida
authors.forename: Shugo
authors.initials: S
authors.lastname: Nakamura
authors.forename: Kentaro
authors.initials: K
authors.lastname: Shimizu
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins