title: SVR_CAF: an integrated score function for detecting native protein structures among decoys.
abstract: An accurate score function for detecting the most native-like models among a huge number of decoy sets is essential to the protein structure prediction. In this work, we developed a novel integrated score function (SVR_CAF) to discriminate native structures from decoys, as well as to rank near-native structures and select best decoys when native structures are absent. SVR_CAF is a machine learning score, which incorporates the contact energy based score (CE_score), amino acid network based score (AAN_score), and the fast Fourier transform based score (FFT_score). The score function was evaluated with four decoy sets for its discriminative ability and it shows higher overall performance than the state-of-the-art score functions.
authors.affiliation: Center for Systems Biology, Soochow University, Suzhou, Jiangsu, 215006, China.
authors.forename: Jianhong
authors.initials: J
authors.lastname: Zhou
authors.forename: Wenying
authors.initials: W
authors.lastname: Yan
authors.forename: Guang
authors.initials: G
authors.lastname: Hu
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology; D005583:Fourier Analysis; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000648:ultrastructure*; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: amino acid network; contact energy; fast Fourier transform; protein native structure selection; support vector regression