title: Prediction of beta-turns with learning machines.
abstract: The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, 200233 Shanghai, China. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Xiao-Jun
authors.initials: XJ
authors.lastname: Liu
authors.forename: Yi-Xue
authors.initials: YX
authors.lastname: Li
authors.forename: Xue-biao
authors.initials: XB
authors.lastname: Xu
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D010455:Peptides / Q000737:chemistry; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry
chemical_list: D010455:Peptides; D011506:Proteins