title: Prediction of beta-turn in protein using E-SSpred and support vector machine.
abstract: Beta-turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of beta-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q (total) barrier and achieved Q (total) = 80.9%, MCC = 0.44, and Q (predicted) higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that beta-turn prediction accuracy can be improved by inclusion of secondary structure information.
authors.affiliation: College of Chemistry, Key Laboratory of Green Chemistry & Technology, Ministry of Education, Sichuan University, Chengdu 610064, China. lr_liu1997@163.com
authors.forename: Lirong
authors.initials: L
authors.lastname: Liu
authors.forename: Yaping
authors.initials: Y
authors.lastname: Fang
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
authors.forename: Cuicui
authors.initials: C
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008956:Models, Chemical; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software
chemical_list: D011506:Proteins