title: Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.
abstract: Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.
authors.affiliation: Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan City, China. yanw@mail.hust.edu.cn
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.forename: Zhi-Dong
authors.initials: ZD
authors.lastname: Xue
authors.forename: Xiao-Hong
authors.initials: XH
authors.lastname: Shi
authors.forename: Jin
authors.initials: J
authors.lastname: Xu
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins