Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.
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/.
Wang
Yan
Y
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan City, China. yanw@mail.hust.edu.cn
Xue
Zhi-Dong
ZD
Shi
Xiao-Hong
XH
Xu
Jin
J
D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins