Prediction of π-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 γ-turns, β-turns, and α-turns in proteins. In the past, studies of π-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying π-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict π-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 π-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/.