Prediction and Analysis of β-Turns in Proteins by Support Vector Machine

Abstract

Tight turn has long been recognized as one of the three important features of proteins after the #-helix and #-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are #-turns. Analysis and prediction of #-turns in particular and tight turns in general are very useful for the design of new molecules such as drugs, pesticides, and antigens. In this paper, we introduce a support vector machine (SVM) approach to prediction and analysis of #-turns. We have investigated two aspects of applying SVM to the prediction and analysis of #-turns. First, we developed a new SVM method, called BTSVM, which predicts #-turns of a protein from its sequence. The prediction results on the dataset of 426 non-homologous protein chains by sevenfold cross-validation technique showed that our method is superior to the other previous methods. Second, we analyzed how amino acid positions support (or prevent) the formation of #-turns based on the "multivariable" classification model of a linear SVM. This model is more general than the other ones of previous statistical methods. Our analysis results are more comprehensive and easier to use than previously published analysis results