Tho Hoan Pham (firstname.lastname@example.org)
Kenji Satou (email@example.com)
Tu Bao Ho (firstname.lastname@example.org)
Japan Advanced Institute of Science and Technology, 1-1 Asahidai,
Tatsunokuchi, Ishikawa 923-1292, Japan
Institute for Bioinformatics Research and Development (BIRD), Japan Science and Technology Corporation (JST)
Tight turn has long been recognized as one of the three important features of proteins after the α-helix and β-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 β-turns. Analysis and prediction of β-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 β-turns. We have investigated two aspects of applying SVM to the prediction and analysis of β-turns. First, we developed a new SVM method, called BTSVM, which predicts β-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 β-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.