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

• Published in: Genome Informatics Vol. 14; pp. 196 - 205
• Main Authors: Pham, Tho Hoan, Satou, Kenji, Ho, Tu Bao
• Format: Journal Article
• Language: English
• Published: Japan Japanese Society for Bioinformatics 2003
• Subjects: Amino Acid Sequence; Amino Acids - chemistry; Bayes Theorem; Crystallography, X-Ray; Databases, Factual; protein secondary structure; Protein Structure, Secondary; Proteins - chemistry; Sequence Alignment; support of amino acid position; support vector machine; β-turns
• ISSN: 0919-9454; 2185-842X
• DOI: 10.11234/gi1990.14.196

Tight turn has long been recognized as one of the three important features of proteins after the a-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.