Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins

Xiaobo Shi, Xiuzhen Hu

College of Sciences Inner Mongolia University of Technology, Hohhot, China.

DOI: 10.4236/eng.2013.510B078                

Abstract

The structure and function of proteins are closely related, and protein structure decides its function, therefore protein structure prediction is quite important.β-turns are important components of protein secondary structure. So development of an accurate prediction method ofβ-turn types is very necessary. In this paper, we used the composite vector with position conservation scoring function, increment of diversity and predictive secondary structure information as the input parameter of support vector machine algorithm for predicting theβ-turn types in the database of 426 protein chains, obtained the overall prediction accuracy of 95.6%, 97.8%, 97.0%, 98.9%, 99.2%, 91.8%, 99.4% and 83.9% with the Matthews Correlation Coefficient values of 0.74, 0.68, 0.20, 0.49, 0.23, 0.47, 0.49 and 0.53 for types I, II, VIII, I’, II’, IV, VI and nonturn respectively, which is better than other prediction.

Keywords

Support Vector Machine Algorithm; Increment of Diversity Value; Position Conservation Scoring Function Value; Secondary Structure Information

Conflicts of Interest

The authors declare no conflicts of interest.

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