Abstract
In this study, we propose a new method to predict β-Hairpins in proteins and its evaluation based on the support vector machine. Different from previous methods, new feature representation scheme based on auto covariance is adopted. We also investigate two structure properties of proteins (protein secondary structure and residue conformation propensity), and examine their effects on prediction. Moreover, we employ an ensemble classifier approach based on the majority voting to improve prediction accuracy on hairpins. Experimental results on a dataset of 1926 protein chains show that our approach outperforms those previously published in the literature, which demonstrates the effectiveness of the proposed method.
Keywords: β-Hairpin, support vector machine, majority voting, protein supersecondary structure prediction
Related Journals
Current Protein & Peptide Science
Related Books
Frontiers in Protein and Peptide Sciences
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