Prediction of β -hairpins in proteins using physicochemical properties and structure information

Jun Feng Xia, Min Wu, Zhu Hong You, Xing Ming Zhao, Xue Ling Li

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1123-1128
Number of pages6
JournalProtein and Peptide Letters
Volume17
Issue number9
DOIs
StatePublished - Sep 1 2010

Keywords

  • Majority voting
  • Protein supersecondary structure prediction
  • Support vector machine
  • β-Hairpin

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry

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