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 Citations (Scopus)

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 2010
Externally publishedYes

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Secondary Protein Structure
Proteins
Politics
Support vector machines
Conformations
Classifiers
Support Vector Machine
Datasets

Keywords

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

ASJC Scopus subject areas

  • Biochemistry
  • Structural Biology

Cite this

Prediction of β -hairpins in proteins using physicochemical properties and structure information. / Xia, Jun Feng; Wu, Min; You, Zhu Hong; Zhao, Xing Ming; Li, Xue Ling.

In: Protein and Peptide Letters, Vol. 17, No. 9, 09.2010, p. 1123-1128.

Research output: Contribution to journalArticle

Xia, Jun Feng ; Wu, Min ; You, Zhu Hong ; Zhao, Xing Ming ; Li, Xue Ling. / Prediction of β -hairpins in proteins using physicochemical properties and structure information. In: Protein and Peptide Letters. 2010 ; Vol. 17, No. 9. pp. 1123-1128.
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