Improved method for predicting π-Turns in proteins using a two-stage classifier

Jun Feng Xia, Zhu Hong You, Min Wu, Shu Lin Wang, Xing Ming Zhao

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

π-turns are irregular secondary structure elements consisting of short backbone fragments (six-amino-acid residues) where the backbone reverses its overall direction. They play an important role in proteins from both the structural and functional points of view. Recently, some methods have been proposed to predict π-turns. In this study, a new method of -turn prediction that uses a two-stage classification scheme is proposed based on support vector machine. In addition, different from previous methods, new coding schemes based on the physicochemical properties and the structural properties of proteins are adopted. Seven-fold cross validation based on a dataset of 640 non-homologue protein chains is used to evaluate the performance of our method. The experiment results show our method can yield a promising performance, which confirms the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)1117-1122
Number of pages6
JournalProtein and Peptide Letters
Volume17
Issue number9
DOIs
StatePublished - Sep 2010
Externally publishedYes

Keywords

  • π-Turns
  • Protein structure prediction
  • Support vector machine
  • Tight turns
  • Two-stage classifier

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