Improved method for predicting β-turn using support vector machine

Qidong Zhang, Sukjoon Yoon, William J. Welsh

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Motivation: Numerous methods for predicting β-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of β-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance. Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting β-turn. The overall prediction accuracy Qtotal was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index.

Original languageEnglish (US)
Pages (from-to)2370-2374
Number of pages5
JournalBioinformatics
Volume21
Issue number10
DOIs
StatePublished - May 15 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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