Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets

Chin Sheng Yu, Jung Ying Wang, Jinn-Moon Yang, Ping Chiang Lyu, Chih Jen Lin, Jenn Kang Hwang*

*Corresponding author for this work

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

In the coarse-grained fold assignment of major protein classes, such as all-α, all-β, α + β, α/β proteins, one can easily achieve high prediction accuracy from primary amino acid sequences. However, the fine-grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0% on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n-peptide and jury voting, to the fine-grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6% on the same independent set - significantly higher than the previous results. On 10-fold cross-validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global sequence-coding schemes can significantly improve the fine-grained fold prediction. Our approach should be useful in structure prediction and modeling.

Original languageEnglish
Pages (from-to)531-536
Number of pages6
JournalProteins: Structure, Function and Genetics
Volume50
Issue number4
DOIs
StatePublished - 1 Mar 2003

Keywords

  • Fine-grained fold prediction
  • Global sequence-coding scheme
  • N-peptide
  • Support vector machines

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