Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences

Yu Ching Chen, Yeong-Shin Lin, Chih Jen Lin, Jenn Kang Hwang*

*Corresponding author for this work

Research output: Contribution to journalArticle

57 Scopus citations

Abstract

The support vector machine (SVM) method is used to predict the bonding states of cysteines. Besides using local descriptors such as the local sequences, we include global information, such as amino acid compositions and the patterns of the states of cysteines (bonded or nonbonded), or cysteine state sequences, of the proteins. We found that SVM based on local sequences or global amino acid compositions yielded similar prediction accuracies for the data set comprising 4136 cysteine-containing segments extracted from 969 nonhomologous proteins. However, the SVM method based on multiple feature vectors (combining local sequences and global amino acid compositions) significantly improves the prediction accuracy, from 80% to 86%. If coupled with cysteine state sequences, SVM based on multiple feature vectors yields 90% in overall prediction accuracy and a 0.77 Matthews correlation coefficient, around 10% and 22% higher than the corresponding values obtained by SVM based on local sequence information.

Original languageEnglish
Pages (from-to)1036-1042
Number of pages7
JournalProteins: Structure, Function and Genetics
Volume55
Issue number4
DOIs
StatePublished - 1 Apr 2004

Keywords

  • Cysteine state sequences
  • Disulfide bonds
  • Multiple feature vectors
  • Support vector machines

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