Prediction of S-glutathionylation sites based on protein sequences

PLoS One. 2013;8(2):e55512. doi: 10.1371/journal.pone.0055512. Epub 2013 Feb 13.

Abstract

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acid Sequence
  • Cysteine / metabolism*
  • Databases, Protein
  • Glutathione / metabolism*
  • Glutathione Disulfide / metabolism*
  • Protein Processing, Post-Translational

Substances

  • Glutathione
  • Cysteine
  • Glutathione Disulfide