Prediction of S-Glutathionylation Sites Based on Protein Sequences

Chenglei Sun, Zheng Zheng Shi, Xiaobo Zhou, Luonan Chen, Xing Ming Zhao

    Research output: Contribution to journalArticlepeer-review

    41 Scopus citations

    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.

    Original languageEnglish (US)
    Article numbere55512
    JournalPLoS ONE
    Volume8
    Issue number2
    DOIs
    StatePublished - Feb 13 2013

    ASJC Scopus subject areas

    • General

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