期刊
PLOS ONE
卷 8, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0055512
关键词
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资金
- National Natural Science Foundation of China [91130032, 61103075, 91029301, 61134013]
- Innovation Program of Shanghai Municipal Education Commission [13ZZ072]
- JSPS/CSTP through the FIRST Program
- Shanghai Pujiang Program
- Chief Scientist Program of SIBS from CAS [2009CSP002]
- Graduate Innovation Funding of Shanghai University [SHUCX102039]
- National Institutes of Health [R01 CA155069-01, R01 LM010185-02]
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.
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