title: Prediction of S-glutathionylation sites based on protein sequences.
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.
authors: Institute of Systems Biology, Shanghai University, Shanghai, China.
authors: Chenglei
authors: C
authors: Sun
authors: Zheng-Zheng
authors: ZZ
authors: Shi
authors: Xiaobo
authors: X
authors: Zhou
authors: Luonan
authors: L
authors: Chen
authors: Xing-Ming
authors: XM
authors: Zhao
mesh_terms: D000595:Amino Acid Sequence; D003545:Cysteine / Q000378:metabolism*; D030562:Databases, Protein; D005978:Glutathione / Q000378:metabolism*; D019803:Glutathione Disulfide / Q000378:metabolism*; D011499:Protein Processing, Post-Translational
chemical_list: D005978:Glutathione; D003545:Cysteine; D019803:Glutathione Disulfide