title: GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity.
abstract: S-glutathionylation, the covalent attachment of a glutathione (GSH) to the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-glutathionylation remains unknown. Based on a total of 1783 experimentally identified S-glutathionylation sites from mouse macrophages, this work presents an informatics investigation on S-glutathionylation sites including structural factors such as the flanking amino acids composition and the accessible surface area (ASA). TwoSampleLogo presents that positively charged amino acids flanking the S-glutathionylated cysteine may influence the formation of S-glutathionylation in closed three-dimensional environment. A statistical method is further applied to iteratively detect the conserved substrate motifs with statistical significance. Support vector machine (SVM) is then applied to generate predictive model considering the substrate motifs. According to five-fold cross-validation, the SVMs trained with substrate motifs could achieve an enhanced sensitivity, specificity, and accuracy, and provides a promising performance in an independent test set. The effectiveness of the proposed method is demonstrated by the correct identification of previously reported S-glutathionylation sites of mouse thioredoxin (TXN) and human protein tyrosine phosphatase 1b (PTP1B). Finally, the constructed models are adopted to implement an effective web-based tool, named GSHSite (http://csb.cse.yzu.edu.tw/GSHSite/), for identifying uncharacterized GSH substrate sites on the protein sequences.
authors: Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
authors: Yi-Ju
authors: YJ
authors: Chen
authors: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan.
authors: Cheng-Tsung
authors: CT
authors: Lu
authors: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan.
authors: Kai-Yao
authors: KY
authors: Huang
authors: Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
authors: Hsin-Yi
authors: HY
authors: Wu
authors: Institute of Chemistry, Academia Sinica, Taipei, Taiwan.
authors: Yu-Ju
authors: YJ
authors: Chen
authors: Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan.
authors: Tzong-Yi
authors: TY
authors: Lee
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003545:Cysteine / Q000378:metabolism; D030562:Databases, Protein; D005978:Glutathione / Q000378:metabolism*; D006801:Humans; D051379:Mice; D008969:Molecular Sequence Data; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D017386:Sequence Homology, Amino Acid; D013379:Substrate Specificity; D060388:Support Vector Machine; D013879:Thioredoxins / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins; D013879:Thioredoxins; D005978:Glutathione; D003545:Cysteine