Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition. (7th June 2016)

• Record Type: Journal Article
• Authors: Zhao, Xiaowei; Ning, Qiao; Ai, Meiyue; Chai, Haiting; Yang, Guifu
• Is Part Of: Journal of theoretical biology. Volume 398(2016)
• Page Start: 96
• Page End: 102
• Publication Date: 2016-06-07
• Subjects: S-glutathionylated sites -- Species-specific analysis -- Support vector machines -- Multiple Features -- Web server; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2016.03.030
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: As a selective and reversible protein post-translational modification, S -glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S -glutathionylation mechanisms, identification of substrates and specific S -Glutathionylated sites is crucial. Experimental identification of S -glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S -glutathionylation Sites) was developed to identify species-specific protein S -glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S -glutathionylated sites. Furthermore, a site-specific analysis showed that S -glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S -glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible athttp://59.73.198.144:8080/SSGlu/ . Highlights: We use support vector machines classifier combined with a two-step feature selection. Identify S-glutathionylated sites in species-specific protein. Incorporate five sequence-derived features into the general pseudo amino acid composition.