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S-sulfenylation site detection software tools | Post-translational modification data analysis

S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant role in redox regulation of protein functions. Although sulfenic acid is transient and labile, most of its physiological activities occur under control of S-hydroxylation. Therefore, discriminating the substrate site of S-sulfenylated proteins is an essential task in computational biology for the furtherance of protein structures and functions.

Source text:
(Bui et al., 2016) MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs. Bioinformatics.

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MDD-SOH
A web server for identifying S-sulfenylation sites. Given a total of 1,096 experimentally verified S-sulfenylated proteins from humans, this study carries out a bioinformatics investigation on SOH sites based on amino acid composition and solvent-accessible surface area. A TwoSampleLogo indicates that the positively and negatively charged amino acids flanking the SOH sites may impact the formulation of S-sulfenylation in closed three-dimensional environments. In addition, the substrate motifs of SOH sites are studied using the maximal dependence decomposition (MDD). Based on the concept of binary classification between SOH and non-SOH sites, Support Vector Machine (SVM) is applied to learn the predictive model from MDD-identified substrate motifs. According to the evaluation results of five-fold cross-validation, the integrated SVM model learned from substrate motifs yields an average accuracy of 0.87, significantly improving the prediction of SOH sites. Furthermore, the integrated SVM model also effectively improves the predictive performance in an independent testing set.
iSulf-Cys
Predicts the cystein sulfenylation sites in proteins. iSulf-Cys is a predictor which incorporate 14 kinds of physicochemical properties of amino acids. It also showed satisfying performance in the independent testing dataset with area under the curve (AUC) 0.7343 and Mathew correlation coefficient (MCC) 0.3315. Features which were constructed from physicochemical properties and position were carefully analyzed. This online web-sever could become a useful tool for both basic research and drug development in the relevant areas
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