A web server for identifying S-glutathionylation sites. GSHSite is based on a statistical method for identifying S-glutathionylation sites and potential consensus motifs by maximal dependence decomposition (MDD). With the application of MDD, a large group of aligned sequences can be moderated into subgroups that capture the most significant dependencies between positions. By further evaluation using five-fold cross-validation, the support vector machine (SVM) models trained with MDD-clustered subgroups could improve predictive accuracy when compared to the model without MDD clustering. Moreover, the experimental S-glutathionylation data from published database (independent set) are used to test the effectiveness of the models in cross-validation.
A web-server specially trained for the Glutathione S-transferase protein. The prediction is based on the basis of amino acid composition, dipeptide composition, tripeptide composition by using support vector machines (SVMs).The prediction result will be displayed on web browser in tabular form with score.
Identifies S-glutathionylated sites from primary protein sequences. PGluS can mine the protein information by using multiple features. In order to reduce the computational time, 5-fold cross validation test instead of jack-knife test were used. The tool was evaluated on an independent testing dataset resulting in an accuracy of 71.25%. It can be useful to assist the discovery of S-glutathionylated sites.