Support Vector Machine Prediction of N- and O-glycosylation Sites Using Whole Sequence Information and Subcellular Localization

Abstract

Background: Glycans, or sugar chains, are one of the three types of chain (DNA, protein and glycan) that constitute living organisms; they are often called “the third chain of the living organism”. About half of all proteins are estimated to be glycosylated based on the SWISS-PROT database. Glycosylation is one of the most important post-translational modifications, affecting many critical functions of proteins, including cellular communication, and their tertiary structure. In order to computationally predict N-glycosylation and O-glycosylation sites, we developed three kinds of support vector machine (SVM) model, which utilize local information, general protein information and/or subcellular localization in consideration of the binding specificity of glycosyltransferases and the characteristic subcellular localization of glycoproteins. Results: In our computational experiment, the model integrating three kinds of information achieved about 90% accuracy in predictions of both N-glycosylation and O-glycosylation sites. Moreover, our model was applied to a protein whose glycosylation sites had not been previously identified and we succeeded in showing that the glycosylation sites predicted by our model were structurally reasonable. Conclusions: In the present study, we developed a comprehensive and effective computational method that detects glycosylation sites. We conclude that our method is a comprehensive and effective computational prediction method that is applicable at a genome-wide level.