Predicting O-glycosylation sites in mammalian proteins by using SVMs

Authors:
Sujun Li;Boshu Liu;Rong Zeng;Yudong Cai;Yixue Li
Affiliations:
Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China and Research Center for Proteome Analysis, Shanghai Institutes for Biological Sciences, Chine ...;Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China;Research Center for Proteome Analysis, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China and Department of Chemistry, College of Sciences, Shanghai University, 99 Sha ...;Department of Biomolecular Sciences, UMIST, P.O. Box 88, Manchester M60 1QD, UK;Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
Venue:
Computational Biology and Chemistry
Year:
2006

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Abstract

O-glycosylation is one of the most important, frequent and complex post-translational modifications. This modification can activate and affect protein functions. Here, we present three support vector machines models based on physical properties, 0/1 system, and the system combining the above two features. The prediction accuracies of the three models have reached 0.82, 0.85 and 0.85, respectively. The accuracies of the three SVMs methods were evaluated by 'leave-one-out' cross validation. This approach provides a useful tool to help identify the O-glycosylation sites in mammalian proteins. An online prediction web server is available at http://www.biosino.org/Oglyc.