Abstract
O-GlcNAcylation is a ubiquitous post-translational modification of proteins that is involved in the majority of cellular processes and is associated with many diseases. To reduce the workload and increase the relevance of experimental identification of protein O-GlcNAcylation sites, O-GlcNAcPRED, a support vector machine (SVM)-based model, was developed to capture potential O-GlcNAcylation sites. By virtue of the novel adapted normal distribution bi-profile Bayes (ANBPB) feature extraction method, O-GlcNAcPRED yielded a sensitivity of 80.83%, a specificity of 78.17% and an accuracy of 79.50% in jackknife cross-validation experiments. In an independent test on 38 recently experimentally identified human O-GlcNAcylated proteins with 67 O-GlcNAcylation sites, O-GlcNAcPRED captured 26 proteins and 39 sites, clearly outperforming the existing predictors, YinOYang and O-GlcNAcscan.
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