Prediction of Kinase-Specific Phosphorylation Sites by One-Class SVMs

Protein phosphorylation is one of the most important post-translational modifications. Detecting possible phosphorylation sites and their corresponding protein kinases is crucial for studying the function of proteins. This task has been formulated as a machine learning problem of discriminating phosphorylation sites from non-phosphorylation sites with features of amino acid sequences. However, as phosphorylation is a dynamic event, it is hard to collect a set of protein sequences which can be safely regarded as non- phosphorylatable. Here we present a new prediction system, called OCSPP, to predict kinase-specific phosphorylation sites according to peptide sequences using one-class support vector machines. This method only needs positive training samples. Experiments on the published datasets show that both the specificity and sensitivity of the method can reach higher than 90% at kinase-family level for some kinase families. Comparison results show the superiority of OCSPP over Scansite, both of which only use positive samples in prediction. OCSPP is available at http://bioinfo.au.tsinghua.edu.cn/OCSPP/.