Evaluation of Protein Phosphorylation Site Predictors

A series of elegant phosphorylation site prediction methods have been developed, which are playing an increasingly important role in accelerating the experimental characterization of phosphorylation sites in phosphoproteins. In this study, we selected six recently published methods (DISPHOS, NetPhosK, PPSP, KinasePhos, Scansite and PredPhospho) to evaluate their performance. First, we compiled three testing datasets containing experimentally verified phosphorylation sites for mammalian, Arabidopsis and rice proteins. Then, we present the prediction performance of the tested methods on these three independent datasets. Rather than quantitatively ranking the performance of these methods, we focused on providing an understanding of the overall performance of the predictors. Based on this evaluation, we found the following results: i) current phosphorylation site predictors are not effective for practical use and there is substantial need to improve phosphorylation site prediction; ii) current predictors perform poorly when used to predict phosphorylation sites in plant phosphoproteins, suggesting that a rice-specific predictor will be required to obtain confident computational annotation of phosphorylation sites in rice proteomics research; and iii) the tested predictors are complementary to some extent, implying that establishment of a meta-server might be a promising approach to developing an improved prediction system.