Computational prediction of phosphorylation sites with their cognate protein kinases (PKs) is greatly helpful for further experimental design. Although ~10 online predictors were developed, the PK classification and control of false positive rate (FPR) were not well addressed. Here we adopted a well-established rule to classify PKs into a hierarchical structure with four levels. Also, we developed a simple approach to estimate the theoretically maximal FPRs. Then GPS 2.0 (Group-based Prediction System, ver 2.0) software was implemented in JAVA and could predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. As an application, we performed a large-scale prediction of >13,000 mammalian phosphorylation sites with high performances. In addition, we also provided a proteome-wide prediction of Aurora-B specific substrates including protein-protein interaction information. As the first stand-alone software for computational phosphorylation, GPS 2.0 will be an excellent tool for further experimental consideration and construction of phosphorylation networks.

Recently, we released GPS 2.1 with a novel Peptide Selection method to improve the prediction performance and robustness greatly.

The GPS 2.1 is freely available at: http://gps.biocuckoo.org

This website is linked in ExPASy Proteomics Tools page.


GPS 2.1 User Interface

For publication of results please cite the following article:

GPS 2.0, a Tool to Predict Kinase-specific Phosphorylation Sites in Hierarchy. Yu Xue#, Jian Ren#, Xinjiao Gao, Changjiang Jin, Longping Wen*, and Xuebiao Yao*. Mol Cell Proteomics. 2008; 7: 1598-1608

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