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 3.0 with novel Weight Training method based on a Logistic model to improve the prediction performance and robustness. Additionally, more than 6,000 phosphorylation sites were used for training and could predict kinase-specific phosphorylation sites for 476 human PKs in hierarchy.

The GPS 3.0 is freely available at: http://gps.biocuckoo.org

This website is linked in ExPASy Proteomics Tools page.

For publication of results please cite the following article:

2019, Submitted

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

[Abstract] [Full Text] [Supplemental Data]

GPS: a comprehensive www server for phosphorylation sites prediction. Yu Xue, Fengfeng Zhou, Minjie Zhu, Kashif Ahmed, Guoliang Chen and Xuebiao Yao*. Nucleic Acids Res. 2005; 33 (suppl 2): W184-W187

[Abstract] [Full Text]

GPS 2.1: enhanced prediction of kinase-specific phosphorylation sites with an algorithm of motif length selection. Yu Xue, Zexian Liu, Jun Cao, Qian Ma, Xinjiao Gao, Qingqi Wang, Changjiang Jin, Yanhong Zhou, Longping Wen, and Jian Ren. Protein Engineering, Design and Selection (2011);24 (3): 255-260

[Abstract] [Full Text] [Supplemental Data]