Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs were developed, numerous PKs were casually classified into sub-groups without a standard rule. For large-scale predictions, the false positive rate (FPR) was also never addressed. In this work, we adopted a well-established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily and single PK. In addition, we developed a simple approach to estimate the theoretically maximal FPRs. The online service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in JAVA, with the modified version of Group-based Phosphorylation Scoring algorithm. As the first stand-alone software for predicting phosphorylation, GPS2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large-scale prediction of more than 13,000 mammalian phosphorylation sites by GPS2.0 was exhibited with great performance and remarkable accuracy. Using Aurora B as an example, we also conducted a proteome-wide search and provided systemic prediction of Aurora-B specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases, which is freely available at: http://bioinformatics.lcd-ustc.org/gps2.