With the recent exponential increase in protein phosphorylation sites identified by mass spectrometry (MS), many researches are undertaken to identify the kinase-specific phosphorylation sites. Our previous work, KinasePhos 1.0, incorporated profile hidden Markov model (HMM) for identifying kinase-specific phosphorylation sites, whose overall predictive accuracy is ∼87% (5,6). NetPhos (7) developed neural networks to predict phosphorylation sites on serine, threonine and tyrosine residues; however, it cannot provide information on the kinases involved and NetPhosK (8) applied an artificial neural network algorithm to predict 17 PK groups-specific phosphorylation sites. DISPHOS (9) took advantage of the position-specific amino acid frequencies and disorder information to improve the discrimination between phosphorylation sites and non-phosphorylation sites. Scansite 2.0 (10) identified short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein serine/threonine, tyrosine kinases or mediate specific interactions with protein or phospholipid ligands. PredPh2ospho (11) predicts phosphorylation sites limited to four protein major kinase families, such as CDK, CK2, PKA and PKC, and four protein kinase groups (AGC, CAMK, CMGC and TK) with predictive accuracy 83–95 and 76–91%, respectively. GPS (12,13), is a
