Protein phosphorylation is an important reversible post-translational modification of proteins, and it orchestrates a variety of cellular processes. Experimental identification of phosphorylation site is labor-intensive and often limited by the availability and optimization of enzymatic reaction. In silico prediction may facilitate the identification of potential phosphorylation sites with ease. Here we present a novel computational method named GPS: group-based phosphorylation site predicting and scoring platform. If two polypeptides differ by only two consecutive amino acids, in particular when the two different amino acids are a conserved pair, e.g., isoleucine (I) and valine (V), or serine (S) and threonine (T), we view these two polypeptides bearing similar 3D structures and biochemical properties. Based on this rationale, we formulated GPS that carries greater computational power with superior performance compared to two existing phosphorylation sites prediction systems, ScanSite 2.0 and PredPhospho. With database in public domain, GPS can predict substrate phosphorylation sites from 52 different protein kinase (PK) families while ScanSite 2.0 and PredPhospho offer at most 30 PK families. Using PKA as a model enzyme, we first compared prediction profiles from the GPS method with those from ScanSite 2.0 and PredPhospho. In addition, we chose an essential mitotic kinase Aurora-B as a model enzyme since ScanSite 2.0 and PredPhospho offer no prediction. However, GPS offers satisfactory sensitivity (94.44%) and specificity (97.14%). Finally, the accuracy of phosphorylation on MCAK predicted by GPS was validated by experimentation, in which six out of seven predicted potential phosphorylation sites on MCAK (Q91636) were experimentally verified. Taken together, we have generated a novel method to predict phosphorylation sites, which offers greater precision and computing power over ScanSite 2.0 and PredPhospho.