Protein kinase-specific phosphorylation

Because of owing to its importance in cellular control, a computational scheme to quickly and efficiently identify phosphorylation sites in protein sequences and the catalytic kinases involved in the phosphorylation is desirable. Such a tool would improve the efficiency of characterization of new protein sequences. Therefore, in this work, a prediction method was designed and implemented to facilitate the identification of the phosphorylation sites and the related catalytic kinases.

NetPhos ( 2 ), DIPHOS ( 3 ) and Berry et al . ( 1 ) presented several prediction methods for identifying the phosphorylation site prediction concentrating on only the substrate specificity. NetPhosK ( 4 ) is an artificial neural network algorithm to identify protein kinase A (PKA) phosphorylation sites with 100% sensitivity and 40% specificity in experiments. Scansite 2.0 ( 5 ) identified short protein motifs that are recognized by phosphorylation protein serine/threonine or tyrosine kinases. Each motif used in the Scansite was constructed from a set of experimentally validated phosphorylation sites and was represented as a position-specific scoring matrix.



Rather than search a protein motif of phosphorylation substrate against the target sequences based on the homolog to the motifs, the KinasePhos web server developed here was based on the concept of machine learning, the same as NetPhos and DIPHOS. Computer models were trained for the detection of phosphorylation sites. By comparison of the prediction accuracy between the predictive computer model methods and the motif search tools, the predictive computer models contribute more specificity for the detection of phosphorylation sites.

The proposed scheme considers the catalytic kinases of protein phosphorylation. The known phosphorylation sites from data sources in public domain were categorized by their annotated protein kinases. Based on the profile hidden Markov model (HMM), computational models were determined from the kinase-specific groups of the phosphorylation sites. A web-based prediction application was implemented to facilitate the identification of protein kinase-specific phosphorylation sites.