As an abundant post-translational modification, reversible phosphorylation is critical for the dynamic regulation of various biological processes. prkC, a critical serine/threonine-protein kinase in bacteria, plays important roles in regulation of the signaling transduction. Identification of prkC-specific phosphorylation sites is fundamental for understanding the molecular mechanism of phosphorylation-mediated signaling. However, experimental identification of substrates for prkC is time-consuming and labor-intensive, and computational methods for kinase-specific phosphorylation prediction in bacteria have yet to be developed.
    In this study, we manually curated the experimentally identified substrates and phosphorylation sites of prkC from the published literature. To develop the predictor, the amino acid location feature extraction method and the support vector machine algorithm were employed. Based on these methods, we developed a novel software of prkC-PSP (prkC-specific Phosphorylation Sites Prediction) for the prediction of prkC-specific phosphorylation sites.