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Cophosk: A Method For Comprehensive Kinase Substrate Annotation Using Co-Phosphorylation Analysis, Marzieh Ayati, Danica D. Wiredja, Daniela M. Schlatzer, Sean Maxwell, Ming Li, Mehmet Koyutürk, Mark R. Chance

Faculty Scholarship

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, …