Uncovering Phosphorylation-Based Specificities through Functional Interaction Networks.

Protein kinases are an important class of enzymes involved in the phosphorylation of their targets, which regulate key cellular processes and are typically mediated by a specificity for certain residues around the target phospho-acceptor residue. While efforts have been made to identify such specificities, only ∼30% of human kinases have a significant number of known binding sites. We describe a computational method that utilizes functional interaction data and phosphorylation data to predict specificities of kinases. We applied this method to human kinases to predict substrate preferences for 57% of all known kinases and show that we are able to reconstruct well-known specificities. We used an in vitro mass spectrometry approach to validate four understudied kinases and show that predicted models closely resemble true specificities. We show that this method can be applied to different organisms and can be extended to other phospho-recognition domains. Applying this approach to different types of posttranslational modifications (PTMs) and binding domains could uncover specificities of understudied PTM recognition domains and provide significant insight into the mechanisms of signaling networks.