diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling

Kinases control most cellular processes through protein phosphorylation. The 518 human protein kinases, i.e., the kinome, are frequently dysregulated in human disease. Kinase activity, localization, and substrate recognition are controlled by dynamic PPI networks composed of scaffolding and adapter proteins, other signaling enzymes, and phospho-substrates. Mapping kinome PPI networks can, therefore, quantify kinome activation states and kinase-mediated cell signaling, and can be used to prioritize kinases for drug discovery. We introduce our 2^nd generation (gen) kinobead competition and correlation analysis (kiCCA) for kinome PPI mapping. 2^nd gen kiCCA utilizes kinome affinity purification with kinase inhibitor soluble competition, data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS), and a redesigned CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. Using neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT), we demonstrate that 2^nd gen kiCCA (1) identified 6-times more kinase PPIs in native cell extracts compared to our 1^st gen approach, (2) determined kinase-mediated signaling pathways that underly the neuroblastoma NMT, and (3) accurately predicted pharmacological targets for altering NMT states. Our 2^nd gen kiCCA approach is broadly useful for cell signaling research and kinase drug discovery.