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 2nd generation
(gen) kinobead competition and correlation analysis (kiCCA) for kinome
PPI mapping. 2nd 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 2nd gen kiCCA (1)
identified 6-times more kinase PPIs in native cell extracts compared
to our 1st gen approach, (2) determined kinase-mediated
signaling pathways that underly the neuroblastoma NMT, and (3) accurately
predicted pharmacological targets for altering NMT states. Our 2nd gen kiCCA approach is broadly useful for cell signaling
research and kinase drug discovery.