diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling

Area of Science:

• Cellular signaling and molecular biology
• Biochemistry and Proteomics
• Cancer Biology and Drug Discovery

Background:

• Protein kinases (kinome) regulate cellular functions via phosphorylation and are implicated in human diseases.
• Kinase activity and signaling are governed by dynamic protein-protein interaction (PPI) networks.
• Mapping these networks is crucial for understanding kinase activation and identifying drug targets.

Purpose of the Study:

• To introduce and validate a second-generation kinobead competition and correlation analysis (kiCCA) for comprehensive kinome PPI mapping.
• To apply 2nd gen kiCCA to neuroblastoma models to uncover kinase-mediated signaling in the noradrenergic-mesenchymal transition (NMT).
• To demonstrate the utility of 2nd gen kiCCA for prioritizing kinases in drug discovery.

Main Methods:

• Utilized kinome affinity purification coupled with kinase inhibitor soluble competition.
• Employed data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS).
• Developed a redesigned correlation coefficient analysis (CCA) algorithm for enhanced PPI prediction.

Main Results:

• 2nd gen kiCCA identified six times more kinase PPIs compared to the first-generation method in native cell extracts.
• The study elucidated kinase-mediated signaling pathways underlying the neuroblastoma NMT.
• Pharmacological targets for modulating NMT states were accurately predicted using the new approach.

Conclusions:

• The 2nd gen kiCCA is a powerful and broadly applicable tool for kinome PPI mapping.
• This method significantly advances cell signaling research and kinase-targeted drug discovery.
• The findings provide insights into neuroblastoma progression and potential therapeutic strategies.