diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling

Area of Science:

• Biochemistry
• Molecular Biology
• Proteomics

Background:

• Protein-protein interactions (PPIs) are crucial for cellular functions and are often dysregulated in diseases like cancer.
• Human kinases (the kinome) play vital roles in cell signaling and are key targets for drug development.
• Understanding kinase PPI networks is essential for elucidating disease mechanisms and identifying therapeutic strategies.

Purpose of the Study:

• To introduce and validate the 2nd generation kinobead competition and correlation analysis (kiCCA) method for high-throughput kinome PPI mapping.
• To demonstrate the enhanced capabilities of 2nd gen kiCCA in identifying kinase-mediated signaling pathways and predicting drug targets.

Main Methods:

• Development of 2nd gen kiCCA utilizing data-independent acquisition (dia) with parallel accumulation serial fragmentation (PASEF) mass spectrometry (MS).
• Implementation of a redesigned correlation analysis (CCA) algorithm with improved selection criteria.
• Application of 2nd gen kiCCA to neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT).

Main Results:

• 2nd gen kiCCA identified 6.1-times more kinase PPIs in native cell extracts compared to the 1st gen approach.
• The study elucidated kinase-mediated signaling pathways underlying the neuroblastoma NMT.
• Pharmacological targets for modulating NMT states were accurately predicted using the 2nd gen kiCCA method.

Conclusions:

• 2nd gen kiCCA represents a significant advancement in mapping kinase PPI networks.
• This improved method facilitates a deeper understanding of cell signaling and kinase dysregulation in disease.
• The 2nd gen kiCCA platform is broadly applicable to cell signaling research and kinase drug discovery.