Project C

Developing MS-Based Proteomics Technologies for Highly Multiplexed, Native Interactome Profiling

     Proteins form interaction networks to cooperatively carry out their biological functions (1).  Changes in protein abundance and post-translational modifications (PTMs) dynamically alter protein-protein interaction networks (PPIs) to enable adaption under different cell-intrinsic and environmental conditions; numerous human diseases highjack signaling pathways and protein homeostasis, thereby rewiring PPI networks in favor of disease progression (2-6).  The 518 protein kinases in the human kinome are central players in cell signaling and control large swaths of cellular PPI networks (7, 8).  Kinases are frequently dysregulated in human disease (9, 10), and a greater understanding of how the kinome integrates with PPI networks will aid us in identifying novel disease mechanisms and specific kinase complexes that are unique drivers of disease; such complexes may serve as valuable drug targets and biomarkers (11).  Yet, sensitive, and high-throughput methods to map native kinase PPIs and their dynamic interactomes are lacking.

 To address this need, we developed kinobead competition and correlation analysis (kiCCA), an MS-based chemoproteomic method for rapid and highly multiplexed profiling of native kinase PPIs in cell and tissue lysates (Fig. 3).  kiCCA uses a panel of multi-targeted kinase probes to compete kinases and their interaction partners from immobilized kinase inhibitor beads (kinobeads or multiplexed inhibitor beads, MIBs, Fig. 3A); co-competed kinase complexes are then identified by correlation analysis (Fig. 3B)(12-16).  We used kiCCA to systematically identify kinase complexes in 18 diverse cancer cell lines, and to quantify PPI changes in the context of cancer types, phenotypic plasticity, and signaling states; this revealed that PPI networks are highly dynamic and context dependent.  Through our profiling efforts, we identified and quantified 1,154 high-confidence PPIs between 238 kinases and 684 non-kinase proteins, which we compiled into an extensive and easily accessible kinase interactome knowledgebase (Table S3 and https://quantbiology.org/kiCCA); this knowledgebase has also been integrated into the BioGRID interaction database (17).

Based on our kiCCA approach, we will develop the next generation chemoproteomic methods for PPI profiling.  These methods will capture dynamic kinome interaction changes in situ with high sensitivity and spatial-temporal resolution, yielding unprecedented insights into physiological and pathological cell signaling.

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