Bonham, ChristopherA, Mandati, Vinay, Singh, RakeshK, Pappin, DarrylJ, Tonks, NicholasK (March 2023) Coupling substrate-trapping with proximity-labeling to identify protein tyrosine phosphatase PTP1B signaling networks. Journal of Biological Chemistry. p. 104582. ISSN 0021-9258
Abstract
The ability to define functional interactions between enzymes and their substrates is crucial for understanding biological control mechanisms; however, such methods face challenges in the transient nature and low stoichiometry of enzyme-substrate interactions. Now, we have developed an optimized strategy that couples substrate-trapping mutagenesis to proximity-labeling mass spectrometry for quantitative analysis of protein complexes involving the protein tyrosine phosphatase PTP1B. This methodology represents a significant shift from classical schemes; it is capable of being performed at near-endogenous expression levels and increasing stoichiometry of target enrichment without a requirement for stimulation of supraphysiological tyrosine phosphorylation levels or maintenance of substrate complexes during lysis and enrichment procedures. Advantages of this new approach are illustrated through application to PTP1B interaction networks in models of HER2-positive and Herceptin-resistant breast cancer. We have demonstrated that inhibitors of PTP1B significantly reduced proliferation and viability in cell-based models of acquired and de novo Herceptin resistance in HER2-positive breast cancer. Using differential analysis, comparing substrate-trapping to wild-type PTP1B, we have identified multiple unreported protein targets of PTP1B with established links to HER2-induced signaling and provided internal validation of method specificity through overlap with previously identified substrate candidates. Overall, this versatile approach can be readily integrated with evolving proximity-labeling platforms (TurboID, BioID2, etc.), and is broadly applicable across all PTP family members for the identification of conditional substrate specificities and signaling nodes in models of human disease.
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