定义酶及其底物之间的功能相互作用的能力对于理解生物防治机制至关重要;然而,这样的方法在瞬时性质和低化学计量的酶-底物相互作用方面面临挑战。现在,我们开发了一种优化的策略,该策略将底物捕获诱变与邻近标记质谱联用,用于定量分析涉及蛋白酪氨酸磷酸酶PTP1B的蛋白复合物.该方法代表了与经典方案的显著转变;它能够在接近内源性表达水平下进行,并增加靶标富集的化学计量,而不需要在裂解和富集过程中刺激超生理酪氨酸磷酸化水平或维持底物复合物。通过在HER2阳性和赫赛汀耐药乳腺癌模型中应用PTP1B相互作用网络来说明这种新方法的优势。我们已经证明,PTP1B的抑制剂在HER2阳性乳腺癌的获得性和从头赫赛汀耐药性的基于细胞的模型中显着降低了增殖和活力。使用差异分析,将底物捕获与野生型PTP1B进行比较,我们已经确定了PTP1B的多个未报告的蛋白质靶标,这些靶标与HER2诱导的信号传导有明确的联系,并通过与先前确定的候选底物重叠提供了方法特异性的内部验证.总的来说,这种通用的方法可以很容易地与不断发展的邻近标签平台(TurboID,BioID2等.),并且广泛适用于所有PTP家族成员,用于鉴定人类疾病模型中的条件性底物特异性和信号传导节点。
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.