蛋白质相互作用（PPI）网络的动态变化是所有生理细胞功能的基础并推动着严重的人类疾病。因此，对PPI网络进行配置能够提供关键的疾病机制洞见并确定新的药物靶点。激酶是许多PPI网络中的调节节点；但是，缺乏系统研究激酶相互作用组学动态的简单方法。我们描述了kinobead竞争和相关分析（kiCCA），这是一种基于定量质谱的化学蛋白质组学方法，可以快速高度多重化的配置内源性激酶相互作用组学。使用kiCCA，在18个不同的癌症细胞系中鉴定了238种激酶的1,154个PPI，并量化了与癌症类型、可塑性和信号状态相关的依赖于上下文的激酶相互作用组学变化，从而组装了一个广泛的细胞信号研究的知识库。我们发现了药物靶点候选者，包括促进癌细胞上皮间充质可塑性和药物耐受性的内吞适配器相关激酶（AAK1）复合物。我们的数据证明了激酶相互作用组学动态对于健康和疾病细胞信号的重要性。版权所有 © 2023 Elsevier Inc.。保留所有权利。

Dynamic changes in protein-protein interaction (PPI) networks underlie all physiological cellular functions and drive devastating human diseases. Profiling PPI networks can, therefore, provide critical insight into disease mechanisms and identify new drug targets. Kinases are regulatory nodes in many PPI networks; yet, facile methods to systematically study kinase interactome dynamics are lacking. We describe kinobead competition and correlation analysis (kiCCA), a quantitative mass spectrometry-based chemoproteomic method for rapid and highly multiplexed profiling of endogenous kinase interactomes. Using kiCCA, we identified 1,154 PPIs of 238 kinases across 18 diverse cancer lines, quantifying context-dependent kinase interactome changes linked to cancer type, plasticity, and signaling states, thereby assembling an extensive knowledgebase for cell signaling research. We discovered drug target candidates, including an endocytic adapter-associated kinase (AAK1) complex that promotes cancer cell epithelial-mesenchymal plasticity and drug resistance. Our data demonstrate the importance of kinase interactome dynamics for cellular signaling in health and disease.Copyright © 2023 Elsevier Inc. All rights reserved.