Woods Kathryn, Chan Alexandria M, Rants'o Thankhoe A, Sapre Tanmay, Mastin Grace E, Maguire Kathleen M, Ong Shao-En, Golkowski Martin
Department of Pharmacology & Toxicology, University of Utah, Salt Lake City 84112, United States.
Huntsman Cancer Institute, University of Utah, Salt Lake City 84112, United States.
J Proteome Res. 2025 Aug 27. doi: 10.1021/acs.jproteome.5c00109.
Kinases control most cellular processes through protein phosphorylation. The 518 human protein kinases, i.e., the kinome, are frequently dysregulated in human disease. Kinase activity, localization, and substrate recognition are controlled by dynamic PPI networks composed of scaffolding and adapter proteins, other signaling enzymes, and phospho-substrates. Mapping kinome PPI networks can, therefore, quantify kinome activation states and kinase-mediated cell signaling, and can be used to prioritize kinases for drug discovery. We introduce our 2 generation (gen) kinobead competition and correlation analysis (kiCCA) for kinome PPI mapping. 2 gen kiCCA utilizes kinome affinity purification with kinase inhibitor soluble competition, data-independent acquisition with parallel accumulation serial fragmentation (diaPASEF) mass spectrometry (MS), and a redesigned CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. Using neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT), we demonstrate that 2 gen kiCCA (1) identified 6-times more kinase PPIs in native cell extracts compared to our 1 gen approach, (2) determined kinase-mediated signaling pathways that underly the neuroblastoma NMT, and (3) accurately predicted pharmacological targets for altering NMT states. Our 2 gen kiCCA approach is broadly useful for cell signaling research and kinase drug discovery.
激酶通过蛋白质磷酸化控制大多数细胞过程。518种人类蛋白激酶，即激酶组，在人类疾病中经常失调。激酶活性、定位和底物识别由由支架蛋白和衔接蛋白、其他信号酶和磷酸化底物组成的动态蛋白质-蛋白质相互作用（PPI）网络控制。因此，绘制激酶组PPI网络可以量化激酶组激活状态和激酶介导的细胞信号传导，并可用于确定激酶在药物发现中的优先级。我们介绍了用于激酶组PPI图谱绘制的第二代（2代）激酶珠竞争与相关性分析（kiCCA）。2代kiCCA利用激酶抑制剂可溶性竞争进行激酶组亲和纯化、采用平行累积串联碎裂（diaPASEF）质谱（MS）进行数据非依赖采集，以及一种重新设计的CCA算法，该算法具有改进的选择标准和预测同一蛋白质的多个激酶相互作用伙伴的能力。使用去甲肾上腺素能-间充质转化（NMT）的神经母细胞瘤细胞系模型，我们证明2代kiCCA（1）与我们的1代方法相比，在天然细胞提取物中鉴定出的激酶PPI多6倍，（2）确定了神经母细胞瘤NMT背后的激酶介导的信号通路，以及（3）准确预测了改变NMT状态的药理学靶点。我们的2代kiCCA方法在细胞信号研究和激酶药物发现中具有广泛的用途。
