Naegle Kristen M, White Forest M, Lauffenburger Douglas A, Yaffe Michael B
The David H. Koch Institute for Integrative Cancer Research, Washington University in St. Louis, St. Louis, MO 63130, USA.
Mol Biosyst. 2012 Oct;8(10):2771-82. doi: 10.1039/c2mb25200g.
Cell signaling networks propagate information from extracellular cues via dynamic modulation of protein-protein interactions in a context-dependent manner. Networks based on receptor tyrosine kinases (RTKs), for example, phosphorylate intracellular proteins in response to extracellular ligands, resulting in dynamic protein-protein interactions that drive phenotypic changes. Most commonly used methods for discovering these protein-protein interactions, however, are optimized for detecting stable, longer-lived complexes, rather than the type of transient interactions that are essential components of dynamic signaling networks such as those mediated by RTKs. Substrate phosphorylation downstream of RTK activation modifies substrate activity and induces phospho-specific binding interactions, resulting in the formation of large transient macromolecular signaling complexes. Since protein complex formation should follow the trajectory of events that drive it, we reasoned that mining phosphoproteomic datasets for highly similar dynamic behavior of measured phosphorylation sites on different proteins could be used to predict novel, transient protein-protein interactions that had not been previously identified. We applied this method to explore signaling events downstream of EGFR stimulation. Our computational analysis of robustly co-regulated phosphorylation sites, based on multiple clustering analysis of quantitative time-resolved mass-spectrometry phosphoproteomic data, not only identified known sitewise-specific recruitment of proteins to EGFR, but also predicted novel, a priori interactions. A particularly intriguing prediction of EGFR interaction with the cytoskeleton-associated protein PDLIM1 was verified within cells using co-immunoprecipitation and in situ proximity ligation assays. Our approach thus offers a new way to discover protein-protein interactions in a dynamic context- and phosphorylation site-specific manner.
细胞信号网络通过对蛋白质 - 蛋白质相互作用的动态调节，以依赖于上下文的方式从细胞外信号传导信息。例如，基于受体酪氨酸激酶（RTK）的网络会响应细胞外配体使细胞内蛋白质磷酸化，从而导致驱动表型变化的动态蛋白质 - 蛋白质相互作用。然而，大多数用于发现这些蛋白质 - 蛋白质相互作用的常用方法是针对检测稳定、寿命较长的复合物进行优化的，而非像RTK介导的那些动态信号网络的基本组成部分——瞬时相互作用。RTK激活下游的底物磷酸化会改变底物活性并诱导磷酸特异性结合相互作用，从而导致形成大型瞬时大分子信号复合物。由于蛋白质复合物的形成应遵循驱动它的事件轨迹，我们推测，在磷酸蛋白质组数据集中挖掘不同蛋白质上测量的磷酸化位点的高度相似的动态行为，可用于预测先前未被识别的新型瞬时蛋白质 - 蛋白质相互作用。我们应用此方法来探索EGFR刺激下游的信号事件。基于定量时间分辨质谱磷酸蛋白质组数据的多重聚类分析，我们对稳健共调节的磷酸化位点进行的计算分析，不仅识别了已知的蛋白质在EGFR上逐个位点的特异性募集，还预测了新型的先验相互作用。使用共免疫沉淀和原位邻近连接分析在细胞内验证了EGFR与细胞骨架相关蛋白PDLIM1相互作用的一个特别有趣的预测。因此，我们的方法提供了一种以动态、依赖上下文和磷酸化位点特异性的方式发现蛋白质 - 蛋白质相互作用的新途径。
