Stites Edward C, Aziz Meraj, Creamer Matthew S, Von Hoff Daniel D, Posner Richard G, Hlavacek William S
Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri.
Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona.
Biophys J. 2015 Apr 7;108(7):1819-1829. doi: 10.1016/j.bpj.2015.02.030.
Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment.
细胞信号网络中的蛋白质往往通过低亲和力相互作用进行混杂性相互作用。因此，评估已绘制相互作用的生理重要性可能会很困难。为此所做的尝试往往集中在单一的、可测量的物理化学因素上，比如亲和力或丰度。例如，相互作用的重要性是根据结合伴侣对感兴趣蛋白质（如受体）的相对亲和力来评估的。然而，可以预期多种因素会同时影响蛋白质向受体的募集（以及这些蛋白质对受体信号传导的潜在贡献），包括亲和力、丰度和竞争，而竞争是一种网络特性。在这里，我们证明蛋白质拷贝数和结合亲和力的测量可以整合到一个考虑质量作用和竞争的机械计算模型框架内。我们使用细胞系特异性模型对11种不同细胞系的表皮生长因子受体（EGFR）信号网络中蛋白质 - 蛋白质相互作用的相对重要性进行排名。每个模型考虑了EGFR中六个自磷酸化位点与含有Src同源2和/或磷酸酪氨酸结合结构域的蛋白质之间经实验表征的相互作用。我们将重要性衡量为在EGFR信号经配体刺激激活后蛋白质向EGFR募集的预测最大程度。我们发现，按此指标排名靠前的相互作用包括实验检测到的相互作用。在多个细胞系中具有高重要性排名的蛋白质包括在EGFR信号传导中具有公认的、特征明确作用的蛋白质，如GRB2和SHC1，以及一个作用定义不太明确的蛋白质YES1。我们的结果揭示了募集方面潜在的细胞系特异性差异。
