Wofsy C, Goldstein B, Lund K, Wiley H S
Department of Mathematics and Statistics, University of New Mexico, Albuquerque 87131.
Biophys J. 1992 Jul;63(1):98-110. doi: 10.1016/S0006-3495(92)81572-2.
To investigate the role of receptor aggregation in EGF binding, we construct a mathematical model describing receptor dimerization (and higher levels of aggregation) that permits an analysis of the influence of receptor aggregation on ligand binding. We answer two questions: (a) Can Scatchard plots of EGF binding data be analyzed productively in terms of two noninteracting receptor populations with different affinities if EGF induced receptor aggregation occurs? No. If two affinities characterize aggregated and monomeric EGF receptors, we show that the Scatchard plot should have curvature characteristic of positively cooperative binding, the opposite of that observed. Thus, the interpretation that the high affinity population represents aggregated receptors and the low affinity population nonaggregated receptors is wrong. If the two populations are interpreted without reference to receptor aggregation, an important determinant of Scatchard plot shape is ignored. (b) Can a model for EGF receptor aggregation and EGF binding be consistent with the "negative curvature" (i.e., curvature characteristic of negatively cooperative binding) observed in most Scatchard plots of EGF binding data? Yes. In addition, the restrictions on the model parameters required to obtain negatively curved Scatchard plots provide new information about binding and aggregation. In particular, EGF binding to aggregated receptors must be negatively cooperative, i.e., binding to a receptor in a dimer (or higher oligomer) having one receptor already bound occurs with lower affinity than the initial binding event. A third question we consider is whether the model we present can be used to detect the presence of mechanisms other than receptor aggregation that are contributing to Scatchard plot curvature. For the membrane and cell binding data we analyzed, the best least squares fits of the model to each of the four data sets deviate systematically from the data, indicating that additional factors are also important in shaping the binding curves. Because we have controlled experimentally for many sources of receptor heterogeneity, we have limited the potential explanations for residual Scatchard plot curvature.
为了研究受体聚集在表皮生长因子(EGF)结合中的作用,我们构建了一个描述受体二聚化(以及更高水平聚集)的数学模型,该模型允许分析受体聚集对配体结合的影响。我们回答两个问题:(a)如果发生EGF诱导的受体聚集,EGF结合数据的Scatchard图能否根据具有不同亲和力的两个非相互作用受体群体进行有效分析?不能。如果聚集的和单体的EGF受体具有两种亲和力,我们表明Scatchard图应具有正协同结合特征的曲率,这与观察到的相反。因此,认为高亲和力群体代表聚集受体而低亲和力群体代表非聚集受体的解释是错误的。如果在不考虑受体聚集的情况下解释这两个群体,就会忽略Scatchard图形状的一个重要决定因素。(b)一个关于EGF受体聚集和EGF结合的模型能否与在大多数EGF结合数据的Scatchard图中观察到的“负曲率”(即负协同结合特征的曲率)一致?可以。此外,获得负曲率Scatchard图所需的模型参数限制提供了有关结合和聚集的新信息。特别是,EGF与聚集受体的结合必须是负协同的,即与已经结合了一个受体的二聚体(或更高聚体)中的受体结合时的亲和力低于初始结合事件。我们考虑的第三个问题是,我们提出的模型是否可用于检测除受体聚集之外的其他机制对Scatchard图曲率的贡献。对于我们分析的膜结合和细胞结合数据,该模型对四个数据集中每个数据集的最佳最小二乘拟合都系统地偏离了数据,表明其他因素在塑造结合曲线方面也很重要。由于我们已经通过实验控制了受体异质性的许多来源,因此我们限制了对Scatchard图剩余曲率的潜在解释。