Casadó Vincent, Cortés Antoni, Ciruela Francisco, Mallol Josefa, Ferré Sergi, Lluis Carmen, Canela Enric I, Franco Rafael
IDIBAPS and CIBERNED Centro de Investigación en Red sobre Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain.
Pharmacol Ther. 2007 Dec;116(3):343-54. doi: 10.1016/j.pharmthera.2007.05.010. Epub 2007 Jun 14.
Almost all existing models that explain heptahelical G-protein-coupled receptor (GPCR) operation are based on the occurrence of monomeric receptor species. However, an increasing number of studies show that many G-protein-coupled heptahelical membrane receptors (HMR) are expressed in the plasma membrane as dimers. We here review the approaches for fitting ligand binding data that are based on the existence of receptor monomers and also the new ones based on the existence of receptor dimers. The reasons for equivocal interpretations of the fitting of data to receptor dimers, assuming they are monomers, are also discussed. A recently devised model for receptor dimers provides a new approach for fitting data that eventually gives more accurate and physiological relevant parameters. Fitting data using the new procedure gives not only the equilibrium dissociation constants for high- and low-affinity binding to receptor dimers but also a "cooperativity index" that reflects the molecular communication within the dimer. A comprehensive way to fit binding data from saturation isotherms and from competition assays to a dimer receptor model is reported and compared with the traditional way of fitting data. The new procedure can be applied to any receptor forming dimers; from receptor tyrosine kinases to intracellular receptors (e.g., estrogen receptor) and in general for ligand binding to proteins forming dimers.
几乎所有解释七螺旋G蛋白偶联受体(GPCR)作用机制的现有模型都是基于单体受体的存在。然而,越来越多的研究表明,许多G蛋白偶联的七螺旋膜受体(HMR)在质膜中以二聚体形式表达。我们在此综述基于受体单体存在的配体结合数据拟合方法,以及基于受体二聚体存在的新方法。还讨论了在假设受体为单体的情况下,对数据拟合到受体二聚体时产生模棱两可解释的原因。最近设计的一种受体二聚体模型为数据拟合提供了一种新方法,最终能给出更准确且与生理相关的参数。使用新方法拟合数据不仅能得到与受体二聚体高亲和力和低亲和力结合的平衡解离常数,还能得到一个反映二聚体内分子通讯的“协同指数”。本文报道了一种将饱和等温线和竞争试验中的结合数据拟合到二聚体受体模型的综合方法,并与传统的数据拟合方法进行了比较。新方法可应用于任何形成二聚体的受体;从受体酪氨酸激酶到细胞内受体(如雌激素受体),一般适用于配体与形成二聚体的蛋白质的结合。