Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona (J.G.), Bellaterra, Spain; Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (J.G.), CIBERSAM, Spain; Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona (J.G.), Spain; Global and Planetary Health, College of Public Health (J.J.M.), Center for Global Health and Infectious Diseases Research, College of Public Health (J.J.M.), Department of Molecular Medicine, Morsani College of Medicine (J.J.M.), Department of Molecular Biosciences (X.W., L.Y.), University of South Florida, Tampa, Florida; Department of Pharmacology and Chemical Biology, University of PittsburghSchool of Medicine (L.W., C.Z.), University of Pittsburgh, Pittsburgh, Pennsylvania; and Lee Moffitt Cancer Center & Research Institute, Tampa, Florida (L.Y.)
Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona (J.G.), Bellaterra, Spain; Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental (J.G.), CIBERSAM, Spain; Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona (J.G.), Spain; Global and Planetary Health, College of Public Health (J.J.M.), Center for Global Health and Infectious Diseases Research, College of Public Health (J.J.M.), Department of Molecular Medicine, Morsani College of Medicine (J.J.M.), Department of Molecular Biosciences (X.W., L.Y.), University of South Florida, Tampa, Florida; Department of Pharmacology and Chemical Biology, University of PittsburghSchool of Medicine (L.W., C.Z.), University of Pittsburgh, Pittsburgh, Pennsylvania; and Lee Moffitt Cancer Center & Research Institute, Tampa, Florida (L.Y.).
Mol Pharmacol. 2023 Dec 15;105(1):54-62. doi: 10.1124/molpharm.123.000754.
G protein-coupled receptors (GPCRs) exhibit a wide range of pharmacological efficacies, yet the molecular mechanisms responsible for the differential efficacies in response to various ligands remain poorly understood. This lack of understanding has hindered the development of a solid foundation for establishing a mathematical model for signaling efficacy. However, recent progress has been made in delineating and quantifying receptor conformational states and associating function with these conformations. This progress has allowed us to construct a mathematical model for GPCR signaling efficacy that goes beyond the traditional ON/OFF binary switch model. In this study, we present a quantitative conformation-based mathematical model for GPCR signaling efficacy using the adenosine A receptor (AR) as a model system, under the guide of F quantitative nuclear magnetic resonance experiments. This model encompasses two signaling states, a fully activated state and a partially activated state, defined as being able to regulate the cognate G nucleotide exchange with respective G protein recognition capacity. By quantifying the population distribution of each state, we can now in turn examine GPCR signaling efficacy. This advance provides a foundation for assessing GPCR signaling efficacy using a conformation-based mathematical model in response to ligand binding. SIGNIFICANCE STATEMENT: Mathematical models to describe signaling efficacy of GPCRs mostly suffer from considering only two states (ON/OFF). However, research indicates that a GPCR possesses multiple active-(like) states that can interact with Gαβγ independently, regulating varied nucleotide exchanges. With the guide of F-qNMR, the transitions among these states are quantified as a function of ligand and Gαβγ, serving as a foundation for a novel conformation-based mathematical signaling model.
G 蛋白偶联受体 (GPCRs) 表现出广泛的药理学效力，但对于导致对各种配体的不同效力的分子机制仍了解甚少。这种缺乏理解阻碍了为建立信号效力的数学模型奠定坚实基础的发展。然而，最近在描绘和量化受体构象状态以及将功能与这些构象相关联方面取得了进展。这一进展使我们能够构建超越传统 ON/OFF 二进制开关模型的 GPCR 信号效力的数学模型。在这项研究中，我们使用腺苷 A 受体 (AR) 作为模型系统，在 F 定量核磁共振实验的指导下，提出了一种基于定量构象的 GPCR 信号效力的数学模型。该模型包含两种信号状态，完全激活状态和部分激活状态，定义为能够调节同源 G 核苷酸与相应 G 蛋白识别能力的交换。通过量化每个状态的种群分布，我们现在可以反过来检查 GPCR 信号效力。这一进展为使用基于构象的数学模型评估配体结合时的 GPCR 信号效力提供了基础。意义陈述：描述 GPCR 信号效力的数学模型大多存在仅考虑两种状态（ON/OFF）的问题。然而，研究表明，GPCR 具有多个类似于活性的状态，这些状态可以与 Gαβγ 独立相互作用，调节不同的核苷酸交换。在 F-qNMR 的指导下，这些状态之间的转换被量化为配体和 Gαβγ 的函数，为新的基于构象的数学信号模型奠定了基础。
