A 19 F-qNMR-Guided Mathematical Model for G Protein-Coupled Receptor Signaling .
Jesús GiraldoJesper J MadsenXudong WangLei WangCheng ZhangLibin YePublished in: Molecular pharmacology (2023)
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 2A receptor (A 2A R) as a model system, under the guide of 19 F quantitative NMR ( 19 F-qNMR) experiments. This model encompasses two signaling states, a fully activated state, and a partially activated state, defined as being able to regulate the cognate Ga s 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 G protein-coupled receptors (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 Gabg independently, regulating varied nucleotide exchanges. With the guide of 19 F-qNMR, the transitions among these states are quantified as a function of ligand and Gabg, serving as a foundation for a novel conformation-based mathematical signaling model.