Molecular mechanisms of native ligand selectivity in catecholamine G protein-coupled receptors

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Authors: Nour Aldin Kahlous, Maiju K. Rinne, Xin Zhang, Yanying Li, Yue Chen, Aikaterini Motso, Kaixuan Gao, Christina Bergqvist, Hongda Sheng, Yi Wang, Israel Cabeza de Vaca, Alejandro Díaz‐Holguín
+9 more Philip Ullmann, Tore Bengtsson, Volker M. Lauschke, Jyrki P. Kukkonen, Lucie Delemotte, Shane C. Wright, Xiangyu Liu, Dan Larhammar, Jens Carlsson
Cited by: 1
References: 84

Abstract

Abstract Activation of G protein-coupled receptors (GPCRs) by extracellular ligands is crucial for cellular communication and modulates numerous physiological processes. Despite sharing highly similar orthosteric binding sites, catecholamine GPCRs exhibit exquisite selectivity for their native agonists, even among nearly identical chemical messengers. However, the molecular basis and evolution of receptor selectivity remain poorly understood. To elucidate the structural mechanisms of GPCR selectivity, we focus on the prototypical human β 2 -adrenergic and D 1 dopaminergic receptors, which are important drug targets and respond to the catecholamines adrenaline/noradrenaline and dopamine, respectively. Guided by structural and sequence data, we identify a small set of residues responsible for ligand selectivity. By exchanging residues at four positions in the β-adrenergic receptors and seven in the D 1 -like dopaminergic receptors, we swap the pharmacological profiles of the two subfamilies. Unexpectedly, the switch in selectivity not only involves residues interacting with the ligand, but is also controlled by regions outside the orthosteric binding site. Cryo-electron microscopy structures and computational models of the mutant receptors identify distinct molecular mechanisms contributing to selectivity in a concerted manner. Our findings provide insights into GPCR evolution and highlight strategies for protein engineering and drug design.

Fields of Study & Subjects

Receptor Mechanisms and Signaling Nicotinic Acetylcholine Receptors Study Machine Learning in Bioinformatics

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
23 April 2026
Language
EN