Ultra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139
Year: 2025
Type: article
Abstract: The orphan G protein-coupled receptor (GPCR) GPR139 attracts interest as a target for neuropsychiatric disorders. Whereas the physiological functions of GPR139 remain elusive, a high-resolution receptor structure is now available. To assess whether structural information enables ligand discovery, we computationally dock 235 million compounds to the GPR139 binding site. Of 68 top-ranked compounds evaluated experimentally, five are full agonists with potencies ranging from 160 nM to 3.6 µM. Structure-guided optimization identifies one of the most potent GPR139 agonists, and a cryo-EM structure of the receptor-ligand complex confirms the predicted binding mode. Functional characterization provides insights into GPR139 signalling, and one agonist elicits behavioural effects in mice. We also explore the potential to replace experimental structure determination with the deep-learning method AlphaFold3, revealing a limited capability of artificial intelligence to model receptor-ligand interactions for understudied GPCRs. The results demonstrate how high-resolution GPCR structures combined with large-library docking can accelerate drug discovery.
Source: Nature Communications
Authors: Israel Cabeza de Vaca, Boris Trapkov, Ling Shen, Duy Duc Vo, Xiaoqun Zhang, Yunting Yang, Mitra Pezeshki, Xuehan Zhang, Frida Bällgren, Aljona Saleh, Andrii V. Tarnovskiy, Dmytro S. Radchenko, Yurii S. Moroz, Hans Bräuner-Osborne, Per Svenningsson, Jan Kihlberg, Zhi-Jie Liu, Alexander Sebastian Hauser, Jens Carlsson
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: Eli Lilly and Company, Hjärnfonden, Linköpings Universitet, Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet, Uppsala Universitet, Science for Life Laboratory, Faculty of Health and Medical Sciences, University of Western Australia, Det Sundhedsvidenskabelige Fakultet, Københavns Universitet, National Supercomputer Centre, Linköpings Universitet
OpenAlex ID: W4417146434
