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<p>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.</p>

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Ultra-large virtual screening unveils potent agonists of the neuromodulatory orphan receptor GPR139

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • 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

摘要

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.