Steric control of signaling bias in the immunometabolic receptor GPR84

Abstract Biased signaling in G protein-coupled receptors offers therapeutic promise, yet rational design of biased ligands remains challenging due to limited mechanistic understanding. Here, we report a molecular basis for controlling signaling bias at the immunometabolic receptor GPR84. We identify three structurally-matched ligands (OX04529, OX04954, and OX04539) with varying steric profiles that exhibit comparable G i protein activation but markedly different β-arrestin recruitment capacities. A high-resolution cryo-EM structure of GPR84-G i in complex with OX04529, complemented by molecular dynamics simulations and targeted mutagenesis, reveals that steric interactions between ligand substituents and Leu336 6.52 and Phe187 5.47 indirectly disrupt a critical polar network involving Tyr332 6.48 , Asn104 3.36 and Asn362 7.45 essential for β-arrestin recruitment. Based on these insights, we develop a steric-dependent model that enables rational design of G protein-biased agonists with predictable β-arrestin recruitment profiles. This mechanistic framework provides the means to design biased agonists with customized signaling profiles at GPR84 and potentially other class A GPCRs.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2026-09-05
DOI
https://doi.org/10.1038/s41467-026-77312-7
Citations
1
Primary Topic
Receptor Mechanisms and Signaling
Type
article
Field-Weighted Citation Impact
2.87

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Steric control of signaling bias in the immunometabolic receptor GPR84

Angela J. Russell, Arun Raja, Listiana Oktavia, Pinqi Wang et al.
1 citations
Nature Communications
Receptor Mechanisms and Signaling
2.87
article

Steric control of signaling bias in the immunometabolic receptor GPR84

Angela J. Russell, Arun Raja, Listiana Oktavia, Pinqi Wang, Philip C. Biggin, Jonathan D. Colburn, Graeme Milligan, Irina G. Tikhonova, Sara Marsango, Vincent B. Luscombe, David R. Greaves, Laura Jenkins, Xuan Zhang, Cheng Zhang, A. -K. Guseinov, Carl von Hallerstein
article en
1 citations

Abstract

Abstract Biased signaling in G protein-coupled receptors offers therapeutic promise, yet rational design of biased ligands remains challenging due to limited mechanistic understanding. Here, we report a molecular basis for controlling signaling bias at the immunometabolic receptor GPR84. We identify three structurally-matched ligands (OX04529, OX04954, and OX04539) with varying steric profiles that exhibit comparable G i protein activation but markedly different β-arrestin recruitment capacities. A high-resolution cryo-EM structure of GPR84-G i in complex with OX04529, complemented by molecular dynamics simulations and targeted mutagenesis, reveals that steric interactions between ligand substituents and Leu336 6.52 and Phe187 5.47 indirectly disrupt a critical polar network involving Tyr332 6.48 , Asn104 3.36 and Asn362 7.45 essential for β-arrestin recruitment. Based on these insights, we develop a steric-dependent model that enables rational design of G protein-biased agonists with predictable β-arrestin recruitment profiles. This mechanistic framework provides the means to design biased agonists with customized signaling profiles at GPR84 and potentially other class A GPCRs.

Nature Communications
Queen's University Belfast (GB), Mansfield University (US), University of Pittsburgh (US), Oxford Research Group (GB), University of Oxford (GB), University of Glasgow (GB)
University of Pittsburgh, Wellcome Trust, British Heart Foundation, European Commission, Lembaga Pengelola Dana Pendidikan, National Institutes of Health, Directorate for Biological Sciences, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council, Clarendon Fund
Openalex Percentile: Top 17%
Receptor Mechanisms and Signaling
2.87
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.