G protein-coupled receptors (GPCRs) regulate diverse physiological responses by engaging distinct heterotrimeric G proteins, yet the basis of Gα selectivity in promiscuous receptors remains unclear. Although ligand bias holds therapeutic promise, selectivity has been assumed to reside mainly in the ligand-binding site (LBS) or G protein interface (GPI). Here, we combine whole-receptor mutagenesis, functional Gα protein assays, molecular dynamics simulations, interpretable machine learning method, and Bayesian network modeling to identify residue networks governing Gαq/11 and Gα12/13 coupling and to define the molecular basis of G protein preference and promiscuity at two vasopressor GPCRs, the angiotensin II type 1 and prostaglandin F2α receptors. While residues within the LBS and GPI domains contribute to coupling efficiency and subtype discrimination, we find that long-range allosteric communication across the receptor, including from structurally unresolved domains, is the principal determinant of Gα protein preference and promiscuity. These allosteric pathways integrate multiple receptor domains, confer signaling robustness to mutation, and hierarchically govern coupling preferences. Our findings suggest that Gα protein selectivity is an allosterically encoded property of GPCRs and provide a conceptual framework for designing ligands and receptors with tailored Gα protein-biased signaling.
Tegvir S. Boora, Han-Yu Chen, Aaron Cho et al.· bioRxiv· 0 citations
Allosteric modulation of G protein–coupled receptors (GPCRs) offers major advantages in receptor selectivity and signaling control; yet systematic approaches to identify allosteric modulators, define their binding sites, and map the underlying allosteric networks remain limited. Current molecular dynamics (MD) and machine learning (ML)-based methods often rely on correlation-driven or black-box models that provide limited mechanistic insight. We developed an interpretable probabilistic framework that extracts residue-level dependencies from MD ensembles using Bayesian network modeling (BNM). By representing each residue through its local interaction energy, BNM identifies both local and long-range energetic couplings and maps the allosteric communication pathways linking the AngII binding site to the G-protein interface in the angiotensin II type 1 receptor (AT1R). To functionally prioritize these pathways, we integrated BNM with comprehensive mutational analysis, combining whole-receptor alanine mutagenesis data with exhaustive in silico deep mutational scanning to validate BNM-predicted hotspots. This approach recovered state-dependent allosteric communities, revealed residues in noncanonical regions that regulate Gαq coupling and identified positions whose functional importance emerged only with specific, predicted substitutions, as well as highlighted a cryptic intracellular pocket enriched in communication hubs. Guided by these network-derived residues and pocket geometries, structure-based virtual screening identified a small, fragment-like molecule negative allosteric modulator (NAM) named Q2 that attenuates AngII-mediated Gαq signaling. Mutational mapping supports Q2 binding adjacent to the G-protein interface, consistent with its mechanism of action. Together, these results establish a generalizable and interpretable framework for uncovering GPCR allosteric communication networks and discovering modulators that exploit these networks.
This study leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor.
Wenyuan Wei, Roland Del Mundo, Tianyi Yang et al.· Journal of Chemical Informat...· 0 citations
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