The development of G protein-biased agonists for the μ-opioid receptor (MOR) offers a clear drug discovery rationale for improved analgesia and reduced side-effects of opiate pharmacotherapy. However, our understanding of the molecular mechanisms governing ligand bias is limited, which hinders our ability to rationally design biased compounds. We have investigated the role of MOR binding site residues W320 and Y328 in controlling bias, by receptor mutagenesis. The pharmacology of a panel of ligands in a cAMP and a β-arrestin2 assay were compared between the wildtype and mutated receptors, with bias factors calculated by operational analysis using ΔΔlog(τ/K) values. [H]diprenorphine competition binding was used to estimate affinity changes. Introducing the mutations W320A and Y328F caused changes in pathway bias, with different patterns of change between ligands. For example, DAMGO increased relative β-arrestin2 activity at the W320A mutant, whilst its β-arrestin2 response was completely lost at Y328F. In contrast, endomorphin-1 gained activity with Y328F but lost activity at W320A, in both pathways. For endomorphin-2 there was a directional shift from cAMP bias at the wildtype towards more β-arrestin2 bias at W320A. We also observe clear uncoupling between mutation-driven changes in function and binding affinity. These findings suggest that the mutations influenced the balance of pathway activation in a ligand-specific manner, thus identifying residues in the MOR binding pocket that govern ligand bias. This increases our understanding of how ligand/receptor binding interactions can be translated into agonist-specific pathway activation.
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http://dx.doi.org/10.1016/j.neuropharm.2017.03.007 | DOI Listing |
Pharmacol Ther
January 2025
School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China; School of Life Science and Technology, China Pharmaceutical University, Nanjing 211198, China.
G protein-coupled receptors (GPCRs) can transmit signals via G protein-dependent or independent pathways due to the conformational changes of receptors and ligands, which is called biased signaling. This concept posits that ligands can selectively activate a specific signaling pathway after receptor activation, facilitating downstream signaling along a preferred pathway. Biased agonism enables the development of ligands that prioritize therapeutic signaling pathways while mitigating on-target undesired effects.
View Article and Find Full Text PDFInflamm Regen
January 2025
Division of Immunobiology, Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan.
Background: For the treatment of liver fibrosis, several novel cell therapies have been proposed. Autologous macrophage therapy has been reported as one of the promising treatments. So far, most studies have used colony-stimulating factor 1 (CSF-1) to induce the differentiation of macrophage progenitor cells.
View Article and Find Full Text PDFJ Infect Dis
January 2025
School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Background: Inflammation and innate immune activation are associated with chronic HIV infection, despite effective treatment. Although gut microbiota alterations are linked to systemic inflammation, the relationships between the gut microbiome, inflammation and HIV remain unclear.
Methods: The UPBEAT-CAD sub-study, examining cardiovascular disease (CVD) risk in HIV, enrolled participants matched on HIV status and traditional CVD risk factors.
Diabetologia
January 2025
Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, CO, USA.
Type 1 diabetes is an autoimmune disease characterised by the destruction of pancreatic beta cells, resulting in lifelong insulin dependence. Although exogenous insulin can maintain glycaemic control, this approach does not protect residual or replacement pancreatic beta cells from immune-mediated death. Current therapeutics designed to protect functional beta cell mass or promote beta cell proliferation and regeneration can have off-target effects, resulting in higher dose requirements and adverse side effects.
View Article and Find Full Text PDFJ Cheminform
January 2025
Department of Mathematics, University of Tennessee, Knoxville, TN, 37996, USA.
Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)-based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory.
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