Broughton, H. | Search bioRxiv

bioRxiv2026

Structural dynamics underlying agonist activation of a GLP-1R-Gs precoupled complex

G-protein-coupled receptors (GPCRs) act as allosteric transmembrane signalling machines, generating distinct cellular responses depending on the conformational states induced by ligand binding. The glucagon-like peptide-1 receptor (GLP-1R), a class B GPCR central to insulin secretion and body-weight regulation, is a key therapeutic target for obesity-associated metabolic disease. Here, we used hydrogen-deuterium exchange mass spectrometry to characterize ligand-evoked structural dynamics within a pre-coupled GLP-1R-Gs protein complex. Non-peptide agonists Chu-128 and danuglipron elicited overlapping dynamic perturbation profiles, with distinct drug-specific effects within the transmembrane bundle. In contrast, the natural GLP-1 hormone produced a weaker stabilizing effect on receptor backbone dynamics, while its inactive metabolite exerted opposing localised destabilization. Notably, both peptides uniquely modulated the highly flexible G-protein switch III loop, a key mediator of downstream signalling. These findings pinpoint areas where structural dynamics shape agonist efficacy and facilitate functional dynamics-integrated drug discovery of non-peptide agonists. SignificanceThe development of non-peptide agonists of the glucagon-like peptide-1 receptor (GLP-1R) represents a major advance in metabolic therapeutics, addressing key limitations of current peptide-based incretin therapies while enabling improved control over receptor signalling and pharmacokinetic properties. The recent FDA approval of a first-in-class small-molecule oral GLP-1R agonist, LY3502970, highlights the translational potential of this approach. However, the molecular basis by which distinct ligands modulate GLP-1R conformational dynamics and signalling remains poorly understood. Here we report how non-peptide agonists (Chu-128 and danuglipron) and endogenous GLP-1 peptide and its inactive metabolite shape the structural dynamics of a pre-coupled GLP-1R-Gs complex, revealing patterns linked to receptor activation. Our findings provide insights that guide the rational design of next-generation GLP-1R therapeutics.

biochemistry↗

bioRxiv2022

MAVEN: An R/Shiny app for compound mechanism of action analysis and visualisation using transcriptomics and compound structure data

BackgroundUnderstanding the mechanism of action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. ResultsTo address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MsigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. ConclusionsMAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.

systems biology↗