bioRxivBiophysicsPreprint
Molecular characterization of an adhesion GPCR signal transduction
M. MarfogliaiD, Louis DumasiD, Byeongseon YangiD, Aurélien Oggier, Melinda Pedraza, Mahdi HijaziiD, Amédé Larabi, Kelvin LauiD, Florence PojeriD, Michael A. NashiD, P. BarthiD
Abstract
Key cellular processes rely on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein-coupled receptors (aGPCRs). While recent studies support aGPCR activation via shedding of the extracellular GAIN domain, shedding-independent signaling mechanisms have also been observed. However, the molecular basis underlying these distinct activation modes remains poorly understood. Here, we integrate single-molecule force spectroscopy, molecular dynamics simulations, and cell-based assays to elucidate the structural and dynamic mechanisms of ADGRG1 mechanotransduction. We show that shear stress induces distinct deformation pathways in the isolated GAIN domain, promoting tethered agonist (TA) exposure through loop rearrangements prior to domain shedding. In the full-length receptor, defined GAIN orientations and specific loop contacts with the 7-transmembrane (7TM) core enable allosteric TA engagement and signaling in the absence of GAIN dissociation. The directionality of the applied force dictates the activation pathway, favoring either GAIN shedding or intact GAIN-7TM coupling. These mechanisms align with both the basal activity and collagen-enhanced signaling of ADGRG1. Using deep learning-guided design, we engineered GAIN variants with tailored mechanical sensitivity, validating our model through predictable shifts in constitutive and ligand-induced signaling. Together, our findings establish a unified framework for aGPCR activation governed by GAIN dynamics and orientation, bridging mechanical and allosteric models of receptor function and providing new strategies for engineering mechanosensitive receptors and precision therapeutics.
- organism: human
- research focus: cell signaling & trafficking
- approach: mechanistic / dynamical model
- approach: deep learning
- Citations
- 1
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