Biology subjects

Marfoglia, M.

Publications and source records attributed to Marfoglia, M..

3 recordsLinked to original sources

bioRxiv2025

Molecular characterization of an adhesion GPCR signal transduction

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.

biophysics↗

bioRxiv2024

AlloPool: An Adaptive Graph Neural Network for Dynamic Allosteric Network Prediction in Protein Systems

Allosteric communication is essential to protein function, facilitating the dynamic regulation of biological responses through the propagation of structural and dynamic changes between regulatory and effector sites in response to stimuli. Traditional approaches to studying protein allostery often rely on static protein structures or abstract representations involving fully connected interaction graphs, which do not capture the temporal and state-dependent nature of these dynamic systems. Here, we introduce AlloPool, a graph neural network (GNN)-based model that iteratively prunes residue interactions to identify minimal, time-dependent interaction networks that govern long-range structural and dynamic responses to chemical or mechanical stimuli. Using temporal attention and graph aggregation, AlloPool accounts for evolving protein conformations in both molecular dynamics (MD) and steered MD (SMD) simulations to predict MD and SMD trajectories. We validate AlloPool on the Pin-1 protein and the ADGRG1 and B1AR receptors, showcasing its ability to accurately recapitulate protein motions, infer allosteric communication pathways, and identify critical allosteric sites. Additionally, AlloPool identifies force-dependent changes in GAIN domain structure and reconstructs directed information flow under mechanical load. Comparative analyses indicate that AlloPool outperforms existing models in MD and SMD trajectory reconstruction, presenting a new framework for analyzing force- and ligand-induced allosteric motions. This work advances the modeling of allosteric systems and offers broad potential for applications in drug discovery, synthetic biology, and protein engineering.

bioinformatics↗

bioRxiv2023

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors including adhesion G-protein coupled receptors (aGPCRs). While recently solved structures support aGPCR activation through shedding of the extracellular GAIN domain, the molecular mechanisms underpinning receptor mechanosensing remain poorly understood. When probed using single-molecule atomic force spectroscopy and molecular simulations, ADGRG1 GAIN dissociated from its tethered agonist at forces significantly higher than other reported signaling mechanoreceptors. Strong mechanical resistance was achieved through specific structural deformations and force propagation pathways under mechanical load. ADGRG1 GAIN variants computationally designed to lock the alpha and beta subdomains and rewire mechanically-induced structural deformations were found to modulate the GPS-Stachel rupture forces. Our study provides unprecedented insights into the molecular underpinnings of GAIN mechanical stability and paves the way for engineering mechanosensors, better understanding aGPCR function, and informing drug-discovery efforts targeting this important receptor class.

biophysics↗