Computational Study of Cannabinoid Receptors CB1 and CB2
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Date
2025-05
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The Ohio State University
Abstract
Marijuana has been used both recreationally and therapeutically, including to alleviate symptoms of cancer. The plant produces several interesting compounds, including the psychoactive D9-tetrahydrocannabinol (THC) and the structurally similar cannabidiol (CBD) which have also been chemically extracted from the plant and administered in pure form. THC and CBD interact with receptors within the cannabinoid system, the major ones being cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2). Both are seven transmembrane G-protein coupled receptors (GPCRs). This report used the Catalogue of Somatic Mutations in Cancers (COSMIC) to map and analyze mutations arising in CB1 and CB2. Identified were several hotspot mutations in both CB1 and CB2. In silico 3-D structure protein analysis reveals that these hotspots cluster on the intracellular regions of the cannabinoid receptors and certain residues may be likely to destabilize the interaction with the G-alpha protein. Our analysis identifies two CB1 residues (R336 and M337) that are most likely to interact with G-alpha. Docking analysis reveals that hotspot substitution mutations at these two positions may affect G-alpha binding. Hotspot mutations in CB2 were not predicted to be as potentially destabilizing as those identified for CB1. Our data suggest that CB1 and CB2 mutations acquired during cancer development may impact the therapeutic effectiveness of cannabinoids. An additional study composed of molecular cross docking between CB1, CB2, and glucagon-like peptide type 1-receptor (GLP-1R) ligands was conducted. Like CB1 and CB2, GLP-1R is a GPCR. Due to the structural similarity and recent rise in cannabis use as well as GLP-1R targeted weight loss drugs, this study aimed to identify potential linkage between the systems. Results indicated that danuglipron, a small GLP-1R agonist, had a higher binding affinity for CB1 and CB2 when compared to cannabinoid ligands.
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Keywords
Endocannabinoid System, Molecular Modeling, Molecular Docking, Protein-Protein Docking