G protein‐coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over‐represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K97 2.68×67 , F194 ECL2 , S203 5.42×43 , S204 5.43×44 , S207 5.46×641 , H296 6.58×58 , and K305 7.32×31 . Meanwhile, the antagonist ligands made interactions with W286 6.48×48 and Y316 7.43×42 , both residues considered to be important in GPCR activation. The interpretation of ML analysis in human understandable form allowed us to construct an exquisitely detailed structure‐activity relationship that identifies small changes to the ligands that invert their pharmacological activity and thus helps to guide the drug discovery process. This approach can be readily applied to any drug target.
G protein coupled receptors (GPCRs) translate the actions of hormones and neurotransmitters into intracellular signalling events. Mutations in GPCRs can prevent their correct expression and trafficking to the cell surface and cause disease. Single cell subcellular localisation measurements reveal that while some cells appear to traffic the majority of the vasopressin 2 receptor (V2R) molecules to the cell surface, others retain a greater number of receptors in the ER or have approximately equal distribution. Mutations in the V2R affect the proportion of cells able to send this GPCR to their cell surface but surprisingly they do not prevent all cells from correctly trafficking the mutant receptors. These findings reveal the potential for rescue of mutant receptor cell surface expression by pharmacological manipulation of the GPCR folding and trafficking machinery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.