A simple open source bioinformatic methodology for initial exploration of GPCR ligands' agonistic/antagonistic properties. Issue 4 (13th July 2020)

• Record Type: Journal Article
• Authors: Panagiotopoulos, Athanasios A.; Papachristofi, Christina; Kalyvianaki, Konstantina; Malamos, Panagiotis; Theodoropoulos, Panayiotis A.; Notas, George; Calogeropoulou, Theodora; Castanas, Elias; Kampa, Marilena
• Is Part Of: Pharmacology research & perspectives. Volume 8:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-13
• Subjects: agonist -- antagonist -- biological activity prediction -- docking -- GPCR -- in silico -- OXER1; Pharmacology -- Periodicals; Drug development -- Periodicals; 615.105
• DOI: 10.1002/prp2.600
• Languages: English
• ISSNs: 2052-1707

Abstract

Abstract: Drug development is an arduous procedure, necessitating testing the interaction of a large number of potential candidates with potential interacting (macro)molecules. Therefore, any method which could provide an initial screening of potential candidate drugs might be of interest for the acceleration of the procedure, by highlighting interesting compounds, prior to in vitro and in vivo validation. In this line, we present a method which may identify potential hits, with agonistic and/or antagonistic properties on GPCR receptors, integrating the knowledge on signaling events triggered by receptor activation (GPCRs binding to Gα, β, γ proteins, and activating Gα, exchanging GDP for GTP, leading to a decreased affinity of the Gα for the GPCR). We show that, by integrating GPCR‐ligand and Gα ‐GDP or ‐GTP binding in docking simulation, which correctly predicts crystallographic data, we can discriminate agonists, partial agonists, and antagonists, through a linear function, based on the ΔG (Gibbs‐free energy) of liganded‐GPCR/Gα ‐GDP. We built our model using two Gαs (β2‐adrenergic and prostaglandin‐D2 ), four Gαi (μ‐opioid, dopamine‐D3, adenosine‐A1, rhodopsin), and one Gαo (serotonin) receptors and validated it with a series of ligands on a recently deorphanized Gαi receptor (OXER1). This approach could be a valuable tool for initial in silico validation and design of GPRC‐interacting ligands.