Multi‐state modeling of G‐protein coupled receptors at experimental accuracy. Issue 11 (16th May 2022)

• Record Type: Journal Article
• Authors: Heo, Lim; Feig, Michael
• Is Part Of: Proteins. Volume 90:Issue 11(2022)
• Page Start: 1873
• Page End: 1885
• Publication Date: 2022-05-16
• Subjects: AlphaFold -- computer‐aided drug design -- G‐protein coupled receptors -- GPCR activation -- multi‐state modeling -- protein dynamics -- protein structure prediction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.26382
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: The family of G‐protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high‐accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi‐state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state‐annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 Å and 1.41 Å for the active and inactive state models, respectively. The models were more suitable for protein‐ligand docking than the original AlphaFold2 models and template‐based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR‐peptide complex structures in GPCR Dock 2021, a blind GPCR‐ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high‐accuracy via machine‐learning methods.