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 towards the inactive conformation. Here,

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... 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. The models were also highly successful in predicting ligand binding poses via protein-ligand docking. 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.