Multi-State Modeling of G-protein Coupled Receptors at Experimental Accuracy

By Lim Heo, Michael Feig. 2021

181 citations recorded by the source corpus.

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 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.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging, Computational Chemistry & Drug Discovery

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Citations in the corpus

Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, methodology): “Many methods have been developed, particularly around MSA resampling, that assist in generating diversity from previous AlphaFold models 43–45 and may also assist in multistate prediction with AF3.”
Evaluation of AlphaFold2 structures as docking targets (2022, background): “…power of AF2-predicted structures in the context of a smaller set of 38 proteins (Zhang et al., 2022), and with a study that suggests AF2 has a tendency to predict holo structures (Saldaño et al., 2022), and in the case of GPCRs can be biased to produce the desired conformation (Heo & Feig, 2022).”
Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties (2022, methodology): “Another protocol removed the MSA (noMSA) and prepared a local database of state-annotated GPCRs to perform AF2 template-based modeling (Heo and Feig, 2022).”