ArticleProteins2022
Multi-state modeling of G-protein coupled receptors at experimental accuracy.
Article in Proteins, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Integrating AI and Molecular Modeling for Structural Prediction of a Closed-Pore State of the hERG Channel.Journal of chemical information and modeling · 2026Article
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- GαNature communications · 2026Article
- Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins.bioRxiv : the preprint server for biology · 2026Article
- Tyrosine kinases sample unique activation ensembles.bioRxiv : the preprint server for biology · 2026Article
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Omega-3 DHA as a blank canvas: A molecular approximation to how lipid mediators modulate LGR6 in inflammation resolution.Protein science : a publication of the Protein Society · 2026Article
- Structural basis and physiological significance of non-canonical GNature communications · 2026Article
- Biophysical and structural analysis of human green cone opsin.Biophysical journal · 2026Article
- Hidden structural states of proteins revealed by conformer selection.Nature communications · 2026Article
- Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence.The journal of physical chemistry letters · 2026Article
- Bias in the AlphaFold3 prediction of ligand-induced domain motion in enzymes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Dual interference with host neuropeptide signaling allows parasitoid wasp to hijack host sugar metabolism.The EMBO journal · 2026Article
- De novo design of GPCR exoframe modulators.Nature · 2026Article
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Defining the Active Conformation of Typical Protein Kinase Domains from Substrate-Bound PDB Structures Enables Active-State AlphaFold2 Models for All 437 Human Catalytic Protein Kinases.bioRxiv : the preprint server for biology · 2026Article
60 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.