Multi‐state modeling of G‐protein coupled receptors at experimental accuracy
Open accessPublished version of a preprint
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Well-folded globular proteins such as GPCRs and kinases sample a wide range of conformations to carry out their biological function.
“Apart from IDPs, well-folded globular proteins, such as G-protein coupled receptors and kinases, also sample a wide range of conformations to carry out their biological function102,103.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [103]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
UCSF Chimera—A visualization system for exploratory research and analysis
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20084 · Cited by 45,260 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977 · see citing works
Krogh, A., Larsson, B., von Heijne, G., & Sonnhammer, E. L. L. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2000.4315 · Cited by 12,654 · see citing works
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Self-citationCrystal Structure of Rhodopsin: A G Protein-Coupled Receptor
Palczewski, K., Kumasaka, T., Hori, T., Behnke, C. A., Motoshima, H., Fox, B. A., Trong, I. L., Teller, D. C., Okada, T., Stenkamp, R. E., et al. (2000) · Science · doi:10.1126/science.289.5480.739 · Cited by 5,637 · see citing works
The I-TASSER Suite: protein structure and function prediction
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
Protein complex prediction with AlphaFold-Multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al. (2021) · doi:10.1101/2021.10.04.463034 · Cited by 3,808 · see citing works
Comparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169 · see citing works
Trends in GPCR drug discovery: new agents, targets and indications
Hauser, A. S., Attwood, M. M., Rask‐Andersen, M., Schiöth, H. B., & Gloriam, D. E. I. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.178 · Cited by 2,609 · see citing works
Protein Structure Prediction and Structural Genomics
Baker, D., & Sali, A. (2001) · Science · doi:10.1126/science.1065659 · Cited by 1,587 · see citing works
The Molecular Basis of G Protein–Coupled Receptor Activation
Weis, W. I., & Kobilka, B. K. (2018) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-060614-033910 · Cited by 1,227 · see citing works
HH-suite3 for fast remote homology detection and deep protein annotation
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145 · see citing works
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138 · see citing works
Clustering huge protein sequence sets in linear time
Steinegger, M., & Söding, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04964-5 · Cited by 875 · see citing works
GPCR Dynamics: Structures in Motion
Latorraca, N. R., Venkatakrishnan, A., & Dror, R. O. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00177 · Cited by 843 · see citing works
AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility
Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J., & Sanner, M. F. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004586 · Cited by 700 · see citing works
G protein-coupled receptors: structure- and function-based drug discovery
Yang, D., Zhou, Q., Labroska, V., Qin, S., Darbalaei, S., Wu, Y., Yuliantie, E., Xie, L., Tao, H., Cheng, J., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00435-w · Cited by 641 · see citing works
Activation mechanism of the β 2 -adrenergic receptor
Dror, R. O., Arlow, D. H., Maragakis, P., Mildorf, T. J., Pan, A. C., Xu, H., Borhani, D. W., & Shaw, D. E. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110499108 · Cited by 625 · see citing works
GPCRdb in 2018: adding GPCR structure models and ligands
Pándy‐Szekeres, G., Munk, C., Tsonkov, T. M., Mordalski, S., Harpsøe, K., Hauser, A. S., Bojarski, A. J., & Gloriam, D. E. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1109 · Cited by 557 · see citing works
Sampling alternative conformational states of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · see citing works
Common activation mechanism of class A GPCRs
Zhou, Q., Yang, D.-H., Wu, M., Guo, Y., Guo, W.-J., Zhong, L., Cai, X.-Q., Dai, A., Jang, W., Shakhnovich, E., et al. (2019) · eLife · doi:10.7554/elife.50279 · Cited by 491 · see citing works
Bjarnadóttir, T. K., Gloriam, D. E. I., Hellstrand, S. H., Kristiansson, H., Fredriksson, R., & Schiöth, H. B. (2006) · Genomics · doi:10.1016/j.ygeno.2006.04.001 · Cited by 433 · see citing works
Targeting adenosine A2A receptors in Parkinson's disease
Schwarzschild, M. A., Agnati, L. F., Fuxé, K., Chen, J., & Morelli, M. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.09.004 · Cited by 433 · see citing works
Impact of GPCR Structures on Drug Discovery
Congreve, M., de Graaf, C., Swain, N. A., & Tate, C. G. (2020) · Cell · doi:10.1016/j.cell.2020.03.003 · Cited by 380 · see citing works
Applying and improving AlphaFold at CASP14
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26257 · Cited by 370 · see citing works
GPCRdb in 2021: integrating GPCR sequence, structure and function
Kooistra, A. J., Mordalski, S., Pándy-Szekeres, G., Esguerra, M., Mamyrbekov, A., Munk, C., Keserű, G. M., & Gloriam, D. E. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1080 · Cited by 351 · see citing works
High‐accuracy protein structure prediction in <scp>CASP14</scp>
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314 · see citing works
Structural basis of CXC chemokine receptor 2 activation and signalling
Liu, K., Wu, L., Yuan, S., Wu, M., Xu, Y., Sun, Q., Li, S., Zhao, S., Hua, T., & Liu, Z. (2020) · Nature · doi:10.1038/s41586-020-2492-5 · Cited by 228 · see citing works
Lu, S., He, X., Yang, Z., Chai, Z., Zhou, S., Wang, J., Rehman, A. U., Ni, D., Pu, J., Sun, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25020-9 · Cited by 225 · see citing works
The G protein-coupled receptor subset of the rat genome
Gloriam, D. E. I., Fredriksson, R., & Schiöth, H. B. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-338 · Cited by 215 · see citing works
Zhang, J., Yang, J., Jang, R., & Zhang, Y. (2015) · Structure · doi:10.1016/j.str.2015.06.007 · Cited by 170 · see citing works
Structural insights into differences in G protein activation by family A and family B GPCRs
Hilger, D., Kumar, K. K., Hu, H., Pedersen, M. F., O’Brien, E. S., Giehm, L., Jennings, C., Eskici, G., Inoue, A., Lerch, M. T., et al. (2020) · Science · doi:10.1126/science.aba3373 · Cited by 154 · see citing works
Modern Homology Modeling of G-Protein Coupled Receptors: Which Structural Template to Use?
Mobarec, J. C., Sánchez, R., & Filizola, M. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm9005252 · Cited by 139 · see citing works
Wright, S. C., Kozielewicz, P., Kowalski-Jahn, M., Petersen, J., Bowin, C.-F., Slodkowicz, G., Martí-Solano, M., Rodriguez, D. J., Hot, B., Okashah, N., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08630-2 · Cited by 84 · see citing works
A combined activation mechanism for the glucagon receptor
Mattedi, G., Acosta‐Gutiérrez, S., Clark, T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921851117 · Cited by 81 · see citing works
GalaxyDock: Protein–Ligand Docking with Flexible Protein Side-chains
Shin, W., & Seok, C. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300342z · Cited by 76 · see citing works
Structural basis of neuropeptide Y signaling through Y1 receptor
Park, C., Kim, J., Ko, S.-B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28510-6 · Cited by 65 · see citing works
Exploring the free-energy landscape of GPCR activation
Alhadeff, R., Vorobyov, I. V., Yoon, H. W., & Warshel, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810316115 · Cited by 64 · see citing works
Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs
Bender, B. J., Marlow, B., & Meiler, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007597 · Cited by 44 · see citing works
Kinch, L. N., Pei, J.-M., Kryshtafovych, A., Schaeffer, R. D., & Grishin, N. V. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26172 · Cited by 43 · see citing works
GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models
Miszta, P., Pasznik, P., Jakowiecki, J., Sztyler, A., Latek, D., & Filipek, S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky429 · Cited by 40 · see citing works
Kinch, L. N., Schaeffer, D. R., Kryshtafovych, A., & Grishin, N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26202 · Cited by 38 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [11]Bristow MR. Treatment of chronic heart failure with beta‐adrenergic receptor antagonists: a convergence of receptor pharmacology and clinical cardiology. Circ Res. 2011; 109(10): 1176‐ 1194. 22034480
- [45]Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron. 1980; 36(22): 3219‐ 3228.
Cited by
Sorted by citations
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
“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.”
MethodThe power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155 · see citing works
“Apart from IDPs, well-folded globular proteins, such as G-protein-coupled receptors and kinases, also sample a wide range of conformations to carry out their biological function 102,103 .”
BackgroundSala, D., Hildebrand, P. W., & Meiler, J. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1121962 · Cited by 80 · see citing works
Representing structures of the multiple conformational states of proteins
Ramelot, T. A., Tejero, R., & Montelione, G. T. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102703 · Cited by 26 · see citing works
Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1
Nicoli, A., Haag, F., Marcinek, P., He, R., Kreißl, J., Stein, J., Marchetto, A., Dunkel, A., Hofmann, T., Krautwurst, D., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00752 · Cited by 28 · see citing works
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
Brotzakis, Z. F., Zhang, S., & Vendruscolo, M. (2023) · doi:10.1101/2023.01.19.524720 · Cited by 22 · see citing works
Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19 · see citing works
“GPCRs undergo conformational changes to transmit signals across the cell membrane, and accurately modeling their multiple conformational states is crucial for drug development and for understanding their functional mechanisms 57,58 .”
Ohnuki, J., Jaunet-Lahary, T., Yamashita, A., & Okazaki, K.-I. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c03052 · Cited by 14 · see citing works
Structural insights into pink-eyed dilution protein (Oca2)
Mesdaghi, S., Murphy, D. L., Simpkin, A. J., & Rigden, D. J. (2023) · Bioscience Reports · doi:10.1042/bsr20230060 · Cited by 13 · see citing works
Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5 · see citing works
Liu, L., Liu, Y., Ye, X., Zhang, S., Li, Y., Zhu, K., Xue, Y., Zhou, J., Zhang, X., & Fang, X. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01264-2 · Cited by 1 · see citing works
- Authors
- Heo, L., & Feig, M.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Feig, Michael
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2022-05-16
- Volume
- 90
- Issue
- 11
- Pages
- 1873-1885
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC9561049
- OpenAlex
- W4229000770
- Cited by
- 215
- References
- 46
- Citations by year
- 2026: 42 · 2025: 43 · 2024: 65 · 2023: 50 · 2022: 15
- FWCI
- 14.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R35 GM126948)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- G protein-coupled receptors, AlphaFold2, template-based modeling, GPCR activation mechanism, protein-ligand docking, transmembrane structure
- MeSH terms
- Humans, Ligands, Peptides, Protein Binding, Protein Conformation, Receptors, G-Protein-Coupled
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
