Resolving the conformational ensemble of a membrane protein by integrating small-angle scattering with AlphaFold
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Abstract
The function of a protein is enabled by its conformational landscape. For non-rigid proteins, a complete characterization of this landscape requires understanding the protein’s structure in all functional states, the stability of these states under target conditions, and the transition pathways between them. Several strategies have recently been developed to drive the machine learning algorithm AlphaFold2 (AF) to sample multiple conformations, but it is more challenging to a priori predict what states are stabilized in particular conditions and how the transition occurs. Here, we combine AF sampling with small-angle scattering curves to obtain a weighted conformational ensemble of functional states under target environmental conditions. We apply this to the pentameric ion channel GLIC using small-angle neutron scattering (SANS) curves, and identify apparent closed and open states. By comparing experimental SANS data under resting and activating conditions, we can quantify the subpopulation of closed channels that open upon activation, matching both experiments and extensive simulation sampling using Markov state models. The predicted closed and open states closely resemble crystal structures determined under resting and activating conditions respectively, and project to predicted basins in free energy landscapes calculated from the Markov state models. Further, without using any structural information, the AF sampling also correctly captures intermediate conformations and projects onto the transition pathway resolved in the extensive sampling. This combination of machine learning algorithms and low-dimensional experimental data appears to provide an efficient way to predict not only stable conformations but also accurately sample the transition pathways several orders of magnitude faster than simulation-based sampling.
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Cites
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · 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
Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 29,588 · see citing works
GROMACS: Fast, flexible, and free
van der Spoel, D., Lindahl, E. R., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20291 · Cited by 19,187 · see citing works
Self-citationLINCS: A linear constraint solver for molecular simulations
Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199709)18:12 · Cited by 18,369 · see citing works
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
The missing term in effective pair potentials
Berendsen, H. J. C., Grigera, J. R., & Straatsma, T. P. (1987) · The Journal of Physical Chemistry · doi:10.1021/j100308a038 · Cited by 13,313 · 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
Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models
Miyamoto, S., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130805 · Cited by 7,268 · see citing works
Improved side-chain torsion potentials for the Amber ff99SB protein force field
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,037 · 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
UCSF ChimeraX: Meeting modern challenges in visualization and analysis
Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018) · Protein Science · doi:10.1002/pro.3235 · Cited by 5,267 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
MolProbity: all-atom contacts and structure validation for proteins and nucleic acids
Davis, I., Leaver‐Fay, A., Chen, V. B., Block, J. N., Kapral, G. J., Wang, X., Murray, L. W., Arendall, W. B., Snoeyink, J. S., Richardson, J. S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm216 · Cited by 3,915 · 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
Evans, D. J., & Holian, B. L. (1985) · The Journal of Chemical Physics · doi:10.1063/1.449071 · Cited by 3,644 · 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
The pore dimensions of gramicidin A
Smart, O. S., Goodfellow, J. M., & Wallace, B. A. (1993) · Biophysical Journal · doi:10.1016/s0006-3495(93)81293-1 · Cited by 671 · see citing works
Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · 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
Biophysical experiments and biomolecular simulations: A perfect match?
Bottaro, S., & Lindorff‐Larsen, K. (2018) · Science · doi:10.1126/science.aat4010 · Cited by 285 · see citing works
SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2
Stein, R. A., & Mchaourab, H. S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010483 · Cited by 258 · see citing works
Knight, C. J., & Hub, J. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv309 · Cited by 200 · see citing works
High-throughput prediction of protein conformational distributions with subsampled AlphaFold2
Monteiro da Silva, G., Cui, J. Y., Dalgarno, D. C., Lisi, G. P., & Rubenstein, B. M. (2024) · Nature Communications · doi:10.1038/s41467-024-46715-9 · Cited by 196 · see citing works
<scp>AlphaFold2</scp> fails to predict protein fold switching
Chakravarty, D., & Porter, L. L. (2022) · Protein Science · doi:10.1002/pro.4353 · Cited by 193 · see citing works
Validating Solution Ensembles from Molecular Dynamics Simulation by Wide-Angle X-ray Scattering Data
Chen, P., & Hub, J. S. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2014.06.006 · Cited by 166 · see citing works
AFsample: improving multimer prediction with AlphaFold using massive sampling
Wallner, B. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad573 · Cited by 164 · see citing works
AlphaFold predictions of fold-switched conformations are driven by structure memorization
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159 · see citing works
Structural basis for ion permeation mechanism in pentameric ligand‐gated ion channels
Sauguet, L., Poitevin, F., Murail, S., Renterghem, C. V., Moraga‐Cid, G., Malherbe, L., Thompson, A., Koehl, P., Corringer, P., Baaden, M., et al. (2013) · The EMBO Journal · doi:10.1038/emboj.2013.17 · Cited by 147 · see citing works
CHAP: A Versatile Tool for the Structural and Functional Annotation of Ion Channel Pores
Klesse, G., Rao, S., Sansom, M. S. P., & Tucker, S. J. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.06.003 · Cited by 146 · see citing works
Accelerating Cryptic Pocket Discovery Using AlphaFold
Meller, A., Bhakat, S., Solieva, S. O., & Bowman, G. R. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01189 · Cited by 132 · see citing works
Grishaev, A., Guo, L., Irving, T. C., & Bax, A. (2010) · Journal of the American Chemical Society · doi:10.1021/ja106173n · Cited by 129 · see citing works
Ayaz, P., Lyczek, A., Paung, Y., Mingione, V. R., Iacob, R. E., de Waal, P. W., Engen, J. R., Seeliger, M. A., Shan, Y., & Shaw, D. E. (2023) · Nature Communications · doi:10.1038/s41467-023-36956-5 · Cited by 105 · see citing works
Structure prediction of alternative protein conformations
Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105 · see citing works
Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins
Thomasen, F. E., & Lindorff‐Larsen, K. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210499 · Cited by 92 · see citing works
String method solution of the gating pathways for a pentameric ligand-gated ion channel
Lev, B. B., Murail, S., Poitevin, F., Cromer, B. A., Baaden, M., Delarue, M. H., & Allen, T. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617567114 · Cited by 78 · see citing works
Henriques, J., Arleth, L., Lindorff‐Larsen, K., & Skepö, M. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.03.002 · Cited by 69 · see citing works
Challenges in antibody structure prediction
Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R. (2023) · mAbs · doi:10.1080/19420862.2023.2175319 · Cited by 55 · see citing works
Structural basis of promiscuous substrate transport by Organic Cation Transporter 1
Zeng, Y. C., Sobti, M., Quinn, A., Smith, N. J., Brown, S. H. J., Vandenberg, J. I., Ryan, R. M., O’Mara, M. L., & Stewart, A. G. (2023) · Nature Communications · doi:10.1038/s41467-023-42086-9 · Cited by 54 · see citing works
The role of G protein conformation in receptor–G protein selectivity
Jang, W., Lu, S., Xu, X., Wu, G., & Lambert, N. A. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01231-z · Cited by 48 · see citing works
Elephants in the Dark: Insights and Incongruities in Pentameric Ligand-gated Ion Channel Models
Howard, R. J. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167128 · Cited by 45 · see citing works
Self-citationWolf, S., Sohmen, B., Hellenkamp, B., Thurn, J., Stock, G., & Hugel, T. (2021) · Chemical Science · doi:10.1039/d0sc06134d · Cited by 43 · see citing works
Amos, S.-B. T. A., Schwarz, T. C., Shi, J., Cossins, B. P., BAKER, T. S., Taylor, R. J., Konrat, R., & Sansom, M. S. P. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c01281 · Cited by 36 · see citing works
Concerted conformational changes control metabotropic glutamate receptor activity
Lecat‐Guillet, N., Quast, R. B., Liu, H., Bourrier, E., Møller, T. C., Rovira, X., Soldevila, S., Lamarque, L., Trinquet, E., Liu, J., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf1378 · Cited by 32 · see citing works
Bergh, C. C., Heusser, S. A., Howard, R. J., & Lindahl, E. R. (2021) · eLife · doi:10.7554/elife.68369 · Cited by 28 · see citing works
Self-citationDynamics and structural changes of calmodulin upon interaction with the antagonist calmidazolium
Leger, C. C., Pitard, I., Sadi, M., Carvalho, N., Brier, S., Méchaly, A., Raoux‐Barbot, D., Davi, M., Hoos, S., Weber, P., et al. (2022) · BMC Biology · doi:10.1186/s12915-022-01381-5 · Cited by 28 · see citing works
AFsample2: Predicting multiple conformations and ensembles with AlphaFold2Preprint
Kalakoti, Y., & Wallner, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596195 · Cited by 26 · see citing works
Brookes, E., Rocco, M., Vachette, P., & Trewhella, J. (2023) · Journal of Applied Crystallography · doi:10.1107/s1600576723005344 · Cited by 21 · see citing works
Dynamic closed states of a ligand-gated ion channel captured by cryo-EM and simulations
Rovšnik, U., Zhuang, Y., Forsberg, B. O., Carroni, M., Yvonnesdotter, L., Howard, R. J., & Lindahl, E. R. (2021) · Life Science Alliance · doi:10.26508/lsa.202101011 · Cited by 20 · see citing works
Self-citationPond, M. P., Eells, R., Treece, B. W., Heinrich, F., Lösche, M., & Roux, B. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.11.024 · Cited by 20 · see citing works
Chinnam, N. B., Syed, A., Hura, G. L., Hammel, M., Tainer, J. A., & Tsutakawa, S. E. (2022) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.09.023 · Cited by 19 · see citing works
Peter, M. F., Ruland, J. A., Kim, Y., Hendricks, P., Schneberger, N., Siebrasse, J. P., Thomas, G. H., Kubitscheck, U., & Hagelueken, G. (2024) · Nature Communications · doi:10.1038/s41467-023-44327-3 · Cited by 17 · see citing works
Lycksell, M., Rovšnik, U., Bergh, C. C., Johansen, N. T., Martel, A., Porcar, L., Arleth, L., Howard, R. J., & Lindahl, E. R. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2108006118 · Cited by 17 · see citing works
Self-citationAssessing AF2’s ability to predict structural ensembles of proteins
Riccabona, J. R., Spoendlin, F. C., Fischer, A.-L. M., Loeffler, J. R., Quoika, P. K., Jenkins, T. P., Ferguson, J. A., Smorodina, E., Laustsen, A. H., Greiff, V., et al. (2024) · Structure · doi:10.1016/j.str.2024.09.001 · Cited by 17 · see citing works
Bharambe, N. G., Li, Z., Seiferth, D., Balakrishna, A. M., Biggin, P. C., & Basak, S. (2024) · Nature Communications · doi:10.1038/s41467-024-47370-w · Cited by 16 · see citing works
Predicting solution scattering patterns with explicit-solvent molecular simulations
Chatzimagas, L., & Hub, J. S. (2022) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2022.08.035 · Cited by 13 · see citing works
Werner, C., Lindenblatt, D., Viht, K., Uri, A., & Niefind, K. (2023) · Kinases and Phosphatases · doi:10.3390/kinasesphosphatases1040018 · Cited by 9 · see citing works
Thomasen, F. E., Cuneo, M. J., Mittag, T., & Lindorff-Larsen, K. (2023) · eLife · doi:10.7554/elife.84147 · Cited by 9 · see citing works
Huang, L., Wright, M. H., Yang, S., Blachowicz, L., Makowski, L., & Roux, B. (2020) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2020.129604 · Cited by 6 · see citing works
Erramilli, S. K., Dominik, P. K., Deneka, D., Tokarz, P., Kim, S. S., Reddy, B. G., Skrobek, B. M., Dalmas, O., Perozo, E., & Kossiakoff, A. A. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168192 · Cited by 4 · see citing works
Schwartz, A. C., Stein, R. A., Gil‐Iturbe, E., Quick, M., & Mchaourab, H. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.08.587957 · Cited by 2 · see citing works
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Shugaeva, T. E., Howard, R. J., Haloi, N., & Lindahl, E. R. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01751-4 · Cited by 14 · see citing works
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Sabzian‐Molaei, F., Orädd, F., Magkakis, K., & Andersson, M. (2026) · FEBS Open Bio · doi:10.1002/2211-5463.70200 · Cited by 2 · see citing works
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Behnam, M. A. M., Cavalli, A., Lousa, D., Soares, C. M., & Klein, C. D. P. (2025) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.5c00677 · Cited by 1 · see citing works
Lidbrink, S. E., Nissen, I., Ahrlind, J. K., Howard, R. J., & Lindahl, E. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.02.716037
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- Authors
- Lidbrink, S. E., Howard, R. J., Haloi, N., & Lindahl, E.
- Affiliations
- Stockholm University (SE)
- Science for Life Laboratory (SE)
- Corresponding author
- Haloi, Nandan
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2025-06-27
- Volume
- 21
- Issue
- 6
- Pages
- e1013187
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC12251176
- OpenAlex
- W4411724732
- Cited by
- 9
- References
- 62
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 1
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Update notices
- new_version (2025-07-11)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vetenskapsrådet (2019-02433, 2021-05806)
- Swedish e-Science Research Centre
- Sven och Lilly Lawskis Fond för Naturvetenskaplig Forskning
- H2020 Marie Skłodowska-Curie Actions (101107036)
- Knut och Alice Wallenbergs Stiftelse (Berzelius-2023-244)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- conformational ensembles, membrane protein structure, GLIC, pentameric ion channel, small-angle neutron scattering, AlphaFold2, conformational sampling, channel activation, Markov state models, free energy landscape, transition pathways, intermediate conformation
- MeSH terms
- Machine Learning, Algorithms, Membrane Proteins, Models, Molecular, Protein Conformation, Computational Biology, Neutron Diffraction, Scattering, Small Angle
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