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Cited by 17Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Assessing AF2’s ability to predict structural ensembles of proteins

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Riccabona, Jakob R., Spoendlin, Fabian C., Fischer, Anna-Lena M., Loeffler, Johannes R., Quoika, Patrick K., Jenkins, Timothy P., Ferguson, James A., Smorodina, Eva, Laustsen, Andreas H., Greiff, Victor, Forli, Stefano, Ward, Andrew B., Deane, Charlotte M., Fernández-Quintero, Monica L.·2024·Structure
DOI 10.1016/j.str.2024.09.001

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Abstract

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

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Metadata

Authors
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., Forli, S., Ward, A. B., Deane, C. M., & Fernández-Quintero, M. L.
Affiliations
  • Universität Innsbruck (AT)
  • University of Oxford (GB)
  • Scripps Research Institute (US)
  • Technical University of Munich (DE)
  • Technical University of Denmark (DK)
  • University of Oslo (NO)
Corresponding author
  • Ward, Andrew B.
  • Deane, Charlotte M.
ORCID
Venue
Structure
Publisher
Elsevier BV
Published
2024-11
Volume
32
Issue
11
Pages
2147-2159.e2
ISSN
0969-2126 (ISSN-L)
DOI
10.1016/j.str.2024.09.001
OpenAlex
W4402860010
Cited by
17
References
7
Citations by year
2026: 9 · 2025: 7 · 2024: 1
FWCI
2.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Structure (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Kreftforeningen (215817)
  • Norges Forskningsråd (311341, 300740, 331890)
  • Horizon 2020 Framework Programme (850974)
  • European Research Council
  • Royal Commission for the Exhibition of 1851
  • European Commission
  • Roche
  • Engineering and Physical Sciences Research Council (EP/S024093/1)
  • Bill and Melinda Gates Foundation (INV-004923)
  • Villum Fonden (00025302)
  • Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
AlphaFold2, protein structural ensembles, ensemble forecasting, conformational substates, molecular dynamics simulation, NMR ensembles, benchmarking, protein conformational dynamics
MeSH terms
Machine Learning, Protein Conformation, Proteins, Software, Nuclear Magnetic Resonance, Biomolecular, Molecular Dynamics Simulation

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