Improved AlphaFold modeling with implicit experimental information
Open accessPreprint — published as 10.1038/s41592-022-01645-6 · Paper page
Abstract
Machine learning prediction algorithms such as AlphaFold 1 and RoseTTAFold 2 can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy 3–6 . We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.
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Cites
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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works
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State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works
- Authors
- Terwilliger, T. C., Poon, B. K., Afonine, P. V., Schlicksup, C. J., Croll, T. I., Millán, C., Richardson, J. S., Read, R. J., & Adams, P. D.
- Affiliations
- Los Alamos National Laboratory (US)
- Lawrence Berkeley National Laboratory (US)
- University of Cambridge (GB)
- Duke University (US)
- Corresponding author
- Terwilliger, Thomas C.
- ORCID
- Venue
- Nature Methods
- Publisher
- openRxiv
- Published
- 2022-01-07
- Volume
- 19
- Issue
- 11
- Pages
- 1376-1382
- ISSN
- 1548-7091 (ISSN-L)
- PMC
- PMC9636017
- OpenAlex
- W4306888844
- Cited by
- 140
- References
- 2
- Citations by year
- 2026: 11 · 2025: 35 · 2024: 29 · 2023: 53 · 2022: 12
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- U.S. Department of Energy (#89233218CNA000001, -AC02-05CH11231, DE-AC52-06NA2-5396, 05CH11231, DE AC02 05CH11231, AC52-06NA25396, DE-AC02-)
- Wellcome Trust (209407/Z/17/Z)
- National Institutes of Health (DE-AC02–05CH11231, DE-AC52-06NA25396, GM063210)
- National Nuclear Security Administration (AC02-05CH11231, DE-AC02-05CH11231, DEAC52- 06NA25396, 89233218C-NA000001)
- National Institute of General Medical Sciences (P01GM063210)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, 89233218CNA000001, 05CH11231)
- Los Alamos National Laboratory (89233218CNA 000001, AC52-06NA25396, DE-AC52-06NA25396, DE-AC02-05CH11231)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold, protein structure prediction, model rebuilding, iterative refinement, template-based modeling, map interpretation, RoseTTAFold
- MeSH terms
- Machine Learning, Algorithms, Models, Molecular, Protein Conformation, Proteins, Cryoelectron Microscopy
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