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OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
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Abstract
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
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“allow users to run a modified AF2 protocol on open access servers in minutes”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [9]
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FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters
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Metadata
- Authors
- Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M. M., Zhang, S., Ojewole, A., Guney, M. E., Biderman, S., Watkins, A. M., Ra, S., Lorenzo, P. R., Nivon, L., Weitzner, B., Ban, Y.-E. A., Chen, S., Zhang, M., Li, C., Song, S. L., He, Y., Sorger, P. K., Mostaque, E., Zhang, Z., Bonneau, R., & AlQuraishi, M.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- Icahn School of Medicine at Mount Sinai (US)
- Courant Institute of Mathematical Sciences (US)
- Flatiron Institute (US)
- University of Utah (US)
- Nvidia (United States) (US)
- Booz Allen Hamilton (United States) (US)
- Genentech (US)
- Arzeda (United States) (US)
- Rutgers, The State University of New Jersey (US)
- University of Illinois Urbana-Champaign (US)
- Microsoft (United States) (US)
- Corresponding author
- Bouatta, Nazim
- AlQuraishi, Mohammed
- ORCID
- Ahdritz, Gustaf
- Bouatta, Nazim
- Floristean, Christina
- Kadyan, Sachin
- Xia, Qinghui
- Gerecke, William
- O’Donnell, Timothy J.
- Berenberg, Daniel
- Fisk, Ian
- Zanichelli, Niccolò
- Zhang, Bo
- Nowaczynski, Arkadiusz
- Wang, Bei
- Stepniewska-Dziubinska, Marta M.
- Zhang, Shang
- Ojewole, Adegoke
- Biderman, Stella
- Watkins, Andrew M.
- Ra, Stephen
- Lorenzo, Pablo Ribalta
- Weitzner, Brian
- Ban, Yih-En Andrew
- Chen, Shiyang
- Zhang, Minjia
- Song, Shuaiwen Leon
- He, Yuxiong
- Sorger, Peter K.
- Zhang, Zhao
- Bonneau, Richard
- AlQuraishi, Mohammed
- Venue
- Nature Methods
- Published
- 2024-05-14
- Volume
- 21
- Issue
- 8
- Pages
- 1514-1524
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11645889
- OpenAlex
- W4396895148
- Cited by
- 439
- References
- 75
- Citations by year
- 2026: 133 · 2025: 138 · 2024: 107
- FWCI
- 51.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM150546)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54-CA225088)
- National Science Foundation (OAC-2106661, OAC-2112606)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- AlphaFold2, protein structure prediction, protein folding, model generalization, secondary structure elements, learning mechanisms, hierarchical folding
- MeSH terms
- Algorithms, Models, Molecular, Protein Conformation, Proteins, Software, Protein Structure, Secondary, Protein Folding, Computational Biology
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 ›
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