Current structure predictors are not learning the physics of protein folding
Open accessAbstract
Summary: Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/ Supplementary information: Supplementary data are available at Bioinformatics online.
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AlphaFold accurately predicts protein folding but was found not to improve understanding of the underlying biochemical processes.
“For example, AlphaFold accurately predicts protein folding but was found not to improve understanding of the underlying biochemical processes (Outeiral et al., 2022).”
From Robust agents learn causal world models (2024) · cites this paper as Outeiral et al., 2022
The Introduction and Discussion distinguish predicting a protein’s final structure from predicting how it folds: the paper describes structure predictors as successful at their primary role but says they do not enhance understanding of folding principles. Thus the claim is supported if “predicts protein folding” means predicting the final structure, but not if it means predicting the folding process or pathway. (LLM, conf=0.88)
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- Authors
- Outeiral, C., Nissley, D. A., & Deane, C. M.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Deane, Charlotte M
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-01-31
- Volume
- 38
- Issue
- 7
- Pages
- 1881-1887
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8963306
- OpenAlex
- W4210460281
- Cited by
- 82
- References
- 53
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 19 · 2023: 21 · 2022: 17
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- UK's Engineering and Physical Sciences Research Council (EP/M013243/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein folding pathways, protein structure prediction, deep learning, AlphaFold2, RoseTTAFold, Rosetta, folding intermediates, folding rate constants, knowledge-based prediction
- MeSH terms
- Physics, Proteins, Protein Folding
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