AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation
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Abstract
AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy ΔΔG. Unexpectedly, we find that physical strain alone-without any additional data or computation-correlates almost as well with ΔΔG as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution.
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Cites
Highly accurate protein structure prediction with AlphaFold
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Unifying Constraints Linking Protein Folding and Native Dynamics Decoded from AlphaFold
Zhang, Z., Ren, W., Xie, L., Zheng, Y., Guan, X., Wang, J., Li, W., & Tang, Q.-Y. (2026) · Physical Review Letters · doi:10.1103/7j2j-f8f7 · Cited by 5 · see citing works
- Authors
- McBride, J. M., & Tlusty, T.
- Affiliations
- Institute for Basic Science (KR)
- Corresponding author
- McBride, John M.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2024-08-26
- Volume
- 133
- Issue
- 9
- Pages
- 098401-098401
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W4401895170
- Cited by
- 17
- References
- 6
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 7 · 2023: 1
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Physical Review Letters (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Institute for Basic Science (IBS-R020-D1)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- point mutations, protein folding free energy, energy landscape, AlphaFold2, protein stability, strain prediction, protein design, protein evolution
- MeSH terms
- Algorithms, Artificial Intelligence, Mutation, Protein Conformation, Proteins, Thermodynamics, Protein Folding
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