AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins
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Abstract
AlphaFold 2 (AF2) has revolutionised protein structure prediction but, like any new tool, its performance on specific classes of targets, especially those potentially under-represented in its training data, merits attention. Prompted by a highly confident prediction for a biologically meaningless, randomly permuted repeat sequence, we assessed AF2 performance on sequences composed of perfect repeats of random sequences of different lengths. AF2 frequently folds such sequences into β-solenoids which, while ascribed high confidence, contain unusual and implausible features such as internally stacked and uncompensated charged residues. A number of sequences confidently predicted as β-solenoids are predicted by other advanced methods as intrinsically disordered. The instability of some predictions is demonstrated by molecular dynamics. Importantly, other deep learning-based structure prediction tools predict different structures or β-solenoids with much lower confidence suggesting that AF2 alone has an unreasonable tendency to predict confident but unrealistic β-solenoids for perfect repeat sequences. The potential implications for structure prediction of natural (near-)perfect sequence repeat proteins are also explored.
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Cites
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“In conclusion, as computed structures [47,53] become as broadly available as experimental structures, probing the performance boundaries of each new method is crucially important: only when all limitations and biases are comprehensively understood, can computational methods illuminate all uncharted…”
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- Authors
- Pratt, O. S., Elliott, L. G., Haon, M., Mesdaghi, S., Price, R. M., Simpkin, A. J., & Rigden, D. J.
- Affiliations
- University of Liverpool (GB)
- Corresponding author
- Rigden, Daniel J.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2025-01-01
- Volume
- 27
- Pages
- 467-477
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC11795689
- OpenAlex
- W4406714966
- Cited by
- 27
- References
- 9
- Citations by year
- 2026: 17 · 2025: 9 · 2024: 1
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/T008695/1)
- 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
- AlphaFold2, AlphaFold3, protein structure prediction, sequence repeats, intrinsically disordered proteins, prediction confidence, molecular dynamics
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