Has AlphaFold 3 achieved success for RNA?
Open accessPublished version of a preprint
Abstract
Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to the fundamental differences between proteins and RNA, which hinder its direct adaptation. The latest release of AlphaFold , AlphaFold 3, has broadened its scope to include multiple different molecules such as DNA, ligands and RNA. While the AlphaFold 3 article discussed the results for the last CASP-RNA data set, the scope of its performance and the limitations for RNA are unclear. In this article, we provide a comprehensive analysis of the performance of AlphaFold 3 in the prediction of 3D structures of RNA. Through an extensive benchmark over five different test sets, we discuss the performance and limitations of AlphaFold 3. We also compare its performance with ten existing state-of-the-art ab initio , template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform at https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.
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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
Accurate structure prediction of biomolecular interactions with AlphaFold 3
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Survey of Hallucination in Natural Language Generation
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Protein complex prediction with AlphaFold-Multimer
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- Authors
- Bernard, C., Postic, G., Ghannay, S., & Tahi, F.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Université Paris-Saclay (FR)
- Corresponding author
- Tahi, Fariza
- Venue
- Acta Crystallographica Section D Structural Biology
- Published
- 2025-01-27
- Volume
- 81
- Issue
- 2
- Pages
- 49-62
- ISSN
- 2059-7983 (ISSN-L)
- PMC
- PMC11804252
- OpenAlex
- W4406877127
- Cited by
- 61
- References
- 6
- Citations by year
- 2026: 25 · 2025: 27 · 2024: 9
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Acta Crystallographica Section D Structural Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (UDOPIA-ANR-20-THIA-0013)
- Ministère de l'Enseignement Supérieur et de la Recherche (ANR11LABEX0045DIGICOSME)
- Université Paris-Saclay (ANR11IDEX000302)
- Grand Équipement National De Calcul Intensif (AD011014250)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold3, RNA 3D structure prediction, ab initio structure prediction, benchmarking, deep learning, template-based structure prediction
- MeSH terms
- Models, Molecular, Nucleic Acid Conformation, RNA, Software, Computational Biology
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