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Has AlphaFold 3 reached its success for RNAs?
Open accessPreprint — published as 10.1107/s2059798325000592 · Paper page
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 fundamental differences between proteins and RNAs, which hinder direct adaptation. The latest release of AlphaFold, AlphaFold 3, has broadened its scope to include multiple different molecules like DNA, ligands and RNA. While the article discusses the results of the last CASP-RNA dataset, the scope of performances and the limitations for RNAs are unclear. In this article, we provide a comprehensive analysis of the performances of AlphaFold 3 in the prediction of RNA 3D structures. Through an extensive benchmark over five different test sets, we discuss the performances and limitations of AlphaFold 3. We also compare its performances with ten existing state-of-the-art ab initio , template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/ .
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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
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Metadata
- 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
- Publisher
- openRxiv
- Published
- 2024-06-14
- Volume
- 81
- Issue
- 2
- Pages
- 49-62
- ISSN
- 2059-7983 (ISSN-L)
- PMC
- PMC11804252
- OpenAlex
- W4406877127
- Cited by
- 61
- References
- 1
- Citations by year
- 2026: 25 · 2025: 27 · 2024: 9
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- 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 (ANR-11-IDEX-0003, ANR-11-LABX-0045, 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › 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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