NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation
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Abstract
Abstract RNA is not only playing a core role in the central dogma as mRNA between DNA and protein, but also many non-coding RNAs have been discovered to have unique and diverse biological functions. As genome sequences become increasingly available and our knowledge of RNA sequences grows, the study of RNA’s structure and function has become more demanding. However, experimental determination of three-dimensional RNA structures is both costly and time-consuming, resulting in a substantial disparity between RNA sequence data and structural insights. In response to this challenge, we propose a novel computational approach that harnesses state-of-the-art deep learning architecture NuFold to accurately predict RNA tertiary structures. This approach aims to offer a cost-effective and efficient means of bridging the gap between RNA sequence information and structural comprehension. NuFold implements a nucleobase center representation, which allows it to reproduce all possible nucleotide conformations accurately.
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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
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391 · see citing works
Sanger, H. L., Klotz, G., Riesner, D., Gross, H. J., & Kleinschmidt, A. K. (1976) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.73.11.3852 · Cited by 2,296 · see citing works
Harnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205 · see citing works
Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., Zong, L., Hong, L., Xiao, J., Shen, T., et al. (2022) · doi:10.1101/2022.08.06.503062 · Cited by 247 · see citing works
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Assessment of Nucleic Acid Structure Prediction in <scp>CASP16</scp>
Kretsch, R. C., Hummer, A. M., He, S., Yuan, R., Zhang, J., Karagianes, T., Cong, Q., Kryshtafovych, A., & Das, R. (2026) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.70072 · Cited by 51 · see citing works
- Authors
- Kagaya, Y., Zhang, Z., Ibtehaz, N., Wang, X., Nakamura, T., Punuru, P. D., & Kihara, D.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Kihara, Daisuke
- ORCID
- Venue
- Nature Communications
- Published
- 2025-01-21
- Volume
- 16
- Issue
- 1
- Pages
- 881-881
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11751094
- OpenAlex
- W4406636378
- Cited by
- 70
- References
- 5
- Citations by year
- 2026: 39 · 2025: 27 · 2024: 4
- FWCI
- 19.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM133840, R01GM123055)
- NSF | BIO | Division of Biological Infrastructure (DBI2003635, DBI2146026)
- NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS2151678)
- NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (IIS2211598)
- NSF | BIO | Division of Molecular and Cellular Biosciences (MCB1925643)
- NSF | ENG/OAD | Division of Civil, Mechanical and Manufacturing Innovation (CMMI1825941)
- 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 › Molecular Biology › RNA regulation and disease
- Keywords
- RNA tertiary structure prediction, end-to-end deep learning, multiple sequence alignment
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