Open Structure-Guided RNA Foundation Model for Generalizable RNA Prediction

The source material is a bibliography of research on RNA structure, function and language models, spanning foundational folding algorithms, deep-learning methods, RNA databases, three-dimensional structure assessment and applications such as splicing, translation and non-coding RNA classification. It includes peer-reviewed studies, conference papers, preprints, software and datasets published from 1981 through 2026, with particular attention to modern foundation models and structure-prediction systems.

RNA structure and function research

RNA molecules perform biological functions that depend on how their sequences fold into secondary and three-dimensional structures. The references below cover computational methods for predicting those structures, models for learning RNA sequence patterns and resources for evaluating predicted models.

Foundation models and deep learning

1. Abramson, J. et al. “Accurate structure prediction of biomolecular interactions with AlphaFold 3.” Nature 630, 493–500 (2024). Article.
2. He, Y. et al. “Generalized biological foundation model with unified nucleic acid and protein language.” Nature Machine Intelligence 7, 942–953 (2025). Article.
3. Brixi, G. et al. “Genome modelling and design across all domains of life with Evo 2.” Nature 652, 1349–1361 (2026). Article.
4. Shen, T. et al. “Accurate RNA 3D structure prediction using a language model-based deep learning approach.” Nature Methods 21, 2287–2298 (2024). Article.
5. Chen, K. et al. “Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.” Briefings in Bioinformatics 25, bbae163 (2024). Article.
6. Wang, H., Zhang, Y., Chen, J., Zhan, J. & Zhou, Y. “Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.” Briefings in Bioinformatics 27, bbag098 (2026). Article.
7. Chen, J. et al. “Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions.” Preprint at arXiv (2022), doi: 10.48550/arXiv.2204.00300.
8. Penić, R. J., Vlašić, T., Huber, R. G., Wan, Y. & Šikić, M. “RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.” Nature Communications 16, 5671 (2025). Article.
9. Wang, N. et al. “Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning.” Nature Machine Intelligence 6, 548–557 (2024). Article.
10. Zou, S. et al. “A large-scale foundation model for RNA function and structure prediction.” Preprint at bioRxiv (2024), doi: 10.1101/2024.11.28.625345.
11. Yu, H. et al. “An interpretable RNA foundation model for exploring functional RNA motifs in plants.” Nature Machine Intelligence 6, 1616–1625 (2024). Article.
12. Akiyama, M. & Sakakibara, Y. “Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning.” NAR Genomics and Bioinformatics 4, lqac012 (2022). Article.
13. Yin, W. et al. “ERNIE-RNA: an RNA language model with structure-enhanced representations.” Nature Communications 16, 10076 (2025). Article.
14. Yang, H. & Li, K. “MP-RNA: unleashing multi-species RNA foundation model via calibrated secondary structure prediction.” In Findings of the Association for Computational Linguistics: EMNLP 2024 (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.) 5278–5296 (ACL, 2024).
15. Zhang, Z. et al. “RNAGenesis: Foundation model for enhanced RNA sequence generation and structural insights.” Preprint at bioRxiv (2024), doi: 10.1101/2024.12.30.630826.
16. Gu, A. & Dao, T. “Mamba: linear-time sequence modeling with selective state spaces.” Preprint at arXiv (2023), doi: 10.48550/arXiv.2312.00752.
17. Vaswani, A. et al. “Attention is all you need.” In 31st Conference on Neural Information Processing Systems (eds Guyon, I. et al.) 5998–6008 (NeurIPS, 2017).
18. Devlin, J., Chang, M., Lee, K. & Toutanova, K. “BERT: pre-training of deep bidirectional transformers for language understanding.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Burstein, J., Doran, C. & Solorio, T.) 4171–4186 (ACL, 2019).
19. Paszke, A. et al. “PyTorch: An imperative style, high-performance deep learning library.” In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 (eds Wallach, H. M. et al.) 8024–8035 (2019).

Secondary-structure prediction

1. Hofacker, I. L. et al. “Fast folding and comparison of RNA secondary structures.” Monatshefte für Chemie 125, 167–188 (1994). Article.
2. Lorenz, R. et al. “ViennaRNA package 2.0.” Algorithms for Molecular Biology 6, 26 (2011). Article.
3. Do, C. B., Woods, D. A. & Batzoglou, S. “CONTRAfold: RNA secondary structure prediction without physics-based models.” Bioinformatics 22, e90–e98 (2006). Article.
4. Zhu, H. et al. “Deep generalizable prediction of RNA secondary structure via base pair motif energy.” Nature Communications 16, 5856 (2025). Article.
5. Sato, K., Akiyama, M. & Sakakibara, Y. “RNA secondary structure prediction using deep learning with thermodynamic integration.” Nature Communications 12, 941 (2021). Article.
6. Tan, Z., Fu, Y., Sharma, G. & Mathews, D. H. “TurboFold II: RNA structural alignment and secondary structure prediction informed by multiple homologs.” Nucleic Acids Research 45, 11570–11581 (2017). Article.
7. Singh, J., Hanson, J., Paliwal, K. & Zhou, Y. “RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.” Nature Communications 10, 5407 (2019). Article.
8. Singh, J. et al. “Improved RNA secondary structure and tertiary base-pairing prediction using evolutionary profile, mutational coupling and two-dimensional transfer learning.” Bioinformatics 37, 2589–2600 (2021). Article.
9. Fu, L. et al. “UFold: fast and accurate RNA secondary structure prediction with deep learning.” Nucleic Acids Research 50, e14 (2022). Article.
10. Chen, X., Li, Y., Umarov, R., Gao, X. & Song, L. “RNA secondary structure prediction by learning unrolled algorithms.” In 8th International Conference on Learning Representations, ICLR 2020 (OpenReview.net, 2020).
11. Zuker, M. & Stiegler, P. “Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information.” Nucleic Acids Research 9, 133–148 (1981). Article.
12. Huang, L. et al. “LinearFold: linear-time approximate RNA folding by 5′-to-3′ dynamic programming and beam search.” Bioinformatics 35, i295–i304 (2019). Article.
13. Zakov, S., Goldberg, Y., Elhadad, M. & Ziv-Ukelson, M. “Rich parameterization improves RNA structure prediction.” Journal of Computational Biology 18, 1525–1542 (2011). Article.
14. Zhang, C., Zhang, Y. & Pyle, A. M. “rMSA: a sequence search and alignment algorithm to improve RNA structure modeling.” Journal of Molecular Biology 435, 167904 (2023). Article.
15. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. “Basic local alignment search tool.” Journal of Molecular Biology 215, 403–410 (1990). Article.

Three-dimensional structure prediction and assessment

1. Das, R. et al. “Assessment of three-dimensional RNA structure prediction in CASP15.” Proteins 91, 1747–1770 (2023). Article.
2. Kretsch, R. C. et al. “Assessment of nucleic acid structure prediction in CASP16.” Proteins 94, 192–217 (2026). Article.
3. Wang, W. et al. “trRosettaRNA: automated prediction of RNA 3D structure with transformer network.” Nature Communications 14, 7266 (2023). Article.
4. Cruz, J. A. et al. “RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction.” RNA 18, 610–625 (2012). Article.
5. Popenda, M. et al. “Entanglements of structure elements revealed in RNA 3D models.” Nucleic Acids Research 49, 9625–9632 (2021). Article.
6. Gren, B. A., Antczak, M., Zok, T., Sulkowska, J. I. & Szachniuk, M. “Knotted artifacts in predicted 3D RNA structures.” PLOS Computational Biology 20, e1011959 (2024). Article.
7. Carrascoza, F., Antczak, M., Miao, Z., Westhof, E. & Szachniuk, M. “Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions.” RNA 28, 250–262 (2022). Article.
8. Miao, Z. et al. “RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.” RNA 26, 982–995 (2020). Article.
9. Yang, H. et al. “Tools for the automatic identification and classification of RNA base pairs.” Nucleic Acids Research 31, 3450–3460 (2003). Article.
10. Xu, J. & Zhang, Y. “How significant is a protein structure similarity with TM-score = 0.5?” Bioinformatics 26, 889–895 (2010). Article.
11. Zemla, A. “LGA: a method for finding 3D similarities in protein structures.” Nucleic Acids Research 31, 3370–3374 (2003). Article.
12. Parisien, M., Cruz, J. A., Westhof, E. & Major, F. “New metrics for comparing and assessing discrepancies between RNA 3D structures and models.” RNA 15, 1875–1885 (2009). Article.
13. Chaudhury, S., Lyskov, S. & Gray, J. J. “PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.” Bioinformatics 26, 689–691 (2010). Article.

RNA biology, translation and splicing

1. Cooper, T. A., Wan, L. & Dreyfuss, G. “RNA and disease.” Cell 136, 777–793 (2009). Article.
2. Fan, X., Yang, Y., Chen, C. & Wang, Z. “Pervasive translation of circular RNAs driven by short IRES-like elements.” Nature Communications 13, 3751 (2022). Article.
3. Jaganathan, K. et al. “Predicting splicing from primary sequence with deep learning.” Cell 176, 535–548 (2019). Article.
4. Ozsolak, F. & Milos, P. M. “RNA sequencing: advances, challenges and opportunities.” Nature Reviews Genetics 12, 87–98 (2011). Article.
5. Chu, Y. et al. “A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions.” Nature Machine Intelligence 6, 449–460 (2024). Article.
6. Chen, C.-K. et al. “Structured elements drive extensive circular RNA translation.” Molecular Cell 81, 4300–4318.e13 (2021). Article.
7. Weingarten-Gabbay, S. et al. “Systematic discovery of cap-independent translation sequences in human and viral genomes.” Science 351, aad4939 (2016). Article.
8. Zhao, J. et al. “IRESfinder: Identifying RNA internal ribosome entry site in eukaryotic cell using framed k-mer features.” Journal of Genetics and Genomics 45, 403–406 (2018). Article.
9. Zhao, J. et al. “DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs.” Briefings in Bioinformatics 25, bbae439 (2024). Article.
10. Fiannaca, A., La Rosa, M., La Paglia, L., Rizzo, R. & Urso, A. “nRC: non-coding RNA classifier based on structural features.” BioData Mining 10, 27 (2017). Article.
11. Chantsalnyam, T., Siraj, A., Tayara, H. & Chong, K. T. “ncRDense: a novel computational approach for classification of non-coding RNA family by deep learning.” Genomics 113, 3030–3038 (2021). Article.

Databases, annotation and visualization

1. Berman, H. M. et al. “The Protein Data Bank.” Nucleic Acids Research 28, 235–242 (2000). Article.
2. Rose, P. W. et al. “The RCSB Protein Data Bank: integrative view of protein, gene and 3D structural information.” Nucleic Acids Research 45, D271–D281 (2017).
3. RNAcentral Consortium. “RNAcentral 2021: secondary structure integration, improved sequence search and new member databases.” Nucleic Acids Research 49, D212–D220 (2021). Article.
4. NCBI Resource Coordinators. “Database resources of the National Center for Biotechnology Information.” Nucleic Acids Research 41, D8–D20 (2013). Article.
5. Frankish, A. et al. “GENCODE 2021.” Nucleic Acids Research 49, D916–D923 (2021). Article.
6. Kalvari, I. et al. “Non-coding RNA analysis using the Rfam database.” Current Protocols in Bioinformatics 62, e51 (2018). Article.
7. Kalvari, I. et al. “Rfam 14: expanded coverage of metagenomic, viral and microRNA families.” Nucleic Acids Research 49, D192–D200 (2021). Article.
8. Sweeney, B. A. et al. “R2DT is a framework for predicting and visualising RNA secondary structure using templates.” Nature Communications 12, 3494 (2021). Article.
9. Kerpedjiev, P., Hammer, S. & Hofacker, I. L. “Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams.” Bioinformatics 31, 3377–3379 (2015). Article.
10. Danaee, P. et al. “bpRNA: large-scale automated annotation and analysis of RNA secondary structure.” Nucleic Acids Research 46, 5381–5394 (2018). Article.

Additional methods, benchmarks and resources

1. Mortimer, S. A., Kidwell, M. A. & Doudna, J. A. “Insights into RNA structure and function from genome-wide studies.” Nature Reviews Genetics 15, 469–479 (2014). Article.
2. Cao, X., Zhang, Y., Ding, Y. & Wan, Y. “Identification of RNA structures and their roles in RNA functions.” Nature Reviews Molecular Cell Biology 25, 784–801 (2024). Article.
3. Van der Maaten, L. & Hinton, G. “Visualizing data using t-SNE.” Journal of Machine Learning Research 9, 2579–2605 (2008).
4. Zhang, Y. et al. “Multiple sequence alignment-based RNA language model and its application to structural inference.” Nucleic Acids Research 52, e3 (2024). Article.
5. Ren, Y. et al. “BEACON: benchmark for comprehensive RNA tasks and language models.” Advances in Neural Information Processing Systems 37, 92891–92921 (2024). Article.
6. Sloma, M. F. & Mathews, D. H. “Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures.” RNA 22, 1808–1818 (2016). Article.
7. Yang, H. et al. “Tools for the automatic identification and classification of RNA base pairs.” Nucleic Acids Research 31, 3450–3460 (2003). Article.
8. Bottaro, S., Di Palma, F. & Bussi, G. “The role of nucleobase interactions in RNA structure and dynamics.” Nucleic Acids Research 42, 13306–13314 (2014). Article.
9. Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China. “RNA language models: zero-shot evaluations in RNA secondary structure and function classification” [data set]. Zenodo (2024), doi: 10.5281/zenodo.14430869.
10. Zhu, H. “structRFM pre-training data.” Zenodo (2026), doi: 10.5281/zenodo.18272040.
11. Zhu, H. “heqin-zhu/structRFM: v0.0.8 (version v0.0.8)” [computer software]. Zenodo (2026), doi: 10.5281/zenodo.18274292.

Source: www.nature.com