Zhang, J., Fei, Y., Sun, L. & Zhang, Q. C. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat. Methods 19, 1193–1207 (2022).
Google Scholar
Wang, W., Su, B., Peng, Z. & Yang, J. Integrated experimental and AI innovations for RNA structure determination. Nat. Biotechnol. 44, 205–214 (2026).
Google Scholar
Kwon, D. RNA function follows form—why is it so hard to predict? Nature 639, 1106–1108 (2025).
Google Scholar
Sharma, S., Ding, F. & Dokholyan, N. V. iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics 24, 1951–1952 (2008).
Google Scholar
Das, R. & Baker, D. Automated de novo prediction of native-like RNA tertiary structures. Proc. Natl Acad. Sci. USA 104, 14664–14669 (2007).
Google Scholar
Das, R., Karanicolas, J. & Baker, D. Atomic accuracy in predicting and designing noncanonical RNA structure. Nat. Methods 7, 291–294 (2010).
Google Scholar
Boniecki, M. J. et al. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 44, e63 (2016).
Google Scholar
Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res. 40, e112 (2012).
Google Scholar
Zhao, Y. et al. Automated and fast building of three-dimensional RNA structures. Sci. Rep. 2, 734 (2012).
Google Scholar
Zhang, Y., Wang, J. & Xiao, Y. 3dRNA: 3D structure prediction from linear to circular RNAs. J. Mol. Biol. 434, 167452 (2022).
Google Scholar
Wang, W. et al. trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Nat. Commun. 14, 7266 (2023).
Google Scholar
Pearce, R., Omenn, G. S. & Zhang, Y. De novo RNA tertiary structure prediction at atomic resolution using geometric potentials from deep learning. Preprint at bioRxiv https://doi.org/10.1101/2022.05.15.491755 (2022).
Li, Y. et al. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nat. Commun. 14, 5745 (2023).
Google Scholar
Shen, T. et al. Accurate RNA 3D structure prediction using a language model-based deep learning approach. Nat. Methods 21, 2287–2298 (2024).
Google Scholar
Kagaya, Y. et al. NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation. Nat. Commun. 16, 881 (2025).
Google Scholar
Baek, M. et al. Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).
Google Scholar
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Justyna, M., Zirbel, C., Antczak, M. & Szachniuk, M. Graph neural network and diffusion model for modeling RNA interatomic interactions. Bioinformatics 41, btaf515 (2025).
Google Scholar
Cruz, J. A. et al. RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18, 610–625 (2012).
Google Scholar
Miao, Z. et al. RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA 23, 655–672 (2017).
Google Scholar
Das, R. et al. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins Struct. Funct. Bioinf. 91, 1747–1770 (2023).
Google Scholar
Schneider, B. et al. When will RNA get its AlphaFold moment? Nucleic Acids Res. 51, 9522–9532 (2023).
Google Scholar
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Google Scholar
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).
Google Scholar
Zhang, C. et al. The historical evolution and significance of multiple sequence alignment in molecular structure and function prediction. Biomolecules 14, 1531 (2024).
Google Scholar
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
Google Scholar
Zhang, T. et al. RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis. Bioinformatics 37, 3494–3500 (2021).
Google Scholar
Zhang, C., Zhang, Y. & Pyle, A. M. rMSA: a sequence search and alignment algorithm to improve RNA structure modeling. J. Mol. Biol. 435, 167904 (2023).
Google Scholar
Degenhardt, M. F. S. et al. Determining structures of RNA conformers using AFM and deep neural networks. Nature 637, 1234–1243 (2025).
Google Scholar
Lee, Y.-T. et al. The conformational space of RNase P RNA in solution. Nature 637, 1244–1251 (2025).
Google Scholar
Tinoco, I. & Bustamante, C. How RNA folds. J. Mol. Biol. 293, 271–281 (1999).
Google Scholar
Brion, P. & Westhof, E. Hierarchy and dynamics of RNA folding. Annu. Rev. Biophys. 26, 113–137 (1997).
Google Scholar
Herschlag, D. RNA chaperones and the RNA folding problem. J. Biol. Chem. 270, 20871–20874 (1995).
Google Scholar
Parisien, M. & Major, F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452, 51–55 (2008).
Google Scholar
Danaee, P. et al. bpRNA: large-scale automated annotation and analysis of RNA secondary structure. Nucleic Acids Res. 46, 5381–5394 (2018).
Google Scholar
Kretsch, R. C. et al. Assessment of nucleic acid structure prediction in CASP16. Proteins Struct. Funct. Bioinform. 94, 192–217 (2026).
Google Scholar
Wang, W., Luo, Y., Peng, Z. & Yang, J. Accurate biomolecular structure prediction in CASP16 with optimized inputs to state-of-the-art predictors. Proteins Struct. Funct. Bioinform. 94, 142–153 (2026).
Google Scholar
Chaudhury, S., Lyskov, S. & Gray, J. J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26, 689–691 (2010).
Google Scholar
Dabrowski-Tumanski, P., Rubach, P., Niemyska, W., Gren, B. A. & Sulkowska, J. I. Topoly: Python package to analyze topology of polymers. Brief. Bioinform. 22, bbaa196 (2021).
Google Scholar
Gren, B. A., Antczak, M., Zok, T., Sulkowska, J. I. & Szachniuk, M. Knotted artifacts in predicted 3D RNA structures. PLoS Comput. Biol. 20, e1011959 (2024).
Google Scholar
Poblete, S., Mlynarczyk, M. & Szachniuk, M. Unknotting RNA: a method to resolve computational artifacts. PLoS Comput. Biol. 21, e1012843 (2025).
Google Scholar
Luwanski, K. et al. RNAspider: a webserver to analyze entanglements in RNA 3D structures. Nucleic Acids Res. 50, W663–W669 (2022).
Google Scholar
Li, Y. et al. DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure prediction. PLoS Biol. 24, e3003659 (2026).
Google Scholar
Tarafder, S. & Bhattacharya, D. RNAbpFlow: base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation. Preprint at bioRxiv https://doi.org/10.1101/2025.01.24.634669 (2025).
Cruz, J. A. & Westhof, E. The dynamic landscapes of RNA architecture. Cell 136, 604–609 (2009).
Google Scholar
Vicens, Q. & Kieft, J. S. Thoughts on how to think (and talk) about RNA structure. Proc. Natl Acad. Sci. USA 119, e2112677119 (2022).
Google Scholar
Ganser, L. R., Kelly, M. L., Herschlag, D. & Al-Hashimi, H. M. The roles of structural dynamics in the cellular functions of RNAs. Nat. Rev. Mol. Cell Biol. 20, 474–489 (2019).
Google Scholar
Li, T. et al. All-atom RNA structure determination from cryo-EM maps. Nat. Biotechnol. 43, 97–105 (2025).
Google Scholar
Li, T., Cao, H., He, J. & Huang, S.-Y. Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps. Nat. Commun. 15, 9367 (2024).
Google Scholar
Jamali, K. et al. Automated model building and protein identification in cryo-EM maps. Nature 628, 450–457 (2024).
Google Scholar
Su, B., Huang, K., Peng, Z., Amunts, A. & Yang, J. CryoAtom improves model building for cryo-EM. Nat. Struct. Mol. Biol. https://doi.org/10.1038/s41594-025-01713-3 (2025).
Google Scholar
Gao, S.-H. et al. Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43, 652–662 (2021).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Shi, Y. et al. Masked label prediction: unified message passing model for semi-supervised classification. In Proc. Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 (ed. Zhou, Z.-H.) 1548–1554 (International Joint Conferences on Artificial Intelligence Organization, 2021).
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017).
Li, W. & Godzik, A. CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Google Scholar
Sloma, M. F. & Mathews, D. H. Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures. RNA 22, 1808–1818 (2016).
Google Scholar
Lu, X.-J., Bussemaker, H. J. & Olson, W. K. DSSR: an integrated software tool for dissecting the spatial structure of RNA. Nucleic Acids Res. 43, e142 (2015).
Du, Z., Peng, Z. & Yang, J. RNA threading with secondary structure and sequence profile. Bioinformatics 40, btae080 (2024).
Google Scholar
Sweeney, B. A. et al. R2DT is a framework for predicting and visualising RNA secondary structure using templates. Nat. Commun. 12, 3494 (2021).
Google Scholar
Liu, X. et al. Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps. Commun. Biol. 9, 293 (2026).
Google Scholar
Kerpedjiev, P., Hammer, S. & Hofacker, I. L. Forna (force-directed RNA): simple and effective online RNA secondary structure diagrams. Bioinformatics 31, 3377–3379 (2015).
Google Scholar
Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins Struct. Funct. Bioinform. 57, 702–710 (2004).
Google Scholar
Gong, S., Zhang, C. & Zhang, Y. RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA. Bioinformatics 35, 4459–4461 (2019).
Google Scholar
Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013).
Google Scholar
Parisien, M., Cruz, J. A., Westhof, É & Major, F. New metrics for comparing and assessing discrepancies between RNA 3D structures and models. RNA 15, 1875–1885 (2009).
Google Scholar
Bu, F. et al. RNA-Puzzles round V: blind predictions of 23 RNA structures. Nat. Methods 22, 399–411 (2025).
Google Scholar
Wang, W., Peng, Z. & Yang, J. Source code for trRosettaRNA2. Zenodo https://doi.org/10.5281/zenodo.18873019 (2026).
