Zhang, J., Fei, Y., Solar, L. & Zhang, Q. C. Advances and alternatives in RNA construction experimental dedication and computational modeling. Nat. Strategies 19, 1193–1207 (2022).
Google Scholar
Wang, W., Su, B., Peng, Z. & Yang, J. Built-in experimental and AI improvements for RNA construction dedication. Nat. Biotechnol. 44, 205–214 (2026).
Google Scholar
Kwon, D. RNA operate follows kind—why is it so laborious to foretell? Nature 639, 1106–1108 (2025).
Google Scholar
Sharma, S., Ding, F. & Dokholyan, N. V. iFoldRNA: three-dimensional RNA construction prediction and folding. Bioinformatics 24, 1951–1952 (2008).
Google Scholar
Das, R. & Baker, D. Automated de novo prediction of native-like RNA tertiary buildings. 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 construction. Nat. Strategies 7, 291–294 (2010).
Google Scholar
Boniecki, M. J. et al. SimRNA: a coarse-grained technique for RNA folding simulations and 3D construction prediction. Nucleic Acids Res. 44, e63 (2016).
Google Scholar
Popenda, M. et al. Automated 3D construction composition for giant RNAs. Nucleic Acids Res. 40, e112 (2012).
Google Scholar
Zhao, Y. et al. Automated and quick constructing of three-dimensional RNA buildings. Sci. Rep. 2, 734 (2012).
Google Scholar
Zhang, Y., Wang, J. & Xiao, Y. 3dRNA: 3D construction prediction from linear to round RNAs. J. Mol. Biol. 434, 167452 (2022).
Google Scholar
Wang, W. et al. trRosettaRNA: automated prediction of RNA 3D construction with transformer community. Nat. Commun. 14, 7266 (2023).
Google Scholar
Pearce, R., Omenn, G. S. & Zhang, Y. De novo RNA tertiary construction prediction at atomic decision utilizing geometric potentials from deep studying. Preprint at bioRxiv (2022).
Li, Y. et al. Integrating end-to-end studying with deep geometrical potentials for ab initio RNA construction prediction. Nat. Commun. 14, 5745 (2023).
Google Scholar
Shen, T. et al. Correct RNA 3D construction prediction utilizing a language model-based deep studying strategy. Nat. Strategies 21, 2287–2298 (2024).
Google Scholar
Kagaya, Y. et al. NuFold: end-to-end strategy for RNA tertiary construction prediction with versatile nucleobase middle illustration. Nat. Commun. 16, 881 (2025).
Google Scholar
Baek, M. et al. Correct prediction of protein–nucleic acid complexes utilizing RoseTTAFoldNA. Nat. Strategies 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. Correct construction prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Justyna, M., Zirbel, C., Antczak, M. & Szachniuk, M. Graph neural community and diffusion mannequin for modeling RNA interatomic interactions. Bioinformatics 41, btaf515 (2025).
Google Scholar
Cruz, J. A. et al. RNA-Puzzles: a CASP-like analysis of RNA three-dimensional construction prediction. RNA 18, 610–625 (2012).
Google Scholar
Miao, Z. et al. RNA-Puzzles Spherical III: 3D RNA construction prediction of 5 riboswitches and one ribozyme. RNA 23, 655–672 (2017).
Google Scholar
Das, R. et al. Evaluation of three-dimensional RNA construction prediction in CASP15. Proteins Struct. Funct. Bioinf. 91, 1747–1770 (2023).
Google Scholar
Schneider, B. et al. When will RNA get its AlphaFold second? Nucleic Acids Res. 51, 9522–9532 (2023).
Google Scholar
Berman, H. M. et al. The Protein Information Financial institution. Nucleic Acids Res. 28, 235–242 (2000).
Google Scholar
Camacho, C. et al. BLAST+: structure and functions. BMC Bioinform. 10, 421 (2009).
Google Scholar
Zhang, C. et al. The historic evolution and significance of a number of sequence alignment in molecular construction and performance prediction. Biomolecules 14, 1531 (2024).
Google Scholar
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold quicker RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
Google Scholar
Zhang, T. et al. RNAcmap: a completely automated pipeline for predicting contact maps of RNAs by evolutionary coupling evaluation. Bioinformatics 37, 3494–3500 (2021).
Google Scholar
Zhang, C., Zhang, Y. & Pyle, A. M. rMSA: a sequence search and alignment algorithm to enhance RNA construction modeling. J. Mol. Biol. 435, 167904 (2023).
Google Scholar
Degenhardt, M. F. S. et al. Figuring out buildings of RNA conformers utilizing AFM and deep neural networks. Nature 637, 1234–1243 (2025).
Google Scholar
Lee, Y.-T. et al. The conformational area of RNase P RNA in resolution. 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 drawback. J. Biol. Chem. 270, 20871–20874 (1995).
Google Scholar
Parisien, M. & Main, F. The MC-Fold and MC-Sym pipeline infers RNA construction from sequence knowledge. Nature 452, 51–55 (2008).
Google Scholar
Danaee, P. et al. bpRNA: large-scale automated annotation and evaluation of RNA secondary construction. Nucleic Acids Res. 46, 5381–5394 (2018).
Google Scholar
Kretsch, R. C. et al. Evaluation of nucleic acid construction prediction in CASP16. Proteins Struct. Funct. Bioinform. 94, 192–217 (2026).
Google Scholar
Wang, W., Luo, Y., Peng, Z. & Yang, J. Correct biomolecular construction 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. & Grey, J. J. PyRosetta: a script-based interface for implementing molecular modeling algorithms utilizing Rosetta. Bioinformatics 26, 689–691 (2010).
Google Scholar
Dabrowski-Tumanski, P., Rubach, P., Niemyska, W., Gren, B. A. & Sulkowska, J. I. Topoly: Python bundle to research topology of polymers. Transient. Bioinform. 22, bbaa196 (2021).
Google Scholar
Gren, B. A., Antczak, M., Zok, T., Sulkowska, J. I. & Szachniuk, M. Knotted artifacts in predicted 3D RNA buildings. PLoS Comput. Biol. 20, e1011959 (2024).
Google Scholar
Poblete, S., Mlynarczyk, M. & Szachniuk, M. Unknotting RNA: a way to resolve computational artifacts. PLoS Comput. Biol. 21, e1012843 (2025).
Google Scholar
Luwanski, Okay. et al. RNAspider: a webserver to research entanglements in RNA 3D buildings. Nucleic Acids Res. 50, W663–W669 (2022).
Google Scholar
Li, Y. et al. DRfold2 is a deep learning-based software that permits environment friendly and correct RNA construction prediction. PLoS Biol. 24, e3003659 (2026).
Google Scholar
Tarafder, S. & Bhattacharya, D. RNAbpFlow: base pair-augmented SE(3)-flow matching for conditional RNA 3D construction technology. Preprint at bioRxiv (2025).
Cruz, J. A. & Westhof, E. The dynamic landscapes of RNA structure. Cell 136, 604–609 (2009).
Google Scholar
Vicens, Q. & Kieft, J. S. Ideas on learn how to assume (and discuss) about RNA construction. 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 within the mobile capabilities of RNAs. Nat. Rev. Mol. Cell Biol. 20, 474–489 (2019).
Google Scholar
Li, T. et al. All-atom RNA construction dedication 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 construction modeling of nucleic acids from cryo-EM maps. Nat. Commun. 15, 9367 (2024).
Google Scholar
Jamali, Okay. et al. Automated mannequin constructing and protein identification in cryo-EM maps. Nature 628, 450–457 (2024).
Google Scholar
Su, B., Huang, Okay., Peng, Z., Amunts, A. & Yang, J. CryoAtom improves mannequin constructing for cryo-EM. Nat. Struct. Mol. Biol. (2025).
Google Scholar
Gao, S.-H. et al. Res2Net: a brand new multi-scale spine structure. IEEE Trans. Sample Anal. Mach. Intell. 43, 652–662 (2021).
Google Scholar
Jumper, J. et al. Extremely correct protein construction prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Shi, Y. et al. Masked label prediction: unified message passing mannequin for semi-supervised classification. In Proc. Thirtieth Worldwide Joint Convention on Synthetic Intelligence, IJCAI-21 (ed. Zhou, Z.-H.) 1548–1554 (Worldwide Joint Conferences on Synthetic Intelligence Group, 2021).
Vaswani, A. et al. Consideration is all you want. Adv. Neural Inf. Course of. Syst. 30, 5998–6008 (2017).
Li, W. & Godzik, A. CD-HIT: a quick program for clustering and evaluating massive units of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Google Scholar
Sloma, M. F. & Mathews, D. H. Precise calculation of loop formation likelihood identifies folding motifs in RNA secondary buildings. RNA 22, 1808–1818 (2016).
Google Scholar
Lu, X.-J., Bussemaker, H. J. & Olson, W. Okay. DSSR: an built-in software program software for dissecting the spatial construction of RNA. Nucleic Acids Res. 43, e142 (2015).
Du, Z., Peng, Z. & Yang, J. RNA threading with secondary construction and sequence profile. Bioinformatics 40, btae080 (2024).
Google Scholar
Sweeney, B. A. et al. R2DT is a framework for predicting and visualising RNA secondary construction utilizing templates. Nat. Commun. 12, 3494 (2021).
Google Scholar
Liu, X. et al. High quality evaluation of RNA 3D construction fashions utilizing deep studying and intermediate 2D maps. Commun. Biol. 9, 293 (2026).
Google Scholar
Kerpedjiev, P., Hammer, S. & Hofacker, I. L. Forna (force-directed RNA): easy and efficient on-line RNA secondary construction diagrams. Bioinformatics 31, 3377–3379 (2015).
Google Scholar
Zhang, Y. & Skolnick, J. Scoring operate for automated evaluation of protein construction template high quality. Proteins Struct. Funct. Bioinform. 57, 702–710 (2004).
Google Scholar
Gong, S., Zhang, C. & Zhang, Y. RNA-align: fast and correct alignment of RNA 3D buildings primarily based on size-independent TM-scoreRNA. Bioinformatics 35, 4459–4461 (2019).
Google Scholar
Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: an area superposition-free rating for evaluating protein buildings and fashions utilizing distance distinction checks. Bioinformatics 29, 2722–2728 (2013).
Google Scholar
Parisien, M., Cruz, J. A., Westhof, É & Main, F. New metrics for evaluating and assessing discrepancies between RNA 3D buildings and fashions. RNA 15, 1875–1885 (2009).
Google Scholar
Bu, F. et al. RNA-Puzzles spherical V: blind predictions of 23 RNA buildings. Nat. Strategies 22, 399–411 (2025).
Google Scholar
Wang, W., Peng, Z. & Yang, J. Supply code for trRosettaRNA2. Zenodo (2026).
