Assessment of Nucleic Acid Structure Prediction in CASP16
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Abstract
Consistently accurate 3D nucleic acid structure prediction would facilitate studies of the diverse RNA and DNA molecules underlying life. In CASP16, blind predictions for 42 targets canvassing a full array of nucleic acid functions, from dopamine binding by DNA to formation of elaborate RNA nanocages, were submitted by 65 groups from 46 different labs worldwide. In contrast to concurrent protein structure predictions, performance on nucleic acids was generally poor, with no predictions of previously unseen natural RNA structures achieving TM‐scores above 0.8. Even though automated server performance has improved, all top‐performing groups were human expert predictors: Vfold, GuangzhouRNA‐human, and KiharaLab. Good performance on one template‐free modeling target (OLE RNA) and accurate global secondary structure prediction suggested that structural information can be extracted from multiple sequence alignments. However, 3D accuracy generally appeared to depend on the availability of closely related 3D structure templates, and predictions still did not achieve consistent recovery of pseudoknots, singlet Watson‐Crick‐Franklin pairs, non‐canonical pairs, or tertiary motifs like A‐minor interactions. For the first time, blind predictions of nucleic acid interactions with small molecules, proteins, and other nucleic acids could be assessed in CASP16. As with nucleic acid monomers, prediction accuracy for nucleic acid complexes was generally poor unless 3D templates were available. Accounting for template availability, there has not been a notable increase in nucleic acid modeling accuracy between previous blind challenges and CASP16.
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“The one target in this second cluster without a template, R1285 OLE RNA [73], had a large amount of evolutionary data, a deep MSA, and functional data in the literature [74] and is further discussed below.”
Self-citationFunctional relevance of CASP16 nucleic acid predictions as evaluated by structure providersPreprint
Kretsch, R., Albrecht, R., Andersen, E., Chen, H.-A., Chiu, W., Das, R., Gezelle, J. G., Hartmann, M. D., Höbartner, C., Hu, Y.-M., et al. (2025) · bioRxiv · doi:10.1101/2025.04.15.649049 · Cited by 29 · cited 3× · see citing works
“…other analyses have evaluated subsets of these targets with particular attention to functionally relevant regions assigned by structure providers [21], protein domains of complexes [22], water and ion networks [23], conformational ensembles [24], and classic RNA-specific features developed in…”
InfluentialSelf-citationAssessment of Protein Complex Predictions in CASP16: Are we making progress?Preprint
Zhang, J., Yuan, R.-Q., Kryshtafovych, A., Kretsch, R., Schaeffer, R. D., Zhou, J., Das, R., Grishin, N. V., & Cong, Q. (2025) · bioRxiv · doi:10.1101/2025.05.29.656875 · Cited by 27 · cited 2× · see citing works
“…have evaluated subsets of these targets with particular attention to functionally relevant regions assigned by structure providers [21], protein domains of complexes [22], water and ion networks [23], conformational ensembles [24], and classic RNA-specific features developed in RNA-puzzles [25].”
Self-citationLigand‐Induced Folding in a Dopamine‐Binding DNA Aptamer
Kaiyum, Y. A., Chao, E. H. P., Dhar, L., Shoara, A. A., Nguyen, M.-D., Mackereth, C., Dauphin-Ducharme, P., & Johnson, P. E. (2024) · ChemBioChem · doi:10.1002/cbic.202400493 · Cited by 25 · cited 1× · see citing works
“…personal communication with structure authors P. Johnson and D. Mackereth), deviates from the secondary structure previously proposed in the literature [70–72] or in any of the CASP16 predictions (Figure 2D), revealing that DNA structures are a significant challenge for existing prediction methods.”
ATOM-1: A Foundation Model for RNA Structure and Function Built on Chemical Mapping DataPreprint
Boyd, N. R., Anderson, B., Townshend, B., Chow, R., Stephens, C. J., Rangan, R., Kaplan, M., Corley, M., Tambe, A., Ido, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.13.571579 · Cited by 23 · see citing works
A generalizable scaffold-based approach for structure determination of RNAs by cryo-EM
Langeberg, C. J., & Kieft, J. S. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad784 · Cited by 21 · see citing works
Chen, K., Litfin, T., Singh, J., Zhan, J., & Zhou, Y.-Q. (2024) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae018 · Cited by 20 · see citing works
Structure and catalytic activity of the SAM-utilizing ribozyme SAMURI
Chen, H., Okuda, T., Lenz, A., Scheitl, C. P. M., Schindelin, H., & Höbartner, C. (2025) · Nature Chemical Biology · doi:10.1038/s41589-024-01808-w · Cited by 19 · see citing works
Coevolution of RNA and protein subunits in RNase P and RNase MRP, two RNA processing enzymes
Zhou, B., Wan, F.-T., Lei, K. X., Lan, P., Wu, J., & Lei, M. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.105729 · Cited by 19 · cited 1×
“RNAse P must recognize the correct tRNA and position the 5′ leader correctly for scission accomplished through 3 major interaction sites: the T-loop anchor, the A-anchor, and a set of base pairs involving the 3′ tRNA tail [83] (Figure 6B).”
Assessment of Pharmaceutical Protein–Ligand Pose and Affinity Predictions in CASP16
Gilson, M. K., Eberhardt, J., Škrinjar, P., Durairaj, J., Robin, X., & Kryshtafovych, A. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70061 · Cited by 18 · cited 1× · see citing works
“…as i-lDDT), lDDT of the ligand-pocket nucleotides (lDDT pocket), the Root- Mean- Square-Deviation (RMSD) of the ligand-pocket nucleotides, and the Binding-site superposed Symmetry-corrected pose Root- Mean- Square-Deviation (BiSyRMSD) of the ligand pose were calculated using OpenStructure [44–46].”
Self-citationThe RNA‐Puzzles Assessments of RNA‐Only Targets in CASP16
Westhof, E., Sun, H., Bu, F., & Miao, Z. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.70052 · Cited by 17 · cited 1× · see citing works
“…have evaluated subsets of these targets with particular attention to functionally relevant regions assigned by structure providers [21], protein domains of complexes [22], water and ion networks [23], conformational ensembles [24], and classic RNA-specific features developed in RNA-puzzles [25].”
ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure PredictionPreprint
Zhang, L., Zhang, H., Mathews, D., & Huang, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12796 · Cited by 17 · cited 1× · see citing works
“Further, we generated base pair probability matrices using ViennaRNA, EternaFold, and CONTRAfold, and used a linear-sum assignment algorithm [61] or a ThreshKnot heuristic [62] to generate potentially pseudoknotted structures.”
de Lajarte, A. A., Taillades, Y. J. M. D., Aruda, J., Bongrand, P., Wightman, F. F., Salazar, D., Allan, M. F., Kalicki, C. H., L’Esperance-Kerckhoff, C., Kashi, A., et al. (2026) · Science Advances · doi:10.1126/sciadv.adz4967 · Cited by 16 · cited 1× · see citing works
“…deep learning-based methods with physics-based methods, evolutionary information [11, 12], and functional data; and increased the amount of training data by accelerating experimental structural determination [13, 14], self-distillation [15], and high throughput, low-resolution experiments [16–19].”
Scaffold-enabled high-resolution cryo-EM structure determination of RNA
Haack, D. B., Rudolfs, B., Jin, S., Khitun, A., Weeks, K. M., & Toor, N. (2025) · Nature Communications · doi:10.1038/s41467-024-55699-5 · Cited by 16
Structure of Fanzor2 reveals insights into the evolution of the TnpB superfamily
Schargel, R. D., Qayyum, M., Tanwar, A., Kalathur, R. C., & Kellogg, E. H. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01394-4 · Cited by 16 · cited 1× · see citing works
“First, we assessed the global 3D fold and local accuracy of the entire complex using the same metrics as the NA monomer category, GDT_TS, TM-score, and lDDT (Figure S7C).”
RiboDraw: semiautomated two-dimensional drawing of RNA tertiary structure diagrams
Das, R., & Watkins, A. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab091 · Cited by 16 · cited 1×
“Rosetta rna_motif [63] was used to identify tertiary RNA motifs in PDB structures.”
Self-citationNEFFy: a versatile tool for computing the number of effective sequences
Haghani, M., Bhattacharya, D., & Murali, T. (2024) · bioRxiv · doi:10.1101/2024.12.01.625733 · Cited by 15 · cited 1× · see citing works
“The results were compared to a different implementation of Neff calculations, Neffy [31].”
AnAnaS: Software for Analytical Analysis of Symmetries in Protein Structures.
Pagès, G., & Grudinin, S. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0708-4_14 · Cited by 15 · cited 1×
“Potential symmetries for each RNA multimer model were collected from AnAnaS [65].”
Breaker, R., Harris, K. A., Lyon, S. E., Wencker, F., & Fernando, C. (2023) · Molecular Microbiology · doi:10.1111/mmi.15129 · Cited by 14 · see citing works
Model Quality Assessment for CASP16
Fadini, A., Studer, G., & Read, R. J. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.70037 · Cited by 11 · cited 1× · see citing works
“The inclusion of nucleic acid targets in CASP's model quality assessment category [89] in the future may benefit the community by encouraging more accurate self-assessment.”
Advances in chaperone-assisted RNA crystallography using synthetic antibodies
Banna, H. A., Das, N., Ojha, M., & Koirala, D. (2023) · BBA Advances · doi:10.1016/j.bbadva.2023.100101 · Cited by 11 · cited 1× · see citing works
“(B) For every group, their positive Z CASP16NA scores were summed to obtain a final ranking.”
A Generalizable Scaffold-Based Approach for Structure Determination of RNAs by Cryo-EM
Langeberg, C. J., & Kieft, J. (2023) · bioRxiv · doi:10.1101/2023.07.06.547879 · Cited by 8 · cited 1×
“…deep learning-based methods with physics-based methods, evolutionary information [11, 12], and functional data; and increased the amount of training data by accelerating experimental structural determination [13, 14], self-distillation [15], and high throughput, low-resolution experiments [16–19].”
Engaging the Community: CASP Special Interest Groups
Elofsson, A., Kretsch, R., Magnus, M., & Montelione, G. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26833 · Cited by 6
Self-citationGezelle, J. G., Korn, S. M., McDonald, J. T., Gong, Z., Erickson, A., Huang, C.-H., Yang, F., Cronin, M., Kuo, Y.-W., Wimberly, B., et al. (2024) · bioRxiv · doi:10.1101/2024.12.17.628992 · Cited by 4 · cited 3×
“Instead, the non-canonical triplet base interactions of A5-G26 and A5-A42 appear to be the conserved and functionally important interactions [81], suggesting the need to analyze pairs beyond canonical base pairs.”
InfluentialDeenalattha, D. H. S., Lange, B., Nein, K., Armstrong, D., & Yesselman, J. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2023.11.583 · Cited by 2 · cited 1×
“…deep learning-based methods with physics-based methods, evolutionary information [11, 12], and functional data; and increased the amount of training data by accelerating experimental structural determination [13, 14], self-distillation [15], and high throughput, low-resolution experiments [16–19].”
Flagged first, then by citations
6 of the 92 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Wang, W.-K., Peng, Z.-L., & Yang, J.-Y. (2025) · bioRxiv · doi:10.1038/s42256-026-01223-x · Cited by 14 · see citing works
Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps
Liu, X., Wang, W., Du, Z., Wang, Z., Peng, Z., & Yang, J. (2026) · Communications Biology · doi:10.1038/s42003-026-09582-2 · Cited by 5 · see citing works
Atomic functional site scaffolding in biopolymers with RFdiffusion3
Butcher, J., Krishna, R., Mitra, R., Woodbury, S. M., Kim, D., Brent, R. I., Li, Y., Corley, N., Favor, A., Chen, A., et al. (2026) · Science · doi:10.1126/science.aeh2982 · Cited by 3 · see citing works
Pradeep, B. K., Chen, S., Dima, R., Hassan, M., Joachimiak, A., Kihara, D., Kloczkowski, A., Law, J., Liwo, A., Meller, J., et al. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169949 · Cited by 2 · see citing works
RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation
Tarafder, S., & Bhattacharya, D. (2026) · Nature Methods · doi:10.1038/s41592-026-03128-4 · Cited by 3
Machine Learning for RNA-Targeting Drug Design
Karroucha, W., Oliver, C., Stoven, V., & Mallet, V. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01094 · Cited by 3 · see citing works
De novo design of RNA pseudoknots with deep learning
Townley, J., Kladwang, W., Baker, D. A., Blair, H. M., Choe, C. A., Nesr, G. E., Favor, A., Fisker, E., Haack, D. B., He, S., et al. (2026) · Science · doi:10.1126/science.aeg6829 · Cited by 7 · see citing works
Self-citationThe knotty problem of RNA structure prediction.
Mustoe, A. M., & Guo, J. (2026) · Science · doi:10.1126/science.aek4499 · Cited by 1 · see citing works
The trRosettaRNA server for RNA structure prediction
Wang, W.-K., Liu, X.-C., Peng, Z.-L., & Yang, J.-Y. (2026) · Nature Protocols · doi:10.1038/s41596-026-01356-8 · Cited by 1 · see citing works
gCoSRNA: Generalizable Coaxial Stacking Prediction for RNA Junctions Using Secondary Structure
Li, S., Xu, Q.-Q., Tan, Y.-L., Jiang, J., Shi, Y., & Zhang, B. (2025) · bioRxiv · doi:10.3390/biom16020230 · Cited by 1 · see citing works
“Although AI-based RNA structure prediction methods have advanced rapidly following the success of AlphaFold2 [40,41], recent results from CASP15 and CASP16 suggest that these approaches have not yet outperformed traditional physics-based and fragment assembly methods [42,43].”
InfluentialExploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials
Sandonas, L. M., Nehme, M. T., Cofas-Vargas, L. F., Olivos-Ramirez, G. E., Cuniberti, G., Poblete, S., & Poma, A. B. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c01213
“13,14 Moreover, it has been reported that the limited availability of experimentally resolved structures is a major hindrance to the training of ML methods for structure prediction.”
Machine learning and language models for RNA structure prediction: Progress and perspectives.
Moyon, L., & Marsico, A. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103339
AI-guided prediction of ncRNA biochemical features for therapeutic targeting.
Chan, J.-W., Daly, C., Vargas-Velazquez, A. M., Chow, P., Chen, J.-M., & Tergaonkar, V. (2026) · TIPS - Trends in Pharmacological Sciences · doi:10.1016/j.tips.2026.05.004
- Authors
- Kretsch, R. C., Hummer, A. M., He, S., Yuan, R., Zhang, J., Karagianes, T., Cong, Q., Kryshtafovych, A., & Das, R.
- Affiliations
- Stanford Medicine (US)
- Howard Hughes Medical Institute (US)
- The University of Texas Southwestern Medical Center (US)
- Eterna Massive Open Laboratory (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- University of California, Davis (US)
- Corresponding author
- Das, Rhiju
- ORCID
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2025-10-30
- Volume
- 94
- Issue
- 1
- Pages
- 192-217
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- PMC
- PMC12248019
- OpenAlex
- W4415699195
- Cited by
- 51
- References
- 92
- Citations by year
- 2026: 37 · 2025: 14
- FWCI
- 15.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- School of Medicine, Stanford University
- National Institute of Allergy and Infectious Diseases
- Howard Hughes Medical Institute
- Stanford Bio-X
- National Institute of General Medical Sciences
- National Institutes of Health (NIGMS R01GM100482, R35 GM122579, R01 AI165433)
- University of Texas Southwestern Medical Center
- National Science Foundation (2330652)
- Welch Foundation (I‐2095‐20220331)
- 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 › DNA and Nucleic Acid Chemistry
- Keywords
- CASP16, RNA structure prediction, DNA structure prediction, pseudoknot, protein-nucleic acid interactions, multiple sequence alignment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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