ArticleProteins2026
Assessment of Nucleic Acid Structure Prediction in CASP16.
Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Limits of deep-learning-based RNA prediction methods.Nucleic acids research · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction.Briefings in bioinformatics · 2026Article
- Inferring the qualities of protein-RNA models with graph transformers.Bioinformatics (Oxford, England) · 2026Article
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- gCoSRNA: Generalizable Coaxial Stacking Prediction for RNA Junctions Using Secondary Structure.Biomolecules · 2026Article
- Article
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- Article
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
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9 authors.
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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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.