ArticleProteins2023
Assessment of three-dimensional RNA structure prediction in CASP15.
Article in Proteins, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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105 citing papers in PubMed, 143 citations in OpenAlex.
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- Conformational analysis of nucleic acids for optimizing DNA and RNA topological models.Nucleic acids research · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- Tools For Building Artificial Biological Nanostructures.ACS nano · 2026Review
- Thermodynamic prediction of RNA cellular activity from sequence via conformational ensembles.Cell · 2026Article
- Are we there yet with XNA aptamers?RSC advances · 2026Review
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
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- Could statistical potential models achieve comparable or better performance than deep learning models?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
- A graphic and command line protocol for quick and accurate comparisons of protein and nucleic acid structures with US-align.Nature protocols · 2026Review
- A fully automated benchmarking suite to compare macromolecular complexes.Nature methods · 2026Review
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Article
- DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure prediction.PLoS biology · 2026Article
45 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 8 institutions in 5 countries.
Funding
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
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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.