RNA 3D structure prediction

Predict three-dimensional RNA structures from sequence with accuracy comparable to protein structure prediction, including targets for which no structural template exists.

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Cite

@misc{cairn-rna-3d-structure-prediction,
  title        = {RNA 3D structure prediction},
  author       = {{Cairn Commons contributors}},
  howpublished = {\url{https://cairn-commons.com/problems/rna-3d-structure-prediction}},
  year         = {2026},
  note         = {Open problem on Cairn Commons, CC BY 4.0. Accessed 2026-09-29}
}

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Claims

Verified

Disputed

Refuted

On the literature board

The problem

Deep learning transformed protein structure prediction, but RNA tertiary structure prediction has not followed. The open question: can RNA 3D structure — including noncanonical base pairs, coaxial helix stacking and correct global folds — be predicted from sequence when no homologous structure is available?

Known status. In the CASP15 RNA category (12 targets, more than 40 groups, assessment by Das et al., 2023) the four top-ranked groups did not use deep learning; global topology was often acceptable while fine details such as noncanonical pairs were not. RNA-Puzzles Round V (23 targets, 18 groups, Nature Methods 2024) identified missing noncanonical modules, wrong coaxial stacking and strand entanglement as the recurring error sources. The CASP16 nucleic-acid assessment (Kretsch et al., 2025; 42 targets, 65 groups) found accuracy still depends on templates: of 36 monomer targets only 2 of 21 acceptable predictions lacked a suitable template, the AlphaFold 3 server was outperformed by several human groups, and there was no significant improvement over earlier rounds for RNA monomers. Interfaces in RNA-RNA and RNA-protein complexes were substantially worse.

What counts as progress

Reproducible predictions on published blind-test target sets with code and models released, reporting standard metrics (TM-score, lDDT, RMSD, base-pair F1) computed by public tools.
Template-free evaluations: results on targets with the template-similarity filter stated, so that gains are not attributable to homology.
Better scoring or model-selection functions, evaluated on public decoy sets.
Documented negative results: a model class that fails on a specific structural motif, with the inputs and outputs provided.

How it is checked. A reviewer re-runs the pipeline on the stated targets, verifies that the reference structures postdate the training cut-off or are excluded from training, and recomputes the reported metrics with the published evaluation scripts.

Current state

The problem