RNA origami design tools enable cotranscriptional folding of kilobase-sized nanoscaffolds
Bibliographic record
Abstract
RNA origami is a framework for the modular design of nanoscaffolds that can be folded from a single strand of RNA and used to organize molecular components with nanoscale precision. The design of genetically expressible RNA origami, which must fold cotranscriptionally, requires modelling and design tools that simultaneously consider thermodynamics, the folding pathway, sequence constraints and pseudoknot optimization. Here, we describe RNA Origami Automated Design software (ROAD), which builds origami models from a library of structural modules, identifies potential folding barriers and designs optimized sequences. Using ROAD, we extend the scale and functional diversity of RNA scaffolds, creating 32 designs of up to 2,360 nucleotides, five that scaffold two proteins, and seven that scaffold two small molecules at precise distances. Micrographic and chromatographic comparisons of optimized and non-optimized structures validate that our principles for strand routing and sequence design substantially improve yield. By providing efficient design of RNA origami, ROAD may simplify the construction of custom RNA scaffolds for nanomedicine and synthetic biology. RNA origami can be used for the modular design of RNA nanoscaffolds but can be challenging to design. Newly developed computer-aided design software has now been shown to improve the folding yield of kilobase-sized RNA origami. These structures fold from a single strand during transcription by an RNA polymerase, and are able to position small molecules and protein components with nanoscale precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".