An RNA Paranemic Crossover Triangle as A 3D Module for Cotranscriptional Nanoassembly
Bibliographic record
Abstract
RNA nanotechnology takes advantage of structural modularity to build self-assembling nano-architectures with applications in medicine and synthetic biology. The use of paranemic motifs, that form without unfolding existing secondary structure, allows for the creation of RNA nanostructures that are compatible with cotranscriptional folding in vitro and in vivo. In previous work, kissing-loop (KL) motifs have been widely used to design RNA nanostructures that fold cotranscriptionally. However, the paranemic crossover (PX) motif has not yet been explored for cotranscriptional RNA origami architectures and information about the structural geometry of the motif is unknown. Here, a six base pair-wide paranemic RNA interaction that arranges double helices in a perpendicular manner is introduced, allowing for the generation of a new and versatile building block: the paranemic-crossover triangle (PXT). The PXT is self-assembled by cotranscriptional folding and characterized by cryogenic electron microscopy, revealing for the first time an RNA PX interaction in high structural detail. The PXT is used as a building block for the construction of multimers that form filaments and rings and a duplicated PXT motif is used as a building block to self-assemble cubic structures, demonstrating the PXT as a rigid self-folding domain for the development of wireframe RNA origami architectures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".