Turn-on RNA Mango Beacons for<i>trans</i>-acting fluorogenic nucleic acid detection
Bibliographic record
Abstract
The Mango I and II RNA aptamers have been widely used in vivo and in vitro as genetically encodable fluorogenic markers that undergo large increases in fluorescence upon binding to their ligand, TO1-Biotin. However, while studying nucleic acid sequences, it is often desirable to havetrans-acting probes that induce fluorescence upon binding to a target sequence. Here, we rationally design three types of light-up RNA Mango Beacons based on a minimized Mango core that induces fluorescence upon binding to a target RNA strand. Our first design is bimolecular in nature and uses a DNA inhibition strand to prevent folding of the Mango aptamer core until binding to a target RNA. Our second design is unimolecular in nature, and features hybridization arms flanking the core that inhibit G-quadruplex folding until refolding is triggered by binding to a target RNA strand. Our third design builds upon this structure, and incorporates a self-inhibiting domain into one of the flanking arms that deliberately binds to, and precludes folding of, the aptamer core until a target is bound. This design separates G-quadruplex folding inhibition and RNA target hybridization into separate modules, enabling a more universal unimolecular beacon design. All three Mango Beacons feature high contrasts and low costs when compared to conventional molecular beacons, with excellent potential for in vitro and in vivo applications.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".