Fluorophore ligand binding and complex stabilization of the RNA Mango and RNA Spinach aptamers
Bibliographic record
Abstract
The effective tracking and purification of biological RNAs and RNA protein complexes is currently challenging. One promising strategy to simultaneously address both of these problems is to develop high-affinity RNA aptamers against taggable small molecule fluorophores. RNA Mango is a 39-nucleotide, parallel-stranded G-quadruplex RNA aptamer motif that binds with nanomolar affinity to a set of thiazole orange (TO1) derivatives while simultaneously inducing a 10 3 -fold increase in fluorescence. We find that RNA Mango has a large increase in its thermal stability upon the addition of its TO1-Biotin ligand. Consistent with this thermal stabilization, RNA Mango can effectively discriminate TO1-Biotin from a broad range of small molecule fluorophores. In contrast, RNA Spinach, which is known to have a substantially more rigid G-quadruplex structure, was found to bind to this set of fluorophores, often with higher affinity than to its native ligand, 3,5-difluoro-4-hydroxybenzylidene imidazolinone (DFHBI), and did not exhibit thermal stabilization in the presence of the TO1-Biotin fluorophore. Our data suggest that RNA Mango is likely to use a concerted ligand-binding mechanism that allows it to simultaneously bind and recognize its TO1-Biotin ligand, whereas RNA Spinach appears to lack such a mechanism. The high binding affinity and fluorescent efficiency of RNA Mango provides a compelling alternative to RNA Spinach as an RNA reporter system and paves the way for the future development of small fluorophore RNA reporter systems.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".