Single-molecule chemical denaturation of riboswitches
Bibliographic record
Abstract
To date, single-molecule RNA science has been developed almost exclusively around the effect of metal ions as folding promoters and stabilizers of the RNA structure. Here, we introduce a novel strategy that combines single-molecule Förster resonance energy transfer (FRET) and chemical denaturation to observe and manipulate RNA dynamics. We demonstrate that the competing interplay between metal ions and denaturant agents provides a platform to extract information that otherwise will remain hidden with current methods. Using the adenine-sensing riboswitch aptamer as a model, we provide strong evidence for a rate-limiting folding step of the aptamer domain being modulated through ligand binding, a feature that is important for regulation of the controlled gene. In the absence of ligand, the rate-determining step is dominated by the formation of long-range key tertiary contacts between peripheral stem-loop elements. In contrast, when the adenine ligand interacts with partially folded messenger RNAs, the aptamer requires specifically bound Mg(2+) ions, as those observed in the crystal structure, to progress further towards the native form. Moreover, despite that the ligand-free and ligand-bound states are indistinguishable by FRET, their different stability against urea-induced denaturation allowed us to discriminate them, even when they coexist within a single FRET trajectory; a feature not accessible by existing methods.
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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".