Tracking Native <i>Tetrahymena</i> Ribozyme Folding with Fluorescence
Bibliographic record
Abstract
Folding of the Tetrahymena group I intron ribozyme and other structured RNAs has been measured using a catalytic activity assay to monitor the native state formation by cleavage of a radiolabeled oligonucleotide substrate. While highly effective, the assay has inherent limitations present in any radioactivity- and gel-based assay. Administrative and safety considerations arise from the radioisotope, and data collection is laborious due to the use of polyacrylamide gels. Here we describe a fluorescence-based, solution assay that allows for more efficient data acquisition. The substrate is labeled with 6-carboxyfluorescein (6FAM) fluorophore and black hole quencher (BHQ1) at the 5′ and 3′ ends, respectively. Substrate cleavage results in release of the quencher, increasing the fluorescence signal by an average of 30-fold. A side-by-side comparison with the radioactivity-based assay shows good agreement in monitoring Tetrahymena ribozyme folding from a misfolded conformation to the native state, albeit with increased uncertainty. The lower precision of the fluorescence assay is compensated for by the relative ease and efficiency of the workflow. In addition, this assay will allow institutions that do not use radioactive materials to monitor native folding of the Tetrahymena ribozyme, and the same strategy should be amenable to native folding of other ribozymes.
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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 | 0.000 |
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".