Global Folding of a Na<sup>+</sup>‐Specific DNAzyme Studied by FRET
Bibliographic record
Abstract
Abstract Recently, a few RNA‐cleaving DNAzymes have been isolated with excellent specificity for Na+, and some of them contain a Na+‐binding aptamer. This metal recognition mechanism is different from that of most previously reported DNAzymes. When using 2‐aminopurine (2AP) as a probe, interesting local folding induced by Na+ was recently observed. In this work, FRET was used to probe the global folding of the Ce13d DNAzyme; one of the Na+‐specific DNAzymes. FRET pairs were at different locations, which yielded a total of five constructs to probe the three‐way junction structure with a large loop. With endlabeled DNAzymes, the global structure appears to be quite rigid with little folding upon adding up to 200 mm monovalent metal ions, although some minor differences were observed between Li+, Na+, and K+. This lack of significant conformational change is also consistent with circular dichroism spectroscopy data. The loop was then labeled with an internal tetramethylrhodamine fluorophore at the G14 position, and its cleavage activity was partially retained. A clear Na+‐dependent folding was observed with spectral crossover. From a biosensing standpoint, global folding based sensors are unlikely to work due to the overall rigid structure of the DNAzyme. Therefore, the best way to use this DNAzyme to discriminate Na+ from K+ is based on cleavage activity, followed by probing local folding, whereas global folding is the least effective for metal discrimination.
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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) | 0.001 | 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 | 0.001 | 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".