Instantaneous Iodine-Assisted DNAzyme Cleavage of Phosphorothioate RNA
Bibliographic record
Abstract
Metal ions play a critical role in the RNA-cleavage reaction by interacting with the scissile phosphate and stabilizing the highly negatively charged transition state. Many metal-dependent DNAzymes have been selected for RNA cleavage. Herein, we report that the Ce13d DNAzyme can use nonmetallic iodine (I 2 ) to cleave a phosphorothioate (PS)-modified substrate. The cleavage yield exceeded 60% for both the R p and S p stereoisomers in 10 s, while the yield without the enzyme strand was only ∼10%. The Ce13d cleavage with I 2 also required Na +, consistent with the property of Ce13d and confirming the similar role of I 2 as a metal ion. Ce13d had the highest yield among eight tested DNAzymes, with the second highest DNAzyme showing only 20% cleavage. The incomplete cleavage was due to competition from desulfurization and isomerization reactions. This DNAzyme was engineered for fluorescence-based I 2 detection. With EDTA for masking metal ions, I 2 was selectively detected down to 4.7 nM. Oxidation of I – with Fe 3+ produced I 2 in situ, allowing detection of Fe 3+ down to 78 nM. By harnessing nonelectrostatic interactions, such as the I 2 /sulfur interaction observed here, more nonmetal species might be discovered to assist DNAzyme-based RNA cleavage.
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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".