An RNA‐Cleaving DNAzyme That Requires an Organic Solvent to Function
Bibliographic record
Abstract
Engineering functional nucleic acids that are active under unusual conditions will not only reveal their hidden abilities but also lay the groundwork for pursuing them for unique applications. Although many DNAzymes have been derived to catalyze diverse chemical reactions in aqueous solutions, no prior study has been set up to purposely derive DNAzymes that require an organic solvent to function. Herein, we utilized in vitro selection to isolate RNA-cleaving DNAzymes from a random-sequence DNA pool that were "compelled" to accept 35 % dimethyl sulfoxide (DMSO) as a cosolvent, via counter selection in a purely aqueous solution followed by positive selection in the same solution containing 35 % DMSO. This experiment led to the discovery of a new DNAzyme that requires 35 % DMSO for its catalytic activity and exhibits drastically reduced activity without DMSO. This DNAzyme also requires divalent metal ions for catalysis, and its activity is enhanced by monovalent ions. A minimized, more efficient DNAzyme was also derived. This work demonstrates that highly functional, organic solvent-dependent DNAzymes can be isolated from random-sequence DNA libraries via forced in vitro selection, thus expanding the capability and potential utility of catalytic DNA.
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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".