Reselection Yielding a Smaller and More Active Silver-Specific DNAzyme
Bibliographic record
Abstract
Abstract Ag10c is a recently reported RNA-cleaving DNAzyme obtained from in vitro selection. Its cleavage activity selectively requires Ag+ ions, and thus it has been used as a sensor for Ag+ detection. However, the previous selection yielded very limited information regarding its sequence requirement, since only ∼0.1% of the population in the final library were related to Ag10c and most other sequences were inactive. In this work, we performed a reselection by randomizing the 19 important nucleotides in Ag10c in such a way that a purine has an equal chance of being A or G, whereas a pyrimidine has an equal chance of being T or C. The round 3 library of the reselection was carefully analyzed and a statistic understanding of the relative importance of each nucleotide was obtained. At the same time, a more active mutant was identified, containing two mutated nucleotides. Further analysis indicated new base pairs leading to an enzyme with smaller catalytic loops but with ∼200% activity of the original Ag10c, and also excellent selectivity for Ag+. Therefore, a more active mutant of Ag10c was obtained and further truncations were successfully performed, which might be better candidates for developing new biosensors for silver. A deeper biochemical understanding was also obtained using this reselection method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".