Replacing Mg<sup>2+</sup> by Fe<sup>2+</sup> for RNA‐Cleaving DNAzymes
Bibliographic record
Abstract
Abstract It has been proposed that Mg2+ and Fe2+ are very similar in interacting with ribozymes and some protein‐based enzymes, but their activities with DNAzymes have yet to be studied. Here, the activity of Fe2+ as cofactor for a few RNA‐cleaving DNAzymes is investigated. 17E is a well‐studied DNAzyme that is active in the presence of many different divalent metal ions; it is highly active with Fe2+ with an apparent Kd of 29.7±2.3 μm and a kobs of 1.12±0.11 min−1 in the presence of 1 mm Fe2+ at pH 7.5. Fe2+ has 21‐fold higher activity than Mg2+. Six different DNAzymes are then tested, and only the DNAzymes active with Mg2+ (17E, 8–17, and E5) are active with Fe2+. Fe2+ has 25 and one‐ to twofold higher activity than Mg2+ for the 8–17 and E5 DNAzymes, respectively. In pH>7 buffer and in presence of air, 1 mm Fe2+ results in a nonspecific degradation of the DNA strand due to reactive oxygen species (ROS). Cleavage reactions in anoxic environment and antioxidant ascorbate can be used to overcome the effect of oxidation. The findings provide insights for potential DNAzyme catalysis in the early Earth, and they further support the similarity between Mg2+ and Fe2+ in enzyme catalysis.
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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 | 0.001 | 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".