The RNA‐Cleaving Bipartite DNAzyme Is a Distinctive Metalloenzyme
Bibliographic record
Abstract
Much interest has focused on the mechanisms of the five naturally occurring self-cleaving ribozymes, which, in spite of catalyzing the same reaction, adopt divergent strategies. These ribozymes, with the exception of the recently described glmS ribozyme, do not absolutely require divalent metal ions for their catalytic chemistries in vitro. A mechanistic investigation of an in vitro-selected, RNA-cleaving DNA enzyme, the bipartite, which catalyzes the same chemistry as the five natural self-cleaving ribozymes, found a mechanism of significant complexity. The DNAzyme showed a bell-shaped pH profile. A dissection of metal usage indicated the involvement of two catalytically relevant magnesium ions for optimal activity. The DNAzyme was able to utilize manganese(II) as well as magnesium; however, with manganese it appeared to function complexed to either one or two of those cations. Titration with hexaamminecobalt(III) chloride inhibited the activity of the bipartite; this suggests that it is a metalloenzyme that utilizes metal hydroxide as a general base for activation of its nucleophile. Overall, the bipartite DNAzyme appeared to be kinetically distinct not only from the self-cleaving ribozymes but also from other in vitro-selected, RNA-cleaving deoxyribozymes, such as the 8-17, 10-23, and 614.
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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".