DNA and RNA enzymes with peroxidase activity An investigation into the mechanism of action
Bibliographic record
Abstract
A DNAhemin complex (PS2.Mhemin), and its RNA counterpart (rPS2.Mhemin), have previously been reported, in the presence of nitrogenous buffers such as HEPES, to show enhanced peroxidative activity relative to both uncomplexed hemin and a control DNAhemin complex (Chem. Biol. 5, 505, 1998). A kinetic analysis of these two hemin-utilizing nucleic acid enzymes provides key insights into the mechanisms for their catalyzed peroxidation reactions. First, control experiments indicate that charge on the added detergent, required for solubility reasons, has little effect on the efficiency of the nucleic-acid-catalyzed reactions. Second, the key functional impact of the two nucleic acid frameworks, either DNA or RNA, appears to be a reduction in the acidity of a water molecule coordinated to the iron atom of the hemin that is bound to the ribozyme and DNAzyme scaffolds. This effect could result from a polar environment and possibly hydrogen bond(s) at the axial position of the hemin, along with favourable hydrophobic interactions for the periphery of the porphyrin ring. Third, the basic component of the buffer enhances the activities; this likely results from a general-base-catalyzed process. Cumulatively, these data supply important clues as to how biopolymers other than a protein can complex with hemin to form productive peroxidase enzymes.Key words: ribozyme, DNAzyme, hemin, peroxidase, mechanism, guanine quadruplex.
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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".