DNA G-Quadruplexes Activate Heme for Robust Catalysis of Carbene Transfer Reactions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Guanine-rich single-stranded DNAs and RNAs that fold into G-quadruplexes (GQs) are known to complex tightly with Fe II -heme and Fe III -heme (hemin), ubiquitous cellular cofactors. Heme–GQ (DNA) complexes, known as heme·DNAzymes, are able to utilize hydrogen peroxide as an oxidant to vigorously catalyze a variety of one-electron (peroxidase) and two-electron (peroxygenase) oxidation reactions. Herein, we show that complexes of Fe II -heme with GQs also robustly catalyze a mechanistically distinct reaction, carbene transfer to an alkene substrate. Significant enhancements were seen in both reaction kinetics and product turnover (∼180) relative to disaggregated Fe II -heme in the absence of DNA or in the presence of other DNA folds, such as single-stranded or double-stranded DNA. Heme binds to GQs by end-stacking. Simple, intramolecularly folded GQs are unable to provide a complexly structured “distal side” environment to the bound heme; therefore, such DNAzymes do not display significant product stereoselectivity. However, intermolecular GQs with multiple pendant nucleotides show increasing stereoselectivity in addition to their enhanced catalytic rates. These results recapitulate the unique functional synergy and highlight the surprising catalytic versatility of complexes formed between heme and DNA/RNA GQs. Our findings suggest that heme·DNAzymes and heme·ribozymes may prove to be useful reagents for carbon–carbon bond forming “green” reactions carried out in vitro and likely within living cells.
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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".