RNA and DNA complexes with hemin [Fe(III) heme] are efficient peroxidases and peroxygenases: how do they do it and what does it mean?
Bibliographic record
Abstract
Guanine-rich RNAs and DNAs from chromosomal telomeres and elsewhere that fold into guanine quadruplexes (G-quadruplexes), are found to complex tightly with porphyrins such as N-methylmesoporphyrin IX (NMM) and hemin [Fe(III) heme]. By themselves, these DNAs and RNAs are found to be efficient catalysts for porphyrin metallation. When complexed with hemin, under physiological conditions, these nucleic acids display robust peroxidase (one-electron oxidation), as well as peroxygenase (two-electron oxidation, or oxygen transfer) activity. These surprising catalytic properties, that frequently match the catalytic performance of natural peroxidase and P450 monooxygenase enzymes, have been the subject of significant mechanistic analysis, as well as having found utility in a wide range of biosensing and other applications. This review summarizes recent insights into a surprising yet fundamental property of many RNAs and DNAs, a property with undoubted ramifications for cellular oxidative disease, de novo hemoenzyme design, and our understanding of the evolution of early biocatalytic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".