Computational studies of DNA base repair mechanisms by nonheme iron dioxygenases: selective epoxidation and hydroxylation pathways
Bibliographic record
Abstract
DNA base repair mechanisms of alkylated DNA bases is an important reaction in chemical biology and particularly in the human body. It is typically catalyzed by an α-ketoglutarate-dependent nonheme iron dioxygenase named the AlkB repair enzyme. In this work we report a detailed computational study into the structure and reactivity of AlkB repair enzymes with alkylated DNA bases. In particular, we investigate the aliphatic hydroxylation and C[double bond, length as m-dash]C epoxidation mechanisms of alkylated DNA bases by a high-valent iron(iv)-oxo intermediate. Our computational studies use quantum mechanics/molecular mechanics methods on full enzymatic structures as well as cluster models on active site systems. The work shows that the iron(iv)-oxo species is rapidly formed after dioxygen binding to an iron(ii) center and passes a bicyclic ring structure as intermediate. Subsequent cluster models explore the mechanism of substrate hydroxylation and epoxidation of alkylated DNA bases. The work shows low energy barriers for substrate activation and consequently energetically feasible pathways are predicted. Overall, the work shows that a high-valent iron(iv)-oxo species can efficiently dealkylate alkylated DNA bases and return them into their original form.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".