A DFT Study of Nucleobase Dealkylation by the DNA Repair Enzyme AlkB
Bibliographic record
Abstract
Oxidative dealkylation is a unique mechanistic pathway found in the alpha-ketoglutarate-Fe(II)-dependent AlkB family of enzymes to remove the alkylation damage to DNA bases and regenerate nucleobases to their native state. The B3LYP density functional combined with a self-consistent reaction field was used to explore the triplet, quintet, and septet spin-state potential energy surfaces of the multistep catalytic mechanism of AlkB. The mechanism was found to consist of four stages. First, binding of dioxygen to iron in the active-site complex occurs concerted with electron transfer, thereby yielding a ferric-superoxido species. Second, competing initiation for the activation of oxygen to generate the high-valent iron-oxygen intermediates (ferryl-oxo Fe(IV)O and ferric-oxyl Fe(III)O(*) species) was found to occur on the quintet and septet surfaces. Then, conformational reorientation of the activated iron-oxygen ligand was found to be nearly thermoneutral with a barrier of ca. 50 kJ mol(-1). The final stage is the oxidative dealkylation of the damaged nucleobase with the rate-controlling step being the abstraction of a hydrogen atom from the damaging methyl group by the ferryl-oxo ligand. For this step, the calculated barrier of 87.4 kJ mol(-1) is in good agreement with the experimental activation energy of ca. 83 kJ mol(-1) for the enzyme-catalyzed reaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".