Minimal Methylated Substrate and Extended Substrate Range of Escherichia coli AlkB Protein, a 1-Methyladenine-DNA Dioxygenase
Bibliographic record
Abstract
The Escherichia coli AlkB protein, and two human homologs ABH2 and ABH3, directly demethylate 1-methyladenine and 3-methylcytosine in DNA. They couple Fe(II)-dependent oxidative demethylation of these damaged bases to decarboxylation of alpha-ketoglutarate. Here, we have determined the kinetic parameters for AlkB oxidation of 1-methyladenine in poly(dA), short oligodeoxyribonucleotides, nucleotides, and nucleoside triphosphates. Methylated poly(dA) was the preferred AlkB substrate of those tested. The oligonucleotide trimer d(Tp1meApT) and even 5'-phosphorylated 1-me-dAMP were relatively efficiently demethylated, and competed with methylated poly(dA) for AlkB activity. A polynucleotide structure was clearly not essential for AlkB to repair 1-methyladenine effectively, but a nucleotide 5' phosphate group was required. Consequently, 1-me-dAMP(5') was identified as the minimal effective AlkB substrate. The nucleoside triphosphate, 1-me-dATP, was inefficiently but actively demethylated by AlkB; a reaction with 1-me-ATP was even slower. E. coli DNA polymerase I Klenow fragment could employ 1-me-dATP as a precursor for DNA synthesis in vitro, suggesting that demethylation of alkylated deoxynucleoside triphosphates by AlkB could have biological significance. Although the human enzymes, ABH2 and ABH3, demethylated 1-methyladenine residues in poly(dA), they were inefficient with shorter substrates. Thus, ABH3 had very low activity on the trimer, d(Tp1meApT), whereas no activity was detected with ABH2. AlkB is known to repair methyl and ethyl adducts in DNA; to extend this substrate range, AlkB was shown to reduce the toxic effects of DNA damaging agents that generate hydroxyethyl, propyl, and hydroxypropyl adducts.
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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) | 0.001 | 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 | 0.001 |
| 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".