Structural Basis for the Inhibition of Human Alkyladenine DNA Glycosylase (AAG) by 3,N4-Ethenocytosine-containing DNA
Bibliographic record
Abstract
Reactive oxygen and nitrogen species, generated by neutrophils and macrophages in chronically inflamed tissues, readily damage DNA, producing a variety of potentially genotoxic etheno base lesions; such inflammation-related DNA damage is now known to contribute to carcinogenesis. Although the human alkyladenine DNA glycosylase (AAG) can specifically bind DNA containing either 1,N(6)-ethenoadenine (εA) lesions or 3,N(4)-ethenocytosine (εC) lesions, it can only excise εA lesions. AAG binds very tightly to DNA containing εC lesions, forming an abortive protein-DNA complex; such binding not only shields εC from repair by other enzymes but also inhibits AAG from acting on other DNA lesions. To understand the structural basis for inhibition, we have characterized the binding of AAG to DNA containing εC lesions and have solved a crystal structure of AAG bound to a DNA duplex containing the εC lesion. This study provides the first structure of a DNA glycosylase in complex with an inhibitory base lesion that is induced endogenously and that is also induced upon exposure to environmental agents such as vinyl chloride. We identify the primary cause of inhibition as a failure to activate the nucleotide base as an efficient leaving group and demonstrate that the higher binding affinity of AAG for εC versus εA is achieved through formation of an additional hydrogen bond between Asn-169 in the active site pocket and the O(2) of εC. This structure provides the basis for the design of AAG inhibitors currently being sought as an adjuvant for cancer chemotherapy.
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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 | 0.001 | 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".