Multiscale computational investigations of the translesion synthesis bypass of tobacco-derived DNA adducts: critical insights that complement experimental biochemical studies
Bibliographic record
Abstract
Among the numerous agents that damage DNA, tobacco products remain one of the most lethal and result in the most diverse set of DNA lesions. This perspective aims to provide an overview of computational work conducted to complement experimental biochemical studies on the mutagenicity of adducts derived from the most potent tobacco carcinogen, namely 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (nicotine-derived nitrosaminoketone or NNK). Lesions ranging from the smallest methylated thymine derivatives to the larger, flexible pyridyloxobutyl (POB) guanine adducts are considered. Insights are obtained from density functional theory (DFT) calculations and molecular dynamics (MD) simulations into the damaged nucleobase and nucleoside structures, the accommodation of the lesions in the active site of key human polymerases, the intrinsic base pairing potentials of the adducts, and dNTP incorporation opposite the lesions. Overall, the computational data provide atomic level information that can rationalize the differential mutagenic properties of tobacco-derived lesions and uncover important insights into the impact of adduct size, nucleobase, position, and chemical composition of the bulky moiety.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".