Effect of base sequence context on the conformational heterogeneity of aristolactam-I adducted DNA: structural and energetic insights into sequence-dependent repair and mutagenicity
Bibliographic record
Abstract
Abstract Aristolochic acids (AAs) are nephrotoxic and potentially carcinogenic plant mutagens that form bulky DNA adducts at the exocyclic amino groups of the purines. The present work utilizes classical molecular dynamics simulations and free energy calculations to investigate the role of lesion site sequence context in dictating the conformational outcomes of DNA containing ALI-N6-dA, the most persistent and mutagenic adduct arising from the AAs. Our calculations reveal that the anti base-displaced intercalated conformer is the lowest energy conformer of damaged DNA in all sequence contexts considered (CXC, CXG, GXC and GXG). However, the experimentally-observed greater mutagenicity of the adduct in the CXG sequence context does not correlate with the relative thermodynamic stability of the adduct in different sequences. Instead, AL-N6-dA adducted DNA is least distorted in the CXG sequence context, which points toward a possible differential repair propensity of the lesion in different sequences. Nevertheless, the structural deviations between adducted DNA with different lesion site sequences are small, and therefore other factors (such as interactions between the adducted DNA and lesion-bypass polymerases during replication) are likely more important for dictating the observed sequence-dependent mutagenicity of ALI-N6-dA.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".