Environmental Effects on the Enhancement in Natural and Damaged DNA Nucleobase Acidity Because of Discrete Hydrogen-Bonding Interactions
Bibliographic record
Abstract
The present study uses density functional theory to carefully consider the effects of the environment on the enhancement in (natural and damaged) DNA nucleobase acidities because of multiple hydrogen-bonding interactions. Although interactions with one small molecule can increase the acidity of the nucleobases by up to 60 kJ mol-1 in the gas phase, the maximum increase in enzymatic-like environments is expected to be approximately 40 kJ mol-1, which reduces to approximately 30 kJ mol-1 in water. Furthermore, the calculated (simultaneous) effects of two, three, or four molecules are increasingly less than the sum of the individual (additive) effects with an increase in the number and acidity of the small molecules bound or the dielectric constant of the solvent. Regardless of these trends, our calculations reveal that additional hydrogen-bonding interactions will have a significant effect on nucleobase acidity in a variety of environments, where the exact magnitude of the effect depends on the properties of the small molecule bound, the nucleobase binding site, and the solvent. The maximum increase in nucleobase acidity because of interactions with up to four small molecules is approximately 80 kJ mol-1 in enzymatic-like environments (or 65 kJ mol-1 in water). These results suggest that hydrogen-bonding interactions likely play an important role in many biological processes by changing the physical and chemical properties of the nucleobases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".