Effects of Hydrogen-Bonding and Stacking Interactions with Amino Acids on the Acidity of Uracil
Bibliographic record
Abstract
The effects of hydrogen-bonding interactions with amino acids on the (N1) acidity of uracil are evaluated using (B3LYP) density functional theory. Many different binding arrangements of each amino acid to three uracil binding sites are considered. The effects on the uracil acidity are found to significantly depend upon the nature of the amino acid and the binding orientation, but weakly depend on the binding site. Our results reveal that in some instances small models for the amino acids can be used, while for other amino acids larger models are required to properly describe the binding to uracil. The gas-phase acidity of uracil is found to increase by up to approximately 60 kJ mol(-1) due to discrete hydrogen-bonding interactions. Although (MP2) stacking interactions with aromatic amino acids decrease the acidity of uracil, unexpected increases in the acidity are found when any of the aromatic amino acids, or the backbone, hydrogen bond to uracil. Consideration of enzymatic and aqueous environments leads to decreases in the effects of the amino acids on the acidity of uracil. However, we find that the magnitude of the decrease varies with the nature of the molecule bound, as well as the (gas-phase) binding orientations and strengths, and therefore solvation effects should be considered on a case-by-case basis in future work. Nevertheless, the effects of amino acid interactions within enzymatic environments are as much as approximately 35 kJ mol(-1). The present study has general implications for understanding the nature of active site amino acids in enzymes, such as DNA repair enzymes, that catalyze reactions involving anionic nucleobase intermediates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".