Combined Effects of π–π Stacking and Hydrogen Bonding on the (N1) Acidity of Uracil and Hydrolysis of 2′-Deoxyuridine
Bibliographic record
Abstract
M06-2X/6-31+G(d,p) is used to study the simultaneous effects of π-π stacking interactions with phenylalanine (modeled as benzene) and hydrogen bonding with small molecules (HF, H(2)O, and NH(3)) on the N1 acidity of uracil and the hydrolytic deglycosylation of 2'-deoxyuridine (dU) (facilitated by fully (OH(-)) or partially (HCOO(-)···H(2)O) activated water). When phenylalanine is complexed with isolated uracil, the proton affinity of all acceptor sites significantly increases (by up to 28 kJ mol(-1)), while the N1 acidity slightly decreases (by ~6 kJ mol(-1)). When small molecules are hydrogen bound to uracil, addition of the phenylalanine ring can increase or decrease the acidity of uracil depending on the number and nature (acidity) of the molecules bound. Furthermore, a strong correlation between the effects of π-π stacking on the acidity of U and the dU deglycosylation reaction energetics is found, where the hydrolysis barrier can increase or decrease depending on the nature and number of small molecules bound, the nucleophile considered (which dictates the negative charge on U in the transition state), and the polarity of the (bulk) environment. These findings emphasize that the catalytic (or anticatalytic) role of the active-site aromatic amino acid residues is highly dependent on the situation under consideration. In the case of uracil-DNA glycosylase (UNG), which catalyzes the hydrolytic excision of uracil from DNA, the type of discrete hydrogen-bonding interactions with U, the nature of the nucleophile, and the anticipated weak, nonpolar environment in the active site suggest that phenylalanine will be slightly anticatalytic in the chemical step, and therefore experimentally observed contributions to catalysis may entirely result from associated structural changes that occur prior to deglycosylation.
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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".