Characterization of Nucleobase−Amino Acid Stacking Interactions Utilized by a DNA Repair Enzyme
Bibliographic record
Abstract
The present work characterizes the gas-phase stacking interactions between four aromatic amino acid residues (histidine, phenylalanine, tyrosine, and tryptophan) and adenine or 3-methyladenine due to the proposed utilization of these interactions by enzymes that repair DNA alkylation damage. The MP2 potential energy surfaces of the stacked dimers are considered as a function of four variables (vertical displacement, angle of rotation, horizontal displacement, and tilt angle) using a variety of basis sets. It is found that the maximum stacking interaction energy decreases with the amino acid according to TRP > TYR approximately HIS > PHE for both nucleobases. However, the magnitude of the stacking interaction significantly increases upon alkylation (by 50-115%). Comparison of the stacking energies calculated using our surface scans to those estimated from experimental crystal structures indicates that the stacking interactions within the active site of 3-methyladenine DNA glycosylase can account for 65-75% of the maximum possible stacking interaction between the relevant molecules. The decrease in stacking in the crystal structure arises due to significant differences in the relative orientations of the nucleobase and amino acid. Nevertheless, alkylation is found to significantly increase the stacking energy when the crystal structure geometries are considered. Our calculations provide computational support for suggestions that alkylation enhances the stacking interactions within the active site of DNA repair enzymes, and they give a measure of the magnitude of this enhancement. Our results suggest that alkylation likely plays a more important role in substrate identification and removal than the nature of the aromatic amino acid that interacts with the substrate via stacking interactions.
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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".