Computational comparison of the stacking interactions between the aromatic amino acids and the natural or (cationic) methylated nucleobases
Bibliographic record
Abstract
The strongest gas-phase MP2/6-31G*(0.25) stacking energies between the aromatic amino acids and the natural or methylated nucleobases were considered. The potential energy surfaces of dimers were searched as a function of the vertical separation, angle of rotation and horizontal displacement between monomers stacked according to their centers of mass. Our calculations reveal that the stacking interactions of adducts for a given nucleobase are dependent on the methylation site (by up to 20 kJ mol(-1)), where the relative magnitudes of the interactions are determined by the dipole moments of the adducts and the proton affinities of nucleobase methylation sites. Nevertheless, the differences in the (gas-phase) stacking of methylated adducts are small compared with the differences between the stacking of the corresponding natural and methylated nucleobases. Indeed, methylation increases the stacking energy by up to 40 kJ mol(-1) (or 135%). Although immersing the dimers in different solvents decreases the gas-phase stacking energies with an increase in the polarity of the environment, base methylation still has a significant effect on the nucleobase stacking ability in solvents with large dipole moments, and, perhaps more importantly, environments that mimic enzyme active sites. Our results shed light on the workings of DNA repairs enzymes that selectively remove a wide variety of alkylated nucleobases over the natural bases.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".