A computational characterization of the hydrogen-bonding and stacking interactions of hypoxanthine
Bibliographic record
Abstract
The hydrogen-bonding and stacking interactions of hypoxanthine, a potential universal nucleobase, were calculated using a variety of methodologies (CCSD(T), MP2, B3LYP, PWB6K, AMBER). All methods predict that the hydrogen-bonding interaction in the hypoxanthine-cytosine pair is approximately 25 kJ mol(-1) stronger than that in the other dimers. Although the calculations support suggestions from experiments that hypoxanthine preferentially binds with cytosine, the trend in the calculated hydrogen-bond strengths for the remaining natural nucleobases do not show a strong correlation with the experimentally predicted binding preferences. However, our calculations suggest that the stacking interactions of hypoxanthine are similar in magnitude to the hydrogen-bonding interactions at all levels of theory (with the exception of B3LYP, which incorrectly predicts stacked dimers to be unstable). Therefore, stacking interactions should also be considered when analyzing the stability of DNA helices containing hypoxanthine and the use of larger models that account for both hydrogen-bonding and stacking within DNA duplexes will likely result in better agreement with experimental observations. For the majority of the dimers, PWB6K and AMBER provide reasonable binding strengths at reduced computational costs, and therefore will be useful techniques for considering larger models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".