A computational proposal for the experimentally observed discriminatory behavior of hypoxanthine, a weak universal nucleobase
Bibliographic record
Abstract
A computational model composed of six nucleobases was used to investigate why hypoxanthine does not yield duplexes of equal stability when paired opposite each of the natural DNA nucleobases. The magnitudes of all nearest-neighbor interactions in a DNA helix were calculated, including hydrogen-bonding, intra- and interstrand stacking interactions, as well as 1-3 intrastrand stacking interactions. Although the stacking interactions in DNA relevant arrangements are significant and account for at least one third of the total stabilization energy in our nucleobase complexes, the trends in the magnitude of the stacking interactions cannot explain the relative experimental melting temperatures previously reported in the literature. Furthermore, although the total hydrogen-bonding interactions explain why hypoxanthine preferentially pairs with cytosine, the experimental trend for the remaining nucleobases (A, T, G) is not explained. In fact, the calculated pairing preference of hypoxanthine matches that determined experimentally only when the sum of all types of nearest-neighbor interactions is considered. This finding highlights a strong correlation between the relative magnitude of the total nucleobase-nucleobase interactions and measured melting temperatures for DNA strands containing hypoxanthine despite the potential role of other factors (including hydration, temperature, sugar-phosphate backbone). By considering a large range of sequence combinations, we reveal that the binding preference of hypoxanthine is strongly dependent on the nucleobase sequence, which may explain the varied ability of hypoxanthine to universally bind to the natural bases. As a result, we propose that future work should closely examine the interplay between the dominant nucleobase-nucleobase interactions and the overall strand stability to fully understand how sequence context affects the universal binding properties of modified bases and to aid the design of new molecules with ambiguous pairing properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".