How Do Size-Expanded DNA Nucleobases Enhance Duplex Stability? Computational Analysis of the Hydrogen-Bonding and Stacking Ability of xDNA Bases
Bibliographic record
Abstract
Computational chemistry (B3LYP, MP2) is used to study the properties of size-expanded DNA nucleobases generated by inserting a benzene spacer into the natural nucleobases. Although the addition of the spacer does not significantly affect the hydrogen-bonding properties of natural nucleobases, the orientation of the base about the glycosidic bond necessary for Watson-Crick binding is destabilized, which could have implications for the selectivity of expanded bases, as well as the stability of expanded duplexes. Consideration of the (stacked) binding energies in the preferred relative orientation of natural and expanded nucleobases aligned according to their centers of mass reveals that the stacking within natural dimers can be increased by up to 50% upon expansion of one nucleobase and up to 90% upon expansion of two nucleobases. The implications of these findings to the stability of expanded duplexes were revealed by considering simplified models of natural and mixed duplexes composed of four nucleobases. Although intra- and interstrand interactions within double helices are typically less than those predicted when nucleobases are stacked according to their centers of mass, some nucleobases utilize their full stacking potential within double helices, where both intra- and interstrand interactions can be significant. Most importantly, increasing the size of nucleobases within the duplex significantly increases both intra- and interstrand stacking interactions. Specifically, some interactions are double the magnitude of the corresponding intrastrand interactions in natural helices, and even greater increases in interstrand interactions are sometimes found. Thus, our work suggests that mixed duplexes composed of natural bases hydrogen bound to expanded bases may exploit the increase in the inherent stacking ability of the expanded bases in more than one way and thereby afford duplexes with greater stability than natural DNA.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".