Structural Properties of Hachimoji Nucleic Acids and Their Building Blocks: Comparison of Genetic Systems with Four, Six and Eight Alphabets
Bibliographic record
Abstract
Expansion of the genetic alphabet is an ambitious goal. A recent breakthrough has led to the eight-base (hachimoji) genetics having canonical and unnatural bases. However, very little is known on the molecular-level features that facilitate the candidature of unnatural bases as genetic alphabets. Here we amalgamated DFT calculations and MD simulations to analyse the properties of the constituents of hachimoji DNA and RNA. DFT reveals the dominant syn conformation for isolated unnatural deoxyribonucleosides and at the 5'-end of oligonucleotides, although an anti/syn mixture is predicted at the nonterminal and 3'-terminal positions. However, isolated ribonucleotides prefer an anti/syn mixture, but mostly prefer anti conformation at the nonterminal positions. Further, the canonical base pairing combinations reveals significant strength, which may facilitate replication of hachimoji DNA. We also identify noncanonical base pairs that can better tolerate the substitution of unnatural pairs in RNA. Stacking strengths of 51 dimers reveals higher average stacking stabilization of dimers of hachimoji bases than canonical bases, which provides clues for choosing energetically stable sequences. A total of 14.4 μs MD simulations reveal the influence of solvent on the properties of hachimoji oligonucleotides and point to the likely fidelity of replication of hachimoji DNA. Our results pinpoint the features that explain the experimentally observed stability of hachimoji 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 | 0.000 |
| 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) | 0.002 | 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".