Exceptional Nuclease Resistance of DNA and RNA with the Addition of Small-Molecule Nucleobase Mimics
Bibliographic record
Abstract
Nucleases present a formidable barrier to the application of nucleic acids in biology, significantly reducing the lifetime of nucleic acid-based drugs. Here, we develop a novel methodology to protect DNA and RNA from nucleases by reconfiguring their supramolecular structure through the addition of a nucleobase mimic, cyanuric acid. In the presence of cyanuric acid, polyadenine strands assemble into triple helical fibers known as the polyA/CA motif. We report that this motif is exceptionally resistant to nucleases, with the constituent strands surviving for up to 1 month in the presence of serum. The conferred stability extends to adjacent non-polyA sequences, albeit with diminishing returns relative to their polyA sections due to hypothesized steric clashes. We introduce a strategy to regenerate stability through the introduction of free polyA strands or positively charged amino side chains, enhancing the stability of sequences of varied lengths. The proposed protection mechanism involves enzyme failure to recognize the unnatural polyA/CA motif, coupled with the motif's propensity to form long, bundling supramolecular fibers. The methodology provides a fundamentally new mechanism to protect nucleic acids from degradation using a supramolecular approach and increases lifetime in serum to days, weeks, or months.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".