Making target sites in large structured RNAs accessible to RNA-cleaving DNAzymes through hybridization with synthetic DNA oligonucleotides
Bibliographic record
Abstract
The 10-23 DNAzyme is one of the most active DNA-based enzymes, and in theory, can be designed to target any purine-pyrimidine junction within an RNA sequence for cleavage. However, purine-pyrimidine junctions within a large, structured RNA (lsRNA) molecule of biological origin are not always accessible to 10-23, negating its general utility as an RNA-cutting molecular scissor. Herein, we report a generalizable strategy that allows 10-23 to access any purine-pyrimidine junction within an lsRNA. Using three large SARS-CoV-2 mRNA sequences of 566, 584 and 831 nucleotides in length as model systems, we show that the use of antisense DNA oligonucleotides (ASOs) that target the upstream and downstream regions flanking the cleavage site can restore the activity (kobs) of previously poorly active 10-23 DNAzyme systems by up to 2000-fold. We corroborated these findings mechanistically using in-line probing to demonstrate that ASOs reduced 10-23 DNAzyme target site structure within the lsRNA substrates. This approach represents a simple, efficient, cost-effective, and generalizable way to improve the accessibility of 10-23 to a chosen target site within an lsRNA molecule, especially where direct access to the genomic RNA target is necessary.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".