An Efficient Catalytic DNA that Cleaves L-RNA
Bibliographic record
Abstract
Many DNAzymes have been isolated from synthetic DNA pools to cleave natural RNA (D-RNA) substrates and some have been utilized for the design of aptazyme biosensors for bioanalytical applications. Even though these biosensors perform well in simple sample matrices, they do not function effectively in complex biological samples due to ubiquitous RNases that can efficiently cleave D-RNA substrates. To overcome this issue, we set out to develop DNAzymes that cleave L-RNA, the enantiomer of D-RNA, which is known to be completely resistant to RNases. Through in vitro selection we isolated three L-RNA-cleaving DNAzymes from a random-sequence DNA pool. The most active DNAzyme exhibits a catalytic rate constant ~3 min-1 and has a structure that contains a kissing loop, a structural motif that has never been observed with D-RNA-cleaving DNAzymes. Furthermore we have used this DNAzyme and a well-known ATP-binding DNA aptamer to construct an aptazyme sensor and demonstrated that this biosensor can achieve ATP detection in biological samples that contain RNases. The current work lays the foundation for exploring RNA-cleaving DNAzymes for engineering biosensors that are compatible with complex biological samples.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".