Developing Fluorogenic RNA-Cleaving DNAzymes for Biosensing Applications
Bibliographic record
Abstract
Deoxyribozymes (or DNAzymes) are single-stranded DNA molecules that have the ability to catalyze a chemical reaction. Currently, DNAzymes have to be isolated from random-sequence DNA libraries by a process known as in vitro selection (IVS) because no naturally occurring DNAzyme has been discovered. Several IVS studies have led to the isolation of many RNA-cleaving DNAzymes (RNase DNAzymes), which catalyze the transesterification of a phosphodiester linkage in an RNA substrate, resulting in its cleavage. An RNase DNAzyme and its substrate can be modified with a pair of donor and acceptor fluorophores (or a fluorophore and quencher pair) to create a fluorescence-signaling system (a signaling DNAzyme) where the RNA-cleaving activity of the DNAzyme is reported through the generation of a fluorescent signal. A signaling DNAzyme can be further coupled with an aptamer (a target-binding nucleic acid sequence) to generate a fluorogenic aptazyme in which the aptamer-target interaction confers an allosteric control of the coupled RNA-cleaving and fluorescence-signaling activity of the DNAzyme. Fluorogenic aptazymes can be exploited as valuable molecular tools for biosensing applications. In this chapter, we provide both a detailed description of methods for isolation of signaling DNAzymes by IVS and general approaches for rational engineering of fluorogenic aptazymes for target detection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".