Simple Fluorescent Sensors Engineered with Catalytic DNA ‘MgZ’ Based on a Non-Classic Allosteric Design
Bibliographic record
Abstract
Most NAE (nucleic acid enzyme) sensors are composed of an RNA-cleaving catalytic motif and an aptameric receptor. They operate by activating or repressing the catalytic activity of a relevant NAE through the conformational change in the aptamer upon target binding. To transduce a molecular recognition event to a fluorescence signal, a fluorophore-quencher pair is attached to opposite ends of the RNA substrate such that when the NAE cleaves the substrate, an increased level of fluorescence can be generated. However, almost all NAE sensors to date harbor either NAEs that cannot accommodate a fluorophore-quencher pair near the cleavage site or those that can accept such a modification but require divalent transition metal ions for catalysis. Therefore, the signaling magnitude and the versatility of current NAE sensors might not suffice for analytical and biological applications. Here we report an RNA-cleaving DNA enzyme, termed 'MgZ', which depends on Mg(2+) for its activity and can accommodate bulky dye moieties next to the cleavage site. MgZ was created by in vitro selection. The selection scheme entailed acidic buffering and ethanol-based reaction stoppage to remove selfish DNAs. Characterization of MgZ revealed a three-way junction structure, a cleavage rate of 1 min(-1), and 26-fold fluorescence enhancement. Two ligand-responsive NAE sensors were rationally designed by linking an aptamer sequence to the substrate of MgZ. In the absence of the target, the aptamer-linked substrate is locked into a conformation that prohibits MgZ from accessing the substrate. In the presence of the target, the aptamer releases the substrate, which induces MgZ-mediated RNA cleavage. The discovery of MgZ and the introduction of the above NAE sensor design strategy should facilitate future efforts in sensor engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".