Visible Fluorescent Light-up Probe for DNA Three-Way Junctions Provides Host–Guest Biosensing Applications
Bibliographic record
Abstract
DNA three-way junctions (3WJs) consist of a Y-shaped hydrophobic branch point connecting three double-stranded stems and are viewed as druggable targets for cancer treatment. They are also important building blocks for the construction of DNA nanostructures and serve as recognition elements for DNA aptasensors for a wide variety of diagnostic applications. However, visible fluorescent light-up probes for specific staining of DNA 3WJs are currently lacking. Herein, we report that a merocyanine containing the N -methylbenzothiazolium (Btz) acceptor vinyl linked to a 2-fluorophenolic (FPhO) donor (FPhOBtz) serves as a universal fluorescent turn-on dye for DNA 3WJs. Our evidence is based on a multifaceted approach to define the specificity and affinity of FPhOBtz for 3WJ DNA aptamers; the cocaine binding aptamer MN4, the cholic acid binding aptamer (CABA), and four steroid aptamers (DOGS.1, DISS.1, BES.1, DCAS.1). FPhOBtz exhibits impressive turn-on (up to 730-fold) fluorescence at 580 nm upon aptamer binding with low micromolar affinity. Direct FPhOBtz displacement from the 3WJ binding domain through competitive alkaloid and steroid binding provides immediate fluorescent read out for host–guest detection strategies in human blood serum in the low micromolar regime. Our results present the first visible light-up fluorescent probe for DNA 3WJ detection strategies.
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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) | 0.000 | 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 | 0.001 | 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".