Harnessing a Fluorescent Nucleobase Surrogate for Supramolecular FRET-Aptamer Detection and Target-Site Mapping
Bibliographic record
Abstract
DNA aptamers can bind small molecule ligands with high affinity and specificity to produce a unique supramolecular structure. Methods to obtain structural information about the binding interaction coupled with sensitive diagnostics is a gold standard for aptasensor design. However, most sensing strategies afford ligand detection without structural insight, while NMR- or crystallography-based structural methods lack sensitivity required for diagnostics. FRET-based strategies can afford both, especially with internal fluorescent nucleobase probes that are spatially fixed within the helix, but dual aptamer labeling can compromise aptamer affinity toward its target. Herein, we showcase a nucleobase surrogate-ligand FRET-based strategy that affords target-site mapping combined with sensitive target detection that addresses these challenges. A fluorescent molecular rotor (FMR) thiophene chalcone (Th6HI) nucleobase surrogate was incorporated into the tetracycline (TC) 42-mer DNA binding aptamer OTC2 to serve as an acceptor for the TC donor. Time-resolved fluorescence anisotropy experiments predict a compact prefolded OTC2 aptamer that is hardly impacted by TC binding. Consequently, direct excitation of the internal FMR Th6HI at 530 nm affords little response to TC binding, as probe rigidity is not strongly altered. In contrast, indirect excitation of the Th6HI probe through TC donor excitation at 378 nm affords site-dependent sensitized fluorescence ( F sen ) of the Th6HI acceptor to afford enhanced sensitivity for TC detection compared to a native platform, which utilizes the intrinsic TC fluorescence. Furthermore, the FRET response provides target-site mapping to build a new binding model for the TC-OTC2 complex that is akin to the three-helical structure of the hammerhead ribozyme.
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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".