Fluorescent modifications in aptamer switches—positional, structural, and neighboring pair effects on sensor performance
Bibliographic record
Abstract
Aptamers are modular nucleic acid ligands increasingly used as recognition elements in biosensors for precision medicine. Their structure facilitates their adaptation into fluorescent switches through molecular design and conjugation with reporter molecules, which are typically selected based on optimal photophysical compatibility. However, a critical challenge persists: reporter molecules are not inert; their structure and underlying conjugation chemistry can introduce structural perturbations that adversely affect aptamer functional properties and DNA-duplex stability, subsequently impacting biosensing performance. Here, we evaluate the effect of fluorophore-quencher modifications by probing the interplay between reporter identity, local environment, and labeling positions within different switch architectures. We demonstrate that applying a combinatorial array of modifications to an aptamer switch sequence can tune its binding properties over a 2-order-of-magnitude range, with apparent binding affinities from ~230 to 2.3 μM and apparent kinetic response rates from ~1000 s to sub-second resolution, while also influencing the switch's specificity and signal resolution. We also establish that the environment and spatial positioning of a reporter pair are critical design parameters, enabling order-of-magnitude performance differences even at theoretically suboptimal sites. This work provides a comprehensive study to help design optimal switches when using fluorescent modifications, enabling the development of next-generation biosensors with tailored performance.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".