A proof of concept application of aptachain: ligand-induced self-assembly of a DNA aptamer
Bibliographic record
Abstract
A challenge for the use of aptamers as biosensors is how to signal the occurrence of their ligand binding event into a signal that can be exploited in a detection scheme. Here, we present the concept of "aptachain" formation, where an aptamer is split into two overlapping or staggered strands and assembles into an extended oligomer upon ligand binding. This assembly of aptamers can then be used as a way to detect ligand binding by the aptamer. As an example of this concept, we employed the cocaine-binding aptamer as a model system, used its ability to tightly bind quinine and demonstrated its capability in a gold nanoparticle-based biosensing application. We used isothermal titration calorimetry to demonstrate that, when split into two overlapping DNA strands, the aptamer remains functional. Size-exclusion chromatography showed that the quinine-bound oligos form a larger assembly of aptamer units than in the absence of ligand. Finally, we used the oligomer forming ability of the aptachain oligos in a biosensor application for quinine that brings gold nanoparticles closer together resulting in a shift in their plasmonic resonance to a longer wavelength and an observed colour shift. We propose that splitting aptamers into overlapping strands that form oligomers in the presence of a ligand, aptachain formation, will be generally applicable to aptamers and prove useful in a variety of biotechnology applications.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".