Pushing Adenosine and ATP SELEX for DNA Aptamers with Nanomolar Affinity
Bibliographic record
Abstract
The classical DNA aptamer for adenosine and ATP has been the most used small molecule binding aptamer for biosensing, imaging, and DNA nanotechnology. This sequence has recurred multiple times in previous aptamer selections, and all previous selections used a high concentration of ATP as the target. Herein, two separate selections were performed using adenosine and ATP as targets. By pushing the target concentrations down to the low micromolar range, two new aptamers with K d as low as 230 nM were obtained, showing around 30-fold higher affinity compared to the classical aptamer. The classical aptamer sequence still dominated the library in the early rounds of the selections, but it was suppressed in the later rounds. The new aptamers bind to one target molecule instead of two. Mutation studies confirmed their secondary structures and specific binding. Using the deep sequencing data from the selections, long-standing questions such as the existence of one-site aptamers and mutation distribution in the classical aptamer were addressed. Comparisons were made with previously reported DNA aptamers for ATP. Finally, a strand-displacement biosensor was tested showing selectivity for adenosine and its nucleotides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".