Cross‐Binding of Adenosine by Aptamers Selected Using Theophylline
Bibliographic record
Abstract
We recently reported that some adenosine binding aptamers can also bind caffeine and theophylline with around 20-fold lower affinities. This discovery led to the current work to examine the cross-binding of adenosine to theophylline aptamers. For the DNA aptamer for theophylline, cross-binding to adenosine was observed, and the affinity was 18 to 38-fold lower for adenosine based on assays using isothermal titration calorimetry and ThT fluorescence spectroscopy. The binding complexes were characterized using NMR spectroscopy, and both adenosine and theophylline showed an overall similar binding structure to the DNA theophylline aptamer, although small differences were also observed. In contrast, the RNA aptamer did not show binding to adenosine, although both aptamers have very similar relative selectivity for various methylxanthines including caffeine. After a negative selection, a few new aptamers with completely different primary sequences for theophylline were obtained and they did not show binding to adenosine. Thus, there are many ways for aptamers to bind theophylline and some can have cross-binding to adenosine. In biology, theophylline, caffeine, and adenosine can bind to the same protein receptors to regulate sleep, and their binding to the same DNA motifs may suggest an early role of nucleic acids in similar regulatory functions.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".