Highly Specific Recognition of Guanosine Using Engineered Base‐Excised Aptamers
Bibliographic record
Abstract
Abstract Purines and their derivatives are highly important molecules in biology for nucleic acid synthesis, energy storage, and signaling. Although many DNA aptamers have been obtained for binding adenine derivatives such as adenosine, adenosine monophosphate, and adenosine triphosphate, success for the specific binding of guanosine has been limited. Instead of performing new aptamer selections, we report herein a base‐excision strategy to engineer existing aptamers to bind guanosine. Both a Na+‐binding aptamer and the classical adenosine aptamer have been manipulated as base‐excising scaffolds. A total of seven guanosine aptamers were designed, of which the G16‐deleted Na+ aptamer showed the highest bindng specificity and affinity for guanosine with an apparent dissociation constant of 0.78 mm. Single monophosphate difference in the target molecule was also recognizable. The generality of both the aptamer scaffold and excised site were systematically studied. Overall, this work provides a few guanosine binding aptamers by using a non‐SELEX method. It also provides deeper insights into the engineering of aptamers for molecular recognition.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".