An engineered one-site aptamer with higher sensitivity for label-free detection of adenosine on graphene oxide
Bibliographic record
Abstract
The 27-nucleotide DNA aptamer for adenosine and ATP, originally selected by the Szostak lab in 1995, has been a very popular model system for biosensor development. This unique aptamer has two target binding sites, and we recently showed that it is possible to remove either site while the other one still retains binding. From an analytical perspective, tuning the number of binding sites has important implications in modulating sensitivity of the resulting biosensors. In this work, we report that the engineered one-site aptamer showed excellent signaling properties with a 2.6-fold stronger signal intensity and also a 4.2-fold increased detection limit compared with the wild-type two-site aptamer. The aptamer has a hairpin structure, and the length of the hairpin stem was systematically varied for the one-site aptamers. Isothermal titration calorimetry and a label-free fluorescence signaling method with graphene oxide and SYBR Green I were respectively used to evaluate binding and sensor performance. Although longer stemmed aptamers produced better adenosine binding affinity, the signaling was quite independent of the stem length as long as more than three base pairs were left. This was explained by the higher affinity of binding to GO by the longer aptamers, cancelling out the higher affinity for adenosine binding. This work further confirms the analytical applications of such one-site adenosine aptamers, which are potentially useful for improved ATP imaging and for developing new biosensors.
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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 | 0.001 | 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".