Solution‐Based Biophysical Methods for Guiding Design of Aptamers into Electrochemical Biosensors
Bibliographic record
Abstract
Structure‐switching aptamers are utilized in various applications and have increasingly been translated into electrochemical biosensors, largely thanks to post‐SELEX sequence engineering through computational and enzymatic approaches. In the context of sequence engineering, it is envisioned that folding and binding thermodynamics could likewise contribute to accelerating translation of aptamers into sensors. Herein, this is explored by first characterizing a series of quinine‐binding aptamers using the biophysical methods isothermal titration calorimetry and nano differential scanning calorimetry. The folding and binding thermodynamics obtained are compared with the resulting analytical performance when aptamers are adapted into sensors. The findings show that the magnitude of sensor response is strongly correlated with aspects of the binding and unfolding thermodynamics of the aptamer as measured in solution. Using a similar approach, a recently reported adenosine monophosphate aptamer is successfully engineered to support electrochemical sensing. It is envisioned that relying on solution‐based biophysical methods will further improve post‐SELEX sequence engineering.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".