Optimizing Stem Length To Improve Ligand Selectivity in a Structure-Switching Cocaine-Binding Aptamer
Bibliographic record
Abstract
Understanding how aptamer structure and function are related is crucial in the design and development of aptamer-based biosensors. We have analyzed a series of cocaine-binding aptamers with different lengths of their stem 1 in order to understand the role that this stem plays in the ligand-induced structure-switching binding mechanism utilized in many of the sensor applications of this aptamer. In the cocaine-binding aptamer, the length of stem 1 controls whether the structure-switching binding mechanism for this aptamer occurs or not. We varied the length of stem 1 from being one to seven base pairs long and found that the structural transition from unfolded to folded in the unbound aptamer is when the aptamer elongates from 3 to 4 base pairs in stem 1. We then used this knowledge to achieve new binding selectivity of this aptamer for quinine over cocaine by using an aptamer with a stem 1 two base pairs long. This selectivity is achieved by means of the greater affinity quinine has for the aptamer compared with cocaine. Quinine provides enough free energy to both fold and bind the 2-base pair-long aptamer while cocaine does not. This tuning of binding selectivity of an aptamer by reducing its stability is likely a general mechanism that could be used to tune aptamer specificity for tighter binding ligands.
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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) | 0.000 | 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 | 0.001 |
| 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".