A nanomolar affinity dopamine aptamer: rethinking of negative selections
Bibliographic record
Abstract
In 2018, a DNA aptamer for dopamine was selected after extensive negative selection steps. Herein, a new selection experiment was carried out under the same conditions except that no negative selections were performed. This selection yielded a single family of binding sequences, which has a Kd 424 nM from isothermal titration calorimetry, 6-fold lower compared to the previous aptamer, although they differ only by one nucleotide. Using the fluorescence strand-displacement reaction, the newly selected aptamer had 4-fold faster release rate, 2-fold lower affinity, and 15-fold lower apparent Kd to a quencher-labeled DNA acting as a capture strand surrogate. All these properties would favor the selection of this new aptamer. We rationalize the missing of this aptamer in the previous work by the too stringent negative selections. The positive selection needs to be carried out at a concentration a few folds higher the Kd of the best aptamer, whereas the negative selection need to be done at a negative target concentration that does not induce significant removal of the best aptamers. This work not only discovers a nanomolar dopamine aptamer mutant but prompts thinking of the type and concentration of negative targets for aptamer selection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".