Selection of Smart Aptamers by Equilibrium Capillary Electrophoresis of Equilibrium Mixtures (ECEEM)
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Bibliographic record
Abstract
We coin a term of "smart aptamers", which describes aptamers with predefined binding parameters of their interaction with the target. Here, we introduce a method for selection of smart aptamers with predefined values of Kd: equilibrium capillary electrophoresis of equilibrium mixtures (ECEEM). Conceptually, a mixture of a target with a DNA (RNA) library is prepared and equilibrated. A plug of the equilibrium mixture is injected into a capillary prefilled with a run buffer containing the target at the concentration identical to the target concentration in the equilibrium mixture. The components of the equilibrium mixture are separated by capillary electrophoresis while equilibrium is maintained between the target and aptamers. The unique feature of ECEEM is that aptamers with different Kd values migrate with different and predictable mobilities. Thus, collecting fractions with different mobilities results in smart aptamers with different and predefined Kd values. In this proof-of-principle work, we used ECEEM to select smart aptamers for MutS protein, for which aptamers have never been previously selected. Three rounds of ECEEM-based selection were sufficient to obtain smart aptamers with Kd values approaching theoretically predicted ones. ECEEM is the first method for aptamer selection whose ability to generate smart aptamers has been experimentally proven.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it