Selection of Smart Aptamers by Methods of Kinetic Capillary Electrophoresis
Bibliographic record
Abstract
We coin the term "smart aptamers" -- aptamers with predefined binding parameters (k(on), k(off), Kd) of aptamer-target interaction. Aptamers, in general, are oligonucleotides, which are capable of binding target molecules with high affinity and selectivity. They are considered as potential therapeutic targets and also thought to rival antibodies in immunoassay-like analyses. Aptamers are selected from combinatorial libraries of oligonucleotides by affinity methods. Until now, technological limitations have precluded the development of smart aptamers. Here, we report on two kinetic capillary electrophoresis techniques applicable to the selection of smart aptamers. Equilibrium capillary electrophoresis of equilibrium mixtures was used to develop aptamers with predefined equilibrium dissociation constants (Kd), while nonequilibrium capillary electrophoresis of equilibrium mixtures facilitated selection of aptamers with different dissociation rate constants (k(off)). Selections were made for MutS protein, for which aptamers have never been previously developed. Both theoretical and practical aspects of smart aptamer development are presented, and the advantages of this new type of affinity probes are described.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".