Quantitative Characterization of Partitioning in Selection of DNA Aptamers for Protein Targets by Capillary Electrophoresis
Bibliographic record
Abstract
Partitioning of protein–DNA complexes from protein-unbound DNA is a key step in selection of DNA aptamers. Conceptually, the partitioning step is characterized by two parameters: transmittance for protein-bound DNA (binders) and transmittance for unbound DNA (nonbinders). Here we present the first study to reveal how these transmittances depend on experimental conditions; such studies are pivotal to the effective planning and control of selection. Our focus was capillary electrophoresis (CE) which is a partitioning approach of high efficiency. By combining a theoretical model and experimental data, we evaluated the dependence of transmittances of binders and nonbinders on the molecular weight of protein target in two modes of CE-based partitioning: nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) and ideal-filter capillary electrophoresis (IFCE). Our data suggest that as the molecular weight of the protein target decreases: (i) the transmittance for binders remains close to unity in NECEEM but decreases drastically in IFCE and (ii) the transmittance for nonbinders increases orders of magnitude in NECEEM but remains relatively stable at a very low level in IFCE. To determine the optimal CE conditions for a given size of protein target, a balance between transmittances of binders and nonbinder must be reached; such a balance would ensure the collection of binders of sufficient purity and quantity. We conclude that, as a rule of thumb, IFCE is preferable for large-size protein targets while NECEEM should be the method of choice for small-size protein targets
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".