Emulsion PCR Significantly Improves Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures-Based Aptamer Selection: Allowing for Efficient and Rapid Selection of Aptamer to Unmodified ABH2 Protein
Bibliographic record
Abstract
Nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM), a homogeneous approach to select DNA aptamers, is among the most efficient partitioning techniques. In contrast with surface-based systematic evolution of ligands by exponential enrichment (SELEX) approaches, the ability of NECEEM to select aptamers to unmodified proteins in solution is preferable for identifying aptamers for eventual in vivo use. The high stringency and low sample volumes of NECEEM, although generally beneficial, can result in binding of very few aptamers, requiring highly efficient amplification to propagate them. When amplified with standard PCR, detectable library enrichment can fail due to the fast conversion of the aptamers into byproducts and preferential amplification of nonbinders. As an alternative, we proposed the use of emulsion PCR (ePCR), which is known to reduce byproduct formation, as a PCR mode for coupling with NECEEM partitioning. For the first time, we tested the advantages of ePCR in NECEEM-based aptamer selection to a medically relevant DNA repair enzyme, ABH2. We report that the combination of ePCR with NECEEM allowed for the selection of aptamers in the first three rounds of SELEX, while SELEX with conventional PCR failed in a number of attempts. Selected aptamers to an unmodified ABH2 protein have potential use in diagnostics and as leads for anticancer cotherapies, used as enhancements of alkylating agents in chemotherapy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".