Faculty Opinions recommendation of Nonequilibrium capillary electrophoresis of equilibrium mixtures: a universal tool for development of aptamers.
Bibliographic record
Abstract
Aptamers are DNA (or RNA) ligands selected from large libraries of random DNA sequences and capable of binding different classes of targets with high affinity and selectivity. Both the chances for the aptamer to be selected and the quality of the selected aptamer are largely dependent on the method of selection. Here we introduce selection of aptamers by nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM). The new method has a number of advantages over conventional approaches. First, NECEEM-based selection has exceptionally high efficiency, which allows aptamer development with fewer rounds of selection. Second, NECEEM can be equally used for selecting aptamers and finding their binding parameters. Finally, due to its comprehensive kinetic capabilities, the new method can potentially facilitate selection of aptamers with predefined K(d), k(off), and k(on) of the aptamer-target interaction. In this proof-of-principle work, we describe the theoretical bases of the method and demonstrate its application to a one-step selection of DNA aptamers with nanomolar affinity for protein farnesyltransferase. PMID: 15740156
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.023 |
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".