Affinity Analysis of a Protein−Aptamer Complex Using Nonequilibrium Capillary Electrophoresis of Equilibrium Mixtures
Bibliographic record
Abstract
We propose a new method that allows the use of low-affinity aptamers as affinity probes in quantitative analyses of proteins. The method is based on nonequilibrium capillary electrophoresis of the equilibrium mixture (NECEEM) of a protein with its fluorescently labeled aptamer. In general, NECEEM of a protein with a fluorescently labeled aptamer generates an electropherogram with three characteristic features: two peaks and an exponential curve. Two peaks correspond to (i) the equilibrium amount of free aptamer in the equilibrium mixture and (ii) the amount of the protein-aptamer complex that remains intact at the time of detection. The exponential part is ascribed to the complex decaying during separation under nonequilibrium conditions. Simple analysis of the three features in experiments with known concentrations of the protein can be used for the determination of the equilibrium dissociation constant, Kd, of the aptamer-protein complex. Similar analysis of the three features in the experiment with unknown concentration of the protein and known Kd value allows the determination of the protein concentration. In this proof-of-principle work, the NECEEM method was applied to the analysis of thrombin using a fluorescein-labeled aptamer under the conditions at which the protein-aptamer complex completely decayed during the separation. We demonstrated that, despite the decay, as few as 4 x 10(6) molecules of the protein could be detected with NECEEM without sacrificing the accuracy. This sensitivity is comparable with that reported by others for the aptamer-based equilibrium method. Thus, the proposed NECEEM-based method allows the use of aptamers for highly sensitive affinity analysis of proteins even when protein-aptamer complexes are unstable.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".