Smart Aptamers Facilitate Multi-Probe Affinity Analysis of Proteins with Ultra-Wide Dynamic Range of Measured Concentrations
Bibliographic record
Abstract
Protein concentration can vary over several orders of magnitude in many physiological, pathological, and biotechnological processes. Studies of these processes require affinity analysis of proteins with a wide dynamic range of accurately measured concentrations. The wide dynamic range can be achieved with multiple affinity probes that bind the target with significantly different equilibrium constants ( K d ). Every probe in such a multi-probe affinity analysis is responsible for detection of the target in a range of concentrations around its K d value. A multi-probe affinity analysis of proteins has not become practical so far due to the lack of generic affinity probes with a wide range of K d and high selectivity. Kinetic capillary electrophoresis (KCE) has been recently proven to generate smart DNA aptamers with a wide range of predefined values of K d and high selectivity. Here, we demonstrate, for the first time, that such aptamers can facilitate multi-probe affinity analysis of a protein with an ultra-wide dynamic range of measured concentrations. Our results showed that a three-aptamer analysis had a concentration dynamic range of more than 4 orders of magnitude. To the best of our knowledge, this is the widest dynamic range ever reported for affinity analyses of proteins. Advantageously, protein concentration in a multi-aptamers analysis can be determined using a simple calibration-free approach. This work proves that the wide range of predefined binding parameters of smart aptamers can bring new capabilities to quantitative affinity analyses. The same feature of smart aptamers makes them potentially indispensable molecular tools in studies of intracellular processes.
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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 | 0.001 |
| 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".