Exploring the Lower Limit of Target Concentration in Capture‐SELEX Using Guanine as a Model Target
Bibliographic record
Abstract
Abstract During an aptamer selection, using a lower target concentration may result in aptamers with a higher binding affinity. Consequently, this begs the question of whether there is a lower limit for target concentration. In this work, we conducted three aptamer selections using 5 μM, 500 nM and 50 nM guanine as the targets, respectively. Successful enrichment of the same guanine aptamers was achieved at both 5 μM and 500 nM guanine, but not with 50 nM. Using 5 μM guanine, the aptamer was enriched in eight rounds of selection, compared to that for 500 nM, which was accomplished in 17 rounds. We discuss the relation of optimal target concentration to the observed Kd value of the resulting aptamers, of which the highest affinity aptamer had a measured Kd of 200 nM. Additionally, we investigated the binding of the aptamers through mutation studies, revealing a critical cytosine. Mutating this cytosine to a thymine switched the selectivity from guanine to adenine, which is reminiscent of the guanine riboswitch. This study revealed a limit in using low target concentration, and the insights described in this article will be useful for guiding the choice of target concentration during capture‐SELEX.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".