Using Distal-Site Mutations and Allosteric Inhibition To Tune, Extend, and Narrow the Useful Dynamic Range of Aptamer-Based Sensors
Bibliographic record
Abstract
Here we demonstrate multiple, complementary approaches by which to tune, extend, or narrow the dynamic range of aptamer-based sensors. Specifically, we employ both distal-site mutations and allosteric control to tune the affinity and dynamic range of a fluorescent aptamer beacon. We show that allosteric control, achieved by using a set of easily designed oligonucleotide inhibitors that competes against the folding of the aptamer, allows rational fine-tuning of the affinity of our model aptamer across 3 orders of magnitude of target concentration with greater precision than that achieved using mutational approaches. Using these methods, we generate sets of aptamers varying significantly in target affinity and then combine them to recreate several of the mechanisms employed by nature to narrow or broaden the dynamic range of biological receptors. Such ability to finely control the affinity and dynamic range of aptamers may find many applications in synthetic biology, drug delivery, and targeted therapies, fields in which aptamers are of rapidly growing importance.
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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 | 0.000 | 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 | 0.000 |
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".