An in solution assay for interrogation of affinity and rational minimer design for small molecule-binding aptamers
Bibliographic record
Abstract
Aptamers are short single-stranded oligonucleotides that fold into unique three-dimensional structures, facilitating selective and high affinity binding to their cognate targets. It is not well understood how aptamer-target interactions affect regions of structure in an aptamer, particularly for small molecule targets where binding is often not accompanied by a dramatic change in structure. The DNase I footprinting assay is a classical molecular biology technique for studying DNA-protein interactions. The simplest application of this method permits identification of protein binding where DNase I digestion is inhibited. Here, we describe a novel variation of the classical DNase I assay to study aptamer-small molecule interactions. Given that DNase I preferentially cleaves duplex DNA over single-stranded DNA, we are able to identify regions of aptamer structure that are affected by small molecule target binding. Importantly, our method allows us to quantify these subtle effects, providing an in solution measurement of aptamer-target affinity. We applied this method to study aptamers that bind to the mycotoxin fumonisin B1, allowing the first identification of high affinity putative minimers for this important food contaminant. We confirmed the binding affinity of these minimers using a magnetic bead binding assay.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".