Kinetic and Equilibrium Binding Characterization of Aptamers to Small Molecules using a Label-Free, Sensitive, and Scalable Platform
Bibliographic record
Abstract
Nucleic acid aptamers function as versatile sensing and targeting agents for analytical, diagnostic, therapeutic, and gene-regulatory applications, but their limited characterization and functional validation have hindered their broader implementation. We report the development of a surface plasmon resonance-based platform for rapid characterization of kinetic and equilibrium binding properties of aptamers to small molecules. Our system is label-free and scalable and enables analysis of different aptamer-target pairs and binding conditions with the same platform. This method demonstrates improved sensitivity, flexibility, and stability compared to other aptamer characterization methods. We validated our assay against previously reported aptamer affinity and kinetic measurements and further characterized a diverse panel of 12 small molecule-binding RNA and DNA aptamers. We report the first kinetic characterization for six of these aptamers and affinity characterization of two others. This work is the first example of direct comparison of in vitro selected and natural aptamers using consistent characterization conditions, thus providing insight into the influence of environmental conditions on aptamer binding kinetics and affinities, indicating different possible regulatory strategies used by natural aptamers, and identifying potential in vitro selection strategies to improve resulting binding affinities.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".