Kinetic ITC of DNA Aptamers Binding for Small Molecules and Implications for Binding Assays and Biosensors
Bibliographic record
Abstract
Abstract The determination of k on and k off values through kinetic analysis is crucial for understanding the intricacies of aptamer‐target binding interactions. By employing kinetic ITC, we systematically analyzed a range of ITC data of various aptamers. Upon plotting their k on and k off values as a function of their K d values, a notable trend emerged. Across a range of K d values spanning from 28 nM to 864 μM, the k on value decreased from 2×10 5 M −1 s −1 to 96 M −1 s −1 , whereas the k off value increased from 1.03×10 −3 s −1 to 0.012 s −1 . Thus, both k on and k off contributed to the change of K d in the same direction, although the range of k on change was larger. Since experiments are often run at close to the K d value, this concentration effect also played an important role in the observed binding kinetics. The effect of these kinetic parameters on two common sensing mechanisms, including aptamer beacons and strand‐displacement assays, are discussed. This work has provided the kinetic values of small molecule binding aptamers and offered insights into aptamer‐based biosensors.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".