Determining the folding and binding free energy of DNA-based nanodevices and nanoswitches using urea titration curves
Bibliographic record
Abstract
DNA nanotechnology takes advantage of the predictability of DNA interactions to build complex DNA-based functional nanoscale structures. However, when DNA functional and responsive units that are based on non-canonical DNA interactions are employed it becomes quite challenging to predict, understand and control their thermodynamics. In response to this limitation, here we demonstrate the use of isothermal urea titration experiments to estimate the free energy involved in a set of DNA-based systems ranging from unimolecular DNA-based nanoswitches to more complex DNA folds (e.g. aptamers) and nanodevices. We propose here a set of fitting equations that allow to analyze the urea titration curves of these DNA responsive units based on Watson-Crick and non-canonical interactions (stem-loop, G-quadruplex, triplex structures) and to correctly estimate their relative folding and binding free energy values under different experimental conditions. The results described herein will pave the way toward the use of urea titration experiments in the field of DNA nanotechnology to achieve easier and more reliable thermodynamic characterization of DNA-based functional responsive units. More generally, our results will be of general utility to characterize other complex supramolecular systems based on different biopolymers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".