Enzyme-Operated DNA-Based Nanodevices
Bibliographic record
Abstract
Functional molecular nanodevices and nanomachines have attracted a growing interest for their potential use in life science and nanomedicine. In particular, due to their versatility and modularity DNA-based nanodevices appear extremely promising. However, a limitation of such devices is represented by the limited number of molecular stimuli and cues that can be used to control and regulate their function. Here we demonstrate the possibility to rationally control and regulate DNA-based nanodevices using biocatalytic reactions catalyzed by different enzymes. To demonstrate the versatility of our approach, we have employed three model DNA-based systems and three different enzymes (belonging to several classes, i.e., transferases and hydrolases). The possibility to use enzymes and enzymatic substrates as possible cues to operate DNA-based molecular nanodevices will expand the available toolbox of molecular stimuli to be used in the field of DNA nanotechnology and could open the door to many applications including enzyme-induced drug delivery and enzyme-triggered nanostructures assembly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".