Regulation of DNA Strand Displacement Using an Allosteric DNA Toehold
Bibliographic record
Abstract
Toehold-mediated DNA strand displacement is the fundamental basis for the construction and operation of diverse DNA devices, including circuits, machines, sensors, and reconfigurable structures. Controllable activation and regulation of toeholds are critical to construct devices with multistep, autonomous, and complex behaviors. A handful of unique toehold activation mechanisms, including toehold-exchange, associative toehold, and remote toehold, have been developed and are often combined to achieve desired strand displacement behaviors and functions. Here we report an allosteric DNA toehold (A-toehold) design that allows the flexible regulation of DNA strand displacement by splitting an input strand into an A-toehold and branch migration domain. Because of its simplicity, the A-toehold mechanism can be a useful addition to the current toolbox of DNA strand displacement techniques. We demonstrated that A-toehold enabled a number of interesting functions that were previously shown using more sophisticated DNA strand displacement systems, including (1) continuously tuning the rate of strand displacement, (2) dynamic control of strand displacement reactions, and (3) selective activation of multiple strand displacement reactions. Moreover, by combining A-toehold and toehold-exchange mechanisms, we have successfully constructed a noncovalent DNA catalysis network that resembles an allosteric enzyme.
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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 | 0.000 | 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) | 0.001 | 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".