Self-Assembly of a Repeatable DNA Nanohinge System Supporting Higher Order Structure Formation
Bibliographic record
Abstract
ABSTRACT DNA base pairs can both encode biological information and be used as a programmable material to build nanostructures with potential application in nanofabrication, data processing and storage, biosensing and drug delivery. Over several decades development of these DNA origami nanostructures has led to increasingly advanced self-assembling nanostructures and molecular machines actuated by various mechanisms such as toehold-mediated strand displacement (TMSD), magnetism and even light. However, scalability remains challenging as using larger scaffold strands can increase the likelihood of kinetic traps and misfolded conformations. Here we describe a repeatable DNA nanohinge system to increase the scalability of existing nanohinge designs for hierarchical assembly of more complex structures with greater degrees of mobility and functionality. The components of this system, comprising two distinct nanohinges, were designed in caDNAno. Structure conformation and stability were simulated using CanDo and MrDNA, and hinge assembly was validated by TEM. Electron micrographs revealed hinge-shaped nanostructures capable of self-assembly into more complex structures, as well as actuation using TMSD through a reversible locking mechanism incorporated into the design. Our work expands the existing utility of DNA nanohinges as building blocks for scalable DNA nanostructures and demonstrates the feasibility of polymerizing hinges in a novel manner for higher order assembly. The enhanced functionality of our dual hinge systems can be employed in future applications requiring greater control and mobility of DNA nanostructures.
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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".