Self-Replicating DNA-Based Nanoassemblies
Bibliographic record
Abstract
The properties of DNA that make it an effective genetic material also allow it to be ideal for programmed self-assembly. Such DNA-programmed assembly has been utilized to construct responsive DNA origami and wireframe nanoassemblies, yet replicating these hybrid nanomaterials remains challenging. Here we report a strategy for replicating DNA wireframe nanoassemblies using the isothermal ligase chain reaction lesion-induced DNA amplification (LIDA). We designed a triangle wireframe structure that can be formed in one step by ring-closing of its linear analog. Introducing a small amount of the wireframe triangle to an excess of the linear analog and complementary fragments, one of which contains a destabilizing abasic lesion, leads to rapid, sigmoidal self-replication of the wireframe triangle via cross-catalysis. Using the same cross-catalytic strategy we also demonstrate rapid self-replication of a hybrid wireframe triangle containing synthetic vertices as well as the self-replication of circular DNA. This work reveals the suitability of isothermal ligase chain reactions such as LIDA to self-replicate complex DNA architectures, opening the door to incorporating self-replication, a hallmark of life, into biomimetic DNA nanotechnology.
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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 | 0.001 |
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".