Synergy of Two Assembly Languages in DNA Nanostructures: Self-Assembly of Sequence-Defined Polymers on DNA Cages
Bibliographic record
Abstract
DNA base-pairing is the central interaction in DNA assembly. However, this simple four-letter (A-T and G-C) language makes it difficult to create complex structures without using a large number of DNA strands of different sequences. Inspired by protein folding, we introduce hydrophobic interactions to expand the assembly language of DNA nanotechnology. To achieve this, DNA cages of different geometries are combined with sequence-defined polymers containing long alkyl and oligoethylene glycol repeat units. Anisotropic decoration of hydrophobic polymers on one face of the cage leads to hydrophobically driven formation of quantized aggregates of DNA cages, where polymer length determines the cage aggregation number. Hydrophobic chains decorated on both faces of the cage can undergo an intrascaffold "handshake" to generate DNA-micelle cages, which have increased structural stability and assembly cooperativity, and can encapsulate small molecules. The polymer sequence order can control the interaction between hydrophobic blocks, leading to unprecedented "doughnut-shaped" DNA cage-ring structures. We thus demonstrate that new structural and functional modes in DNA nanostructures can emerge from the synergy of two interactions, providing an attractive approach to develop protein-inspired assembly modules in 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".