Complex Donuts: Small Variations in DNA Sequence Dictate Pathway Complexity in DNA Nanotoroids
Bibliographic record
Abstract
Abstract The formation of higher‐order structures in natural biopolymers, such as polypeptides and nucleic acids, is governed by sequence specificity and monomer chemistry. Although nucleic acids can assemble into programmable nanostructures through base‐pairing interactions, their chemical diversity is limited to four nucleobases. DNA amphiphiles overcome this limitation by introducing orthogonal interactions through non‐nucleosidic modifications. These amphiphiles self‐assemble into diverse morphologies, such as spheres, fibers, or sheets, with closely packed, parallel DNA strands on their exterior. This unusual arrangement can give rise to emergent properties absent in simple DNA strands. Here, we show that the precise sequence of single‐stranded DNA, independent of double helix base‐pairing, can be used to program the self‐assembled morphology of DNA amphiphiles. Remarkably, small sequence variations can drive the formation of nonequilibrium DNA nanotoroids, rather than conventional morphologies. The DNA nanotoroids were formed as on‐pathway structures via a competitive mechanism, only when a toroid‐selective DNA sequence was used. They could be stabilized noncovalently by a small molecule cross‐linker or coassembly with a secondary DNA amphiphile. Molecular dynamics simulations demonstrated the dependence of toroid formation on the structure of the end π‐stacking unit. This work introduces a new class of DNA‐based nanotoroid materials with assembly properties controlled by unique sequences, akin to proteins, for applications in cell delivery, nanofiltration, nanoreactors, and materials templation.
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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".