Assembly of 3D DNA architectures: towards minimal design and maximal function
Bibliographic record
Abstract
DNA serves as the essential biomacromolecule responsible for encoding, transmitting and expressing genetic information in all forms of life. However, when taken out of this biological role, the unique self-assembling properties, information rich content and easy means of chemical synthesis make DNA an ideal material for solving some of the challenging problems in chemical construction at the nanometer length scale. The emerging field of supramolecular DNA assembly presents chemical solutions to DNA construction, by synthetically modifying DNA with small molecules and supramolecular motifs. This thesis specifically examines how DNA building blocks modified with synthetic organic, inorganic and polymeric molecules can be designed to efficiently assemble into well-defined 3D structures. In part 1, a modular assembly strategy is developed whereby 2D DNA triangles are efficiently prepared and connected to create the first triangular prismatic structure that can be site-specifically coordinated with transition metals. In part 2, selective introduction of sequence symmetry is utilized to both simplify design and generate an expanded set of 3D DNA geometries in a mild, facile and high yield manner. In parts 3 and 4, a 3D DNA construction method that assembles a minimum number of DNA strands in near quantitative yield, to give a scaffold with a large number of single-stranded arms, is introduced. As demonstrated in part 3, site-specific hybridization of DNA-polymer conjugates to the single-stranded arms of this 3D-DNA scaffold gives efficient access to nanostructured DNA-block copolymer cages with enhanced nuclease resistance. In part 4, it is demonstrated that unfunctionalized 3D DNA cubes efficiently accumulate in the cytoplasm of human cervical cancer cells (HeLa) without the aid of any transfection agent. Collectively, this work develops 'DNA-economic' strategies to assemble 3D DNA structures in a facile manner and excellent yield. These assembly methods lay the foundation for fundamental assessment and future integration of 3D DNA structures as cellular probes or drug delivery tools and as a means to help solve some of the challenges facing researchers in biophysics and nanoscience.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".