Sequence-Defined DNA Polymers: New Tools for DNA Nanotechnology and Nucleic Acid Therapy
Bibliographic record
Abstract
Conspectus Structural DNA nanotechnology offers a unique self-assembly toolbox to construct soft materials of arbitrary complexity, through bottom-up approaches including DNA origami, brick, wireframe, and tile-based assemblies. This toolbox can be expanded by incorporating interactions orthogonal to DNA base-pairing such as metal coordination, small molecule hydrogen bonding, π-stacking, fluorophilic interactions, or the hydrophobic effect. These interactions allow for hierarchical and long-range organization in DNA supramolecular assemblies through a DNA-minimal approach: the use of fewer unique DNA sequences to make complex structures. Here we describe our research group’s work to integrate these orthogonal interactions into DNA and its supramolecular assemblies. Using automated solid phase techniques, we synthesized s equence-defined D NA p olymers (SDPs) featuring a wide range of functional groups, achieving high yields in the process. These SDPs can assemble into not only isotropic spherical morphologies─such as spherical nucleic acids (SNAs)─but also into anisotropic nanostructures such as 1D nanofibers and 2D nanosheets. Our structural and molecular modeling studies revealed new insights into intermolecular chain packing and intramolecular chain folding, influenced by phosphodiester positioning and SDP sequence. Using these new self-assembly paradigms, we created hierarchical, anisotropic assemblies and developed systems exhibiting polymorphism and chiroptical behavior dependent on the SDP sequence. We could also precisely control the size of our nanofiber assemblies via nucleation-growth supramolecular polymerization and create compartmentalized nanostructures capable of precise surface functionalization. The exquisite control over sequence, composition, and length allowed us to combine our SDPs with nanostructures including DNA wireframe assemblies such as prisms, nanotubes, and cubes to create hybrid, stimuli-responsive assemblies exhibiting emergent structural and functional modes. The spatial control of our assemblies enabled their use as nanoreactors for chemical transformations in several ways: via hybridization chain reaction within SNA coronas, through chemical conjugation within SNA cores, and through a molecular “printing” approach within wireframe assemblies for nanoscale information transfer and the creation of anisotropic “DNA-printed” polymer particles. We have also employed our SDP nanostructures toward biological and therapeutic applications. We demonstrated that our SNAs could serve as both extrinsic and intrinsic therapeutic platforms, with improved cellular internalization and biodistribution profiles, and excellent gene silencing activities. Using SDPs incorporating hydrophobic dendrons, high-affinity and highly specific oligonucleotide binding to human serum albumin was demonstrated. These structures showed an increased stability to nuclease degradation, reduced nonspecific cellular uptake, no toxicity even at high concentrations, and excellent biodistribution beyond the liver, resulting in unprecedented gene silencing activity in various tissues. Control over the sequence has thus presented us with a unique polymeric building block in the form of the SDP, which combines the chemical and structural diversity of polymers with the programmability of DNA. By linking these orthogonal assembly languages, we have discovered new self-assembly rules, created DNA-minimal nanostructures, and demonstrated their utility through a range of applications. Developing this work further will open new avenues in the fields of DNA nanomaterials, nucleic acid therapeutics, as well as block copolymer self-assembly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".