PEG-Grafted Oligolysines Stabilize DNA Origami While Enhancing Receptor-Specific Cell Binding
Bibliographic record
Abstract
DNA nanostructures (DNs) offer programmable platforms for targeted biomedical applications, but their limited stability under physiological conditions has hindered their utility. Protective surface modifiers, or “coatings”, can improve DN stability but often impede access of surface-displayed ligands to cell receptors, reducing receptor engagement and target cell binding. Here, we report polyethylene glycol (PEG)-grafted oligolysine coatings that simultaneously enhance DN structural stability and preserve receptor-specific cell binding. We synthesized a 36-member coating library varying in lysine valency, PEG molecular weight, and grafting density, and identified three formulations that bound DNs with ∼ 6-fold higher affinity and conferred ∼ 30-fold greater cargo stability than the widely used K 10 - b -PEG 5k block copolymer. When functionalized with antibodies, coated DNs selectively engaged Fcγ receptors on DC2.4 dendritic cells─a phagocytic cell line prone to nonspecific interactions with uncoated DNs─achieving a 12-fold increase in binding specificity relative to K 10 - b -PEG 5k . Statistical modeling revealed that optimal performance required coordinated tuning of multiple parameters, underscoring the importance of multiparametric design. This work identifies improved protective coatings for DNA origami and establishes a design framework for engineering biostable, receptor-targeted DNA nanodevices for biological applications.
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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".