Defining the Design Parameters for <i>in Vivo</i> Enzyme Delivery Through Protein Spherical Nucleic Acids
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The translation of proteins as effective intracellular drug candidates is limited by the challenge of cellular entry and their vulnerability to degradation. To advance their therapeutic potential, cell-impermeable proteins can be readily transformed into protein spherical nucleic acids (ProSNAs) by densely functionalizing their surfaces with DNA, yielding structures that are efficiently taken up by cells. Because small structural changes in the chemical makeup of a conjugated ligand can affect the bioactivity of the associated protein, structure–activity relationships of the linker bridging the DNA and the protein surface and the DNA sequence itself are investigated on the ProSNA system. In terms of attachment chemistry, DNA-based linkers promote a sevenfold increase in cellular uptake while maintaining enzymatic activity in vitro as opposed to hexaethylene glycol (HEG, Spacer18) linkers. Additionally, the employment of G-quadruplex-forming sequences increases cellular uptake in vitro up to fourfold. When translating to murine models, the ProSNA with a DNA-only shell exhibits increased blood circulation times and higher accumulation in major organs, including lung, kidney, and spleen, regardless of sequence. Importantly, ProSNAs with an all-oligonucleotide shell retain their enzymatic activity in tissue, whereas the native protein loses all function. Taken together, these results highlight the value of structural design in guiding ProSNA biological fate and activity and represent a significant step forward in the development of intracellular protein-based therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".