Development of DNA Nanostructures for High-Affinity Binding to Human Serum Albumin
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Bibliographic record
Abstract
The development of nucleic acid therapeutics has been hampered by issues associated with their stability and in vivo delivery. To address these challenges, we describe a new strategy to engineer DNA structures with strong binding affinity to human serum albumin (HSA). HSA is the most abundant protein in the blood and has a long circulation half-life (19 days). It has been shown to hinder phagocytosis, is retained in tumors, and aids in cellular penetration. Indeed, HSA has already been successfully used for the delivery of small-molecule drugs and nanoparticles. We show that conjugating dendritic alkyl chains to DNA creates amphiphiles that exhibit high-affinity (Kd in low nanomolar range) binding to HSA. Notably, complexation with HSA did not hinder the activity of silencing oligonucleotides inside cells, and the degradation of DNA strands in serum was significantly slowed. We also show that, in a site-specific manner, altering the number and orientation of the amphiphilic ligand on a self-assembled DNA nanocube can modulate the affinity of the DNA cage to HSA. Moreover, the serum half-life of the amphiphile bound to the cage and the protein was shown to reach up to 22 hours, whereas unconjugated single-stranded DNA was degraded within minutes. Therefore, adding protein-specific binding domains to DNA nanostructures can be used to rationally control the interface between synthetic nanostructures and biological systems. A major challenge with nanoparticles delivery is the quick formation of a protein corona (i.e., protein adsorbed on the nanoparticle surface) upon injection to biological media. We foresee such DNA cage-protein complexes as new tools to study the role of this protein adsorption layer with important implications in the efficient delivery of RNAi therapeutics in vitro and in vivo.
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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 | 0.000 |
| 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 | 0.001 | 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 it