Modulating Nanoparticle Superlattice Structure Using Proteins with Tunable Bond Distributions
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Herein, we investigate the use of proteins with tunable DNA modification distributions to modulate nanoparticle superlattice structure. Using beta-galactosidase (βgal) as a model system, we have employed the orthogonal chemical reactivities of surface amines and thiols to synthesize protein–DNA conjugates with 36 evenly distributed or 8 specifically positioned oligonucleotides. When these are assembled into crystalline superlattices with gold nanoparticles, we find that the distribution of DNA modifications modulates the favored structure: βgal with uniformly distributed DNA bonding elements results in body-centered cubic crystals, whereas DNA functionalization of cysteines results in AB 2 packing. We probe the role of protein oligonucleotide number and conjugate size on this observation, which revealed the importance of oligonucleotide distribution in this observed assembly behavior. These results indicate that proteins with defined DNA modification patterns are powerful tools for controlling nanoparticle superlattices architecture, and establish the importance of oligonucleotide distribution in the assembly behavior of protein–DNA conjugates.
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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 | 0.000 |
| 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 | 0.001 |
| 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".