Modulating the Activity of Protein Conjugated to Gold Nanoparticles by Site-Directed Orientation and Surface Density of Bound Protein
Bibliographic record
Abstract
The key property of protein-nanoparticle conjugates is the bioactivity of the protein. The ability to accurately modulate the activity of protein on the nanoparticles at the interfaces is important in many applications. In the work reported here, modulation of the activity of protein-gold nanoparticle (AuNP) conjugates by specifically orienting the protein and by varying the surface density of the protein was investigated. Different orientations were achieved by introducing cysteine (Cys) residues at specific sites for binding to gold. We chose Escherichia coli inorganic pyrophosphatase (PPase) as a model protein and used site-directed mutagenesis to generate two mutant types (MTs) with a single Cys residue on the surface: MT1 with Cys near the active center and MT2 with Cys far from the active center. The relative activities of AuNP conjugates with wild type (WT), MT1, and MT2 were found to be 44.8%, 68.8%, and 91.2% of native PPase in aqueous solution. Site-directed orientation with the binding site far from the active center thus allowed almost complete preservation of the protein activity. The relative activity of WT and MT2 conjugates did not change with the surface density of the protein, while that of MT1 increased significantly with increasing surface density. These results demonstrate that site-directed orientation and surface density can both modulate the activity of proteins conjugated to AuNP and that orientation has a greater effect than density. Furthermore, increasing the surface density of the specifically oriented protein MT2, while having no significant effect on the specific activity of the protein, still allowed increased protein loading on the AuNP and thus increased the total protein activity. This is of great importance in the study on the interface of protein and nanoparticle and the applications for enzyme immobilization, drug delivery, and biocatalysis.
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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) | 0.000 | 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 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".