Mode of Protein Complexes on Gold Nanoparticles Surface: Synthesis and Characterization of Biomaterials for Hemocompatibility and Preferential DNA Complexation
Bibliographic record
Abstract
By using in situ synthesis of gold nanoparticles (Au NPs) in the presence of binary mixtures of cytochrome c (Cyc,c) and bovine serum albumen (BSA) model proteins, we demonstrated a new method of studying protein–protein interactions on the surfaces of nanomaterials. Such interactions were simultaneously evaluated and supported by the molecular dynamics studies in terms of protein docking. Both experimental and theoretical studies collectively indicated a strong complexation among Cyc,c and BSA on the surface of Au NPs with a multipoint anchoring mechanism to Au surface. They also highlighted that the Cyc,c–BSA complex exhibited much stronger surface adsorption rather than Cyc,c or BSA alone. Biofunctional Au NPs thus obtained were tested for hemocompatibility for their possible applications as drug delivery vehicles in systemic circulation by employing the hemolysis. The hemolysis was done for the Au NPs which were coated with entire mixing range of Cyc,c–BSA mixtures to explore the most appropriate mixing compositions of Cyc,c–BSA mixtures for hemocompatibility. In addition, protein coated Au NPs demonstrated strong complexation with DNA which were significantly pronounced for the Cyc,c–BSA complex coated NPs rather than Cyc,c or BSA alone coated NPs. The Cyc,c–BSA docked complex on Au NP surface behaved like a typical helix–turn–helix motif because of the size disparity between a much larger BSA and smaller Cyc,c protein that resulted in stronger complexation with DNA in comparison to surface adsorbed Cyc,c or BSA alone. These finding bear important relevance in biotechnology in terms of gene expression and transcription factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".