Protein mixtures of environmentally friendly zein to understand protein–protein interactions through biomaterials synthesis, hemolysis, and their antimicrobial activities
Bibliographic record
Abstract
Industrially important zein protein has been employed to understand its interactions with two model proteins bovine serum albumin (BSA) and cytochrome c (Cyc,c) following the in vitro synthesis of Au NPs so as to expand its applicability for biological applications. Interactions were studied under the effect of temperature variation by UV-visible and fluorescence emission studies. Temperature induced unfolding in the protein mixtures indicated their degree of mutual interactions through simultaneous nucleation of gold nanoparticles (Au NPs) and their subsequent shape control effects. Zein + BSA mixtures showed favorable protein-protein interactions over the entire mole fraction range with maximum close to x(BSA) = 0.24, whereas zein + Cyc,c showed such interactions only in the zein rich region with significant demixing in the Cyc,c rich region of the mixtures. Both hydrophobic as well as hydrophilic domains in the unfolded states were driving such interactions in the case of zein + BSA mixtures while demixing was the result of the predominant hydrophilic nature of Cyc,c and its self-aggregation behavior in the Cyc,c rich region in contrast to the predominant hydrophobic nature of zein. Zein + BSA mixtures produced small roughly spherical Au NPs fully coated with protein, whereas the demixing zone of zein + Cyc,c mixtures generated highly anisotropic NPs with little protein coating. To explore their biological applications, protein conjugated NPs of both mixtures were subjected to hemolysis where NPs coated with the former mixture showed little hemolysis and may act as drug delivery vehicles in systemic circulation in comparison to the latter. Both kinds of NPs further demonstrated their extraordinary antimicrobial activities with different kinds of strains and proved to be highly important environmentally friendly biomaterials.
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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".