Purification of Extracellular Protease from Staphylococcus simulans QB7and Its Ability in Generating Antioxidant and Anti-inflammatory Peptides from Meat Proteins
Bibliographic record
Abstract
Proteases, especially microbial proteases, are widely used in food processing. The purpose of this study was aimed to purify an extracellular protease produced by the strain Staphylococcus simulans QB7 and to evaluate its ability in hydrolyzing meat proteins and generating antioxidant and anti-inflammatory peptides. The optimal conditions for producing the enzyme were as follows: inoculum ratio, 10%; initial pH, 6.5; temperature, 32 °C; incubation time, 36 h; and rotation speed, 160 rpm. The protease had a molecular weight of approximately 47 kDa, possessing the optimal activity at 50 °C, pH 7.0, The protease was stable at pH 4.0–8.0 and 30–60 °C, and the activity was improved by Na+, Mg2+, Ca2+, and Zn2+ ions, whereas it was inhibited by Cu2+, Co2+, Fe3+, Ba2+, Fe2+, β-M, and ethylene diamine tetraacetic acid disodium salt (EDTA). The protease could effectively hydrolyze meat proteins, and the generated hydrolysate could significantly inhibit tumor necrosis factor-alpha (TNFα)-induced oxidative stress, including superoxide and malondialdehyde levels and inflammation (vascular adhesion molecule-1 [VCAM-1] and cyclooxygenase 2 [COX2)) in human vascular EA.hy926 cells. The present findings support the ability of S. simulans QB7 protease in generating antioxidant and anti-inflammatory peptides during the fermentation of meat products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".