Anti-Salmonella Activity and Peptidomic Profiling of Peptide Fractions Produced from Sturgeon Fish Skin Collagen (Huso huso) Using Commercial Enzymes
Bibliographic record
Abstract
This study investigated peptide fractions from fish skin collagen for antibacterial activity against Escherichia coli and Salmonella strains. The collagen was hydrolyzed with six commercial proteases, including trypsin, Alcalase, Neutrase, Flavourzyme, pepsin and papain. Hydrolyzed samples obtained with trypsin and Alcalase had the largest number of small peptides (molecular weight <10 kDa), while the hydrolysate produced with papain showed the lowest degree of hydrolysis and highest number of large peptides. Four hydrolysates were found to inhibit the growth of the Gram-negative bacteria, with papain hydrolysate showing the best activity against E. coli, and Neutrase and papain hydrolysates showing the best activity against S. abony; hydrolysates produced with trypsin and pepsin did not show detectable antibacterial activity. After acetone fractionation of the latter hydrolysates, the peptide fractions demonstrated enhanced dose-dependent inhibition of the growth (colony-forming units) of four Salmonella strains, including S. abony (NCTC 6017), S. typhimurium (ATCC 13311), S. typhimurium (ATCC 14028) and S. chol (ATCC 10708). Shotgun peptidomics analysis of the acetone fractions of Neutrase and papain hydrolysates resulted in the identification of 71 and 103 peptides, respectively, with chain lengths of 6–22 and 6–24, respectively. This work provided an array of peptide sequences from fish skin collagen for pharmacophore identification, structure–activity relationship studies, and further investigation as food-based antibacterial agents against pathogenic microorganisms.
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 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 | 0.000 |
| 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".