Combining statistical, machine learning and experimental approaches for screening of novel antimicrobial peptides of calf cruor hydrolysates
Bibliographic record
Abstract
Producing bioactive peptides through enzymatic hydrolysis is one of the most promising strategies for valorizing food by-products such as slaughterhouse blood. However, identifying the peptides responsible for the bioactivity of raw hydrolysates is difficult due to the large number of peptide sequences released during the enzymatic process. This study presents for the first time an integration of conventional statistical and machine learning tools to discover new antimicrobial peptides from calf cruor (C-cru), based on experimental data of the antimicrobial activity of raw hydrolysates and their peptide population. Pearson correlation (P-corr), linear regression (LR), and Random Forest (RF) were used to explain the relationship between peptide population abundance and antimicrobial activities (antibacterial, anti-mold, and anti-yeast) of raw hydrolysates of C-cru peptides. Peptides having greater importance in explaining the antimicrobial activities of hydrolysates were selected and their in vitro antimicrobial activity was further assessed by chemical synthesis. As a result, three new peptide sequences with fungicidal effect were identified: α(87–98), β(126–145), and β(128–145). This innovative approach can accelerate the discovery of new antimicrobial peptides from hemoglobin hydrolysates, which could be useful for further separation and application of peptides in food biopreservation. • Statistical/ML method proposed to elucidate bioactive peptides in raw hydrolysates. • This approach identified potential antimicrobial peptides from C-cru hydrolysates. • Three new antifungal peptide α(87–98), β(126–145), and β(128–145) were identified.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".