Isolation and identification of cholesterol esterase and pancreatic lipase inhibitory peptides from brewer's spent grain by consecutive chromatography and mass spectrometry
Bibliographic record
Abstract
The isolation and identification of cholesterol esterase (CE) and pancreatic lipase (PL) inhibitory peptides obtained from the protein hydrolysate of brewer's spent grain (BSG) was performed. BSG peptides were fractionated and purified sequentially by anion exchange, gel filtration (FPLC), and reversed phase high-performance liquid chromatography (RP-HPLC). The fractions obtained from each chromatographic step were collected and the in vitro enzyme inhibitory activity was evaluated. The chromatographic purification process increased the in vitro activities. The most active fractions were evaluated using MALDI-TOF tandem mass spectrometry, which identified three peptides: a peptide with the highest CE inhibition capacity (WNIHMEHQDLTTME) and two peptides with PL inhibition capacity (DFGIASF and LAAVEALSTNG). These three peptides showed hydrophobic and acidic amino acid residues (Asp and Glu) and/or their amines (Asn and Gln), which could be a common feature among lipid-lowering peptides related to CE and PL enzyme inhibition. The in silico studies showed that the three peptides had high hydrophobicity and were susceptible to enzymatic hydrolysis performed by trypsin, pepsin, and pancreatin. The BSG byproduct was a good source of CE and PL inhibitory peptides, thus adding value to this byproduct of the beer industry. This is the first report to demonstrate that BSG peptides can inhibit CE and PL enzymes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".