Identification of zinc-binding peptides in ADAM17-inhibiting whey protein hydrolysates using IMAC-Zn2+ coupled with shotgun peptidomics
Bibliographic record
Abstract
Abstract Food components possessing zinc ligands can be used to inhibit zinc-dependent enzymes. In this study, zinc-binding peptides were derived from whey protein hydrolysates, and their ultrafiltration (> 1 and < 1 kDa) fractions, produced with Esperase (WPH-Esp), Everlase and Savinase. Immobilized metal affinity chromatography (IMAC-Zn2+) increased the zinc-binding capacity of the peptide fraction (83%) when compared to WPH-Esp (23%) and its < 1 kDa fraction (40%). The increased zinc-binding capacity of the sample increased the inhibitory activity against the zinc-dependent “a disintegrin and metalloproteinase 17”. LC-MS/MS analysis using a shotgun peptidomics approach resulted in the identification of 24 peptides originating from bovine β-lactoglobulin, α-lactalbumin, serum albumin, β-casein, κ-casein, osteopontin-k, and folate receptor-α in the fraction. The identified peptides contained different combinations of the strong zinc-binding group of residues, His+Cys, Asp+Glu and Phe+Tyr, although Cys residues were absent in the sequences. In silico predictions showed that the IMAC-Zn2+ peptides were non-toxins. However, the peptides possessed poor drug-like and pharmacokinetic properties; this was possibly due to their long chain lengths (5–19 residues). Taken together, this work provided an array of food peptide-based zinc ligands for further investigation of structure-function relationships and development of nutraceuticals against inflammatory and other zinc-related diseases. Graphical abstract
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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 | 0.000 |
| 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) | 0.001 | 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".