Identification of zinc-binding peptides in ADAM17-inhibiting whey protein hydrolysates using IMAC-Zn2+ coupled with shotgun peptidomics
Why this work is in the frame
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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-Zn 2+ ) 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-Zn 2+ 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 it