Metal-Chelating Peptides Derived from Sunflower Meal Protein: Preparation, Isolation, Identification, and Antioxidant Properties
Why this work is in the frame
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Bibliographic record
Abstract
Sunflower meal (SM), a byproduct of sunflower seed oil extraction, contains approximately 30–50% proteins. Recognized for its high protein content and bioavailability, it is suitable for bioactive peptides production. Yet, research exploring the potential of sunflower proteins for generating metal-chelating peptides (MCPs) is sparse. Therefore, this study aimed to evaluate SM as a protein source to produce MCPs. After protein extraction to obtain a sunflower protein isolate, single and sequential enzymatic treatments were applied to produce hydrolysates using protamex (Prot) and protamex followed by Flavourzyme (Prot + Flav), respectively. Upon sequential treatment, effectively a large number of peptide bonds were cleaved, releasing mainly small-sized peptides. Prot hydrolysates exhibited the highest Fe 2+ -chelating properties, inhibition of Cu 2+ -induced reactive oxygen species (ROS) production, and ABTS scavenging activities. Besides, sequential hydrolysis with both enzymes enhanced the inhibition of Fe 3+ -induced ROS production and reducing power. The Cu 2+ -chelating peptides present in hydrolysates were separated by using immobilized metal ion affinity chromatography (IMAC-Cu 2+ ) and identified by LC–MS/MS analysis. MS/MS analysis of enriched Cu 2+ -binding peptide fractions unveiled twenty-nine potential His-containing MCPs from SMPI hydrolysate. The molecular weight of Cu 2+ -identified peptides ranged from 0.8 to 1.7 kDa, with the larger-sized peptides (>1 kDa) presenting the most effective bioactive properties. His, Glu, and Asp residues were crucial for metal-chelating and antioxidant properties. Due to their high potential to bind Cu 2+, Fe 2+, and Fe 3+, SM peptides could serve as potential MCP candidates for use as food or pharmaceutical agents to prevent metal-induced oxidation and related diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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