Metal-Chelating Peptides Derived from Sunflower Meal Protein: Preparation, Isolation, Identification, and Antioxidant Properties
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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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) | 0.000 | 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".