Effect of Cu2+ coordination on peptide self-assembly, mechanical properties, and antioxidant activity of peptide hydrolysates obtained from sunflower meal protein isolate
Bibliographic record
Abstract
Due to the diversity of their sequence, peptides can arrange themselves into organized nanostructures through a spontaneous and reversible process. Several strategies have proven to trigger peptide self-assembly to design biomaterials; yet, the use of coordinating Cu 2+ in sunflower hydrolysates has not been exploited to date. Thus, the effect of Cu 2+ coordination on peptide self-assembly of hydrolysates from sunflower meal-by-product was studied. Two enzymatic treatments (Protamex®, Prot; and Protamex® followed by Flavourzyme®, Prot+Flav) were applied to produce whole and ≤ 1 kDa hydrolysates. Cu 2+ coordination by peptides in hydrolysates was demonstrated by Uv-vis and CD, pyrocatechol violet assay and nano-LC-MS/MS, while the self-assembly pattern of peptides upon Cu 2+ coordination was revealed by ThT, ANS, DLS and TEM techniques. Upon Cu 2+ coordination, tunable self-assembling peptide nanostructures endowed with improved physico-chemical were generated. Besides, the yield stress and G’ values across most hydrolysates increased in the Peptide-Cu 2+ complexes. These properties depended not only on the number of Cu 2+ binding peptides but also on the binding affinity, which were different among the hydrolysates. Finally, the antioxidant assay revealed a distinct activity of the ≤ 1 kDa Prot containing ATCUN peptides. Indeed, similar to the activity of EDTA, this fraction extended the lifespan of C. elegans over 3 generations under Cu 2+ -induced oxidative stress conditions. This study represents a feasible strategy to explore Cu 2+ coordination for the formation of food-derived materials via His and Cys-containing peptides, and may provide the basis for applications of Cu 2+ -chelated supramolecular materials, including injectable drug delivery systems and nutraceutical products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".