Anti Amyloid‐Beta Aggregation Activity of Kefir‐Derived Peptides
Bibliographic record
Abstract
BACKGROUND: Kefir is a fermented beverage rich in beneficial probiotics, and its water-soluble <10kDa fraction has demonstrated antioxidant activity, acetylcholinesterase inhibition, and neuroprotection in Drosophila melanogaster Alzheimer's disease (AD) models. Using in silico mutagenesis, we designed mutated versions of two kefir-derived peptides (KDPs) to enhance their binding affinity to amyloid-beta (Aβ) and their potential to cross the blood-brain barrier (BBB). We then evaluated their effectiveness in preventing or disrupting Aβ plaque formation in vitro. METHOD: KDPs were mutated using ToxinPred. ExPASy PeptideCutter yielded digested KDPs (dKDPs). Bioactivity and BBB permeability were predicted with PeptideRanker and BBPpred. Mutated KDPs (mKDPs) and dKDPs were docked with Aβ monomers using ClusPro. Top mKDPs (1, 2, 3) and dKDP were synthesized and tested in a thioflavin T aggregation assay. For early treatment, peptides (1, 10 and 100 µM) were added with Aβ, and fluorescence was measured hourly for 24h. For late treatment, peptides (10 µM) were added after 48h of the addition of Aβ, with a reading at 96h. Statistical analysis used repeated measures one-way ANOVA. RESULT: In early treatment, mKDP1 reduced Aβ aggregation by 23%, mKDP2 by 56%, mKDP3 by 16%, and dKDP by 57% after 24 hours (p<0.0001 for all comparisons). In late treatment, mKDP1 and mKDP2 reduced Aβ aggregation by approximately 45% (p=0.0002 and p=0.0001, respectively), while mKDP3 and dKDP showed no significant effects. CONCLUSION: All peptides showed anti-Aβ aggregation effects in early administration, and mKDP1 and mKDP2 in late stages. Further in vivo studies are needed to validate these findings.
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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) | 0.000 | 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".