Insights into Molecular Mechanisms of Polyphenols’ Inhibition of Amyloid β Aggregation in Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s Disease (AD) is characterized by the aggregation of amyloid beta (Aβ) peptides, particularly Aβ42, into toxic oligomers and fibrils. Polyphenols, natural compounds with low toxicity and good permeability across the blood-brain barrier, have shown promise in inhibiting Aβ aggregation; however, their mechanisms remain unclear. Using molecular dynamics simulations, we investigated the interactions of six polyphenols—apigenin, catechin, curcumin, EGCG, nobiletin, and resveratrol—with the Aβ42 pentamer, analyzing binding positions, residue contacts, and binding affinities. Binding free energy calculations revealed EGCG and nobiletin as the most effective, with strong binding affinities and the ability to disrupt Aβ aggregates. Detailed structural analyses identified hydrogen bonding with the peptide backbone and hydrophobic interactions with side-chain residues as key mechanisms driving the stable binding. EGCG, with its high hydroxyl group content and strong hydrophobic interactions, emerged as the top candidate. Direct dimer assembly simulations confirmed EGCG’s ability to inhibit Aβ42 aggregation by reducing inter-peptide hydrogen bonding and suppressing secondary structure formation. These findings provide insights for designing optimized polyphenol-based therapeutics to combat AD, advancing the development of effective treatments for neurodegenerative 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".