The Interaction between Amyloid-β Peptides and Anionic Lipid Membranes Containing Cholesterol and Melatonin
Bibliographic record
Abstract
One of the hallmarks of Alzheimer's disease is the formation of senile plaques, primarily consisting of amyloid-β (Aβ) peptides. Peptide-membrane and peptide-lipid interactions are thought to be crucial in this process. We studied the interaction of Aβ₁₋₄₂ and Aβ₂₅₋₃₅ peptides with anionic lipid membranes made of dimyristoylphosphatidylcholine (DMPC) and dimyristoylphosphoserine (DMPS) using X-ray diffraction. We compare the experimentally determined electron densities in the gel state of the membranes with density calculations from peptide structures reported in the Protein Data Bank in order to determine the position of the peptide in the bilayers. The full length peptide Aβ₁₋₄₂ was found to embed in the hydrocarbon core of the anionic lipid bilayers. Two populations were found for the Aβ₂₅₋₃₅ peptide: (1) membrane-bound states in the hydrophilic head group region of the bilayers, where the peptides align parallel to the membranes, and (2) an embedded state in the bilayer center. Aging plays an important role in the development of Alzheimer's, in particular with respect to changes in cholesterol and melatonin levels in the brain tissue. Immiscible cholesterol plaques were created by addition of 30 mol% cholesterol to the anionic membranes. The Aβ₂₅₋₃₅ peptides were found to strongly interact with the lipid bilayers, displacing further cholesterol molecules into the plaques, effectively lowering the cholesterol concentration in the membranes and increasing the total fraction of cholesterol plaques. Addition of 30 mol% melatonin molecules to the anionic membranes drastically reduced the population of the membrane-embedded Aβ state. These results present experimental evidence for an interaction between Aβ peptides, melatonin and cholesterol in lipid membranes.
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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) | 0.000 | 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".