Amyloid-β<sub>25–35</sub>peptides aggregate into cross-β sheets in unsaturated anionic lipid membranes at high peptide concentrations
Bibliographic record
Abstract
One of the hallmarks of Alzheimer's disease is the formation of protein plaques in the brain, which mainly consist of amyloid-β peptides of different lengths. While the role of these plaques in the pathology of the disease is not clear, the mechanism behind peptide aggregation is a topic of intense research and discussion. Because of their simplicity, synthetic membranes are promising model systems to identify the elementary processes involved. We prepared unsaturated zwitterionic/anionic lipid membranes made of 1-palmitoyl-2-oleoyl-sn-glycero-phosphocholine (POPC) and 1,2-dimyristoyl-sn-glycero-3-phospho-l-serine (DMPS) at concentrations of POPC/3 mol% DMPS containing 0 mol%, 3 mol%, 10 mol%, and 20 mol% amyloid-β25-35 peptides. Membrane-embedded peptide clusters were observed at peptide concentrations of 10 and 20 mol% with a typical cluster size of ∼11 μm. Cluster density increased with peptide concentration from 59 (±3) clusters per mm(2) to 920 (±64) clusters per mm(2), respectively. While monomeric peptides take an α-helical state when embedded in lipid bilayers at low peptide concentrations, the peptides in peptide clusters were found to form cross-β sheets and showed the characteristic pattern in X-ray experiments. The presence of the peptides was accompanied by an elastic distortion of the bilayers, which can induce a long range interaction between the peptides. The experimentally observed cluster patterns agree well with Monte Carlo simulations of long-range interacting peptides. This interaction may be the fundamental process behind cross-β sheet formation in membranes and these sheets may serve as seeds for further growth into amyloid fibrils.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".