Analytical model and multiscale simulations of Aβ peptide aggregation in lipid membranes: towards a unifying description of conformational transitions, oligomerization and membrane damage
Bibliographic record
Abstract
The mechanisms underlying the formation of extracellular amyloid plaques on neuronal membranes, a major hallmark of Alzheimer's disease, are the subject of intense debate. Here we use multiscale simulations and analytical theory to unveil the early steps of the spontaneous self-assembly of membrane-embedded α-helical Aβ (1-40) peptides. Based on a simple analytical model describing the electrostatic repulsions among water-exposed charged residues, the presence of distorted structures called "frustrated helices" is predicted. Large scale (20 μs) Coarse Grained simulations of 36 replicas of Aβ (1-40) performed within a POPC lipid matrix confirmed the formation of supramolecular assemblies which resemble a twisted ribbon. Fully atomistic simulations have demonstrated the stability of these helical structures. Concomitant to the formation of these large assemblies, CG simulations evidenced membrane curvature and substantiate the view that these assemblies may entail mechanical stress on membrane structure. We think that these findings provide an alternative view to the traditional models that consider a conformational transition towards β-sheet rich structures as a prerequisite for triggering membrane damage and, eventually, neurotoxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".