Back

Ahead of the membrane curve: in silico insights into amyloid-β aggregation

Maximiano, P.; Hashemi, M.

2026-08-25 biophysics
10.64898/2026.08.22.746319 bioRxiv
Show abstract

Membrane surfaces can accelerate amyloid $\beta$ (A$\beta$) aggregation, yet the role of membrane curvature in this process remains poorly understood. Here, we used multi-million atom all-atom molecular dynamics simulations to compare the adsorption, conformational dynamics, and oligomerization of four A$\beta$42 peptides at a planar neuronal membrane and a highly curved lipid vesicle. For both systems, all peptides adsorbed within the first 2 $\mu$s, but their subsequent behavior differed substantially. The curved membrane exhibited a larger area per lipid and more extensive hydrophobic packing defects, allowing A$\beta$42 to penetrate more deeply and form strong contacts with lipid tails through its central hydrophobic core and C-terminal region. These interactions disrupted a solution-formed dimer and limited peptide-peptide association during the simulated interval. Additionally, vesicle-bound peptides adopted more extended conformations with increased $\beta$-structure and $\beta$-hairpin formation compared with peptides at the planar membrane. A$\beta$42 adsorption was also corelated to lipid reorganization in the vesicle. In contrast, the planar membrane supported weaker adsorption and stable dimer-to-trimer growth but showed little large-scale lipid segregation. These findings reveal that curvature reshapes the early A$\beta$42 aggregation landscape by strengthening peptide-lipid interactions, altering aggregation-prone conformations, and reorganizing membrane domains. Membrane geometry should therefore be considered alongside lipid composition in mechanistic models of A$\beta$42 oligomerization and membrane-associated toxicity.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.