De Novo Linear Peptides as Potential Recognition Elements and/or Aggregation Modulators of Amyloid-β42 in Alzheimer’s Disease
Year: 2026
Type: preprint
Abstract: The development of deep learning algorithms for de novo protein design has opened the way for novel in silico strategies to develop peptide binders targeting protein aggregation implicated in neurodegenerative diseases. When combined with molecular simulations, these approaches provide a promising framework for investigating protein-peptide recognition and its impact on aggregation. Herein, we studied, through molecular dynamics, four de novo peptides of sizes ranging between 7 and 15 residues, generated with RFdiffusion, combined with ProteinMPNN and AlphaFold, using different constrained sequences of the Aβ42 peptide and a common hotspot region (residues 16-22). The shorter peptides formed cross-β sheet structures with the hotspot sequence, whereas the longer peptides adopted α-helical conformations. The hotspot sequence in trans and the segment Aβ42(16-20) linked to an hexalysine sequence were also investigated. Aβ42 was modeled using CHARMM36m (C36m) and AMBER99SB-DISP force fields, with the latter showing an aggregation propensity comparable to C36m under 8 M urea denaturing conditions. The peptides’ potential to modulate aggregation was assessed for C36m Aβ42 model tetramers at 1:1 peptide-to-monomer ratios. Although none of the peptides prevented the formation of all possible oligomeric species (i.e., dimer, trimer, and tetramer), several, selectively inhibited or delayed tetramer formation within the simulation timescale, resulting in less compact assemblies. The underlying mechanisms differed among peptides, with some altering intermolecular β-sheet organization through persistent interactions with the C-terminal region and others influencing tetramer formation with limited participation in βsheet interactions. The behavior of the peptides 16-22 (KLVFFAE) and the 16-20-KKKKKK closely reproduced experimental observations. At high temperatures none of the peptides precluded tetramerization, suggesting that enhanced hydrophobic interactions outweigh peptide-mediated inhibition. Notably, charged peptides showed the strongest effects on tetramer formation, indicating an important role for electrostatic interactions in inhibiting Aβ42 aggregation.
Source: ChemRxiv
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7211972550