De Novo Linear Peptides as Potential Recognition Elements and/or Aggregation Modulators of Amyloid-β42 in Alzheimer’s Disease
- 1Faculty of Sciences of the University of Lisbon, BioISI - Biosystems and Integrative Sciences Institute, C8, Campo Grande, Lisbon, Portugal, 1749-016
- 2Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais, Lisboa, Portugal, 1049-001
- 3Instituto de Engenharia de Sistemas e Computadores - Investigacão e Desenvolvimento, Rua Alves Redol, Lisboa, Portugal, 1000-029
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.
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BibTeX
@misc{rocha2026novo,
title = {{De Novo Linear Peptides as Potential Recognition Elements and/or Aggregation Modulators of Amyloid-β42 in Alzheimer’s Disease}},
author = {C. Rocha and G. Martins and A. Vieira and G. Simplício and S. Varanda and F. Fernandes and N. Galamba},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008448/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008448/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - De Novo Linear Peptides as Potential Recognition Elements and/or Aggregation Modulators of Amyloid-β42 in Alzheimer’s Disease
AU - C. Rocha
AU - G. Martins
AU - A. Vieira
AU - G. Simplício
AU - S. Varanda
AU - F. Fernandes
AU - N. Galamba
PY - 2026
DA - 2026/09/07
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008448/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008448/v1
AB - 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.
ER -