The Impact of Natural Compounds on S-Shaped Aβ42 Fibril: From Molecular Docking to Biophysical Characterization

Abstract

The pursuit for effective strategies inhibiting the amyloidogenic process in neurodegenerative disorders, such as Alzheimer's disease (AD), remains one of the main unsolved issues, and only a few drugs have demonstrated to delay the degeneration of the cognitive system. Moreover, most therapies induce severe side effects and are not effective at all stages of the illness. The need to find novel and reliable drugs appears therefore of primary importance. In this context, natural compounds have shown interesting beneficial effects on the onset and progression of neurodegenerative diseases, exhibiting a great inhibitory activity on the formation of amyloid aggregates and proving to be effective in many preclinical and clinical studies. However, their inhibitory mechanism is still unclear. In this work, ensemble docking and molecular dynamics simulations on S-shaped Aβ₄₂ fibrils have been carried out to evaluate the influence of several natural compounds on amyloid conformational behaviour. A deep understanding of the interaction mechanisms between natural compounds and Aβ aggregates may play a key role to pave the way for design, discovery and optimization strategies toward an efficient destabilization of toxic amyloid assemblies.

Keywords: Alzheimer’s disease; Amyloid β; S-shape; ensemble docking; molecular dynamics; natural compounds.

Figure 1

The ten best natural compounds that exhibited the lowest MM–GBSA binding energies for the selected S-shape amyloid fibril.

Figure 2

Representative snapshots of the three different mechanisms of action of selected natural compounds: interchain destabilization, pocket distortion and pocket stabilization. For each mechanism the (A) starting configurations after the docking protocol and the (B) final structures after 150 ns of molecular dynamics (MD) simulation are shown. The ligands are represented in red, while the amyloid fibrils and their residues within 0.35 nm from the ligand are represented in grey and yellow, respectively.

Figure 3

(A) Beta-sheet structure probability, (B) order parameter and (C) inter-chain interaction area for the wild type amyloid fibrils and all the receptor–ligand complexes.

Figure 4

Contact probability between the selected natural compounds and the amyloid residues during the MD simulations.

Figure 5

Pharmacophore model based on shared features between (A) 6-shogaol and oleuropein and (B) curcumin, gossypin and piceatannol. HBA identifies a hydrogen bond acceptor, HBD a hydrogen bond donor, AR an aromatic ring and H a hydrophobic interaction.