A computational study of self-assembled hexapeptide inhibitors against amyloid-β (Aβ) aggregation

Abstract

The fibrillation and deposition of amyloid-β (Aβ) peptides in human brains are pathologically linked to Alzheimer's disease (AD). Development of different inhibitors (peptides, organic molecules, and nanoparticles) to prevent Aβ aggregation becomes a promising therapeutic strategy for AD treatment. We recently propose a “like-interacts-like” design principle to computationally design/screen and experimentally validate a new set of hexapeptide inhibitors with completely different sequences from the Aβ sequence. These hexapeptide inhibitors inhibit Aβ aggregation and reduce Aβ-induced cytotoxicity. However, inhibitory mechanisms of these hexapeptides and the underlying interactions between hexapeptides and Aβ remain unclear. Herein we apply multi-scale computational methods (quantum-chemical calculations, molecular docking and explicit-solvent molecular dynamic simulation) to explore the structure, dynamics, and interaction between 3 identified hexapeptides (CTLWWG, GTVWWG, and CTIYWG) and different Aβ-derived fragments and an Aβ17–42 pentamer. When interacting with 6 Aβ-derived fragments, 3 hexapeptide inhibitors show stronger interactions with two lysine-included fragments (₁₆KLVFFA₂₁ and ₂₇NKGAII₃₃) than other fragments, indicating different sequence-specific interactions with Aβ. When interacting with the Aβ17–42 pentamer, the 3 peptides show similar binding modes and interaction mechanisms by preferentially binding to the edge of the Aβ17–42 pentamer to potentially block the Aβ elongation pathway. This work provides structural-based binding information on further modification and optimization of these peptide inhibitors to experimentally enhance their inhibitory abilities against Aβ aggregation.