Discovering Effective Chiral Dipeptides against Aβ(1-42) Aggregation by the Computational Screening Strategy

Area of Science:

• Biochemistry
• Computational Chemistry
• Neuroscience

Background:

• Amyloidogenic aggregation of amyloid-beta (Aβ) is central to Alzheimer's disease pathogenesis.
• β-sheet-breaker (BSB) peptides show promise in inhibiting this aggregation.
• The anti-aggregation potential of ultrashort chiral dipeptides is not well understood.

Purpose of the Study:

• To develop a computational strategy for identifying effective chiral dipeptides as BSBs against Aβ(1-42) aggregation.
• To discover novel dipeptides with optimal anti-aggregation performance.
• To explore their potential as therapeutic agents for Alzheimer's disease.

Main Methods:

• Construction of a comprehensive chiral dipeptide library.
• Screening via cascaded molecular docking and molecular dynamics (MD) simulations.
• Experimental validation of computationally identified candidates.
• Mechanistic investigation using MD and replica-exchange molecular dynamics (REMD) simulations.

Main Results:

• The computational strategy successfully identified the chiral dipeptide D-W-D-P.
• D-W-D-P demonstrated strong interactions with Aβ fibrils and inhibited Aβ aggregation in experiments.
• MD and REMD simulations revealed D-W-D-P inhibits Aβ aggregation via hydrophobic contacts and hydrogen bonds, disrupting intermolecular interactions and salt bridges.

Conclusions:

• The developed computational screening strategy is effective for discovering potent anti-aggregation dipeptides.
• D-W-D-P shows significant potential as a therapeutic agent for Alzheimer's disease by inhibiting Aβ aggregation.
• This approach offers a viable pathway for identifying novel peptide-based treatments for neurodegenerative diseases.