Title:	Peptide inhibitor design via machine learning and molecular dynamics
DNr:	NAISS 2025/22-1484
Project Type:	NAISS Small Compute
Principal Investigator:	Najla Hosseini <najla.hosseini@compchem.lu.se>
Affiliation:	Lunds universitet
Duration:	2025-11-03 – 2026-12-01
Classification:	10610
Homepage:	https://github.com/najla23
Keywords:

Abstract

Alzheimer’s disease (AD) is marked by the accumulation of amyloid plaques, which are strongly influenced by ionic environments. High-salt diets have been linked to elevated phosphorylated tau and AD-like pathology, yet the molecular mechanisms by which salt ions and modulators (Li⁺) interact with amyloids remain poorly understood. Li⁺ has been shown to mitigate tau pathology and slow disease progression, while Mg²⁺, essential for neural function, may exert neuroprotective effects, though its role under salt-rich conditions is unclear. We propose an engineering-oriented, molecular-level investigation combining computational chemistry, and AI-driven modeling to: Quantify the effects of salt and modulator ions on amyloid plaque initiation. Identify mutant structures capable of inhibiting plaque formation. The project aims to rational design of chaperone-like peptide inhibitors, providing a framework for engineering novel AD therapeutics.