Computational identification of promising therapeutics via BACE1 Targeting: Implications for Alzheimer's disease.

Abstract

Alzheimer's disease (AD) is a significant global healthcare challenge, particularly in the elderly population. This neurodegenerative disorder is characterized by impaired memory and progressive decline in cognitive function. BACE1, a transmembrane protein found in neurons, oligodendrocytes, and astrocytes, exhibits varying levels across different neural subtypes. Abnormal BACE1 activity in the brains of individuals with AD leads to the formation of beta-amyloid proteins. The complex interplay between myelin sheath formation, BACE1 activity, and beta-amyloid accumulation suggests a critical role in understanding the pathological mechanisms of AD. The primary objective of this study was to identify molecular inhibitors that target Aβ. Structure-based virtual screening (SBVS) was employed using the MCULE database, which houses over 2 million chemical compounds. A total of 59 molecules were selected after the toxicity profiling. Subsequently, five compounds conforming to the Egan-Egg permeation predictive model of the ADME rules were selected and subjected to molecular docking using AutoDock Vina on the Mcule drug discovery platform. The top two ligands and the positive control, 5HA, were subjected to molecular dynamics simulation for five nanoseconds. Toxicity profiling, physiochemical properties, lipophilicity, solubility, pharmacokinetics, druglikeness, medicinal chemistry attributes, average potential energy, RMSD, RMSF, and Rg analyses were conducted to identify the ligand MCULE-9199128437-0-2 as a promising inhibitor of BACE1.