Ligand-Based Pharmacophore Modelling Targeting α-Synuclein Misfolding for an Effective Treatment of Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disease prominently observed in elderly population. Aggregation and misfolding of α-synuclein protein is strongly implicated as an underlying pathogenesis of the disease. PD is characterised by high amount of intracellular α-synuclein as the main constituents in Lewy bodies. However, PD drugs in clinical conditions elicit unpleasant side effects and develop resistance after long time application, making a search for better candidates imperative. This study is aimed at identifying potent inhibitors of α-synuclein from interbioscreen (IBS) database using ligand-based pharmacophore modelling, Glide standard precision docking, molecular dynamics, and pk-CSM pharmacokinetics parameters. Among the ten models generated, only one of the pharmacophore models was validated with Guner-Henry’s goodness of hits (GH) scoring method and enrichment factor (EF) of 0.87 and 23.43 respectively, making it ideal for database screening. The pharmacophore model with features, HHHHHHDDDDA identified 100 hits from “877,337” IBS database natural compounds. The top hits, STOCK2S-85121, STOCK3S-13122, STOCK2S-57139, STOCK7S-07150, and STOCK4S-24924 demonstrated better docking scores of -4.789, -4.451, -4.413, -4.365 and -4.227 kcal/mol respectively, while the standard, Levodopa has docking score of -3.556 kcal/mol. The retrieved compounds have similar amino acid interactions with the standard compound (levodopa). The ligands are predicted to have good Blood Brain Barrier permeation, central nervous system penetrations and absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Molecular dynamics for 50 ns further suggested that the highest docked compound (STOCK2S-85121) was stable into the binding pocket and displayed strong hydrogen bond interactions. Therefore, the compound is recommended for further evaluations as potential anti-PD agents.

Keywords

• in silico admet
• ligand-based drug design
• natural products
• parkinson’s disease
• pharmacophore model
• α-synuclein inhibitors