E-Pharmacophore modelling, molecular docking and dynamics approaches for in silico identification of acetylcholinesterase inhibitors from natural products against Alzheimer’s disease

Abstract

Abstract Alzheimer's disease (AD) is the most common cause of dementia and one of the major causes of death worldwide. The ability of some natural compounds, such as flavonoids, to cross the blood-brain barrier and decrease the progression of such disorders has been described in the literature. This study aimed to identify potential natural product molecules as acetylcholinesterase inhibitors of Alzheimer's disease using silico computational approaches. A pharmacophore model was developed based on the 3D structure of the protein–ligand complex of the acetylcholinesterase protein (PDB:4EY7) using the Phase module. A natural product library of 30,926 ligands was prepared using LigPrep and was used for virtual screening. Based on the pharmacophore similarity score, the best ligands were identified and further scrutinized by molecular docking, MM/GBSA, induced fit docking, and ADME profiling. Two of the most promising natural products, NPC109925 and NPC170602, were evaluated to understand the stability of these ligands in the binding pocket using molecular dynamics (MD) simulation for 100ns and post dynamic MM/GBSA was performed for 101 frames from MD simulation result which showed better binding energy in comparison with pre dynamic MM/GBSA. These molecules exhibited better binding affinity compared to the co-crystallized ligand in in silico studies. However, further in vitro and in vivo screening is recommended to confirm the acetylcholinesterase inhibitory activity of these ligands which could serve as lead molecules for further development as anti- Alzheimer’s agents.