Identification of AChE targeted therapeutic compounds for Alzheimer's disease: an in-silico study with DFT integration

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by cognitive deterioration and changes in behavior. Acetylcholinesterase (AChE), which hydrolyzes acetylcholine, is a key drug target for treating AD. This research aimed to identify new AChE inhibitors using the IMPPAT database. We used known drugs as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Initial screening of these compounds was performed using molecular docking, followed by an analysis of their drug-likeness and ADMET properties. Compounds with favorable properties underwent density functional theory (DFT) calculations to assess their electronic properties such as HOMO-LUMO gap, electron density, and molecular orbital distribution. These descriptors provided insights into each compound's reactivity, stability, and binding potential with AChE. Promising candidates were further evaluated through molecular dynamics (MD) simulations over 100 ns and MMPBSA analysis for the last 30 ns. Two compounds, Biflavanone (IMPHY013027) with a binding free energy of - 130.394 kcal/mol and Calomelanol J (IMPHY007737) with - 107.908 kcal/mol, demonstrated strong binding affinities compared to the reference molecule HOR, which has a binding free energy of - 105.132 kcal/mol. These compounds exhibited promising drug-ability profiles in both molecular docking and MD simulations, indicating their potential as novel AChE inhibitors for AD treatment. However, further experimental validation is necessary to verify their effectiveness and safety.

Keywords: Alzheimer’s disease; Chemical similarity search; DFT; MD simulation; Molecular docking.

Fig. 1

The schematic workflow depicts the screening procedure for the two leading virtual hits identified in the study.

Fig. 2

Three-dimensional representation of the active binding site of the AChE protein (A), along with the depiction of residues involved in active binding (B).

Fig. 3

(A) Superimposed image of native protein with co-crystallized HOR in PDB, (B) Docked HOR showed interaction with the same amino acid residues as found in the crystal structure in PDB.

Fig. 4

2D interaction profiles of AChE and BChE with their reference inhibitors (HOR and HUN, Respectively) and top ligands (Biflavanone and Calomelanol J), showing hydrogen bonds and hydrophobic interactions. (Dotted green lines represent hydrogen bonds, red ignited arcs indicate hydrophobic interactions, and red circles and ellipses highlight common residues with the reference.)

Fig. 5

Analysis of frontier molecular orbitals (FMOs) presented as LUMO, HOMO, and their energy gap (ΔE in kcal/mol) for hit compounds (B; Biflavanone, C; Calomelanol J) in comparison with the HOR (A).

Fig. 6

Three-dimensional plot of Molecular Electrostatic Potential (MEP) for the reference HOR (A) and the investigated molecules (B; Biflavanone, C; Calomelanol J).

Fig. 7

Graphic depiction of RMSD (A) and RMSF (B) profiles for the protein–ligand complex during a 100 ns MD simulation.

Fig. 8

Graphical representations illustrating the Radius of Gyration (Rg) for the protein, protein–ligand complexes, and Hydrogen Bonds (H-bonds) throughout the 100 ns simulation period.

Fig. 9

Graphic depiction of the SASA profile for the protein–ligand complexes.

Fig. 10

Graphical representations illustrating the free energy of interactions in protein–ligand complexes.