Identification of stigmasterol derived AChE inhibitors for Alzheimer's disease using high throughput virtual screening and molecular dynamics simulations

Abstract

Alzheimer's disease (AD), a progressive neurodegenerative disorder, poses a significant global health burden due to its intricate pathology and the absence of curative treatments. Current therapies primarily offer symptomatic relief, often with limited efficacy and complications, thereby underscoring the urgent need for innovative, safer, and more effective interventions. Stigmasterol, a plant-derived phytosterol, has demonstrated neuroprotective properties, including anti-inflammatory and antioxidant effects, which positions this sterol as a compelling candidate for further investigation in AD treatment. In this investigation, high-throughput virtual screening of 972 stigmasterol analogs (SAs) was conducted to identify potential acetylcholinesterase (AChE) inhibitors, followed by ADMET filtering, molecular dynamics (MD) simulations, MM/GBSA free binding energy estimations, and DFT calculations. Three lead compounds, including SA4 (-10.9 kcal/mol), SA12 (-10.6 kcal/mol), and SA15 (-10.5 kcal/mol), demonstrated superior binding affinities compared to stigmasterol (-9.6 kcal/mol) and the control drug donepezil (-8.6 kcal/mol). Docking interaction analysis revealed strong binding by hydrogen bonds and hydrophobic interactions, whereas pharmacokinetic, pharmacodynamic, and toxicity assessments confirmed the favorable characteristics of these compounds. MD simulations (200 ns) demonstrated the structural compactness of the compounds, which was further supported by principal component analysis and Gibbs free energy landscape experiments. MM/GBSA identified SA4 as the most potent analog (-82.21 kcal/mol), followed by SA15 (-80.40 kcal/mol) and SA12 (-69.72 kcal/mol). A DFT-based molecular reactivity analysis revealed decreased reactivity and increased kinetic stability of the lead candidates in their transition from free to bound states. These findings provide insights into the therapeutic potential of stigmasterol analogs as AChE inhibitors, thus offering the groundwork for in vivo and in vitro validation for advancing AD treatment.

Keywords: Alzheimer’s; DFT; HTVS; MD simulation; MM/GBSA; Stigmasterol.

Fig. 1

Active site analysis of the AChE receptor (PDB ID: 1B41). (a) Pocket 1, (b) Pocket 2. The 3D structural pockets were visualized using CASTp v3.0 (Computed Atlas of Surface Topography of proteins, http://sts.bioe.uic.edu/castp).

Fig. 2

Superimposition of the co-crystallized ligand in its original conformation (green) and its docked pose (blue). The superimposition of 3D structures was prepared and visualized using PyMOL v3.1 (Schrödinger, LLC, https://pymol.org).

Fig. 3

Docked complexes showing ligands bound in the active site. (a) SA4, (b) SA12, (c) SA15, (d) Stigmasterol, (e) Donepezil, (f) Galantamine, (g) Huperzine A. The 3D visualizations were generated using BIOVIA Discovery Studio Visualizer v24.1.0.23298 (Dassault Systèmes, https://discover.3ds.com/discovery-studio-visualizer-download).

Fig. 4

Conventional hydrogen bonding and hydrophobic interactions between the ligands and target protein. (a) SA4, (b) SA12, (c) SA15, (d) Stigmasterol, (e) Donepezil. The 2D molecular interactions were illustrated employing BIOVIA Discovery Studio Visualizer v24.1.0.23298 (Dassault Systèmes, https://discover.3ds.com/discovery-studio-visualizer-download).

Fig. 5

2D structures of the top three stigmasterol analogs (SA4, SA12, SA15) along with stigmasterol and donepezil. The 2D chemical structures were generated using Smi2Depict web server (https://cdb.ics.uci.edu/cgibin/Smi2DepictWeb.py).

Fig. 6

Evaluation of drug-likeness and oral bioavailability of the selected lead candidates, stigmasterol, and the reference drug. LIPO denotes lipophilicity, INSOLU denotes insolubility, INSATU refers to the insaturation index, FLEX represents molecular flexibility, SIZE indicates molecular weight, and POLAR refers to polarity. (a) SA4, (b) SA12, (c) SA15, (d) stigmasterol, (e) donepezil.

Fig. 7

MD simulation trajectories of the lead candidates, stigmasterol, and donepezil. (a) Protein-ligand (PL) RMSD analysis, (b) Ligand (L) RMSD analysis, (c) RMSF analysis.

Fig. 8

MD simulation trajectories of the lead candidates, stigmasterol, and donepezil. (a) Rg analysis, (b) SASA analysis.

Fig. 9

MD simulation trajectories of the lead candidates, stigmasterol, and donepezil. (a) MolSA analysis, (b) PSA analysis.

Fig. 10

Protein-ligand contact analysis over 200 ns simulation time, highlighting the versatile and dynamic binding profiles of the selected lead candidates. (a) SA4, (b) SA12, (c) SA15.

Fig. 11

Protein-ligand contact analysis over 200 ns simulation time, showing the versatile and dynamic binding profiles. (a) Stigmasterol, (b) Donepezil.

Fig. 12

Principal components analysis of the selected compounds. (a) SA4, (b) SA12, (c) SA15, (d) Stigmasterol, (e) Donepezil, (f) Superimposition of the ligands.

Fig. 13

Gibbs free energy landscape analysis of the selected compounds. (a) SA4, (b) SA12, (c) SA15, (d) Stigmasterol, (e) Donepezil.

Fig. 14

DFT-based molecular reactivity analysis of stigmasterol, the lead compounds, and donepezil in the free state.

Fig. 15

DFT-based molecular reactivity analysis of stigmasterol, lead compounds, and donepezil in the bound state.

Fig. 16

Graphical summary illustrating the stepwise workflow employed in the present study.