Identification of Novel Scaffolds Against GSK-3β for Targeting Alzheimer's Disease Through Molecular Modeling Techniques

Abstract

Alzheimer's disease (AD) is one of the most common causes of dementia in elderly populations. A multifactorial and complex etiology has hindered the establishment of successful disease-modifying and retarding treatments. An important molecular target that has a close link with the disease's pathophysiology is glycogen synthase kinase 3β (GSK-3β). GSK-3β is thought to be an important bridge between amyloid and tau pathologies, the two principle pathogenic hallmarks of the disease. In particular, its kinase activity is thought to be a contributing factor for initiating aberrant tau hyperphosphorylation toward neurodegenerative progression. To identify potential inhibitors for GSK-3β, a pharmacophore-based virtual screening was used on the VITAS-M Lab database, a large database of small molecules. A co-crystal ligand employed as the template allowed the screening of roughly 200,000 compounds. Compounds successfully screened were selected on the basis of the Phase screen score combining vector alignments, volume scores, and site matching parameters. Using a cutoff score of 1.7, 174 compounds were docked using the Glide tool for molecular docking to further identify potential high-affinity binding ligands. Finally, four chemicals with the best binding scores (cutoff Glide GScore values of - 8 kcal/mol) were identified. Among these, 3-(2-oxo-2H-chromen-3-yl)-N-(4-sulfamoylphenyl) benzamide (VL-1) and trimethylsilyl trifluoromethanesulfonate (VL-2) showed strong and stable binding interactions, as evidenced by molecular dynamics simulation (MDS). The results suggest that VL-1 and VL-2 may serve as promising lead compounds for GSK-3β-based anti-AD therapeutics. However, further in vivo mechanistic validation is warrantied to confirm their therapeutic applicability.

Keywords: ADMET; Alzheimer’s disease; Dementia; Glycogen synthase kinase 3β; Molecular simulation; Virtual screening.

Fig. 1

Schematic flowchart of the study methodology

Fig. 2

A The alignment of co-crystal ligands to extract common pharmacophoric features. B The common pharmacophoric features represented on template ligand (6LQ). Orange circles show aromatic rings, cyan spheres show hydrogen bond donors, and red sphere shows hydrogen bond acceptor

Fig. 3

A The area under curve of validated query differentiating active ligands from decoys with a value of 0.82. B The optimized pharmacophore hypothesis

Fig. 4

The molecular interactions of hit compounds with the binding pocket of GSK-3β enzyme. Hydrogen bonds are shown with green color, Pi-Sulfur bonds with orange, hydrophobic interactions with pink color spheres

Fig. 5

The potential binding configurations of the active compounds within the GSK-3β binding pocket. The residues of the binding site are illustrated in soft green sticks, while the hit ligands are represented with sticks in various colors

Fig. 6

The binding configurations of the active compounds as they align with the co-crystal ligand. In panel (A), VL-1 is depicted with blue sticks, perfectly aligned alongside the red co-crystal ligand. In panel (B), VL-2 is showcased using red sticks

Fig. 7

A The RMSD graphs illustrating the behavior of the protein and ligands throughout the 100 ns simulation period. B The variations in protein residues during the simulations, assessed through RMSF values. The blue graph represents the RMSF of the VL-1 complex, while the green graph depicts the VL-2 complex

Fig. 8

The interaction of protein–ligand during MDS. A The protein–ligand contacts of VL-1 complex. B The protein–ligand contacts of VL-2 complex. The interacting residues are shown are shown as stacked bars

Fig. 9

Principal component analysis of VL-1 and VL-2 complexes. A The PCA plot of VL-1 complex with overall flexibility of 40.05%. B The PCA plot of VL-2 complex with overall flexibility of 37.36% in three hyper spaces

Fig. 10

The dynamic cross correlation matrix of the protein complexes to find the positive correlation in protein residues. A The DCCM of VL-1 complex. B The DCCM of VL-2 complex

Fig. 11

The contribution of binding energy components in total binding free energy

Fig. 12

Free Energy Landscape (FEL) analysis of VL-1 (A) and VL-2 (B) complexes. Minima points indicate stable conformations, with VL-1 showing a single deep basin and VL-2 displaying two stable states