E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

Area of Science:

• Medicinal Chemistry
• Neuroscience
• Computational Drug Discovery

Background:

• Glycogen synthase kinase-3β (GSK-3β) deregulation is implicated in Alzheimer's disease (AD) pathogenesis, contributing to neuronal loss and memory deficits.
• GSK-3β's role in neurofibrillary tangle formation makes it a key therapeutic target for AD intervention.

Purpose of the Study:

• To identify potent GSK-3β antagonists for potential Alzheimer's disease treatment.
• To develop and optimize pharmacophore models for drug screening against GSK-3β.

Main Methods:

• Generation and optimization of four e-pharmacophore models from twenty crystal structures.
• Shape-based virtual screening of small molecule databases against the optimized pharmacophore models.
• Rigid receptor docking, quantum polarized ligand docking, induced fit docking, and molecular dynamics simulations to evaluate lead compound efficacy and stability.

Main Results:

• Virtual screening identified 1800 compounds with desired pharmacophore features, leading to the selection of 18 potential leads after docking.
• Lead1 exhibited the lowest docking score, superior binding free energy, and optimal binding orientation compared to co-crystal ligands.
• Molecular dynamics simulations confirmed the stable interaction of the GSK-3β-lead1 complex over 50 ns.

Conclusions:

• Lead1 demonstrates potential as a potent antagonist for GSK-3β.
• The identified compound warrants further investigation as a therapeutic candidate for Alzheimer's disease.