Background:Alzheimer's disease (AD) is the leading type of dementia, impacting millions of individuals across the globe. Recent clinical evidence from three therapeutic anti-Aβ antibodies has shown that clearing Aβ-amyloid plaques in the early stages of Alzheimer’s disease (AD) can slow the progression of the condition. γ-Secretase, together with β-secretase, sequentially cleaves amyloid precursor protein (APP) during its processing.

 The aim of this study was to use computational methods, specifically docking, to find molecules that can activate the gamma-secretase enzyme

Methods: The gamma-secretase enzyme structure was prepared using Chimera software by removing non-standard structures and water molecules and then amino acids adjacent to the cholesterol ligand were mapped using PyMOL software. The 3D structure and SMILES notation of cholesterol were retrieved from the PubChem database. The docking results, stored in a pdbqt file (with atomic charges and atom types), were analyzed using Discovery Studio, LigPlus+, and PDBsum. LigPlus+ specifically evaluated protein subunit interactions.

Result :This study evaluated key drug-like properties (solubility, tumorigenicity, LogP, toxicity) of compounds using predictive tools (Swiss Target Prediction, PASS-Way2drug, SwissADME) in alignment with Lipinski's rule of five. The identified amino acids—Trp227, Leu192, Arg186, Leu199, Leu203, Leu206, Tyr155, Leu215, Phe162, Ser223, and Ile230—were situated on the C subunit of the gamma-secretase enzyme. AutoDock Vina's efficient docking process and Chimera's visualization capabilities were leveraged, with Ligplot providing interaction analysis

Conclusion: Gamma-secretase modulators are projected to play a key therapeutic role in combating Alzheimer's disease.