Pharmacophore based virtual screening, molecular docking and density functional theory approaches to discover the potent beta-amyloid precursor protein (B-APP) inhibitor

Beta-amyloid precursor protein (β-APP) is a membrane-bound glycoprotein. It plays an important role in growth factor and a mediator of cell adhesion. Amyloid precursor protein was cut into small fragments during proteolysis process. The fragments of the fibrillogenic amyloid beta-peptide forms the clumps accumulate on the outer side of brain. It accumulates by stages into microscopic amyloid plaques that are considered one hallmark of brains affected by Alzheimer’s disease. Our effort to finding the small molecule inhibitor of β-APP that reduces the formation of beta amyloid plaque. Computational techniques were employed to find the potent β-APP inhibitor. Chemical feature based pharmacophore model was developed for selectivity of β-APP inhibitors. The best hypothesis (Hypo1) was generated consisting of four chemical features (one hydrogen bond donor, one hydrophobic and two ring aromatic). It has exhibited high correlation co-efficient, cost difference and low RMS value. The well validated model was used as 3D query in the virtual screening to retrieve potential leads for β-APP inhibition. Molecular docking was performed to find suitable orientation of compounds in the protein active site. Two hit compounds retrieved from the chemical database satisfies better chemical, Physical and electronic properties and it could help to design the potent β-APP inhibitors.