Pharmacophore based 3D-QSAR modeling, virtual screening and docking for identification of potential inhibitors of β-secretase


The enzyme β-secretase-1 is responsible for the cleavage of the amyloid precursor protein, a vital step in the process of the formation of amyloid-β peptides which are known to lead to neurodegeneration causing Alzheimer's disease. Challenges associated with toxicity and blood brain permeation inability of potential inhibitors, continue to evade a successful therapy, thus demanding the search and development of highly active and effective inhibitors. Towards these efforts, we used a ligand based pharmacophore model generation from a dataset of known inhibitors whose activities against β-secretase hovered in the nano molar range. The identified 5 feature pharmacophore model, AHHPR, was validated via three dimensional quantitative structure activity relationship as indicated by r2, q2 and Pearson R values of 0.9013, 0.7726 and 0.9041 respectively. For a dataset of compounds with nano molar activity, the important pharmacophore features present in the current model appear to be similar with those observed in the models resulting from much wider activity range of inhibitors. Virtual screening of the ChemBridge CNS-Set™, a database having compounds with a better suitability for central nervous system based disorders followed by docking and analysis of the ligand protein interactions resulted in the identification of eight prospective compounds with considerable diversity. The current pharmacophore model can thus be useful for the identification, design and development of potent β-secretase inhibitors which by optimization can be potential therapeutics for Alzheimer's disease.

DOI: 10.1016/j.compbiolchem.2017.03.001

Cite this paper

@article{Palakurti2017PharmacophoreB3, title={Pharmacophore based 3D-QSAR modeling, virtual screening and docking for identification of potential inhibitors of β-secretase}, author={Ravichand Palakurti and Ramakrishna Vadrevu}, journal={Computational biology and chemistry}, year={2017}, volume={68}, pages={107-117} }