Advances in Pharmacology and Pharmacy Vol. 3(3), pp. 53 - 63
DOI: 10.13189/app.2015.030301
Identification of Small Molecule Memapsin Inhibitors via Computation-based Virtual Screening
Afaf H. Al-Nadaf 1,*, Mutasem O. Taha 2
1 Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Applied Science University, Jordan
2 Drug Discovery Unit, Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Jordan
ABSTRACT
Background: Alzheimer's disease (AD) is a degenerative disease of the brain common form of dementia. A variety of therapeutic strategies for modulating the progression or prevention of AD are currently being investigated. The etiology of the disease is characterized by aggregates of amyloid plaques, largely composed of amyloid-β peptide formed from the amyloid precursor protein cleaved by Memapsin 2(Beta-secretase / BACE1). Based on its key role in the β -amyloid cascade, inhibition of Memapsin 2 is widely recognized as one of the most promising therapeutic approaches for the treatment and prevention of AD. However, the development of small molecule, brain penetrant Memapsin 2 inhibitors has been challenging. Method: Molecular docking study using LigandFit Docking and Scoring as well as LibDock Docking functions were performed as a preliminary in-silico screening test for National Cancer Institute (NCI) database using binding pocket of Memapsin. Followed by in-vitro enzyme inhibition assay, High-ranking docked conformers and poses were scored using seven scoring functions. The validation for our docking–scoring procedure was performed through employing the same conditions to dock a well-known Memapsin inhibitor IO2. High ranking compounds were evaluated in vitro using Memapsin fluorescence resonance energy transfer (FRET) assay. Results: The docking simulation resulted in a close model to the crystallographic structure. Five of the important interactions are shared between the co-crystallized ligand and in-silico hits. Virtual screening identified low micromolar inhibitory leads from the NCI list of compounds. The most potent hit exhibited Memapsin IC50 values of 11.1μM in enzymatic assay. Conclusion: We have identified a low micro-molar Memapsin inhibitor with IC50 of 11.1μM. Our results suggest that in silico high-throughput screening approach can serve as useful source to identify new hits which can be used as lead candidate for synthetic modification in order to reach more potent enzyme inhibitors.
KEYWORDS
Alzheimer's Disease, Memapsin 2 Inhibitors, Docking, Virtual Screening, In vitro Validation
