alexa In silico Screening for Identification of Novel Aurora
ISSN: 2329-6674

Enzyme Engineering
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Research Article

In silico Screening for Identification of Novel Aurora Kinase Inhibitors by Molecular Docking, Dynamics Simulations and Ligand-Based Hypothesis Approaches

Sidra Batool1, Saba Ferdous1, Mohammad A. Kamal2, Hira Iftikhar1 and Sajid Rashid1*
1National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, Pakistan
2King Fahd Medical Research Center, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
Corresponding Author : Sajid Rashid
National Center for Bioinformatics
Quaid-i-Azam University, Islamabad, Pakistan
Tel: +925190644107
E-mail: [email protected]
Received December 12, 2012; Accepted February 04, 2013; Published February 09, 2013
Citation: Batool S, Ferdous S, Kamal MA, Iftikhar H, Rashid S (2013) In silico Screening for Identification of Novel Aurora Kinase Inhibitors by Molecular Docking, Dynamics Simulations and Ligand-Based Hypothesis Approaches. Enz Eng 2:106. doi:10.4172/2329-6674.1000106
Copyright: © 2013 Batool S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

Aurora kinase family members are involved in a wide variety of cell cycle events including centrosome separation, cytokinesis, kinetochore formation, spindle assembly, chromosomal segregation and microtubule dynamics. Typically, dysfunction of Aurora proteins is associated with aneuploidy, cell death and mitotic arrest, leading to tumorigenesis. This spurred a vast interest to identify pharmacologically active small-molecule inhibitors of Aurora proteins. In this study, we isolated four novel inhibitors through virtual screening and docking analyses. These hits were subsequently characterized by molecular dynamic simulations to monitor their binding stabilities at ATP binding site. To aid characterizing novel and more potent inhibitors for Aurora kinases, we explored selective features of our ligand dataset by ligand-based pharmacophore modeling approach. The best pharmacophore model was then employed for performing virtual screenings of libraries isolated from Princeton and Uorsy databases. On the basis of common pharmacophore features, Lipinski’s rule of five, absorption, distribution, metabolism and elimination properties, hits were short listed and refined by molecular dockings. Finally, the selected compounds were validated on the basis of binding capabilities, consensus scoring and activity values. We propose that the novel inhibitors described in this study may warrant characterization in designing active lead for clinical studies that may serve as anticancer drugs in future.

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