E-Pharmacophore Model Assisted Discovery of Novel Antagonists of nNOSNalamolu Ravina Madhulitha, Natarajan Pradeep, Swargam Sandeep, Kanipakam Hema, Pasala Chiranjeevi, Katari Sudheer Kumar and Amineni Uma-Maheswari*
Bioinformatics Centre, Department of Bioinformatics, SVIMS University, Tirupati-517507, Andhra Pradesh, India
- *Corresponding Author:
- Uma-Maheswari A
Associate Professor and Coordinator of BIF
SVIMS Bioinformatics Centre
Department of Bioinformatics
Tirupati – 517507, AP, India
E-mail: [email protected]
Received Date: November 03, 2016; Accepted Date: January 21, 2017; Published Date: January 24, 2017
Citation: Madhulitha NR, Pradeep N, Sandeep S, Hema K, Chiranjeevi P, et al. (2017) E-Pharmacophore Model Assisted Discovery of Novel Antagonists of nNOS. Biochem Anal Biochem 6:307. doi: 10.4172/2161-1009.1000307
Copyright: © 2016 Madhulitha NR, 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.
The nitric oxide (NO) synthesized by neuronal nitric oxide synthase (nNOS) acts as a neurotransmitter and plays a crucial role in a series of neurobiological functions. In diseased condition, activated nNOS induces nitrosylation as well as phosphorylation of tau protein and glycogen synthase kinase 3 beta (GSK-3β) respectively. Hyper phosphorylation of tau accelerates tau oligomerization resulting in formation of neurofibrillary tangles (NFT), ensuring the neuronal cell death in hippocampus region; a hallmark of Alzheimer’s disease (AD). Thus, designing inhibitor towards nNOS may reduce the neuronal loss caused by nNOS. Hence nNOS has been one of the revitalizing targets for AD. In the present work, one energetically optimized structure-based pharmacophore (e-pharmacophore) was generated using nNOS co-crystal structure (4D1N) to map important pharmacophoric features of nNOS. Shape based similarity screening performed using e-pharmacophore against in-house library of more than one million compounds resulted 2701 library of compounds. Rigid receptor docking (RRD) was applied and followed by molecular mechanics and generalized Born and surface area (MM-GBSA) calculation which results 22 nNOS ligands. To define the leads, dock complexes were subjected to quantum-polarized ligand docking (QPLD) followed by free energy calculations revealed 3 leads. On comparison with 1 existing inhibitor,it concealed three best leads with lower binding energy and better binding affinity. The best lead was subjected to induced fit docking (IFD) with MM-GBSA calculation and further molecular dynamics (MD) simulations for 50 ns in solvated model system. Potential energy, root mean square deviation (RMSD) and root mean square fluctuations (RMSF) results disclosed constancy of lead 1 interactions throughout 50 ns MD simulations run. Thus proposed three leads are having favorable absorption distribution metabolism excretion toxicity (ADME/T) properties and provide a scaffold for designing nNOS antagonists.