Computational modeling and validation studies of 3-D structure of neuraminidase protein of H1N1 influenza A virus and subsequent in silico elucidation of piceid analogues as its potent inhibitors

Authors

  • Chhedi Lal Gupta Bioinformatics Centre, Indian Veterinary Research Institute, Izatnagar–243122, India
  • Salman Akhtar Department of Biotechnology, Microbiology and Bioinformatics, Integral University, Lucknow-226026, India
  • Preeti Bajpaib Department of Biotechnology, Microbiology and Bioinformatics, Integral University, Lucknow-226026, India
  • K. N. Kandpal Bioinformatics Centre, Indian Veterinary Research Institute, Izatnagar–243122, India
  • G. S. Desai Division of Animal Biotechnology, Indian Veterinary Research Institute, Izatnagar-243122, India
  • Ashok K. Tiwari Bioinformatics Centre, Indian Veterinary Research Institute, Izatnagar–243122, India

Keywords:

neuraminidase, modeler, Ramachandran plot, molecular docking, anti-influenza drugs, piceid

Abstract

Emergence of the drug resistant variants of the Influenza A virus in the recent years has aroused a great need for the development of novel neuraminidase inhibitors for controlling the pandemic. The neuraminidase (NA) protein of the influenza virus has been the most potential target for the anti-influenza. However, in the absence of any experimental structure of the drug targeting NA protein of H1N1 influenza A virus as zanamivir and oseltamivir, the comprehensive study of the interaction of the drug molecules with the target protein has been missing. Hence in this study a computational 3-D structure of neuraminidase of H1N1 influenza A virus has been developed using homology modeling technique, and the same was validated for its reliability by ProSA web server in term of energy profile & Z scores and PROCHECK program followed by Ramachandran plot. Further, the developed 3-D model had been employed for docking studies with the class of compounds as Piceid and its analogs. In this context, two novel compounds (ChemBank ID 2110359 and 3075417) were found to be more potent inhibitors of neuraminidase than control drugs as zanamivir and oseltamivir in terms of their robust binding energies, strong inhibition constant (Ki) and better hydrogen bond interactions between the protein-ligand complex. The interaction of these compounds with NA protein has been significantly studied at the molecular level.

Downloads

Published

2013-03-12

How to Cite

Gupta, C. L., Akhtar, S., Bajpaib, P., Kandpal, K. N., Desai, G. S., & Tiwari, A. K. (2013). Computational modeling and validation studies of 3-D structure of neuraminidase protein of H1N1 influenza A virus and subsequent in silico elucidation of piceid analogues as its potent inhibitors. EXCLI Journal, 12, 215–225. Retrieved from https://www.excli.de/index.php/excli/article/view/1143

Issue

Vol. 12 (2013)

Section

Original articles

License

Authors who publish in this journal agree to the following terms:

  • The authors keep the copyright and grant the journal the right of first publication under the terms of the Creative Commons Attribution license, CC BY 4.0. This licencse permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
  • The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
  • Because the advice and information in this journal are believed to be true and accurate at the time of publication, neither the authors, the editors, nor the publisher accept any legal responsibility for any errors or omissions presented in the publication. The publisher makes no guarantee, express or implied, with respect to the material contained herein.
  • The authors can enter into additional contracts for the non-exclusive distribution of the journal's published version by citing the initial publication in this journal (e.g. publishing in an institutional repository or in a book).

Most read articles by the same author(s)