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International Journal of Computational Bioinformatics and In Silico Modeling
2014: Volume-3 Issue-3
ISSN: 2320-0634

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International Journal of Computational Bioinformatics and In Silico Modeling 3(3) 2014: 398-406

Sequence analysis and physicochemical characterization of Nipah virus proteins through computational approach



Dipika Rungta and Koel Mukherjee*

Bioinformatics Laboratory, Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand- 835215, India.

* Corresponding Author

ABSTRACT

Nipah virus is a zoonotic and negative sense single stranded RNA paramyxovirus which is classified in the genus Henipavirus, under the family of Paramyxoviridae. The Center of Disease Control has declared it as a biosafety level 4 agent due to which it has attracted the interest of many researchers. The present work is aimed towards the characterization of Nipah virus proteins, in order to identify the reason behind virus’ pathogenicity. The nipah virus structural proteins were characterized via various computational tools like CLC protein workbench, protparam, SOPMA, CysRec tool and SOSUI Server. Primary structure analysis shows that most of the proteins in nipah virus are hydrophobic in nature due to the presence of high content of non-polar residues. Secondary structure analysis shows that nipah virus proteins have predominant and high coil structural content which may be due to the rich content of more flexible glycine and hydrophobic proline amino acids. The nipah viral structural proteins were further modelled using Prime application of Schrodinger suit which was further validated through Ramachandran plot and molecular dynamics simulation. The sequence analysis can be further used for the designing of a potent antiviral drug against the virus, thereby aiding knowledge in the field of research.


Copyright © 2014 | AIZEON publishers | All rights reserved

 

 

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Citation: Dipika Rungta and Koel Mukherjee. (2014). Sequence analysis and physicochemical characterization of Nipah virus proteins through computational approach. Int J Comput Bioinfo In Silico Model 3(3): 398-406

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