Literature DB >> 17417832

Graphical representation and numerical characterization of H5N1 avian flu neuraminidase gene sequence.

Ashesh Nandy1, Subhash C Basak, Brian D Gute.   

Abstract

The high degree of virulence and potential for development of a pandemic strain of the H5N1 avian flu has resulted in wide interest in characterization of the various genes of the H5N1 virus genome. We have considered for our analysis all 173 available complete sequences, as of February 2006, of the neuraminidase gene, which is the target of the most effective treatment regimen comprising the inhibitors oseltamivir and zanamivir. We have used a 2D graphical representation of the neuraminidase RNA sequences of H5N1 strains to identify a few distinct structural motifs. The H5N1 strains were split into two main classes: strains that were benign to human beings in the years up to 1996 and the period 1999-2002 and strains that were highly pathogenic to humans in the periods 1997 and 2003 to present. Comparisons with earlier H1N1 pandemic and epidemic strains have also been made to understand the current status of the gene. Our findings indicate that the base composition and distribution patterns are significantly different in the two periods, and this may be of interest in studying mutational changes in such viral genes.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17417832     DOI: 10.1021/ci600558w

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  3 in total

1.  Computational analysis and determination of a highly conserved surface exposed segment in H5N1 avian flu and H1N1 swine flu neuraminidase.

Authors:  Ambarnil Ghosh; Ashesh Nandy; Papiya Nandy
Journal:  BMC Struct Biol       Date:  2010-02-22

Review 2.  Graphical representation and mathematical characterization of protein sequences and applications to viral proteins.

Authors:  Ambarnil Ghosh; Ashesh Nandy
Journal:  Adv Protein Chem Struct Biol       Date:  2011       Impact factor: 3.507

3.  Prediction of novel mouse TLR9 agonists using a random forest approach.

Authors:  Varun Khanna; Lei Li; Johnson Fung; Shoba Ranganathan; Nikolai Petrovsky
Journal:  BMC Mol Cell Biol       Date:  2019-12-20
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.