Back
 JBiSE  Vol.2 No.4 , August 2009
Determination of inter- and intra-subtype/species varia-tions in polymerase acidic protein from influenza A virus using amino-acid pair predictability
Abstract: The polymerase acidic protein is an important family of proteins from influenza A virus, which is classified as many different subtypes or spe-cies. Thus, an important question is if these classifications are numerically distinguishable with respect to the polymerase acidic protein. The amino-acid pair predictability was used to transfer 2432 polymerase acidic proteins into 2432 scalar data. The one-way ANOVA found these polymerase acidic proteins distinguish-able in terms of subtypes and species. However, the large residuals in ANOVA suggested a pos-sible large intra-subtype/species variation. Therefore, the inter- and intra-subtype/species variations were studied using the model II ANOVA. The results showed that the in-tra-subtype/species variations accounted most of variation, which was 100% in total for both inter- and intra- subtype/species variations. Our analysis threw lights on the issue of how to de-termine a wide variety of patterns of antigenic variation across space and time, and within and between subtypes as well as hosts.
Cite this paper: nullYan, S. and Wu, G. (2009) Determination of inter- and intra-subtype/species varia-tions in polymerase acidic protein from influenza A virus using amino-acid pair predictability. Journal of Biomedical Science and Engineering, 2, 273-279. doi: 10.4236/jbise.2009.24041.
References

[1]   I. Capuaa and D. J. Alexander, (2008) Avian influenza vaccines and vaccination in birds, Vaccine, 26(4), D70-D73.

[2]   N. Hehme, T. Colegate, B Palache, and L. Hessel, (2008) Influenza vaccine supply: Building long-term sustain-ability, Vaccine, 26(4), D23-D26.

[3]   M. Hoelscher, S. Gangappa, W. Zhong, L. Jayashankar, and S. Sambhara, (2008) Vaccines against epidemic and pandemic influenza, Expert Opin. Drug Deliv., 5, 1139 -1157.

[4]   B. Palache, (2008) New vaccine approaches for seasonal and pandemic influenza, Vaccine, 26, 6232-6236.

[5]   J. C. Tilburt, P. S. Mueller, A. L. Ottenberg, G. A. Poland, and B. A. Kioenig, (2008) Facing the challenges of in-fluenza in healthcare settings: The ethical rationale for mandatory seasonal influenza vaccination and its impli-cations for future pandemics, Vaccine, 26(4), D27-D30.

[6]   M. Peyre, G. Fusheng, S. Desvaux, and F. Roger, (2009) Avian influenza vaccines: A practical review in relation to their application in the field with a focus on the Asian experience, Epidemiol. Infect., 137, 1-21.

[7]   A. W. Hampson, (2008) Vaccines for pandemic influenza: The history of our current vaccines, their limitations and the requirements to deal with a pandemic threat, Ann. Acad. Med. Singapore, 37, 510-517.

[8]   N. Skeik and F. I. Jabr, (2008) Influenza viruses and the evolution of avian influenza virus H5N1, Int. J. Infect. Dis., 12, 233-238.

[9]   D. Q. Wei, Q. S. Du, H. Sun, and K. C. Chou, (2006) Insights from modeling the 3D structure of H5N1 influ-enza virus neuraminidase and its binding interactions with ligands, Biochem. Biophys. Res. Commun., 344, 1048-1055.

[10]   S. Q. Wang, Q. S. Du, and K. C. Chou, (2007) Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuramini-dases, Biochem. Biophys. Res. Commun., 354, 634-640.

[11]   Q. S. Du, S. Q. Wang, and K. C. Chou, (2007) Analogue inhibitors by modifying oseltamivir based on the crystal neuraminidase structure for treating drug-resistant H5N1 virus, Biochem. Biophys. Res. Commun., 362, 525-531.

[12]   X. L. Guo, L. Li, D. Q. Wei, Y. S. Zhu, and K. C. Chou, (2008) Cleavage mechanism of the H5N1 hemagglutinin by trypsin and furin. Amino Acids, 35, 375-382.

[13]   R. B. Huang, Q. S. Du, C. H. Wang, and K. C. Chou, (2008) An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of in-fluenza A virus, Biochem. Biophys. Res. Comm., 377, 1243-1247.

[14]   Q. S. Du, R. B. Huang, C. H. Wang, X. M. Li, and K. C. Chou, (2009) Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus, J. Theoret. Biol., doi:10.1016/j.jtbi.2009.1003. 1003.

[15]   J. R. Schnell, and J. J. Chou, (2008) Structure and mechanism of the M2 proton channel of influenza A vi-rus, Nature, 451, 591-595.

[16]   R. M. Pielak, J. R. Jason, R. Schnell, and J. J. Chou, (2009) Mechanism of drug inhibition and drug resistance of influenza A M2 channel, Proc. Natl. Acad. Sci. USA, www. pnas.org cgi doi 10.1073 pnas.0902548106.

[17]   K. C. Chou, (2001) Prediction of protein cellular attrib-utes using pseudo amino acid composition, PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol.44, 60), 43, 246-255.

[18]   K. C. Chou, (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21, 10-19.

[19]   X. Xiao, S. H. Shao, Z. D. Huang, and K. C. Chou, (2006) Using pseudo amino acid composition to predict protein structural classes: Approached with complexity measure factor, J. Comput. Chem., 27, 478-482.

[20]   X. Xiao, S. Shao, Y. Ding, Z. Huang, Y. Huang, and K. C. Chou, (2005) Using complexity measure factor to predict protein subcellular location, Amino Acids, 28, 57-61.

[21]   X. Xiao and K. C. Chou, (2007) Digital coding of amino acids based on hydrophobic index, Protein Pept. Lett., 14, 871-875.

[22]   X. Xiao, W. Z. Lin, and K. C. Chou, (2008) Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes, J. Comput. Chem., 29, 2018-2024.

[23]   X. Xiao, P. Wang, and K. C. Chou, (2008) Predicting protein structural classes with pseudo amino acid com-position: An approach using geometric moments of cel-lular automaton image, J. Theoret. Biol., 254, 691-696.

[24]   X. Xiao, P. Wang, and K. C. Chou, (2008) GPCR-CA: A cellular automaton image approach for predicting G- pro-tein-coupled receptor functional classes, J. Comput. Chem., DOI 10.1002/jcc.21163.

[25]   X. Xiao, P. Wang, and K. C. Chou, (2009) Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition, J. Appl. Crystallogr., 42, 169-173.

[26]   G. Wu and S. Yan, (2002) Randomness in the primary structure of protein: Methods and implications, Mol. Biol. Today, 3, 55-69.

[27]   G. Wu and S. Yan, (2006) Mutation trend of hemaggluti-nin of influenza A virus: a review from computational mutation viewpoint, Acta Pharmacol. Sin., 27, 513-526.

[28]   G. Wu and S. Yan, (2008) Lecture notes on computational mutation, Nova Science Publishers, New York.

[29]   O. G. Engelhardt and E. Fodor, (2006) Functional asso-ciation between viral and cellular transcription during in-fluenza virus infection, Rev. Med. Virol., 16, 329-345.

[30]   A. Honda and A. Ishihama, (1997) The molecular anat-omy of influenza virus RNA polymerase, Biol. Chem., 378, 483-488.

[31]   T. Watanabe, S. Watanabe, K. Shinya, J. H. Kim, M. Hatta, and Y. Kawaoka, (2009) Viral RNA polymerase complex promotes optimal growth of 1918 virus in the lower respiratory tract of ferrets, Proc. Natl. Acad. Sci. USA, 106, 588-592.

[32]   K. C. Chou and H. B. Shen, (2008) Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms, Nature Prot., 3, 153-162.

[33]   K. C. Chou and H. B. Shen, (2007) Review: Recent pro-gresses in protein subcellular location prediction, Analyt. Biochem., 370, 1-16.

[34]   K. C. Chou and H. B. Shen, (2007) Euk-mPLoc: A fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites, J. Proteome Res., 6, 1728-1734.

[35]   H. B. Shen and K. C. Chou, (2009) QuatIdent: A web server for identifying protein quaternary structural attrib-ute by fusing functional domain and sequential evolution information, J. Proteome Res., 8, 1577-1584.

[36]   K. C. Chou and H. B. Shen, (2007) MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse- PSSM, Biochem. Biophys. Res. Commun., 360, 339-345.

[37]   T. Wang, J. Yang, H. B. Shen, and K. C. Chou, (2008) Predicting membrane protein types by the LLDA algo-rithm, Protein Pept. Lett., 15, 915-921.

[38]   H. B. Shen and K. C. Chou. (2007) EzyPred: A top-down approach for predicting enzyme functional classes and subclasses, Biochem. Biophys. Res. Commun., 364, 53- 59.

[39]   K. C. Chou and D. W. Elrod, (2002) Bioinformatical analysis of G-protein-coupled receptors, J. Proteome Res., 1, 429-433.

[40]   K. C. Chou, (2005) Prediction of G-protein-coupled re-ceptor classes, J. Proteome Res., 4, 1413-1418.

[41]   K. C. Chou and H. B. Shen, (2008) ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information, Biochem. Biophys, Res. Comm., 376, 321-325.

[42]   H. B. Shen and K. C. Chou. (2009) Identification of pro-teases and their types, Analyt. Biochem., 385, 153-160.

[43]   K. C. Chou, (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in pro-teins, J. Biol. Chem., 268, 16938-16948.

[44]   K. C. Chou, (1996) Review: Prediction of HIV protease cleavage sites in proteins, Analyt. Biochem., 233, 1-14.

[45]   H. B. Shen and K. C. Chou, (2008) HIVcleave: a web- server for predicting HIV protease cleavage sites in pro-teins, Analyt. Biochem., 375, 388-390.

[46]   K. C. Chou and H. B. Shen, (2007) Signal-CF: A subsite- coupled and window-fusing approach for predicting sig-nal peptides, Biochem. Biophys. Res. Commun., 357, 633-640.

[47]   H. B. Shen and K. C. Chou, (2007) Signal-3L: A 3-layer approach for predicting signal peptide, Biochem. Bio-phys. Res. Commun., 363, 297-303.

[48]   K. C. Chou, (2004) Review: Structure bioinformatics and its impact to biomedical science, Curr. Med. Chem., 11, 2105-2134.

[49]   R. Apweiler, A. Bairoch and C. H. Wu. (2005) Protein sequence databases, Curr. Opin. Chem. Biol., 8, 76-80.

[50]   G. Wu and S. Yan, (2008) Prediction of mutations engi-neered by randomness in H5N1 neuraminidases from in-fluenza A virus, Amino Acids, 34, 81-90.

[51]   G. Wu and S. Yan, (2008) Prediction of mutations initi-ated by internal power in H3N2 hemagglutinins of influ-enza A virus from North America, Int. J. Pept. Res. Ther., 14, 41-51.

[52]   G. Wu and S. Yan, (2008) Prediction of mutation in H3N2 hemagglutinins of influenza A virus from North America based on different datasets, Protein Pept. Lett., 15, 144-152.

[53]   G. Wu and S. Yan, (2008) Three sampling strategies to predict mutations in H5N1 hemagglutinins from influ-enza A virus, Protein Pept. Lett., 15, 731-738.

[54]   G. Wu and S. Yan. (2008) Prediction of mutations engi-neered by randomness in H5N1 hemagglutinins of influ-enza A virus, Amino Acids, 35, 365-373.

[55]   E. Spackman, (2008) A brief introduction to the avian influenza virus, Methods Mol. Biol., 436, 1-6.

[56]   SPSS Inc. (1992-2003) SigmaStat for windows version 3.00.

[57]   R. R. Sokal and F. J. Rohlf, (1995) Biometry: The princi-ples and practices of statistics in biological research, 2nd ed, W. H. Freeman, New York, 203-218.

[58]   G, Wu, M. Baraldo, and M. Furlanut, (1999) Inter-patient and intra-patient variations in the baseline tapping test in patients with Parkinson’s disease, Acta Neurol. Belg., 99, 182-184.

[59]   M. Furlanut, G. Wu, and E. Perucca, (2001) Variability in the metabolism of levodopa and clinical implications, In: Interindividual Variability in Drug Metabolism in Man. eds. by Pacifici GM, Pelkonen O, Tayler & Francis, London and New York, Chapter 7, 181-227.

[60]   M. Lipsitch and J. J. O’Hagan, (2007) Patterns of anti-genic diversity and the mechanisms that maintain them, J. R. Soc. Interface, 4, 787-802.

[61]   J. A. Mumford, (2007) Vaccines and viral antigenic di-versity, Rev. Sci. Tech., 26, 69-90.

[62]   F. Carrat and A. Flahault, (2007) Influenza vaccine: The challenge of antigenic drift, Vaccine, 25, 6852-6862.

[63]   K. M. Grebe, J. W. Yewdell, and J. R. Bennink, (2008) Heterosubtypic immunity to influenza A virus, where do we stand? Microbes. Infect., 10, 1024-1029.

 
 
Top