Journal of Mathematical Chemistry

, Volume 51, Issue 8, pp 2238–2255 | Cite as

Novel algorithm for phylogenetic analysis of proteins: application to analysis of the evolution of H5N1 influenza viruses

  • Vladimir R. PerovicEmail author
Original Paper


The highly pathogenic avian influenza virus (HPAIV) A subtype H5N1 is causing threat to human health over the years. Phylogenetic analysis is an important tool for analyzing the evolution of influenza. A novel phylogenetic algorithm based on a new protein distance measure derived from the informational spectrum method (ISM) has been presented. The new phylogenetic approach allows assessment of functional evolution of protein sequences. The new ISM-based phylogenetic approach has been found to overcome some drawbacks of other phylogenetic approaches, particularly concerning sensitivity to a single mutation, deletion and the position of the mutation. The ISM-based approach applied to hemagglutinin subunit 1 protein (HA1) of HPAIV A subtype H5N1 viruses in Egypt between 2006 and 2011, revealed clear clustering in two groups, with one growing group of H5N1 viruses after 2009 with increased number of human infections with H5N1. Four group-specific mutations are identified which are important for increased human tropism and the pandemic potential.


Protein sequence Phylogenetic analysis H5N1 influenza virus Electron-ion interaction potential Informational spectrum method 



This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 173001).

Supplementary material

10910_2013_212_MOESM1_ESM.jpg (2.7 mb)
Online Resource 1. Comparison of the standard and ISM-based phylogenetic analyses; High resolution trees are generated the same as in Fig. 3 with more details.
10910_2013_212_MOESM2_ESM.jpg (2.7 mb)
Online Resource 2. High resolution phylogenetic tree constructed using the ISM-based method. HA1 sequences are colored according to years they were isolated.


  1. 1.
    T.T. Wang, M.K. Parides, P. Palese, Science 335(6075), 1463 (2012)CrossRefGoogle Scholar
  2. 2.
    Influenza at the Human-Animal Interface (HAI). (World Health Organization) Accessed June 2012.
  3. 3.
    C. Scholtissek, Virus Genes. 11(2–3), 209–15 (1995)CrossRefGoogle Scholar
  4. 4.
    Y. Watanabe, M.S. Ibrahim, H.F. Ellakany, N. Kawashita, R. Mizuike, H. Hiramatsu, N. Sriwilaijaroen, T. Takagi, Y. Suzuki, K. Ikuta, PLoS Pathog. 7(5), e1002068 (2011)CrossRefGoogle Scholar
  5. 5.
    V. Veljkovic, N. Veljkovic, C.P. Muller, S. Müller, S. Glisic, V. Perovic, H. Köhler, BMC Struct. Biol. 9, 21 (2009)CrossRefGoogle Scholar
  6. 6.
    V. Veljkovic, A Theoretical Approach to Preselection of Cancerogens and Chemical Carcinogenesis (Gordon & Breach, New York, 1980)Google Scholar
  7. 7.
    V. Veljkovic, I. Slavic, Phys. Rev. Lett. 29, 105–107 (1972)CrossRefGoogle Scholar
  8. 8.
    V. Veljkovic, Phys. Lett. A 45, 41–42 (1973)CrossRefGoogle Scholar
  9. 9.
    V. Veljkovic, I. Cosic, B. Dimitrijevic, D. Lalovic, IEEE Trans. Biomed. Eng. 32, 337–341 (1985)CrossRefGoogle Scholar
  10. 10.
    D. Swofford, G. Olsen, P. Waddell, D.M. Hillis, in Molecular Systematics, 2nd edition, ed. by D.M. Hillis, C. Moritz, B.K. Mable. (Sinauer, Sunderland, Massachusetts, 1996), pp. 407–511.Google Scholar
  11. 11.
    M.A. Hershkovitz, D.D. Leipe, in Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, vol. 39, ed. by A.D. Baxevanis, B.F.F. Ouellette (Wiley, Hoboken, NJ, USA, 2006). doi: 10.1002/9780470110607.ch9.
  12. 12.
    T.H. Jukes, C.R. Cantor, Mammalian Protein Metabolism (Academic Press, New York, 1969), pp. 21–132Google Scholar
  13. 13.
    M. Kimura, J. Mol. Evol. 16, 111–120 (1980)CrossRefGoogle Scholar
  14. 14.
    H. Kishino, M. Hasegawa, J. Mol. Evol. 29, 170–179 (1989)CrossRefGoogle Scholar
  15. 15.
    J.A. Lake, Proc. Natl Acad. Sci. USA 91, 1455–1459 (1994)CrossRefGoogle Scholar
  16. 16.
    R. Sokal, C. Michener, Univ. Kansas Sci. Bull. 38, 1409–1438 (1958)Google Scholar
  17. 17.
    P.H. Sneath, R.R. Sokal, Numerical Taxonomy (W. H. Freeman, San Francisco, 1973)Google Scholar
  18. 18.
    N. Saitou, M. Nei, Mol. Biol. Evol. 4, 406–425 (1987)Google Scholar
  19. 19.
    W.M. Fitch, E. Margoliash, Science 155, 279–284 (1967)CrossRefGoogle Scholar
  20. 20.
    W.M. Fitch, Syst. Zool. 35, 406–416 (1971)CrossRefGoogle Scholar
  21. 21.
    D. Sankoff, R.J. Cedergren, Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (Addison-Wesley, London, 1983)Google Scholar
  22. 22.
    J. Camin, R. Sokal, Evolution 19, 311–326 (1965)CrossRefGoogle Scholar
  23. 23.
    J. Felsenstein, Syst. Biol. 22(3), 240–249 (1973)CrossRefGoogle Scholar
  24. 24.
    B. Larget, D.L. Simon, Mol. Biol. Evol. 16(6), 750 (1999)CrossRefGoogle Scholar
  25. 25.
    W.K. Hastings, Biometrika 57(1), 97–109 (1970)CrossRefGoogle Scholar
  26. 26.
    W.M. David, Bioinformatics: Sequence and Genome Analysis (CSHL Press, New York, 2001)Google Scholar
  27. 27.
    J. Felsenstein, Cladistics 5, 164–166 (1989)Google Scholar
  28. 28.
    J.D. Thompson, T. Gibson, D.G. Higgins, Curr. Protoc. Bioinf. 2–3 (2002)Google Scholar
  29. 29.
    S. Kumar, M. Nei, J. Dudley, K. Tamura, Briefings Bioinf. 9(4), 299–306 (2008)CrossRefGoogle Scholar
  30. 30.
    D.L. Swofford, B. Documentation, Phylogenetic Analysis Using Parsimony (Illinois Natural History Survey, Champaign, 1989)Google Scholar
  31. 31.
    J. Adachi, M. Hasegawa, in MOLPHY Version 2.3: Programs for Molecular Phylogenetics Based on Maximum Likelihood, No. 28. (Tokyo, Japan: Institute of Statistical Mathematics, 1996).Google Scholar
  32. 32.
    H.A. Schmidt, K. Strimmer, M. Vingron, A. von Haeseler, Bioinformatics 18(3), 502–504 (2002)CrossRefGoogle Scholar
  33. 33.
    Z. Yang, Mol. Biol. Evol. 24(8), 1586–1591 (2007)CrossRefGoogle Scholar
  34. 34.
    F. Ronquist, J.P. Huelsenbeck, Bioinformatics 19(12), 1572–1574 (2003)CrossRefGoogle Scholar
  35. 35.
    A.J. Drummond, A. Rambaut, BMC Evol. Biol. 7(1), 214 (2007)CrossRefGoogle Scholar
  36. 36.
    S. Guindon, O. Gascuel, Syst. Biol. 52(5), 696–704 (2003)CrossRefGoogle Scholar
  37. 37.
    T.L. Williams, B.M. Moret, in Bioinformatics and Bioengineering, 2003. Proceedings. (Third IEEE Symposium on, 2003), pp. 79–86.Google Scholar
  38. 38.
    S. Roch, Science 327(5971), 1376–1379 (2010)CrossRefGoogle Scholar
  39. 39.
    G. Bhardwaj, K.D. Ko, Y. Hong, Z. Zhang, N.L. Ho, S.V. Chintapalli, L.A. Kline, M. Gotlin, D.N. Hartranft, M.E. Patterson, F. Dave, E.J. Smith, E.C. Holmes, R.L. Patterson, D.B. van Rossum, PloS One 7(4), e34261 (2012)CrossRefGoogle Scholar
  40. 40.
    É. Jakó, E. Ari, P. Ittzés, A. Horváth, J. Podani, Mol. Phylogenet. Evol. 52(3), 887 (2009)CrossRefGoogle Scholar
  41. 41.
    L. Brocchieri, Theor. Popul. Biol. 59(1), 27–40 (2001)CrossRefGoogle Scholar
  42. 42.
    A. Albayrak, H.H. Otu, U.O. Sezerman, BMC Bioinf. 11(1), 428 (2010)CrossRefGoogle Scholar
  43. 43.
    X. Chen, M. Tompa, Nat. Biotechnol. 28(6), 567–572 (2010)CrossRefGoogle Scholar
  44. 44.
    J.D. Thompson, D.G. Higgins, T.J. Gibson, Nucl. Acids Res. 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  45. 45.
    P. Di Tommaso, S. Moretti, I. Xenarios, M. Orobitg, A. Montanyola, J.M. Chang, J.F. Taly, C. Notredame, Nucl. Acids Res. 39(2), W13–W17 (2011)CrossRefGoogle Scholar
  46. 46.
    K. Katoh, K.I. Kuma, H. Toh, T. Miyata, Nucl. Acids Res. 33(2), 511–518 (2005)CrossRefGoogle Scholar
  47. 47.
    S. Zhang, T. Wang, in Proceedings of the 10th WSEAS International Conference on Applied Computer Science. (WSEAS, 2010), pp. 67–71.Google Scholar
  48. 48.
    S. Zhang, T. Wang, MATCH Commun. Math. Comput. Chem. 63(3), 701–716 (2010)Google Scholar
  49. 49.
    H.H. Otu, K. Sayood, Bioinformatics 19(16), 2122–2130 (2003)CrossRefGoogle Scholar
  50. 50.
    K. Carr, E. Murray, E. Armah, R.L. He, S.S.T. Yau, PloS One 5(3), e9550 (2010)CrossRefGoogle Scholar
  51. 51.
    Y. Bakis, H.H. Otu, N. Tasçi, C. Meydan, S. Yüzbasioglu, O.U. Sezerman, BMC Bioinf. 14(1), 20 (2013)CrossRefGoogle Scholar
  52. 52.
    H. Minkowski, Geometrie der Zahlen (Chelsea, London, 1953)Google Scholar
  53. 53.
    R. Schwarz, M. Dayhoff, Atlas of Protein Sequences and Structure (National Biomedical Research Foundation, Washington, DC, 1978)Google Scholar
  54. 54.
    D.T. Jones, W.R. Taylor, J.M. Thornton, Comput. Appl. Biosci. 8, 275–282 (1992)Google Scholar
  55. 55.
    P. Duhamel, H. Hollmann, Electron. Lett. 20(1), 14–16 (1984)CrossRefGoogle Scholar
  56. 56.
    F. Murtagh, Comput. Stat. Q. 1, 101–113 (1984)Google Scholar
  57. 57.
    K. Tamura, D. Peterson, N. Peterson, G. Stecher, N. Nei, S. Kumar, Mol. Biol. Evol. 28, 2731–9 (2011)CrossRefGoogle Scholar
  58. 58.
    R.C. Edgar, Nucl. Acids Res. 32(5), 1792–1797 (2004)CrossRefGoogle Scholar
  59. 59.
    National Center for Biotechnology Information (NCBI) database. Available: Accessed 15 June 2013.
  60. 60.
    Global Initiative on Sharing All Influenza Data (GISAID) database. Available: Accessed 15 Jan 2013.
  61. 61.
    W.J. Murphy, E. Eizirik, S.J. O’Brien, O. Madsen, M. Scally, C.J. Douady, E. Teeling, O.A. Ryder, M.J. Stanhope, W.W. de Jong, M.S. Springer, Science 294(5550), 2348–2351 (2001)CrossRefGoogle Scholar
  62. 62.
    W.J. Murphy, E. Eizirik, W.E. Johnson, Y.P. Zhang, O.A. Ryder, S.J. O’Brien, Nature 409(6820), 614–618 (2001)CrossRefGoogle Scholar
  63. 63.
    V. Ranwez, F. Delsuc, S. Ranwez, K. Belkhir, M.K. Tilak, E.J. Douzery, BMC Evol. Biol. 7(1), 241 (2007)CrossRefGoogle Scholar
  64. 64.
    World Health Organization (WHO) website. Cumulative number of confirmed human cases of avian influenza A(H5N1) reported to WHO. Available: Accessed Mar 2013.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Institute of Nuclear Sciences Vinca, Center for Multidisciplinary ResearchUniversity of BelgradeBelgradeSerbia

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