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Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

Author

Listed:
  • Mansour Ebrahimi
  • Parisa Aghagolzadeh
  • Narges Shamabadi
  • Ahmad Tahmasebi
  • Mohammed Alsharifi
  • David L Adelson
  • Farhid Hemmatzadeh
  • Esmaeil Ebrahimie

Abstract

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Suggested Citation

  • Mansour Ebrahimi & Parisa Aghagolzadeh & Narges Shamabadi & Ahmad Tahmasebi & Mohammed Alsharifi & David L Adelson & Farhid Hemmatzadeh & Esmaeil Ebrahimie, 2014. "Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0096984
    DOI: 10.1371/journal.pone.0096984
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    as
    1. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Xia-Yu Xia & Meng Ge & Zhi-Xin Wang & Xian-Ming Pan, 2012. "Accurate Prediction of Protein Structural Class," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-8, June.
    3. Tokiko Watanabe & Maki Kiso & Satoshi Fukuyama & Noriko Nakajima & Masaki Imai & Shinya Yamada & Shin Murakami & Seiya Yamayoshi & Kiyoko Iwatsuki-Horimoto & Yoshihiro Sakoda & Emi Takashita & Ryan Mc, 2013. "Characterization of H7N9 influenza A viruses isolated from humans," Nature, Nature, vol. 501(7468), pages 551-555, September.
    4. Mansour Ebrahimi & Amir Lakizadeh & Parisa Agha-Golzadeh & Esmaeil Ebrahimie & Mahdi Ebrahimi, 2011. "Prediction of Thermostability from Amino Acid Attributes by Combination of Clustering with Attribute Weighting: A New Vista in Engineering Enzymes," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    5. Ron A. M. Fouchier & Yoshihiro Kawaoka, 2013. "Gain-of-function experiments on H7N9," Nature, Nature, vol. 500(7461), pages 150-151, August.
    6. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    7. Lembke B., 1918. "√ a. p," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 111(1), pages 709-712, February.
    8. Masaki Imai & Tokiko Watanabe & Masato Hatta & Subash C. Das & Makoto Ozawa & Kyoko Shinya & Gongxun Zhong & Anthony Hanson & Hiroaki Katsura & Shinji Watanabe & Chengjun Li & Eiryo Kawakami & Shinya , 2012. "Experimental adaptation of an influenza H5 HA confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets," Nature, Nature, vol. 486(7403), pages 420-428, June.
    9. David A. Steinhauer, 2013. "Pathways to human adaptation," Nature, Nature, vol. 499(7459), pages 412-413, July.
    10. Faezeh Hosseinzadeh & Mansour Ebrahimi & Bahram Goliaei & Narges Shamabadi, 2012. "Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
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