Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/94781
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Type: Journal article
Title: Understanding the undelaying mechanism of HASubtyping in the level of physic-chemal characteristics of protein
Author: Ebrahimi, M.
Aghagolzadeh, P.
Shamabadi, N.
Tahmasebi, A.
Alsharifi, M.
Adelson, D.
Hemmatzadeh, F.
Ebrahimie, E.
Citation: PLoS One, 2014; 9(5):e96984-1-e96984-14
Publisher: Public Library of Science
Issue Date: 2014
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
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.
Keywords: Animals; Humans; Influenza A virus; Hemagglutinin Glycoproteins, Influenza Virus; Computational Biology; Mutation; Decision Trees; Neural Networks (Computer); Chemical Phenomena; Data Mining; Support Vector Machine
Rights: © 2014 Ebrahimi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0030008781
DOI: 10.1371/journal.pone.0096984
Appears in Collections:Molecular and Biomedical Science publications

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