title: Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.
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.
authors.affiliation: Department of Biology, School of Basic Sciences, University of Qom, Qom, Iran.
authors.forename: Mansour
authors.initials: M
authors.lastname: Ebrahimi
authors.affiliation: Department of Nephrology, Hypertension, and Clinical Pharmacology, University of Bern, Bern, Switzerland.
authors.forename: Parisa
authors.initials: P
authors.lastname: Aghagolzadeh
authors.affiliation: Department of Biology, School of Basic Sciences, University of Qom, Qom, Iran.
authors.forename: Narges
authors.initials: N
authors.lastname: Shamabadi
authors.affiliation: Institute of Biotechnology, Shiraz University, Shiraz, Iran.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Tahmasebi
authors.affiliation: School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Alsharifi
authors.affiliation: School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia.
authors.forename: David L
authors.initials: DL
authors.lastname: Adelson
authors.affiliation: School of Animal and Veterinary Science, The University of Adelaide, Adelaide, Australia.
authors.forename: Farhid
authors.initials: F
authors.lastname: Hemmatzadeh
authors.affiliation: School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
mesh_terms: D000818:Animals; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D003663:Decision Trees; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry* / Q000145:classification* / Q000235:genetics; D006801:Humans; D009980:Influenza A virus; D009154:Mutation; D016571:Neural Networks, Computer; D060388:Support Vector Machine
chemical_list: D019267:Hemagglutinin Glycoproteins, Influenza Virus