title: Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus.
abstract: The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Ren
authors.affiliation: Center for Statistics and Information, National Health and Family Planning Commission, Beijing, China.
authors.forename: Yuefeng
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
authors.forename: Xiaoning
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
authors.forename: Xiping
authors.initials: X
authors.lastname: Shen
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
authors.forename: Wenlong
authors.initials: W
authors.lastname: Gao
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, Gansu, China.
authors.forename: Juansheng
authors.initials: J
authors.lastname: Li
mesh_terms: D000956:Antigens, Viral / Q000276:immunology; D019295:Computational Biology / Q000379:methods*; D006388:Hemagglutinins / Q000276:immunology*; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D009975:Orthomyxoviridae / Q000276:immunology; D060388:Support Vector Machine
chemical_list: D000956:Antigens, Viral; D006388:Hemagglutinins