title: Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2.
abstract: The rapid and consistent mutation of influenza requires frequent evaluation of antigenicity variation among newly emerged strains, during which several in-silico methods have been reported to facilitate the assays. In this paper, we designed a structure-based antigenicity scoring model instead of those sequence-based previously published. Protein structural context was adopted to derive the antigenicity-dominant positions, as well as the physic-chemical change of local micro-environment in correlation with antigenicity change. Then a position specific scoring matrix (PSSM) profile and local environmental change over above positions were integrated to predict the antigenicity variance. Independent testing showed a high accuracy of 0.875, and sensitivity of 0.986, with a significant ability to discover antigenic-escaping strains. When applying this model to the historical data, global and regional antigenic drift events can be successfully detected. Furthermore, two well-known vaccine failure events were clearly suggested. Therefore, this structure-context model may be particularly useful to identify those to-be-failed vaccine strains, in addition to suggest potential new vaccine strains.
authors: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
authors: Jingxuan
authors: J
authors: Qiu
authors: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
authors: Tianyi
authors: T
authors: Qiu
authors: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
authors: Yiyan
authors: Y
authors: Yang
authors: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
authors: Dingfeng
authors: D
authors: Wu
authors: Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
authors: Zhiwei
authors: Z
authors: Cao
mesh_terms: D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry*; D053122:Influenza A Virus, H3N2 Subtype / Q000737:chemistry*; D008958:Models, Molecular; D000072417:Protein Domains
chemical_list: D019267:Hemagglutinin Glycoproteins, Influenza Virus; C483468:hemagglutinin, human influenza A virus