title: Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin.
abstract: We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise dN/dS. In combination, solvent accessibility and proximity explain 32% of the variation in dN/dS; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.
authors.affiliation: Department of Integrative Biology, Institute for Cellular and Molecular Biology and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America; School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America.
authors.forename: Austin G
authors.initials: AG
authors.lastname: Meyer
authors.affiliation: Department of Integrative Biology, Institute for Cellular and Molecular Biology and Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Claus O
authors.initials: CO
authors.lastname: Wilke
mesh_terms: D000914:Antibodies, Viral / Q000276:immunology*; D000956:Antigens, Viral / Q000276:immunology*; D001665:Binding Sites; D016208:Databases, Factual; D018604:Epitope Mapping; D000939:Epitopes / Q000276:immunology*; D019143:Evolution, Molecular; D014644:Genetic Variation / Q000235:genetics; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry* / Q000235:genetics / Q000276:immunology*; D006801:Humans; D053122:Influenza A Virus, H3N2 Subtype / Q000276:immunology*; D007251:Influenza, Human / Q000235:genetics / Q000276:immunology* / Q000821:virology; D017510:Protein Folding; D055550:Protein Stability; D012794:Sialic Acids / Q000378:metabolism; D012997:Solvents / Q000737:chemistry
chemical_list: D000914:Antibodies, Viral; D000956:Antigens, Viral; D000939:Epitopes; D019267:Hemagglutinin Glycoproteins, Influenza Virus; D012794:Sialic Acids; D012997:Solvents