title: In silico structural homology modelling and docking for assessment of pandemic potential of a novel H7N9 influenza virus and its ability to be neutralized by existing anti-hemagglutinin antibodies.
abstract: The unpredictable nature of pandemic influenza and difficulties in early prediction of pandemic potential of new isolates present a major challenge for health planners. Vaccine manufacturers, in particular, are reluctant to commit resources to development of a new vaccine until after a pandemic is declared. We hypothesized that a structural bioinformatics approach utilising homology-based molecular modelling and docking approaches would assist prediction of pandemic potential of new influenza strains alongside more traditional laboratory and sequence-based methods. The newly emerged Chinese A/Hangzhou/1/2013 (H7N9) influenza virus provided a real-life opportunity to test this hypothesis. We used sequence data and a homology-based approach to construct a 3D-structural model of H7-Hangzhou hemagglutinin (HA) protein. This model was then used to perform docking to human and avian sialic acid receptors to assess respective binding affinities. The model was also used to perform docking simulations with known neutralizing antibodies to assess their ability to neutralize the newly emerged virus. The model predicted H7N9 could bind to human sialic acid receptors thereby indicating pandemic potential. The model also confirmed that existing antibodies against the HA head region are unable to neutralise H7N9 whereas antibodies, e.g. Cr9114, targeting the HA stalk region should bind with high affinity to H7N9. This indicates that existing stalk antibodies initially raised against H5N1 or other influenza A viruses could be therapeutically beneficial in prevention and/or treatment of H7N9 infections. The subsequent publication of the H7N9 HA crystal structure confirmed the accuracy of our in-silico structural model. Antibody docking studies performed using the H7N9 HA crystal structure supported the model's prediction that existing stalk antibodies could cross-neutralise the H7N9 virus. This study demonstrates the value of using in-silico structural modelling approaches to complement physical studies in characterization of new influenza viruses.
authors.affiliation: Vaxine Pty Ltd, Bedford Park, Adelaide, South Australia, Australia.
authors.forename: Harinda
authors.initials: H
authors.lastname: Rajapaksha
authors.affiliation: Vaxine Pty Ltd, Bedford Park, Adelaide, South Australia, Australia; Department of Diabetes and Endocrinology, Flinders Medical Centre/Flinders University, Adelaide, South Australia, Australia; Vaxine Pty Ltd, Flinders Medical Centre/Flinders University, Adelaide, South Australia, Australia.
authors.forename: Nikolai
authors.initials: N
authors.lastname: Petrovsky
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D057134:Antibodies, Neutralizing / Q000737:chemistry / Q000276:immunology*; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry / Q000276:immunology*; D006801:Humans; D064766:Influenza A Virus, H7N9 Subtype / Q000737:chemistry / Q000276:immunology*; D007251:Influenza, Human / Q000276:immunology / Q000821:virology; D062105:Molecular Docking Simulation; D008969:Molecular Sequence Data; D009976:Orthomyxoviridae Infections / Q000276:immunology* / Q000821:virology*; D058873:Pandemics; D011487:Protein Conformation; D016415:Sequence Alignment
chemical_list: D057134:Antibodies, Neutralizing; D019267:Hemagglutinin Glycoproteins, Influenza Virus; C109963:hemagglutinin, avian influenza A virus