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.
Highlights
One of the leading challenges when a new influenza strain such as H7N9 is found to be infecting humans is to rapidly appraise its pandemic potential, so as to gauge the importance of allocation of resources to study of the new virus and development of tools and reagents including vaccines [1,2]
In order to characterise the behaviour of H7N9 HA a homology model was generated
This could enable preliminary assessment of pandemic potential of newly emerged influenza strains by providing an estimation of their affinity for human sialic acid receptors and their ability to be neutralized by existing anti-HA antibodies
Summary
One of the leading challenges when a new influenza strain such as H7N9 is found to be infecting humans is to rapidly appraise its pandemic potential, so as to gauge the importance of allocation of resources to study of the new virus and development of tools and reagents including vaccines [1,2]. Bottlenecks in pandemic assessment arise from delays in transporting the new virus to laboratories with the requisite skills, the time to grow and characterise the virus, infect animals and develop an understanding of its behaviour [1]. This process may take 6–12 months, with development of a vaccine against the new strain taking potentially even longer [3]. Advances in bioinformatics including structural modelling and docking tools provide a major opportunity to help assess potential pandemic influenza viruses alongside their physical characterisation [4]. Key questions that could potentially be addressed by structural modelling methods to help assess the pandemic potential of any new influenza virus include capability of human to human transmission, ability to acquire mutations that could increase virulence, ability to be neutralised by existing antibodies and/or antiviral drugs and suitability for egg or cell culture adaptation and largescale vaccine production [4]
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