Sparse Hierarchical Bayesian Models for Detecting Relevant Antigenic Sites in Virus Evolution
Vinny Davies (University of Glasgow)
Friday 2nd December, 2016 15:00-16:00 Maths 203
Emerging viral diseases pose a substantial threat to public health. Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In the current work, we use structural and phylogenetic differences between pairs of virus strains to identify important antigenic sites on the surface of the virus. We discuss a number of different models that can account for the experimental variability in the data and select the variables responsible for antigenic variability. We will look at the differences between the models and then demonstrate their use on Foot-and-Mouth Disease Virus and Influenza datasets.