Developing machine learning tools to investigate evolutionary trajectories in emerging viral infectious diseases

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TBCUnderstanding the evolutionary trajectory of an emerging viral pathogen is crucial to both surveillance and the development of therapeutic interventions. This project aims to advance our understanding of the mutational pathways followed as a virus adapts to its host. The unprecedented and large quantity of genomic and molecular data linked to phenotypic health data that has been generated in response to the current Sars-CoV-2 pandemic, as well as existing data from influenza and other viruses, allows us to address this question at the molecular level. Using a combination of computational techniques, including structural bioinformatics and machine learning / deep learning, the effects of successive combinations of mutations in proteins involved in viral/host interactions will be evaluated and predicted. To test and appraise the computation tools a range of lab based molecular virology techniques will be employed. Including in vitro virus culture, cloning, site-directed mutagenesis, reverse genetics, recombinant protein expression and purification, pseudovirus construction, classical and pseudoneutralisation assays. The computational tools and insights from this project can be applied to future emerging viral pathogens. The range of highly sought after skills developed within the project will give a firm bases for a future career in academia and/or industry.