Antiviral immunity is our central question: how cells and animals sense viral infection, switch on the interferon response and keep it in balance. We pursue it from four directions at once.
We work on RNA viruses, especially respiratory viruses and the arboviruses spread by mosquitoes, ticks and midges. Using cell lines and primary human airway cultures, we study how these viruses replicate, how cells detect them through interferons, and how viruses evade that response, along with how co-infection and pollution change the picture.
Antiviral genes are among the fastest evolving in the genome, and comparing them across species pinpoints where hosts and viruses have been pushing against each other. We study how viruses evolve and how immune systems evolve in response, and tracking how viruses adapt to new hosts helps us reason about which might jump next.
Deep learning is our main computational tool. We use protein language and structure models to predict how immune proteins interact and to pick out the experiments worth doing, and apply them to comparative genomic and surveillance data.
We are working towards new antivirals by two routes: targeting the viral proteins that disarm the immune response, and strengthening the host's own defences. Interferon-lambdas are a particular focus, and we build modified versions to understand what controls their potency.
A typical project runs through several of these: an evolutionary signal points to a protein interface, virology tests what it does in a real infection, computation predicts which changes matter, and the best findings become targets worth designing against.
Across all four runs a One Health view spanning wildlife, livestock and people, shaped by the COVID-19 pandemic, when members of the lab helped build Northern Ireland's genomic surveillance of the virus.
The people page shows who is working on what, and the publications page has the papers.