November 18th, 2020 at 18:00
Speaker: Arup Chakraborty, Massachusetts Institute of Technology
Moderator: Mehran Kardar, Massachusetts Institute of Technology
Abstract: Infectious disease-causing viruses have plagued humanity since antiquity. Vaccination has often protected against these threats, and saved more lives than any other medical procedure. Indeed, the best hope for ending the COVID-19 pandemic is an effective vaccine. I will comment on natural immune responses to COVID-19, and current vaccine development efforts. But, what if SARS-CoV-2 was a highly mutable virus? Efforts to develop effective vaccines against such pathogens have not been successful. HIV is a prominent example. We do not have a universal vaccine that can protect us from diverse strains of influenza either. I will describe how bringing together theory/computation (rooted in statistical physics) with basic and clinical immunology can help address such challenges. Using such an approach, we translated data on HIV protein sequences to knowledge of the HIV fitness landscape – i.e., how the virus’ ability to propagate infection depends on its sequence. Predictions emerging from the fitness landscape were then tested against in vitro and clinical data. I will discuss how a potentially potent T cell-based vaccine was designed based on these findings and tested positively in Macaques. I will then describe work aimed toward eliciting antibodies, the other arm of the immune system, that can protect against diverse strains of highly mutable pathogens. This is a problem at the intersection of statistical physics, immunology, and learning theory.