How to hit HIV where it hurts, with an aside on polio

Vaccination has saved more lives than any other medical procedure. But, some pathogens have evolved which have defied successful vaccination using the empirical paradigms pioneered by Pasteur and Jenner. I will describe how bringing together theoretical/computational approaches from statistical physics, with immunology is beginning to confront this challenge by developing some of the principles necessary for rational design of vaccines that may eliminate these scourges. One characteristic of many pathogens for which successful vaccines do not exist is that they present themselves in various guises. HIV is an extreme example because of its high mutability, and it continues to wreak havoc, especially in developing countries. I will describe how we developed models to translate data on HIV protein sequences to knowledge of the HIV fitness landscape, and tested the resulting predictions against in vitro and clinical data (with collaborators). Based on these studies, a therapeutic T cell-based vaccine was designed, which is now being advanced to pre-clinical studies in monkeys. I will not discuss our work on induction of broadly neutralizing antibodies. Rather, I will briefly describe important differences between the fitness landscapes of polio and HIV, which point to one reason that it was easier to make a vaccine against polio (another highly mutable pathogen).