Computational design of hepatitis C virus immunogens from host-pathogen dynamics over empirical viral fitness landscapes.

Hepatitis C virus (HCV) afflicts 170 million people and kills 700 000 annually. Vaccination offers the most realistic and cost effective hope of controlling this epidemic, but despite 25 years of research, no vaccine is available. A major obstacle is HCV's extreme genetic variability and rapid mutational escape from immune pressure. Coupling maximum entropy inference with population dynamics simulations, we have employed a computational approach to translate HCV sequence databases into empirical landscapes of viral fitness and simulate the intrahost evolution of the viral quasispecies over these landscapes. We explicitly model the coupled host-pathogen dynamics by combining agent-based models of viral mutation with stochastically-integrated coupled ordinary differential equations for the host immune response. We validate our model in predicting the mutational evolution of the HCV RNA-dependent RNA polymerase (protein NS5B) within seven individuals for whom longitudinal sequencing data is available. We then use our approach to perform exhaustive in silico evaluation of putative immunogen candidates to rationally design tailored vaccines to simultaneously cripple viral fitness and block mutational escape within two selected individuals. By systematically identifying a small number of promising vaccine candidates, our empirical fitness landscapes and host-pathogen dynamics simulator can guide and accelerate experimental vaccine design efforts.