In Silico Hepatitis C Vaccine Design
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. Despite 25 years of research, no vaccine is available. A major obstacle is the virus' extreme genetic variability and rapid mutational escape from immune pressure. Improvements in the vaccine design process are urgently needed. Coupling data mining and maximum entropy inference, we have developed a computational approach to translate sequence databases into empirical fitness landscapes. These landscapes explicitly connect viral genotype to phenotypic fitness and reveal vulnerable targets that can be exploited to rationally design vaccines. These landscapes represent the mutational playing field over which the virus evolves. We have integrated them with agent-based models of the viral mutational and host immune response, establishing a data-driven multi-scale immune simulator. Using this simulator we can perform in silico screening of HCV immunogens to rationally design vaccines to both cripple viral fitness and block escape. By systematically identifying a small number of promising vaccine candidates, these models can accelerate the search for a vaccine by massively reducing the experimental search space. However without large scale parallelization, this in silico method will not be fast enough to provide this assistance to clinicians. We plan on extending this method to other diseases.