Hepatitis C virus (HCV) poses a significant threat to public health, particularly due to its high mutation rate, enabling immune evasion and drug resistance. Our research defines innovative fitness landscape and evolutionary models, rooted in biophysics and population genetics, to analyze complex and diverse sequence data for understanding viral evolution and accelerating HCV vaccine development and therapeutic strategies.
Our models identified regions of the E2 glycoprotein that are difficult for the virus to mutate without compromising fitness, making them attractive targets for broadly neutralizing antibodies (bNabs) [1]. We also revealed that differences in evolutionary constraints between HCV subtypes 1a and 1b may explain the higher chronicity rate and disease severity associated with subtype 1b, while also identifying bNabs resilient to both subtypes [2]. Extending our analysis to the E1-E2 heterodimer, we discovered that fitness-compensating E1 mutations could accelerate escape from E2-targeting antibodies, highlighting the importance of considering both proteins in vaccine design. Our approach also identified bNabs predicted to be resilient against escape mutations in both E1 and E2 [3]. In the space of antiviral therapy, our models revealed the significant role of epistasis in the evolution of drug resistance mutations (DRMs) targeting the non-structural 3 (NS3) protein. We identified specific DRMs associated with strong epistatic interactions, which appear to facilitate viral escape from drug-induced selection pressures [4].
Our developed computational models provide valuable insights for rational vaccine design and the development of more effective antiviral therapies, potentially leading to improved strategies for combating HCV infection.