Molecular escape mechanisms due to patterns of resistance-associated amino acid variants in the hepatitis C virus NS3 protease

点击翻译按钮获取中文摘要

Resistance-associated amino acid variants are a challenging problem in the era of direct-acting antiviral agents in hepatitis C (HCV). Clonal and deep sequencing techniques enable the identification of minority variants from HCV-infected patient sera and for gaining more profound insight into the dynamics of resistance acquisition in the viral quasispecies under drug pressure. Complex patterns of variants in treatment escape from ketoamide and macrocyclic NS3 protease inhibitors (PI) are expected to arise with second-site mutations emerging to compensate for resistant variant fitness losses, which is crucial to select variants from the quasispecies population under drug pressure. We perform large scale sequencing of HCV patient sera from PI-containing regimens and machine learning to identify treatment-failure related variant patterns in the NS3 protease domain. We will apply molecular virology tools, i.e. infectious HCV cell culture, for phenotype description of resistant variants and second-site mutations. Using a residue-interaction network approach on crystal structures and molecular dynamics simulation data of the NS3 protease, we aim in identifying protease residues of structural and functional importance and their role in molecular escape mechanisms from PI treatment. Using the data from phenotype characterization of the protease variants, we will be able to differentiate between resistance associated and compensatory sites. The goal of this research proposal is to translate knowledge from molecular virology and structure biology of NS3 protease variants into treatment algorithms to avoid resistance development and treatment failure in future antiviral therapy.