In-silico identification of the vaccine candidate epitopes against the Lassa virus hemorrhagic fever

Abstract

Lassa virus (LASV), a member of the Arenaviridae, is an ambisense RNA virus that causes severe hemorrhagic fever with a high fatality rate in humans in West and Central Africa. Currently, no FDA approved drugs or vaccines are available for the treatment of LASV fever. The LASV glycoprotein complex (GP) is a promising target for vaccine or drug development. It is situated on the virion envelope and plays key roles in LASV growth, cell tropism, host range, and pathogenicity. In an effort to discover new LASV vaccines, we employ several sequence-based computational prediction tools to identify LASV GP major histocompatibility complex (MHC) class I and II T-cell epitopes. In addition, many sequence- and structure-based computational prediction tools were used to identify LASV GP B-cell epitopes. The predicted T- and B-cell epitopes were further filtered based on the consensus approach that resulted in the identification of thirty new epitopes that have not been previously tested experimentally. Epitope-allele complexes were obtained for selected strongly binding alleles to the MHC-I T-cell epitopes using molecular docking and the complexes were relaxed with molecular dynamics simulations to investigate the interaction and dynamics of the epitope-allele complexes. These predictions provide guidance to the experimental investigations and validation of the epitopes with the potential for stimulating T-cell responses and B-cell antibodies against LASV and allow the design and development of LASV vaccines.