Computational designing of multiepitope based vaccine (MEBV) candidate against human metapneumovirus (HMPV) using immunoinformatics approach

Abstract Human metapneumovirus (HMPV) is a paramyxoviridae family respiratory pathogen, which is closely related to respiratory syncytial virus. It produces the symptoms of the flu and it may result in the serious damage of the airways and even though it is a worldwide problem, there is no vaccine available as yet. Three antigenic proteins namely surface glycoprotein, nucleoprotein and phosphoprotein were screened from all the receptors and showed no similarity to host genome. The three viral proteins were then screened for identifying the cytotoxic and helper T cell lymphocytes as well as linear and conformational B-cell lymphocytes. The vaccine had 13 MHC-I epitopes, one MHC-II epitope, and four linear B-cell epitopes, which were combined with linkers and beta-defensin as adjuvant into a 296-amino-acid MEBV with antigenicity score of 0.6644. Molecular docking and MD simulations showed that there is a high degree of interaction with human TLR3 with binding energy of − 14.6 and immune activation. Moreover, the disulfide engineering enhanced construct stability. Cloning and codon optimization was used to verify efficient expression in the target host. Taken together, these findings suggest that the proposed MEBV has potential as an immunogen of HMPV.