Computational Prediction For High Immunogenic Regions Of The African Swine Fever Virus Proteome

Abstract

Vietnamese scientists are facing the urge to inventing the vaccine for African Swine Fever Virus. According to that requirement, this research would be considered to shorten the vaccine invention. By using bioinformatics, the selection of the potential gene for the vaccine could help the scientist to skip many experiments and reduce the explore time. This research aims to predict the most potential genes which could use for vaccine production and the species of pigs that resist the African Swine Fever Virus. This prediction focuses on combining the Major Histocompatibility Complex (MHC) Class II of the pig and 11 peptides of African Swine Fever Virus, which are from Outer, Inner, and Capsid of the viral structure. The pig has 99 alleles of MHC Class II, in which the exon 2 plays a role as the structure of β1 in the MHC Class II that combines directly with the peptides of the virus. The combination ability of the peptide with exon 2 presents the immunogenicity of the peptides. According to the Denmark Technology University's tool, "NetMHCIIpan 3.2 server" is one of the useful tools to build a database of the combination between MHC Class II and the peptides. The peptides which combine powerfully with the exon 2 are the potential materials for the vaccine production. The result demonstrates B646L is the most potential gene, which binds significantly up to 68 alleles of the MHC. Furthermore, the alleles 06 of the pig's MHC Class II has the most robust resistant with the African Swine Fever Virus. However, the previous research demonstrates that a severe ASFV vaccine leads to a couple of clinical problems after the vaccination. Further research is needed to launch in the wet lab to finish the vaccine invention or continue predicting other viral genes.