Using a dual immunoinformatics and bioinformatics approach to design a novel and effective multi-epitope vaccine against human torovirus disease

Abstract

Human Torovirus (HToV), a member of the Coronaviridae family, causes severe enteric diseases with no specific medication available. To develop novel preventative measures, we employed immunoinformatics techniques to design a multi-epitope-based subunit vaccine (HToV-MEV) triggering diverse immune responses. We selected non-allergenic, non-toxic, and antigenic epitopes from structural polyproteins, joined them with suitable linkers, and added an adjuvant 50S ribosomal L7/L12 peptide. The vaccine's solubility score of 0.903678 and physiochemical properties were found effective. Molecular dynamics simulations and free energy calculations revealed strong binding affinity for Toll-like receptor 3 (TLR-3), with a lowest energy score of −303.88, indicating high affinity. In-silico cloning and codon optimization showed significant production potential in E. coli. Immune simulations predicted a human immunological response. Our results are promising, but subsequent in vivo research is recommended. The HToV-MEV vaccine design demonstrates potential for preventing HToV-related diseases, and further investigation is warranted to explore its therapeutic applications.