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

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.

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Déclaration de conflit d'intérêts

Declaration of Competing Interest "I, Sajjad Ahmad, declare that my work on vaccine design aligns with the scope of the journal 'Computational Approaches in Vaccine Design for Biology and Medicine,' which serves as a platform for advancing computational methodologies in vaccine development. My research contributes to the interdisciplinary nature of vaccine design, integrating computational approaches with traditional experimental techniques. I affirm that there are no competing interests to declare.