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

被引：1

作者：

Ahmad, Sajjad ^{[1
]}

Ali, Syed Shujait ^{[1
]}

Iqbal, Arshad ^{[1
]}

Ali, Shahid ^{[1
]}

Hussain, Zahid ^{[1
]}

Khan, Ishaq ^{[1
]}

Khan, Hayat ^{[2
]}

机构：

[1] Centre for Biotechnology and Microbiology, University of Swat, Kpk, Mingora

[2] Department of Genomics, Phenomics, and Bioinformatics, North Dakota State University

来源：

Computational Biology and Chemistry | 2024年 / 113卷

关键词：

E. coli expression; HToV vaccine; In-silico design; Multi-epitope; TLR-3;

D O I：

10.1016/j.compbiolchem.2024.108213

中图分类号：

学科分类号：

摘要：

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. © 2024 Elsevier Ltd

引用

共 75 条

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