Novel Design for Multi-Epitope Vaccines of COVID-19 and Critical In-Silico Assessment Steps

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dc.contributor.authorLan, T.
dc.contributor.authorSu, S.
dc.contributor.authorPing, P.
dc.contributor.authorLi, J.
dc.date.issued2025
dc.description.abstractIntroduction: The coronavirus disease COVID-19, caused by the SARS-CoV-2 virus, was a global pandemic that happened in March of 2020. The virus was mutated into several widelyspread strains such as Alpha, Beta, Gamma, Delta, and Omicron, and is continuing its unpredictable mutation. Method: Multi-Epitope Vaccine (MEV) is one type of recombinant vaccine with its sequence containing multiple epitopes and is considered as an effective way to fight against the infectious disease. Previous in silico approaches to MEV construction have been constrained by their inability to predict molecular conformation structures accurately, consequently leading to inaccurate property evaluations. In this work, we designed a novel MEV for the future prevention of COVID-19 or similar diseases. We set strict thresholds to screen for epitope candidates in order to construct highly effective MEV and use the latest ColabFold (a modified version of AlphaFold2) to predict accurate tertiary structures of the MEV. Results: We especially studied epitopes from the main proteins of SARS-CoV-2 (i.e., the envelope, membrane, nucleo-, and spike proteins) that can provoke immunity response of B-cells, helper Tcells (Th), and cytotoxic T-cells (CTL), then we combined them through amino acid linkers to construct the MEV. We evaluated the vaccine in terms of its physicochemical properties, population coverage, safety for use, secondary and tertiary structure, docking immunity response, and immu nity response eliciting capability. Conclusion: These in silico assessments demonstrate that our proposed vaccine can elicit effective immune responses and it is safe to use with a high population coverage.
dc.description.statementofresponsibilityTian Lan, Shuquan Su, Pengyao Ping, and Jinyan Li
dc.identifier.citationCurrent Bioinformatics, 2025; 20(10):878-889
dc.identifier.doi10.2174/0115748936303461240827061629
dc.identifier.issn1574-8936
dc.identifier.issn2212-392X
dc.identifier.orcidPing, P. [0000-0002-1829-3273]
dc.identifier.urihttps://hdl.handle.net/2440/147821
dc.language.isoen
dc.publisherBentham Science Publishers
dc.relation.granthttp://purl.org/au-research/grants/arc/DP180100120
dc.rights© 2025 Bentham Science Publishers
dc.source.urihttps://doi.org/10.2174/0115748936303461240827061629
dc.subjectMulti-epitope vaccine; SARS-CoV-2; helper T-cells; cytotoxic T-cells; antigens; molecular docking
dc.titleNovel Design for Multi-Epitope Vaccines of COVID-19 and Critical In-Silico Assessment Steps
dc.typeJournal article
pubs.publication-statusPublished

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