Multi-epitope vaccine targeting SARS-CoV-2 omicron S and N proteins promotes enhanced immunity: a computational approach

Abstract

Background The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the COVID-19 pandemic, which resulted in millions of deaths globally and had profound social, economic, and political consequences. Although effective vaccines and antiviral therapies have substantially reduced the global burden of COVID-19, the continued emergence of viral variants highlights the need for next-generation effective vaccine strategies capable of providing broader and more durable immune response. Methods In this work, we provide an immunoinformatic approach for multi-epitope vaccine (MEV) design and prediction. Based on the spike (S) and nucleocapsid (N) proteins of SARS-CoV-2, immunoinformatic methods were used to identify the epitopes for B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL). The B cell, CTL, and HTL epitopes were conjugated with flexible linkers GSG, GSGG, and a Gb-1 peptide conjugated to the C-terminal of the MEV ccandidate. Results The final MEV candidate exhibited favorable predicted characteristics, with a molecular weight of approximately 55.47 kDa and a length of 498 amino acid residues. Computational analyses indicated that the designed construct was antigenic, non-toxic, non-allergenic, and possessed suitable physicochemical properties and predicted solubility, supporting its potential as a vaccine candidate for further investigation. Molecular docking analysis demonstrated favorable interactions between the MEV construct and selected Toll-like receptors (TLRs), while molecular dynamics (MD) simulations suggested the stability of the vaccine-receptor complexes throughout the simulation period. Furthermore, C-ImmSim-based immune simulation predicted the induction of both humoral and cellular immune responses following the proposed immunization schedule. Collectively, these findings highlight the potential of the designed MEV construct as a computationally optimized vaccine candidate and provide a framework for future experimental evaluation. Conclusion This study presents a computationally designed MEV candidate against SARS-CoV-2 by integrating immunoinformatics approaches, structural modeling, molecular docking, molecular dynamics simulations, and immune response prediction. The findings suggest that the proposed MEV construct may possess favorable immunogenic and structural properties; however, experimental validation through in vitro and in vivo studies remains essential to confirm its safety, immunogenicity, and protective efficacy. The proposed approach provides a valuable strategy for accelerating rational vaccine design and may serve as a foundation for future development of experimentally validated vaccine candidates.