Designing a novel vaccine against COVID-19 based on spike SARS-Cov-2 notable mutations using immunoinformatics approaches - Somayyeh Rahimnahal; Shahnaz Yousefizadeh; Yahya Mohammadi (PLoS ONE, 2026)
The rapid emergence of SARS-CoV-2 variants with spike protein mutations undermines the effectiveness of current vaccines, necessitating innovative strategies to ensure broad and lasting immunity. This study leverages an immunoinformatics approach to design two multi-epitope vaccine constructs Cov19-B (649 amino acids, 74 kDa) and Cov19-T (465 amino acids, 48 kDa) specifically targeting mutations in the spike protein observed in the Alpha, Beta, Gamma, and Omicron variants. Using sequence data retrieved from NCBI, GISAID, and UniProt, we predicted a range of epitopes, including linear B-cell, cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and IFN-gamma-inducing epitopes, selected for their high antigenicity, solubility, non-allergenicity, and non-toxicity. These epitopes provide extensive global population coverage: 76.83% for MHC I, 87.43% for MHC II, and 93.8% for combined epitopes. The constructs were enhanced with adjuvants—Human Beta-defensin 3, PADRE, and 50S ribosomal protein L7/L12—and connected with AAY, GPGPG, EAAAK, and KK linkers to optimize structural stability and immune activation. Codon-optimized has done using GenSmart™, and structurally stabilized via disulfide engineering (Disulfide by Design 2). Computational analyses, including molecular docking and dynamics simulations (assessing RMSD, RMSF, gyration, and MMPBSA), validated stable binding interactions with human neutralizing antibodies. Immune response simulations conducted via C-IMMSIM further confirmed the constructs’ capacity to trigger robust humoral and cellular immunity. To enable practical application, codon optimization was performed for efficient expression in prokaryotic systems. This study highlights the vital role of continuous genomic surveillance in tracking SARS-CoV-2 evolution and informs the development of next-generation vaccines. However, the study is limited to computational predictions, requiring experimental validation to confirm efficacy.