tetano
Editor, Senior Moderator
J Struct Biol
. 2025 Feb 11:108177.
doi: 10.1016/j.jsb.2025.108177. Online ahead of print. Computational identification of B and T-cell epitopes for designing a multi-epitope vaccine against SARS-CoV-2 spike glycoprotein
Truc Ly Nguyen[SUP] 1 [/SUP], Thong Ba Nguyen[SUP] 2 [/SUP], Heebal Kim[SUP] 3 [/SUP]
Affiliations
Although the peak of the COVID-19 pandemic has passed, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a significant global threat and remains a public health concern. Given the ongoing risk and the substantial loss of life caused by the virus, continuous research into vaccine development is essential. This study employs immunoinformatics approaches to identify T-cell and B-cell epitopes for designing a multi-epitope peptide vaccine candidate targeting the Omicron variant. The proposed vaccine construct comprises 1435 amino acids, including eight linear B lymphocyte, seven cytotoxic T lymphocyte, and five helper T lymphocyte epitopes, along with appropriate adjuvants and linkers. The evaluation of the vaccine revealed high antigenicity, non-allergenicity, non-toxicity, and favorable physicochemical properties. To further assess its efficacy, molecular docking studies were performed to investigate interactions between the vaccine and key immune components, including Toll-like receptors and major histocompatibility complex molecules. Stability of these interactions was confirmed using molecular dynamics simulations in triplicate, conducted over 100 ns using GROMACS 2023 to compute key metrics, such as root mean square deviation, root mean square fluctuation, solvent-accessible surface area, and radius of gyration. The results demonstrate that the multi-epitope vaccine has the potential to elicit strong immune responses against the Omicron variant, providing a promising foundation for further experimental validation and clinical development in COVID-19 vaccine research.
Keywords: Computational vaccinology; Immunoinformatics; Molecular dynamics simulation; Multi-epitope vaccine; SARS-CoV-2.
. 2025 Feb 11:108177.
doi: 10.1016/j.jsb.2025.108177. Online ahead of print. Computational identification of B and T-cell epitopes for designing a multi-epitope vaccine against SARS-CoV-2 spike glycoprotein
Truc Ly Nguyen[SUP] 1 [/SUP], Thong Ba Nguyen[SUP] 2 [/SUP], Heebal Kim[SUP] 3 [/SUP]
Affiliations
- PMID: 39947305
- DOI: 10.1016/j.jsb.2025.108177
Although the peak of the COVID-19 pandemic has passed, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose a significant global threat and remains a public health concern. Given the ongoing risk and the substantial loss of life caused by the virus, continuous research into vaccine development is essential. This study employs immunoinformatics approaches to identify T-cell and B-cell epitopes for designing a multi-epitope peptide vaccine candidate targeting the Omicron variant. The proposed vaccine construct comprises 1435 amino acids, including eight linear B lymphocyte, seven cytotoxic T lymphocyte, and five helper T lymphocyte epitopes, along with appropriate adjuvants and linkers. The evaluation of the vaccine revealed high antigenicity, non-allergenicity, non-toxicity, and favorable physicochemical properties. To further assess its efficacy, molecular docking studies were performed to investigate interactions between the vaccine and key immune components, including Toll-like receptors and major histocompatibility complex molecules. Stability of these interactions was confirmed using molecular dynamics simulations in triplicate, conducted over 100 ns using GROMACS 2023 to compute key metrics, such as root mean square deviation, root mean square fluctuation, solvent-accessible surface area, and radius of gyration. The results demonstrate that the multi-epitope vaccine has the potential to elicit strong immune responses against the Omicron variant, providing a promising foundation for further experimental validation and clinical development in COVID-19 vaccine research.
Keywords: Computational vaccinology; Immunoinformatics; Molecular dynamics simulation; Multi-epitope vaccine; SARS-CoV-2.