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Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein
Ramadhita Umitaibatin1, Azza Hanif Harisna2, Muhammad Miftah Jauhar2
1Lab-on-Chip Group, Department of Biomedical Engineering, School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung 40132, Indonesia.
Abstract:
The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.
Insights
A novel multiepitope vaccine targeting the COVID-19 spike protein was designed for the Indonesian population using immunoinformatics. This vaccine shows potential for broad protection against SARS-CoV-2 variants with high efficacy and safety.
Area of Science:
- Computational biology and vaccinology
- Infectious disease research
Background:
- The COVID-19 pandemic necessitates vaccines effective against mutating SARS-CoV-2 strains.
- Existing vaccines may not offer optimal protection for specific populations like Indonesians.
- A gap exists in designing multiepitope vaccines for COVID-19 using immunoinformatics for the Indonesian demographic.
Purpose of the Study:
- To design a multiepitope-based vaccine for the Indonesian population against SARS-CoV-2.
- To utilize a comprehensive immunoinformatic approach for vaccine development.
- To predict and analyze key immunological components of the potential vaccine.
Main Methods:
- Selection of SARS-CoV-2 spike glycoprotein sequences from Indonesia (GISAID database).
- Prediction of T-cell epitopes (CD8+ CTL, CD4+ HTL), B-cell epitopes, and IFN-γ induction.
- In silico vaccine modeling, molecular docking, molecular dynamics, immune simulations, and plasmid vector design.
Main Results:
- The designed vaccine candidate is predicted to be antigenic, non-allergenic, and non-toxic.
- It is capable of inducing Interferon-gamma (IFN-γ) production.
- The vaccine demonstrated good stability in molecular dynamics and immune simulations, with an 86.29% population coverage in Indonesia.
Conclusions:
- A promising multiepitope vaccine model for COVID-19 has been designed for the Indonesian population.
- The in silico analysis indicates high potential efficacy and safety.
- Further experimental investigation of this vaccine model is recommended.
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