Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatic approach

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Abstract

Background. An outbreak of SARS-CoV-2 in 2019 has brought a great challenge to public health and rapid identification of immune epitopes for designing an effective vaccine for different variants of SARS-CoV-2 is necessary at the time of the pandemic. Rational, rapid, and precise vaccine design, especially vaccine antigen identification and optimization by in silico methods of bioinformatics, structural biology, and immunoinformatic is critical to efficient vaccine development against the SARS-CoV-2 virus. The aim of this study was to develop a particular novel and effective vaccines vaccine using bioinformatics approaches and resources that can target B- and T-cell epitopes to combat SARS-CoV-2 infection. 

Materials and methods. The variants of SARS-CoV-2 (Alpha, Beta, Delta, and Omicron strains) spike protein were selected for designing the vaccine. The B-cell, T-cell, and IFNg-inducing epitopes were predicted. The beta-defensin-3 protein was selected as adjuvant and predicted epitopes were connected using suitable linkers. The vaccine’s allergenicity, antigenicity, physicochemical characteristics, 2D and 3D structure modeling, and molecular docking were evaluated for the final construct. 

Results. The in silico results showed that the multi-epitope vaccine has a stable structure and can induce humoral and cellular immune responses against SARS-CoV-2. 

Conclusion. B-cell and T-cell epitopes on spike protein were identified and recommended for design and confirmation of in vivo evaluation for multi-epitope peptides as vaccines against SARS-CoV-2.

About the authors

V. Alamdari-Palang

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

MSc, PhD Candidate, Department of Molecular Medicine

Iran, Islamic Republic of, Shiraz

Z. Dehghan

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Researcher, Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies

Iran, Islamic Republic of, Shiraz

M. Kian

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

DVM, PhD Candidate, Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies

Iran, Islamic Republic of, Shiraz

S. Zonar

Islamic Azad University

Email: razban_vahid@yahoo.com

MSc, Researcher, Department of Biology, Sciences and Research Branch

Iran, Islamic Republic of, Tehran

J. Fallahi

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Assistant Professor, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

Iran, Islamic Republic of, Shiraz

M. Sisakht

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Researcher, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

Iran, Islamic Republic of, Shiraz

S. Khajeh

Shiraz University of Medical Sciences

Email: razban_vahid@yahoo.com

PhD, Assistant Professor, Bone and Joint Diseases Research Center

Iran, Islamic Republic of, Shiraz

V. Razban

Shiraz University of Medical Sciences

Author for correspondence.
Email: razban_vahid@yahoo.com

PhD, Associate Professor, Department of Molecular Medicine, School of Advanced Medical Sciences and Technologies

Iran, Islamic Republic of, Shiraz

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Supplementary files

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2. Figure 1. Schematic workflow of in silico prediction and evaluation of the peptide based multi-epitope vaccine

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3. Figure 2. Final construct of the multi-epitope vaccine

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4. Figure 3. Secondary structure analysis of multi-epitope vaccine using PSIPRED server

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5. Figure 4. A) Tertiary structure of final vaccine construct refinement by PyProtModel software, B) Ramachandran plot analysis of structure predicted by PROCHEK server

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6. Figure 5. The three conformational B-cell epitopes predicted by the ElliPro tool in the multi-epitope vaccine

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7. Figure 6. Molecular docking between multi-epitope vaccine and with TLR3, TLR4, and TLR8 (A–C, respectively)

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Copyright (c) 2025 Alamdari-Palang V., Dehghan Z., Kian M., Zonar S., Fallahi J., Sisakht M., Khajeh S., Razban V.

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