Bioinformatic identification of B-cell epitope for designing Multitope Peptide-Based Vaccine against Covid-19

Rashidian, Zahra; Nassiri, Mohammadreza; Haghparast, Alireza; Sekhavati, Mohammad Hadi; Javadmanesh, Ali

doi:10.22092/ari.2026.371288.3932

Bioinformatic identification of B-cell epitope for designing Multitope Peptide-Based Vaccine against Covid-19

Articles in Press

Document Type : Original Articles

Authors

¹ Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad

² Department of Pathobiology, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad

10.22092/ari.2026.371288.3932

Abstract

Introduction: During the global outbreak of COVID-19, the crucial role of vaccination in enhancing population immunity, limiting viral transmission, and reducing disease severity, hospitalizations, and mortality became evident. The purpose of all vaccines is to expose the body to an antigen that will not cause disease, but will stimulate the immune system. There are different vaccines against the corona virus. An important aspect of COVID-19 vaccine development is the wide variety of technological platforms that have been explored for producing effective immunization strategies, among which recombinant protein–based vaccines are characterized by high protein expression efficiency, favorable long-term stability, and the ability to elicit robust immune responses. The aim of this research was to design the first Multi-epitope Peptide Vaccine (MPV) candidate targeting SARS-CoV-2. Materials and Methods: Protein sequences were retrieved from public databases, and key epitope-rich regions were linked using rigid peptide linkers. The physicochemical properties of the construct, including antigenicity, allergenicity, toxicity, stability, half-life, and molecular features, were evaluated using bioinformatics tools. Results: Comprehensive in silico analyses confirmed the construct's promising vaccine profile: it was predicted to be highly antigenic (VaxiJen score up to 1.698), non-allergenic, non-toxic, and stable (instability index: 37.59). Structural modeling and validation yielded a high-quality 3D model with 96.1% of residues in favored Ramachandran regions and a ProSA Z-score of -2.46, indicative of native-like folding. Conclusion: These computational results suggest that the designed multi-epitope construct holds strong potential to have a protective immune response against COVID-19. Nevertheless, further laboratory and in-vivo investigations will be required for validating its safety, immunogenicity, and protective potential.

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