Immunoinformatics Strategy to Develop a Novel Universal Multiple Epitope-Based COVID-19 Vaccine

Abstract

Currently available COVID vaccines are effective in reducing mortality and severity but do not prevent transmission of the virus or reinfection by the emerging SARS-CoV-2 variants. There is an obvious need for better and longer-lasting effective vaccines for various prevailing strains and the evolving SARS-CoV-2 virus, necessitating the development of a broad-spectrum vaccine that can be used to prevent infection by reducing both the transmission rate and re-infection. During the initial phases of SARS-CoV-2 infection, the nucleocapsid (N) protein is one of the most abundantly expressed proteins. Additionally, it has been identified as the most immunogenic protein of SARS-CoV-2. In this study, state-of-the-art bioinformatics techniques have been exploited to design novel multiple epitope vaccines using conserved regions of N proteins from prevalent strains of SARS-CoV-2 for the prediction of B- and T-cell epitopes. These epitopes were sorted based on their immunogenicity, antigenicity score, and toxicity. The most effective multi-epitope construct with possible immunogenic properties was created using epitope combinations. EAAAK, AAY, and GPGPG were used as linkers to connect epitopes. The developed vaccines have shown positive results in terms of overall population coverage and stimulation of the immune response. Potential expression of the chimeric protein construct was detected after it was cloned into the Pet28a/Cas9-cys vector for expression screening in Escherichia coli. The developed vaccine performed well in computer-based immune response simulation and covered a diverse allelic population worldwide. These computational findings are very encouraging for the further testing of our candidate vaccine, which could eventually aid in the control and prevention of SARS-CoV-2 infections globally.