Molecular characterization and prediction of B-cell epitopes for the development of SARS-CoV-2 vaccine through bioinformatics approach

Context: The SARS-CoV-2 virus is the cause of the COVID-19 pandemic, which is a severe public health crisis worldwide. Aims: To analyze the SARS-CoV-2 isolates of Surabaya and predict ORF1ab polyprotein epitopes through the bioinformatics approach for vaccine candidate development. Methods: Three genomic sequences of Surabaya isolates were obtained from the GISAID, NCBI and PDB Gen-bank databases and MEGA-11 software were used to understand the transformations in the isolates. The IEDB and VaxiJen, AllerTop, and ToxinPred web servers were used to predict B-cell epitopes and analyze their antigenicity, non-allergenicity, non-toxicity, respectively. Moreover, these epitopes were linked by EAAAK for 3D modeling, refinement, and validation through Swiss- Model, Galaxy Refine, and RAMPAGE web tools. Results: The Surabaya isolates, RSDS-RCVTD-UNAIR-49-A, 54-A, and 42-A, had 10, 20, and 16 mutations in nucleotides and depicted a phylogenetically close relationship to isolates of Egypt, Pakistan, and Bangladesh, respectively. A total of 71 sequential Orf1ab B-cell epitopes were predicted, and only three peptides were found to be antigenic, non-allergenic, and non-toxic. These epitopes were linked with the EAAAK linker to develop a 3D refined and validated structure. This construct was docked with TLR-3 receptor by the Cluspro webserver and found a high affinity of ORF1ab+TLR3 due to 15 hydrogen bonds. The construct demonstrated good humoral and cellular immune responses in the C-ImmSim server, and cloning in the expression vector pET28a (+) yielded a colon of 846bp. Conclusions: ORF1ab B-cell epitopes could be useful for developing effective vaccines to combat SARS-CoV-2 infection.