Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused COVID-19 disease in China. So far, no vaccine has licensed to protect against infection with COVID-19, therefore an effective COVID-19 vaccine needed. The aim of this study was to predict antigenic peptides of SARS-CoV-2 for designing the COVID-19 vaccine using immunoinformatic analysis. In this study, T and B-cell epitopes of S protein were predicted and screened based on the antigenicity, toxicity, allergenicity, and cross-reactivity with human proteomes. The epitopes were joined by the appropriate linker. LT-IIc as an adjuvant was attached to the end of the structure. The secondary and 3D structure of the vaccine was predicted. The refinement process was performed to improve the quality of the 3D model structure; the validation process is performed using the Ramachandran plot and ProSA z-score. The proposed vaccine's binding affinity to the HLA-A11:01 and HLA-DRB1_01:01 molecule was evaluated by molecular docking. Using molecular dynamics, the stability of vaccine-HLA complexes was also evaluated. Finally, in silico gene cloning was performed in the pET30a (+) vector. The findings suggest that the current vaccine may be a promising vaccine to prevent SARS-CoV-2 infection.

Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches

“…In recent months, several authors already reported the MEBP vaccine candidates against SARS-CoV-2 using reverse vaccinology approach. Studies published by Naz et al ( 2020 ) and Sanami et al ( 2020 ) predicted both B- and T-cell epitopes of S protein alone for designing a MEBP vaccine candidate against SARS-CoV-2. Sarkar et al ( 2020 ) reported N and S proteins as target antigens for prediction of B- and T-cell epitopes.…”

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

“…This is because almost all prediction methods (discussed in Section 2.2.1) require specifying the HLA allele for predicting associated epitopes. However, a close inspection of the related studies revealed that this was due to either non-reporting of HLA allele restriction of the predicted epitopes (e.g., Sarkar et al [ 161 ]), or cases where only the number of unique HLA alleles associated with the predicted epitopes were reported without specifying the individual correspondences (e.g., Sanami et al [ 163 ]). As for the studies leveraging the immunological data of SARS-CoV from public databases, the SARS-CoV epitopes having no HLA class information were assigned the “NA” HLA class.…”

Designing a conserved peptide-based subunit vaccine against SARS-CoV-2 using immunoinformatics approach

“…VaxiJen v2.0 server was used in examining the antigenicity potential of the protein using non-alignment dependent algorithm but focuses on the physiochemical attributes of the selected prototype vaccine (Yasmin et al 2016 ; Sanami et al 2020 ). ANTIGENPro is a free server which functions with specific microarray data for the evaluation of protein antigenicity index.…”

“…The accuracy of the server was reported to be 76% based on cross-validation experiments (Magnan et al 2010 ; Kalita et al 2019 ). AllerTOP v2.0 server (employs machine learning techniques like amino acid E-descriptors, auto and cross variance transformation and the k nearest neighbours, for the allergenic classification with accuracy of 85.3% (Sanami et al 2020 ).…”