5.1.1. Reverse vaccinology model
In recent years, vaccine design has undergone extensive evolutions due
to reverse vaccinology (RV). In this regard, the desired pathogen genome
is first evaluated by bioinformatic analysis and then potential vaccine
candidates are identified [38]. Vaxign is the first web-based system
which applies the RV algorithm to effectively offer the vaccine
candidates for various microbial pathogens. Recently Ong et al. have
achieved a new learning method namely Vaxign-ML machine to enhance the
resolution of candidate prediction [38]. Using Vaxign RV and then
Vaxign-ML systems, they first predicted 6 adhesion protein candidates
including S protein and 5 non-structural nasp3, 3CL-pro, nsp8, nsp9, and
nsp10 proteins for development of the COVID-19 vaccine. Contrary to
previous researches around the COVID-19 vaccine design that focused on
the S protein, it was the first time that the nsp3 and nsp8 were also
announced as alternative candidates with significant antigenicity
scores. Therefore, it seems that the solution to fight against COVID-19
infection is to use a cocktail
vaccine that include a set of candidates (nsp3, nsp8 and S proteins)
instead of a given antigen (S protein) to elicit a significant
protective immunity [39].
A similar study according to in-silico RV strategy tried to render
multi-epitope vaccine candidate against SARS-CoV2 infection and
evaluated its biological activities by computational methods. They
examined three antigens (ORF3a, N and M proteins) with the help of
bioinformatic tools to find potential B-T lymphocyte-stimulating
epitopes. Eventually, specific domains of the M or NOM protein
containing highly scored B and T epitopes was introduced as the main
vaccine candidate that established stable conjugates with Toll-like
receptor (TLR) 4 and HLA-A-11:01 receptors using the imagery molecular
dynamics and docking studies [40]. Therefore, RV seems to guide
furthers research to more rapid access to immunogenic antigen cocktails
in the design of the COVID-19 vaccine.