Reverse vaccinology approach towards the in-silico multiepitope vaccine development against SARS-CoV-2

Abstract

Background: The novel severe acute respiratory syndrome related corona virus-2 (SARS-CoV-2) belongs to the “Coronaviridae” family and order “Nidovirales”, which has caused the pandemic coronavirus disease 2019 (COVID-19). SARS-CoV-2 has been spread in more than a 100 countries, and more than a million have lost their lives. Vaccination and immunization could be an effective strategy to combat fatal COVID-19. Methods: For identification of effective vaccine candidate against COVID-19, various immunoinformatics online tools and softwares were used to predict epitopes. Cytotoxic T cell epitopes, helper T cell epitopes, and B cell epitopes from three structural polyproteins (Spike, Membrane, and Nucleocapsid (SMN) based on the binding affinity towards MHC, antigenicity, non-allergenicity, and non-toxicity) were identified for vaccine development. The multiepitope based vaccine was constructed linking two additional adjuvants human beta-defensin-3 and human beta-defensin-2 at N and C terminal, respectively. Results: The constructed vaccine sequence was found to be a good antigen and non-allergen for the human body. The constructed vaccine was docked with the TLR-3 receptor. The docked complex was further taken for molecular dynamics simulations and RMSD was calculated, which showed stable binding of the complex. The codon adaptation index (CAI) of 0.92 and GC content of 55.5% for E. coli (K12 strain) suggested efficient expression of the predicted vaccine. Conclusion: The current study can be helpful in the reduction of time and cost for further experimental validations and could give a valuable contribution against this pandemic.

Identification of Putative Vaccine and Drug Targets against the Methicillin-Resistant Staphylococcus aureus by Reverse Vaccinology and Subtractive Genomics Approaches

“…This in silico technique optimizes the prediction of drug target and vaccine candidate proteins of different pathogens that are challenging to culture in the laboratory. Recently, such computational approaches had popular results for the rapid discovery of novel targets in various human pathogens including SARS-CoV-2 [ 19 , 20 ], Ebola virus [ 21 ], Mycobacterium tuberculosis [ 22 ], Helicobacter pylori [ 23 ], Salmonella typhi [ 24 ], serogroup B Neisseria meningitides [ 25 ], Streptococcus agalactiae [ 26 ], and Acinetobacter baumannii [ 27 ]. These approaches allowed to fill the gap between genomic data and the identification of 3D structures of therapeutic targets.…”

Insight Into Novel Anti-tuberculosis Vaccines by Using Immunoinformatics Approaches

“…Thus, the current study aimed to use a reverse vaccinology technique to develop a multi-epitope TB vaccine. A reverse vaccinology technique has recently demonstrated several multiple epitopes vaccines against COVID-19 ( Rahman et al, 2020a , b ; Kumar et al, 2021 ), Ebola ( Ullah et al, 2020 ), and Malaria ( Damfo et al, 2017 ). Also, a potential vaccine coding in the Mtb H37Rv genome was created by various immuno-informatics techniques to produce adaptive immunity in numerous B and T cell epitopes ( Yazdani et al, 2020 ).…”

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

“…B cell epitopes were predicted using Antibody Prediction tool of IEDB server. Although several RV based in silico studies prefer conformational B cell epitopes [ [62] , [63] , [64] , [65] ], the current study sought linear epitopes only. As mentioned before, one obvious reason was the expected loss of conformational epitopes in the cropped sequences [ 66 ] and the fact that the predictive accuracy of conformational epitopes for shorter peptides is not great.…”