Identification of potential vaccine candidates againstSARS-CoV-2, A step forward to fight COVID-19: A Reverse Vaccinology Approach

Abstract

The recent Coronavirus Disease 2019 causes an immense health crisis to global public health. The World Health Organization (WHO) declared the COVID-19 as a pandemic. The COVID-19 is the etiologic agent of a recently arose disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Presently, there is no vaccine available against this emerged viral disease. Therefore, it is indeed a need of the hour to develop an effectual and safe vaccine against this decidedly pandemic disease. In the current study, we collected SARS-CoV-2 genome from Indian geographical origin against human host, further more using reverse vaccinology and immunoinformatics tools here we claim effective vaccine candidates that can be mile stone in battle against COVID19. This novel study divulged two promising antigenic peptide GVYFASTEK and NFRVQPTESIV from surface glycoproteins (protein accession no. -QIA98583.1 and QHS34546.1) of SARS-CoV-2, which were predicated to be interacted with class I and class II MHC alleles and showed up to 90% conservancy and high value of antigenicity. Subsequently, the molecular docking studies were verified molecular interaction of these prime antigenic peptides with the residues of HLA-A*11-01 allele for MHC Class I and HLA DRB1*04-01 allele for MHC class II. After vigorous analysis, these peptides were predicted to be suitable epitopes which are capable to elicit the strong cell-mediated immune response against the SARS-CoV-2. Consequences from the present study could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable and virtuous platform to provide timely countermeasure of this dangerous pandemic disease, COVID-19.

AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2

“…Results from a list of selected publications were compared with percentile ranks computed by our method for the same pHLAs. We did not find any significant correlation with the in silico predictions from Grifoni et al 2020, Lee and Koohy 2020, and Gupta et al 2020 highlighting a clear distinction between our methodology and the procedures used in these studies. Although the best candidate selected by Gupta et al is not among our best candidates for HLA-A*11:01, it is scored by the model as the top candidate among those proposed by the authors.…”

“…The substantial difference between the selection of pHLA candidates performed by our methodology with respect to those presented by Grifoni et al 2020, Lee and Koohy 2020and Gupta et al 2020 highlights a clear distinction between these approaches. Nonetheless, our method supported the selection of top candidates in small datasets obtained by applying hand-crafted filtering stages (Baruah and Bose 2020;Gupta et al 2020). The mild correlation with the results from Smith et al 2020 might indicate the usage of equivalent components during some steps of the selection process.…”

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions

“…We used EvalVax to evaluate peptide vaccines and megapools proposed by other publications ( Lee and Koohy, 2020 ; Fast et al., 2020 ; Poran et al., 2020 ; Bhattacharya et al., 2020 ; Baruah and Bose, 2020 ; Abdelmageed et al., 2020 ; Ahmed et al., 2020 ; Srivastava et al., 2020 ; Herst et al., 2020 ; Vashi et al., 2020 ; Akhand et al., 2020 ; Mitra et al., 2020 ; Khan et al., 2020 ; Banerjee et al., 2020 ; Ramaiah and Arumugaswami, 2020 ; Gupta et al., 2020 ; Saha and Prasad, 2020 ; Tahir ul Qamar et al, 2020 ; Singh et al., 2020 ; Yarmarkovich et al., 2020 ; Grifoni et al., 2020a ; Nerli and Sgourakis, 2020 ; Yazdani et al., 2020 ; Ismail et al., 2020 ) on metrics including EvalVax-Unlinked and EvalVax-Robust population coverage at different per-individual number of peptide-HLA hits thresholds, expected per-individual number of peptide-HLA hits in White, Black, and Asian populations, percentage of peptides that are predicted to be glycosylated, peptides observed to mutate with a probability greater than 0.001, or peptides that sit on known cleavage sites. We define “normalized coverage” as the mean expected per-individual number of peptide-HLA hits for a vaccine divided by the number of peptides in the vaccine.…”

Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch vaccines, designed by reverse epitomics approach, show potential to cover large ethnically distributed human population worldwide

“…On the other hand, the multi-epitope vaccine involves fusion of multiple epitopes identified from the proteome of the SARS-CoV-2 by short peptide linkers. Several subunit and multi-epitope-based vaccine designs have been published claiming potential to activate CD4 and CD8 T-cell immune response driving long-term robust adaptive immunity in the vast majority of the population (Abdelmageed et al, 2020 ; Ahmed et al, 2020 ; Akhand et al, 2020 ; An et al, 2000 ; Banerjee et al, 2020 ; Baruah & Bose, 2020 ; Bhattacharya et al, 2020 ; Fast & Chen, 2020 ; Gragert et al, 2013 ; Gupta et al, 2020 ; Herst et al, 2020 ; Ismail et al, 2020 ; Khan et al, 2020 ; Lee & Koohy, 2020 ; Liu et al, 2020 ; Lu et al, 2014 ; Mitra et al, 2020 ; Nerli & Sgourakis, 2020 ; Poran et al, 2020 ; Ramaiah & Arumugaswami, 2020 ; Saha & Prasad, 2020 ; Sheikhshahrokh et al, 2020 ; Singh et al, 2020 ; Srivastava et al, 2020a ; 2020b ; Ul Qamar et al, 2020 ; Vashi et al, 2020 ; Yarmarkovich, Farrel et al, 2020; Yazdani et al, 2020 ). Numerous highly antigenic regions have also been reported from SARS-CoV-2 proteins, which have been recognized with a large population coverage by favorable binding with large number of HLA allele distributed among different ethnic human population across the world (Grifoni et al, 2020b ; Yarmarkovich, Warrington, et al, 2020 ).…”