Design of an epitope-based peptide vaccine against the SARS-CoV-2: a vaccine-informatics approach

Abstract

The recurrent and recent global outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has turned into a global concern which has infected more than 42 million people all over the globe, and this number is increasing in hours. Unfortunately, no vaccine or specific treatment is available, which makes it more deadly. A vaccine-informatics approach has shown significant breakthrough in peptide-based epitope mapping and opens the new horizon in vaccine development. In this study, we have identified a total of 15 antigenic peptides [including thymus cells (T-cells) and bone marrow or bursa-derived cells] in the surface glycoprotein (SG) of SARS-CoV-2 which is nontoxic and nonallergenic in nature, nonallergenic, highly antigenic and non-mutated in other SARS-CoV-2 virus strains. The population coverage analysis has found that cluster of differentiation 4 (CD4+) T-cell peptides showed higher cumulative population coverage over cluster of differentiation 8 (CD8+) peptides in the 16 different geographical regions of the world. We identified 12 peptides ((LTDEMIAQY, WTAGAAAYY, WMESEFRVY, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF, FGAGAALQI, YGFQPTNGVGYQ, LPDPSKPSKR, QTQTNSPRRARS and VITPGTNTSN) that are $80\hbox{--} 90\%$ identical with experimentally determined epitopes of SARS-CoV, and this will likely be beneficial for a quick progression of the vaccine design. Moreover, docking analysis suggested that the identified peptides are tightly bound in the groove of human leukocyte antigen molecules which can induce the T-cell response. Overall, this study allows us to determine potent peptide antigen targets in the SG on intuitive grounds, which opens up a new horizon in the coronavirus disease (COVID-19) research. However, this study needs experimental validation by in vitro and in vivo.

Keywords: COVID-19; T- and B-cell; allergenicity; epitope prediction; immunogenicity; molecular docking; population coverage; vaccinomics.

Figure 1

(A) The 3D structure of HLA molecules, including HLA-B^*53:01, HLA-B^*44:03 and HLA-DRB1^*01:01. (B) The peptide structure of three peptides for CD8+ T-cells (LTDEMIAQY, WTAGAAAYY and WMESEFRVY). (C). The structure of six CD4+ T-cells peptides, including FVSNGTHWF, IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF and FGAGAALQ.

Figure 2

Six Peptides are representing the B-cell epitopes which have highest antigenic propensity and are surrounded by six different colored lines, with each line indicating the different analysis methods (Bepipred linear epitope prediction, Chou & Fasman beta-turn prediction, Emini surface accessibility prediction, Karplus & Schulz flexibility prediction, Kolasker & Tongaonkar antigenicity prediction and Parker hydrophilicity prediction) with the maximum scores.

Figure 3

Population coverage of the identified peptides in 16 geographical regions of the world: (A) Population coverage represents the fraction of individuals expected to respond to a given epitope set, (B) average number of epitope hits/HLA combinations recognized by the population and (C) minimum number of epitope hits/HLA combinations recognized by 90% of the population (PC_90).

Figure 4a

Population coverage of selected peptides binding to the MHC class-I molecules in the 16 geographical regions of the world.

Figure 4b

Population coverage of selected peptides binding to the MHC class II molecules in 16 geographical regions of the world.

Figure 5

Peptide LTDEMIAQY (yellow) binds in the groove of the HLA-B^*53:01. While other two peptides WTAGAAAYY (magenta) and WMESEFRVY (blue) bind in the groove of the HLA-B^*44:03. All H-bonds and other type of interactions are represented in dotted lines with bond length (in À).

Figure 6

Five peptides including IRASANLAA (green), FGAISSVLN (magenta), VKQLSSNFG (pale yellow), FAMQMAYRF (blue) and FGAGAALQI (red) bind in the groove of HLA-DRB1^*01:01. All H-bonds and other type of interactions are represented in dotted lines with bond length (in À).