From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein

Abstract

Zika virus (ZikV) has emerged as a potential threat to human health worldwide. A member of the Flaviviridae, ZikV is transmitted to humans by mosquitoes. It is related to other pathogenic vector-borne flaviviruses including dengue, West Nile and Japanese encephalitis viruses, but produces a comparatively mild disease in humans. As a result of its epidemic outbreak and the lack of potential medication, there is a need for improved vaccine/drugs. Computational techniques will provide further information about this virus. Comparative analysis of ZikV genomes should lead to the identification of the core characteristics that define a virus family, as well as its unique properties, while phylogenetic analysis will show the evolutionary relationships and provide clues about the protein's ancestry. Envelope glycoprotein of ZikV was obtained from a protein database and the most immunogenic epitope for T cells and B cells involved in cell-mediated immunity, whereas B cells are primarily responsible for humoral immunity. We mainly focused on MHC class I potential peptides. YRIMLSVHG, VLIFLSTAV and MMLELDPPF, GLDFSDLYY are the most potent peptides predicted as epitopes for CD4⁺ and CD8⁺ T cells, respectively, whereas MMLELDPPF and GLDFSDLYY had the highest pMHC-I immunogenicity score and these are further tested for interaction against the HLA molecules, using in silico docking techniques to verify the binding cleft epitope. However, this is an introductory approach to design an epitope-based peptide vaccine against ZikV; we hope that this model will be helpful in designing and predicting novel vaccine candidates.

Keywords: Immune Epitope Database; MHC class; Zika virus; artificial neural network; epitopes; immunogenomics.

Figure 1

Depicts the most infected countries and territories with active transmission of the Zika virus (CDC_report).

Figure 2

Multiple sequence alignment, showing the fully conserved region highlighted in yellow blocks; identical and similar regions are indicated by ‘*’ and ‘:’, respectively, including all species. Moreover, the rectangles designate the predicted T‐cell epitope regions in two colours: red (CD8⁺) and green (CD4⁺).

Figure 3

Represents the composition of secondary structure from amino acid residues of Zika virus envelope glycoprotein. Only 35·7% residues form sheet, 60·5% form helices and 3·8% residues form the turn region.

Figure 4

Modelled peptide structure of seven CD8⁺ T‐cell epitopes represented by sequence as (a) CTAAFTFTK, (b) MMLELDPPF, (c) RLKGVSYSL, (d) HQIFGAAFK, (e) GLDFSDLY, (f) SYSLCTAAF, (g) IRCIGVSNR, respectively.

Figure 5

(a) Modelled three‐dimenasional structure of Zika virus envelope glycoprotein; (b) Top three templates superimposed with our modelled protein with an average of RMSD = 0·481; (c) Ramachandran plot of our modelled protein showing the residues in allowed the region.

Figure 6

Represents the epitope MMLELDPPF, which binds in the groove of the HLA‐B*53:01 molecules. Residues ASN‐63, GLN‐70, ASN‐80, TYR‐84 and THR‐143 form regular hydrogen bonds while residues TYR‐7, TYR‐9, ILE‐66, THR‐73, SER‐77, TYR‐99 and GLU‐163 form bonds as a result of electron sharing (which may happen due to charge distribution) between oxygen atoms with covalent characters.

Figure 7

Epitope MMLELDPPF binding in the groove of the HLA‐B*53:01 molecules. Residues GLN‐70, SER‐77 and ASN‐80 form regular hydrogen bonds while residues ILE‐66, THR‐73, ASP‐74, TYR‐84, SER‐143, TYR‐ 116, ASN‐114 and GLU‐163 form bonds as a result of sharing electrons (may happen due to charge distribution) between oxygen atoms with covalent characters.

Figure 8

Combined B‐cell linear epitope prediction showed the region from 150 to 170 amino acid residues had the highest antigenic propensity for B‐cell linear epitopes. Surrounded by six differently coloured lines, which cover the region 150–170 amino acid residues in Zika virus envelope glycoprotein, each line indicating different analysis methods with the maximum scores.