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. 2022 Apr 30;8(5):e09364.
doi: 10.1016/j.heliyon.2022.e09364. eCollection 2022 May.

Immunogenic epitope prediction to create a universal influenza vaccine

R R Mintaev  1   2 , D V Glazkova  1 , E V Bogoslovskaya  1 , G A Shipulin  1
Affiliations

Immunogenic epitope prediction to create a universal influenza vaccine

R R Mintaev et al. Heliyon. .

Abstract

Influenza virus is one of the most rapidly evolving human pathogens and causes significant morbidity and mortality worldwide. This feature enables the virus to avoid natural or vaccine-induced immunity. For this reason, there is an intensive search for new approaches to create a universal influenza vaccine. Here, we propose pipelines based on modern prediction algorithms that allowed us to select 10 B-cell epitopes, 10 CD8+ T-cell epitopes and 6 CD4+ T-cell epitopes from influenza viruses that were characterized by high conservation and antigenicity. These epitopes could be used to create universal vaccines against influenza viruses. In addition, the scripts used in these pipelines are universal and can be used to select epitopes from other pathogens.

Keywords: Computational approaches; Influenza; T- and B-cell epitopes; Universal vaccine.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Pipeline for predicting B-cell epitopes. Third-party software used in the pipeline is shown in ovals. The script was fed the output results of the BepiPred 2.0 program and multiple sequence alignments (MSAs) generated using the Mafft program. The script calculated the conservation of the positions in the reference sequence, which were used to obtain the conserved fragments in the original set of epitopes. Then, immunogenic fragments of the longest length were selected using requests to the VaxiJen 2.0 server. The obtained epitopes were mapped to the structure of HA to select epitopes available on the surface of the protein.
Figure 2
Figure 2
Pipeline for predicting CD4+ T-cell epitopes. Third-party software used in the pipeline is shown in ovals. The script was fed the results from the IEDB prediction service using a consensus algorithm and multiple alignments (MSAs) generated using the Mafft program. The output results from the IEDB service contain the predictions of several algorithms at once, so the script selected peptides that were included in the overlapping sets of peptides from several algorithms.
Figure 3
Figure 3
The result of docking the A/M1:58-GILGFVFTL-66 peptide to the HLA-A∗02:01 molecule. The surface shows the electron density of MHCI amino acid residues located no further than 4 Å from the amino acid residues of the peptide. The carbon atoms on the surface of the MHCI molecule are shown in gray. Green color indicates the carbon atoms of the peptide. Blue color indicates nitrogen atoms and red indicates oxygen atoms.
See this image and copyright information in PMC

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