Multi Epitope Vaccine Prediction Against Aichi Virus using Immunoinformatic Approach

Aichi virus, AiV is single stranded negative sense RNA genome belonging to the genus Kobuviru, a family of Picornaviridae that causes severe gastroenteritis. There is no treatment or vaccine for it, thus the aim of this study is to design a peptide vaccine using immunoinformatic approaches to analyze the viral Protein VP1 of AiV-1 strain, to determine the conserved region which is further studied to predict all possible epitopes that can be used as a peptide vaccine. A total of 38 Aichi virus VP1 retrieved from NCBI database were aligned to determine the conservancy and to predict the epitopes using IEDB analysis resource. Three epitopes predicted as a peptide vaccine for B cell was (PLPPDT, PPLPTP, and LPPLPTP). For T cell, two epitopes showed high affinity to MHC class I (FSIPYTSPL and TMVSFSIPY) and high coverage against the whole world population. Also, in MHC class II, three epitopes that interact with most frequent MHC class II alleles (FTYIAADLR and YMAEVPVSA) with high coverage in the whole world population. For both MHCI and MHCII the T-cell peptide with the strongest affinity to the worldwide population was FSIPYTSPL. Peptide vaccine against AiV is powerfully displace the normal produced vaccines based on the experimental biochemistry tools, as it designed to handle with a wide range of mutated strains, which will effectively minimize the frequent outbreaks and their massive economical wastage consequences.

Producing vaccine with the experimental biochemistry tools are high-cost, laborious and sometimes does not work effectively, moreover, the vaccine that formulated from attenuated or inactivated microorganism contains immunity induction proteins of that probably develops allergenic or reactogenic responses. For that reason, in silico proper protein residues epitopes prediction is considered to be helpful in peptide vaccine production with a great impact immunogenic and little amount of allergenic effect (13)(14)(15)(16). Numerous researches demonstrated the immunological efficacy of peptide-based vaccines against infectious illnesses. The advancement of peptide-based immunizations has fundamentally progressed with the particular epitope's identification gotten from infectious pathogens. Comprehension of the antigen recognition molecular basis and HLA binding motifs has brought about the improvement of the designed vaccine depending on motifs prediction to bind to host class I or class II MHC (17). There are several types of research have been conducted considering immunoinformatic predication and in sillico modeling of epitope-based peptide vaccine against many viruses (18)(19)(20)(21)(22).

Sequence of protein recovery:
A total of 38 protein strains sequences of Aichi virus vp1 were retrieved from NCBI (https://www.ncbi.nlm.nih.gov/) in October 2018. Those 38 strains sequences were collected from different parts in the world (Japan, Germany and South Korea), The Achi virus VP1 strains, area of collection and their accession numbers are listed in the table (1).

Phylogenetic and alignment:
The retrieved sequences were submitted to Phylogenetic and alignment tools MEGA7.0 to determine the common ancestor of each strain and the conservancy (23) (https://www.megasoftware.net/). The alignment and phylogenetic tree were presented in Figure (2).

Determination of conserved regions:
The chosen sequences were aligned by using multiple sequence alignment (MSA) BioEdit software (version 7.2.5.0) (24) to obtain the sequences of the conserved regions, aligned with Clustal W were used to determine the conserved regions in all Aichi virus VP1, protein sequences shown in figure (3). Peptides chose as epitopes were analyzed by different prediction tools from Immune Epitope Database, IEDB analysis resource (https://www.iedb.org/ ) (25).

Binding prediction of B cell epitope:
The reference sequence of Aichi virus VP1 was subjected to many B cell tests in IEDB webpage ( http://tools.iedb.org/bcell/ ) (26).

linear B cell epitopes prediction:
The linearity of the peptide was studied using Bepipered Linear Epitope Prediction in the immune epitope database ( http://toolsiedb.ofg/bcell/ ) (27), which had a threshold value of 0.35.

Binding prediction of T cell epitope: 2.5.1 Binding predictions of MHC class 1:
The peptide binding analysis to major histocompatibility class I molecules was evaluated by IEDB MHC I estimated tool at ( http://tools.iedb.org/mhci/). Prediction methods were achieved by Artificial Neural Network (ANN), The analysis was done for alleles with peptides length of 9-mers and which have scored equal or less than 500 Half Maximal Inhibitory Concentration (IC50) (30) which was chosen for further analysis.

Binding Predictions of MHC class 2:
Analysis of peptide binding to MHC2 molecules was assessed by the IEDB MHC II prediction tool at (http://tools.immuneepitope.org/mhcii/) (31) For MHCII binding Prediction human allele references set were used (32). We used Artificial Neural Networks (ANN) to identify both the binding affinity and MHCII binding core epitopes. All conserved epitopes that bind to many alleles with a score equal or less than 500 half maximal inhibitory concentration (IC50) were selected for further analysis.

Population Coverage Calculation:
All proposed MHC class I & class II epitopes from Aichi virus vp1 protein were used for population coverage to whole world population with selected MHC I and MHC II binding alleles using IEDB population coverage calculation tool at (http://tools.iedb.org/tools/population/iedb_input) (33).

figure (1):
Flowchart of the epitope prediction processes for B cell and T cell.

B-cell epitope prediction:
The Bepipred linear epitope prediction, Kolaskar, and Tongaonkar results and Emini surface accessibility prediction results were recorded by subjected reference sequence of Aichi virus (vp1) in IEDB table 2, figures 4, 5 and 6.

Prediction of cytotoxic T-lymphocyte epitope and interaction with MHC 1
The (vp1) reference sequence protein of Aichi virus was submitted in the IEDB MHC-1 binding prediction tool to predict epitopes interact with MHC-1 alleles.

Prediction of T-cell epitopes and interaction with MHC 11:
The (vp1) reference sequence protein of Aichi virus was submitted in the IEDB MHC-11 binding prediction tool to predict epitopes interact with MHC-11 alleles.   In the current study, an immunoinformatic-driven approach used to screen emergent immunogen against Aichi virus. B-cell immunity is given the priority to design vaccine but T-cell was also shown to induce strong immune response (36)  fragments and their responses are exquisitely antigen-specific, and they are important as antibodies in defending against infection (37,38).
T cell immune response is long-lasting immunity as foreign particles and can avoid the effect of memory produced via an immune system. In the prediction results of IEDB, the peptides that have good affinity with HLA molecules were FSIPYTSPL and TMVSFSIPY for MHC1. FTYIAADLR and YMAEVPVSA for MHC class II.
We installed threshold associated with all epitopes in both MHC1 and MHC11 by reformulating the peptides bind with an IC50 value below 500 nM, this allowed computing the number of true negatives, true positives, false negatives, and false positives. An overarching approach to gain most protection against viral infections is to design a successful peptide-based vaccine following the identification of essential epitopes by using the immunoinformatic approach combined with an effective adjuvant choice. Computational immunology is now regarded to contribute to vaccine design in the way of computational chemistry contributes to drug design, before the wet lab confirmation, an advance bioinformatics software should be employed to predict these properties (37,39).
Immunoinformatic focuses mostly on small peptides ranging from 8 to 11residues, just one epitope per protein can be sufficient to create an immune response in the host (40)(41)(42). Bioinformatic techniques to search for epitopes are well understood and available, however can sometimes lead to high false positive rates (43) . Despite this drawback, epitope predictors are successful of identifying weak or even strong epitope motifs that have been experimentally ignored (44).
With the advent of next-generation sequencing (NGS) methods, an extraordinary wealth of information has become available that requires moreadvanced immunoinformatic tools. this has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines (45).

5.Conclusion:
World population coverage results for total epitopes binding for both MHC1 and 11 alleles was 99.8% and the most promising T-cell peptides was FSIPYTSPL from 159 to 167 that considered as a unique domain which successfully interacted with both MHC1 and MHC11 alleles together, and it can be binding with 19 distinctive alleles and provided the highest population coverage epitope set (87.42%) this region is probably promising and This peptide should be considered as a viable peptide vaccine for Aichi virus.

Recommendation:
We recommend Further in vitro and in vivo studies to undertake the effectiveness of these predicted epitopes as peptide vaccine. and also, to do further studies in other strains, there will be a possibility to find common conserved promising epitopes for multiple strains. this work considered for further investigation.

7.Acknowledgment:
This research was supported by Africa city of technology, for whom the authors would like to show their gratitude for their provided insight and expertise that greatly assisted the research.

Confect of interest:
the authors declare that there is no conflict of interest regarding the publication of this paper and the authors declare that they have no competing interests.

Data availability:
All relevant data used to support the findings of this study are included within the manuscript and supplementary information files.