Highly Conserved Epitopes of ZIKA Envelope Glycoprotein May Act as a Novel Peptide Vaccine with High Coverage: Immunoinformatics Approach

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From structural polyproteins, Envelope Glycoprotein-E was selected for epitopes prediction using NCBI Reference Sequence: (YP_002790881), Table 2.

Table 2. Structural proteins of Zika Virus obtained from NCBI

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2.2. Determination of Conserved Regions

The retrieved sequences were used as platform to obtain conserved regions using multiple sequence alignment (MSA) ^[14]. Sequences aligned with the aid of ClustalW as implemented in the BioEdit program, version 7.0.9.0 (Hall, 1999) for finding the conserved regions among international strains and for South America strains ^[15]. Later on, the candidate epitopes were analyzed by different prediction tools from Immune Epitope Database IEDB analysis resource (https://www.iedb.org/), Figure 1.

2.3. B-cell Epitope Prediction

B cell epitope is the portion of an immunogen, which interacts with B-lymphocytes. As a result, the B-lymphocyte is differentiated into antibody-secreting plasma cell and memory cell. B cell epitope is characterized by being hydrophilic, accessible and in a beta turn region ^[16]. Thus, the classical propensity scale methods and hidden Markov model programmed softwares from IEDB analysis resource were used for the following aspects:

Prediction of linear B-cell Epitopes: BepiPred from immune epitope database (https://toolsiedb.ofg/bcell/) ^[17] was used as linear B-cell epitopes prediction from the conserved region with a default threshold value of 0.023.

Prediction of surface accessibility: by using Emini surface accessibility prediction tool of the immune epitope database (IEDB) (https://tools.immuneepitope.org/tools/bcell/iedb) ^[18] the surface accessible epitopes were predicted from the conserved region holding the default threshold value 1.480.

Prediction of Epitopes antigenicity sites: (https://tools.immuneepitope.org/bcell/) ^[19] the kolaskar and tongaonker antigenicity method was used to determine the antigenic sites with a default threshold value of 0.860.

Prediction of epitopes hydrophilicity: parker hydrophilicity prediction tool of the IEBD database (https://tools.immuneepitope.org/bcell/) ^[20]was used to determine the hydrophilicity of the conserved regions; the threshold default value was 1.480.

Prediction of beta turns sites: Chou and Fasman beta turn prediction method (https://tools.immuneepitope.org/bcell/) was used with the default threshold 0.988 to determine the sites contain beta turns.

2.4. MHC Class I Binding Predictions

Analysis of peptide binding to MHC class I molecules was assessed by the IEDB MHC I prediction tool at https://tools.iedb.org/mhci/n, MHC-I peptide complex presentation to T lymphocytes undergo several steps. The attachment of cleaved peptides to MHC molecules step was predicted. prediction methods can be achieved by Artificial Neural Network (ANN), Stabilized Matrix Method (SMM), or Scoring Matrices derived from Combinatorial Peptide Libraries (Comblib_Sidney2008), consensus method was used which combines ANN, SMM and comblib different methods ^{[21, 22, 23, 24, 25]}. Prior to prediction, all epitope lengths were set as 9mers, all internationally conserved epitopes binded to alleles at score equal or less than 1.0 percentile rank was selected for further analysis. ^[26]

2.5. MHC Class II Binding Predictions

Analysis of peptide binding to MHC class II molecules was assessed by the IEDB MHC II prediction tool at https://tools.immuneepitope.org/mhcii/ ^{[27, 28]}. For MHC-II binding predication, certain HLA-DR alleles were analyzed including: DRB1*01:01, DRB1*04:01, DRB1*07:01, DRB1*11:01, DRB1*15:01. MHC class II groove has the ability to bind to peptides with different lengths. This variability in binding makes prediction as difficult as less accurate ^[29]. There are four prediction methods for IEDB MHC II prediction tool: ARB, SMM_align, Sturniolo's method and a consensus method. ARB predict IC (50) values through combination of searches different peptide sizes and alleles into a single global prediction based on ARB matrices. SMM-align is a matrix-based method with extensions incorporating flanking residues outside of binding grooves. It also predict the IC50 values of peptides. The consensus approach is used to combine the outcome of the three methods. Firstly, a random scan set of Swiss-Prot proteins and achieve scores for 2,000,000 random peptides. Thereafter, act as reference to rank new predictions. The consensus method uses the median rank of the three approaches as the final prediction score ^[30]. All internationally conserved epitopes binded to alleles at score equal or less than 10-percentile rank was selected for further analysis.

2.6. Population Coverage Calculation:

All potential MHC I and MHC II binders from Zika virus envelope glycoprotein E was assessed for population coverage against the whole world population and South America population with the selected MHC-I and MHC-II interacted alleles by the IEDB population coverage calculation tool at https://tools.iedb.org/tools/population/iedb_input ^[31].

2.7. Homology Modeling

ZIKA envelope glycoprotein 3D structure was obtained by phyre2, (https://www.sbg.bio.ic.ac.uk/phyre2) which uses advanced remote homology detection methods to build 3D models, 99% of residues of envelope glycoprotein modelled at > 90% confidence. UCSF Chimera (version 1.8) was used to visualize the 3D structure, Chimera currently available within the Chimera package and available from the chimera web site (https://www.cgl.ucsf.edu/cimera). Homology modeling was achieved for further verification of the service accessibility and hydrophilicity of B lymphocyte epitopes predicted, as well as to visualize all predicted T cell epitopes in the structural level ^{[32, 33]}.

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Figure 1. Workflow of prediction of potential B lymphocyte epitopes and T lymphocyte epitopes for the development of peptide vaccine or diagnostic tool for ZIKV infection

3. Results

3.1. Prediction of B-cell Epitope

Envelope protein was subjected to Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity and Chou and Fasman beta turn prediction methods in IEDB, that predict the probability of specific regions in the protein to bind to B cell receptor, being in the surface, being immunogenic, being in a hydrophilic region and being in a beta turn region, respectively, Figure 2.

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Figure 2. Prediction of B-cell epitopes by different scales

In Bepipred Linear Epitope Prediction method; the average binders score of Envelope Glycoprotein to B cell was 0.023, with a maximum of 1.831 and a minimum of -2.366, all values equal or greater than the default threshold 0.023 were predicted to be a potential B cell binders.

In Emini surface accessibility prediction; the average surface accessibility areas of the protein was scored as 1.000, with a maximum of 6.354 and a minimum of 0.074, all values equal or greater than the default threshold 1.0 were potentially in the surface.

The default threshold of antigenicity of the protein was 1.026; all values greater than 1.026 are potential antigenic determinants, the average of the antigenicity was 1.026, with a maximum of 1.186 and minimum of 0.861.

In Parker hydrophilicity prediction; the average hydrophilicity score of the protein was 1.480, with a maximum of 7.057and a minimum of -6.471, all values equal or greater than the default threshold 1.480 were potentially hydrophilic.

None of international strains epitopes had succeeded the Kolaskar and Tongaonkar antigenicity prediction method, however six B cell epitopes were found from international strains to satisfy all other scales of predicting B cell epitope. AQDKP from 35 to 39 was found to have the highest score, followed by TPNSPRAE from 170 to 177. The result is summarized in Table 3 and proposed epitopes are shown in Figure 3 at the structural level.

Table 3. list of B- cell epitopes predicted by different scales from international strains

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Figure 3. proposed B-Cell Epitopes in international strains

In South America strains, the number of conserved Epitopes and their lengths were increased compared to international strains. Two epitopes “from South America” were satisfied the threshold values for all predicted scales: LDKQSDTQYV from 82 to 91 and EVQYAGTDGPCK from 329 to 340, as shown in Figure 4. The result of South America regions B cell epitopes is summarized in Table 4.

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Figure 4. proposed B-Cell epitopes in South America strains

The Chou and Fasman beta turn prediction method was used with the default threshold 0.988 with maximum of 1.373 and a minimum of 0.739 for more confirmation for the prediction of the epitope to elicit B cell employed.

Table 4. list of B- cell epitopes predicted by different scales from South America strains

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Table 5. list of epitopes that had binding affinity with the MHC Class I alleles

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3.2. Prediction of Cytotoxic T-lymphocyte Epitopes and Interaction with MHC Class I

Envelope Glycoprotein from international strains (including South America) was analyzed using IEDB MHC-1 binding prediction tool to predict T cell epitope suggested interacting with different types of MHC Class I alleles. Based on Consensus (ann/smm/comblib_sidney2008) with percentile rank ≤1; 19 peptides were predicted to interact with different MHC-1 alleles. The peptide MMLELDPPF from 374 to 382 had higher affinity to interact with 8 alleles (HLA-B*15:01, HLA-B*46:01, HLA-A*32:01, HLA-B*35:01, HLA-B*48:01, HLA-A*23:01, HLA-A*29:02 and HLA-A*02:06), followed by MAVLGDTAW from 421 to 429, and KEWFHDIPL from 215 to 223 that had affinity to interact with 4 alleles for each. The epitopes and their corresponding MHC-1 alleles are shown in Table 5.

Table 6. List of epitopes that had Binding affinity with the Class II alleles

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3.3. Prediction of T Helper Cell Epitopes and Interaction with MHC Class II

By the same way in IEDB MHC-1 binding prediction tool, T-cell epitopes from international strains (including South America), were analyzed using MHC-II binding prediction method; based on Consensus (smm/nn/sturniolo) with percentile rank ≤10.There were 30 predicted epitopes found to interact with MHC-II alleles for which the peptide (core) FKSLFGGMS and LITANPVIT had high affinity to interact with six alleles (HLA-DRB1*11:01, HLA-DRB1*08:02, HLA-DRB1*01:01, HLA-DRB1*04:0,1 HLA-DRB1*07:01, HLA-DRB1*15:01) and (HLA-DRB1*08:02, HLA-DRB1*11:01, HLA-DRB1*04:01, HLA-DRB1*01:01, HLA-DQA1*05:01, HLADQB1*03:01) respectively. Moreover, LVEFKDAHA epitopes that could interact with five alleles (HLA-DRB1*04:01, HLA-DRB1*15:01, HLA-DRB1*08:02, HLA-DRB1*11:01 and HLA-DRB1*07:01). The result is listed in Table 6.

3.4. Analysis of the Population Coverage

Epitopes that are suggested interacting with MHC-I and II alleles (especially high affinity binding epitopes and that can bind to different set of alleles) were selected for population coverage analysis. The results of population coverage of all epitopes are listed in Table 7.

Table 7. population coverage of all epitopes in both MHC class I and II in the world

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In MHC class I, Four epitopes that interact with most frequent MHC class I alleles (MMLELDPPF, KEWFHDIPL, MAVLGDTAW and DTAWDFGSV) gave high percentage against the whole world population by IEDB population coverage tool. The maximum population coverage (80.45%) for these proposed epitopes was found in the Guinea-Bissau, while the higher population coverage in South America: (77.93%) was found in American Samoa followed by Venezuela (72.26%) then Chile Amerindian, Brazil Mixed and Chile (54.46%, 52.85% and 51.01% respectively). Table 8 represents the populations for which the coverage is greater than 50 % and Figure 5 shows these proposed Epitopes at the structural level.

Also in MHC class II, three epitopes that interact with most frequent MHC class II alleles (LITANPVIT, VHTALAGAL and FKSLFGGMS) gave high percentage against the whole world population by IEDB population coverage tool. The maximum population coverage for these proposed epitopes were found in two countries of South America: New Caledonia(95.75%) followed by Fiji (95.07%). Table 9 represents the populations for which the coverage is greater than 50 % and Figure 6 shows these proposed Epitopes at the structural level.

FEATVRGAK and LHGTVTVEV that bind to (HLA-DRB1*11:01, HLA-DQA1*05:01 and HLA-DQB1*03:01) and (HLA-DQA1*04:01 and HLA-DQB1*04:02); respectively were found to increase the population coverage in most South America regions and the surrounding areas. Moreover, they show their best coverage scores in Argentine, Brazil and Brazil Amerindians. The result is shown in Table 10.

Table 8. population coverage for Class I proposed epitopes throughout the world

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Table 9. population coverage for Class II proposed epitopes throughout the world

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Figure 5. proposed T-Cell epitopes that interact with MHC Class I

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Figure 6. proposed T-Cell epitopes that interact with MHC Class II

Table (10.A). MHC class II proposed epitopes’ population coverage in South America regions

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Table (10.B). MHC class II proposed epitopes’ population coverage in South America regions

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Table (10.C). MHC class II proposed epitopes’ population coverage in South America regions

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