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

Marwan Mustafa Badawi*, Marwa Mohamed Osman, Afra AbdElhamid Fadl Alla, Ammar Mohammed Ahmedani, Mohamed hamed Abdalla, Mosab Mohamed Gasemelseed, Ahmed Abubakar Elsayed, Mohamed Ahmed Salih

Department of Biotechnology, Africa city of Technology- Khartoum, Sudan

Abstract: Zika virus (ZIKV) is positive sense single stranded RNA of Flavivirus genus belonging to the Flaviviridae family. It has neither drug nor protective vaccine, and considered to be in relatedness to neurological abnormalities such as Guillain Barre Syndrome and microcephaly of neonates. The aim of this study is to analyze envelope glycoprotein E of all Zika strains using in silico approaches looking for conservancy, which is further studied to predict all potential epitopes that can be used after in vitro and in vivo confirmation as a therapeutic peptide vaccine. A total of 50 Zikavirusvariants’ (include 12 from South America) polyproteins retrieved from NCBI database were aligned, and the conserved regions of Envelope Glycoprotein-E were selected for epitopes prediction. IEDB analysis resource was used to predict B and T cell epitopes and to calculate the population coverage. Epitopes with high scores in both B cell and T cell epitopes predicting tools were suggested. Three epitopes were proposed for international therapeutic peptide vaccine for B cell (AQDKP, TPNSPRAE and TPHWNNK) and two other epitopes designed especially for South America strains (LDKQSDTQYV and EVQYAGTDGPCK). For T cell epitopes, MMLELDPPF epitope was highly recommended as therapeutic peptide vaccine to interact with MHC class I along with three other epitopes (MAVLGDTAW, KEWFHDIPL and DTAWDFGSV) which showed very good population coverage against the whole world population. Three epitopes showed high affinity to interact with MHC class II alleles (FKSLFGGMS, LITANPVIT and VHTALAGAL) with excellent population coverage throughout the world and South America region. Herd immunity protocols can be achieved in countries with low population coverage percentage to minimize the active transmission of the virus, especially among pregnant women and other groups at risk.We recommend in vitro and in vivo proving the effectiveness of these proposed epitopes as a vaccine, as well as to be used as a diagnostic screening test.

Keywords: Zika virus (ZIKV), arboviruses, peptide vaccine, Immune Epitope Database IEDB, epitopes, Herd immunity and Vaccine

Citation: Marwan Mustafa Badawi, Marwa Mohamed Osman, Afra AbdElhamid Fadl Alla, Ammar Mohammed Ahmedani, Mohamed hamed Abdalla, Mosab Mohamed Gasemelseed, Ahmed Abubakar Elsayed, Mohamed Ahmed Salih, Highly Conserved Epitopes of ZIKA Envelope Glycoprotein May Act as a Novel Peptide Vaccine with High Coverage: Immunoinformatics Approach. American Journal of Biomedical Research, 4 (3). 46-60. January 2016. doi:10.12691/ajbr-4-3-1

1. Introduction

Zika virus (ZIKV) is positive sense single stranded RNA, an arbovirus of Flavivirus genus belonging to the Flaviviridae family, which includes dengue, yellow fever, St. Louis encephalitis, West Nile and Japanese encephalitis viruses, among others [1,2,3].

ZIKA was initially isolated in 1947 from blood of a febrile sentinel rhesus monkey during a yellow fever study in the Zika forest of Uganda .The virus was subsequently isolated from a pool of Aedes africanus mosquitoes collected in 1948 from the same region of the Zika forest; a serological survey conducted at that time showed that 6.1% of the residents in nearby regions of Uganda had speciﬁc antibodies to ZIKV [3,4]. Up to 2006, only sporadic cases of ZIKV human infections were reported in literature [1,2,4]. In 2007, ZIKV has caused a large epidemic on Yap Island, Federated States of Micronesia and involved in infecting three quarters of local population. This outbreak shows that, ZIKV has been detected outside of Africa and Asia, having the potential as an emerging pathogen [5,6]. By October 2015, a single state in the northeast of Brazil (Bahia), reported 56,318 suspected cases of Zika virus disease. Brazilian national authorities estimate that between 497,593 and 1,048,701 cases of Zika virus infection have occurred since the outbreak began, after Brazil, Colombia has been the most-affected country so far, with 20,297 cases reported (up to 23 January 2016) since the country’s first cases were detected in October 2015. There has been a rapid regional spread of the virus. By 4 February 2016, 31 countries and territories in the Americas reported local transmission of the virus [7,8]. From 1 January 2007 to 25 February 2016, Zika virus transmission was documented in a total of 52 countries and territories. This includes 40 countries that reported local transmission between 2015 and 2016 [7]. It is likely to be transmitted and detected in other countries within the geographical range of competent mosquito vectors, especially Aedes aegypti [7]. Humans are get infected by infective mosquito bites, however recent report suggests that there is a possibility of secondary sexual transmission [9].

ZIKA is classified as Bio Safety Level 2 pathogen in the EU (with exception of the UK) and USA, and thought to be in relatedness to neurological diseases such as Guillain Barre Syndrome and microcephaly in neonates of infected mothers. Although ZIKV is a BSL2 pathogen, laboratories should assess the risks for pregnant laboratory personnel [5,10].

Currently, there is neither medicine against ZIKA fever nor specific antiviral treatment for clinical ZIKV infection, someone protects him/herself by preventing mosquito bites. Therefore, the development of a vaccine against ZIKV is very much important and this development has not been achieved yet. Surface or envelope proteins of the virus are the most antigenic ones and often considered as good candidates for immunization. They are important for vaccine development as it mediates the viral entry, and they are the primary targets of adaptive immune response [11,12]. Several institutions now are encouraging varieties of approaches to Zika virus vaccine development, using different strategies include DNA-based vaccine, a live-attenuated vaccine, a recombinant VSV-virus expressing Zika virus E glycoprotein and other approaches related to vector control [13].

The aim of this study is to analyze envelope glycoprotein E of all Zika strains using in silico approaches looking for conservancy, which is further studied to predict all potential B and T cell epitopes that can be used after in vitro and in vivo confirmation as a therapeutic peptide vaccine or diagnostic screening protocol, and regarding the current endemic in South America region; Zika isolated from these countries are separately further tested for identification of unique conserved epitopes.

2. Materials and Methods

2.1. Protein Sequence Retrieval

A total of 50 Zika virus strains’ polyproteins from different geographic regions (include 12 strains from South America) were retrieved from NCBI (http://www.ncbi.nlm.nih.gov/protein/?term=zika+virus+polyprotein) database in March 2016. These 50 strains sequences retrieved are from different parts of the world; 18 isolates were collected in South America region, Central America and the Caribbean; particularly from Brazil, Colombia, Guatemala, Martinique, Puerto Rico, Haiti and Suriname. Thirty-two International strains were collected in different countries, Mexico, Thailand, Philippines, China, Italy, Uganda, Senegal, Nigeria, Malaysia, Cambodia, French Polynesia and Central African Republic. Retrieved Strains and their Accession numbers are listed in Table 1.

Table 1. Virus Strains retrieved and their Accession numbers and area of collection

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

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 (http://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 (http://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) (http://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: (http://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 (http://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 (http://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 http://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 http://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 http://tools.iedb.org/tools/population/iedb_input [31].

2.7. Homology Modeling

ZIKA envelope glycoprotein 3D structure was obtained by phyre2, (http://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 (http://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].

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.

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

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.

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

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

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

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

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

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

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

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

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

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

4. Discussion

Vaccination has proven to be the mainstay in prevention of various deadly infectious diseases [34,35,36]. Historically, live-attenuated or inactivated forms of microbial pathogens (viruses, bacteria, etc.) have been used for induction of antigen-specific responses that protect the host against subsequent infections. Based on the pathogen being used, such vaccine formulations can contain anywhere between tens of to a few hundred proteins. However, protective immunity is usually dependent upon a few select proteins within such formulations, whereas the majority of proteins are unnecessary for the induction of protective immunity. Furthermore, these additional proteins may induce allergenic responses, thus emphasizing the need to eliminate them from vaccine formulations. This rationale led to an interest in subunit vaccines using single, or a select few, proteins of the microbes in vaccine formulations for induction of protective immunity [37,38]. An extension of this logic would be that even single proteins contain many hundreds of antigenic epitopes, all of which are not necessary; whereas some may even be detrimental to the induction of protective immunity. This has created an interest in peptide vaccines containing only epitopes capable of inducing positive, desirable T cell and B cell mediated immune responses [39]. There are many peptide vaccines under development, such as vaccine for human immunodeficiency virus (HIV) [40], hepatitis C virus (HCV) [41], malaria [42], foot and mouth disease [43], swine fever [44], influenza [45], anthrax [46], human papilloma virus (HPV) [47], therapeutic anti-cancer vaccines [48-53] for pancreatic cancer, melanoma, non-small cell lung cancer, advanced hepatocellular carcinoma, coetaneous T-cell lymphoma and B-Cell chronic lymphocytic leukemia. A database of publicly and privately conducted clinical studies is maintained on ClinicalTrials.gov, which is a service of the U.S. National Institute of Health. Around 562 clinical studies of peptide vaccines for preventive or therapeutic purposes on multiple disease conditions are registered with this database until 30th -March 2016.

In this study, we aimed to determine the highly potential immunogenic epitopes for B and T cells, the prime molecules of cell mediated and humoral immunity, as vaccine candidate for ZIKA virus from envelope glycoprotein.

Among the retrieved strains, the earliest strain (YP_002790881) was collected at 1947 from Uganda, and several strains were collected in the sixties, seventies, eighties and the first decade of 21st century, 2013, 2014 and 2015. Last strains collected among retrieved were (AMQ34003 and AMQ34004), as they are collected at 25 February 2016. As a result, we think that the conserved regions among international strains accompanied with good level of confidence.

To determine a potential and effective peptide antigen for B cell, epitopes should get above threshold scores in Bepipred linear epitope prediction, Emini surface accessibility, Parker hydrophilicity, Kolaskar and Tongaonkar antigenicity and Chou and Fasman beta turn prediction methods in IEDB. According to epitopes illustrated in Table 3, these epitopes are the only conserved regions from international strains of zika virus envelope glycoprotein that have high probability of activating B lymphocyte, however, these epitopes predicted were satisfied most of scales of predicting B cell epitope but none of them succeeded the Kolaskar and Tongaonkar antigenicity prediction method. The linear epitope 35 AQDKP 39 showed the highest score in Emimi surface and Parker hydrophilicity followed by 128 KSIQPEN 134 epitope. Shawan et al. (2014) suggested different epitope 123 DAHAKRQTVVVLGSQEGAV 141 with 19 a.a length as elicit B lymphocyte response, but this epitope was not located in any conserved regions of envelope glycoprotein in zika virus strains [54].

According to Table 4; South America strains showed a very great similarity, so the number and length of the conserved Epitopes were increased. The linear epitope 64 SISDMASDSRCPTQGEAYLDKQSDTQYV 91, 28 amino acid length in South America strains showed high score in Emini surface and Parker hydrophilicity, but not the antigenicity scale, however, when we decreased the epitope length to 10 amino acid LDKQSDTQYV the antigenicity score jumped above the default threshold reaching 1.041. In addition, the linear epitope 329 EVQYAGTDGPCK 340, 12 amino acid gave high antigenicity score equal 1.034. Therefore epitopes that are suggested to activate B cell in South America region had more affinity to induce an immune response than the universal epitopes regarding activation of B lymphocyte independently of T lymphocyte.

Since the immune response of T cell is long lasting response comparing with B cell, where the antigen can easily escape the antibody memory response [54] and CD8+ T and CD4+ T cell responses play a major role in antiviral immunity [55], designing of vaccine against T cell epitope is much more promising. For MHC Class I Alleles prediction, we chose the most common HLA-A and HLA-B alleles [56]. So, according to Table 5; among 19 T cell internationally conserved epitopes predicted to interact with MHC Class I, we found the epitope 374 MMLELDPPF 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). The same epitope was predicted by Shawan et al. (2014) as the highest immunogenicity epitope as 0.9139 I PMHC score [54]. In addition, we found epitopes 421 MAVLGDTAW 429 and 215 KEWFHDIPL 223, had the affinity to interact with four alleles of each, while 426 DTAWDFGSV 434 epitope had the affinity to interact with three alleles Table 5. These four epitopes had very good population coverage for class I alleles throughout the world with high coverage percentage in American Samoa and Venezuela. As shown in Table 8, we found MMLELDPPF epitope covering 33.02% of the world population and it gave higher coverage percentage in West and North Africa while KEWFHDIPL epitope showed higher population coverage in American Samoa (66.36%).

A. Arnaiz- villena et. al (2006) reported that there were four HLA-A alleles and four HLA-B alleles (A*02, A*24, A*31, A*68, B*35, B*39, B*40 and B*48) with frequencies higher than 5% found in all Amerindian populations which is distributed differently in south America and the surrounding regions, reaching their highest in Bolivia, Guatemala, Peru and Honduras as 55%, 47%, 46% and 44% of the country population, respectively [57,58]. So, according to our result in Table 5; we found that noticeable, and we found that the 215 KEWFHDIPL 223 epitope interact with 2 of HLA-B alleles that are reported in Amerindian population (B*40 and B*48) while MMLELDPPF interact with one HLA-A allele (A*02) and two HAL-B allele (B*35 and B*48), Therefore these epitopes may show high affinity in Amerindian populations.

We found that 30 predicted internationally conserved epitopes interacting with MHC-II alleles and we represented this result in Table 6. Epitopes FKSLFGGMS and LITANPVIT had high affinity to interact with six alleles while VHTALAGAL epitope could interact with three alleles.

Related to our result represented in Table 9, we found these three epitopes had excellent population coverage for Class II alleles throughout the world and high coverage percentage in South America region (82.03%). Epitopes FKSLFGGMS and LHGTVTVEV interact with two of class II haplotype found in high frequency in several Meso and South American groups (HLA-DRB1*08:02 and HLA-DQB1*04:02) alleles that reported in A. Arnaiz-Villena et.a.l (2006). However, in our study FKSLFGGMS epitope showed slightly low coverage percentage in South America comparing to other epitopes [57]. FEATVRGAK epitope interact with HLA-DQB1*03:01 allele which found in different Amerindian populations [57]. In our result listed in Table 6; we found both FEATVRGAK and LHGTVTVEV epitopes are conserved in South America region. LITANPVIT alone showed high coverage percentage in population of most of South America countries reached up to 91.52% in Brazil Amerindians and 84.66% of Brazil. We suggested special set of epitopes in Table (10A, 10B, 10C) for South America region, and we showed that all these epitopes had very high coverage percentage in Brazil, one of the epidemic countries with zika virus according to WHO report for 2015-2016 [59].

It is to be noted that for efficient induction of either B-cell or cytotoxic T cell responses, the induction of a robust helper T cell response is crucial [60,61].

5. Conclusion

Conventional peptide vaccine development methods are costly, and time consuming, the role in silico prediction tools do is highly appreciated as they select specific peptides in protein, which then tested in vitro and in vivo to verify and prove the effectiveness of the proposed epitopes to induce an immune response, as well as to be used as a diagnostic screening test. Herd immunity protocols can be achieved in countries with low percentage of population coverage to minimize the active transmission of the virus, especially among pregnant women and other groups at risk.

Acknowledgments

Authors would like to thank African City of Technology members for their cooperation.

Competing Interest

The authors declare that they have no competing interests.

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