1. Introduction

Middle East respiratory syndrome (MERS) is a new viral emergent human disease caused by a novel coronavirus called Middle East Respiratory Syndrome Coronavirus (MERS-CoV) ^[1]. Although the disease was first reported in the Kingdom of Saudi Arabia (KSA) when the virus was isolated from a patient with fatal pneumonia and acute kidney injury in Jeddah in June 2012, however, through retrospective investigations, health officials later identified that the first known cases of MERS occurred in Jordan in April 2012 ^{[2, 3, 4, 5, 6]}. In September 2012 the World Health Organization (WHO) reported two cases of severe community-acquired pneumonia caused by MERS-CoV ^[7]. By December 2015, the disease had already struck 1,621 persons of whom 584 died from respiratory failure and diarrhea majority of them were reported from the Arabian Peninsula, with one large outbreak involving 186 cases in the Republic of Korea; infection is typically associated with considerable morbidity and mortality ^{[8, 9, 10, 11]}.

Most infections were geographically linked to the Middle East, i.e., Jordan, Saudi Arabia, Qatar, and United Arab Emirates (UAE), but cases also occurred in the United Kingdom, Germany, France, and Italy ^[12]. As known cases have been directly or indirectly related to countries in the Arabian Peninsula, all cases of MERS have been linked through travel to or residence in countries in this region ^{[6, 13]}. Dromedary camels (Camelus dromedarius) were implicated for the first time as a possible source for human infection on the basis of the presence of MERS-CoV neutralizing antibodies in dromedaries from Oman and the Canary Islands of Spain. Since then, the presence of MERS-CoV antibodies in dromedaries has been reported in Jordan, Egypt, UAE and KSA ^[14]. MERS affects the respiratory system (lungs and breathing tubes). Most MERS patients developed severe acute respiratory illness with symptoms of fever, cough and shortness of breath. About 3-4 out of every 10 patients reported with MERS have died ^[6].

As all coronaviruses, MERS-CoV has a non segmented, single stranded, positive polarity RNA genome. It is an enveloped virus with a helical nucleocapsid. There is no virion polymerase. In the electron microscope, prominent club-shaped spikes in the form of a “corona” (halo) can be seen ^[15]. The MERS-CoV genome contains 30,119 nucleotides and contains at least 10 predicted open reading frames, 9 of which are predicted to be expressed from a nested set of seven subgenomic mRNAs ^[16].

Among the structural proteins of MERS-CoV, the spike (S) glycoprotein plays the most important roles in virus infection and pathogenesis as it uses a novel coronavirus receptor for entry and is targeted by neutralizing antibodies, thus, it is considered to be a promising target for effective MERS vaccine design ^{[17, 18]}. More importantly, T-cell-based cellular immunity is essential for cleaning MERS-CoV infection, yet the vaccine against the S protein mainly elicits neutralizing antibody response. Further, the high mutation rate of the S protein may result in the escape of neutralizing antibodies against MERS-CoV. Therefore, a highly conserved target that elicits both neutralizing antibody and cellular immunity against MERS-CoV is essential for an effective vaccine development ^[19].

Although the mortality of the infection is alarming (30-50%), as is its uncanny resemblance-at least in its clinical features to severe acute respiratory syndrome (SARS), there are no clinically approved vaccines or antiviral drugs available for either of these infections; thus, the development of effective therapeutic and preventive strategies that can be readily applied to new emergent strains is a research priority to save human lives and address the pandemic concerns ^{[20, 21, 22, 23]}. Past efforts to develop coronavirus vaccines have used whole-inactivated virus, live-attenuated virus, recombinant protein subunit or genetic approaches ^[24]. Although these methods are useful for vaccine development and successful in many cases, but they are time-consuming and fail when the pathogens cannot be cultivated in vitro, or when the most abundant antigens are variable in sequence. Now genomic approaches allow prediction of all antigens independent of their abundance and immunogenicity during infection, without the need to grow the pathogen in vitro ^[25]. The use of peptides as therapeutics is experiencing renewed enthusiasm owing to advances in delivery, stability and design. Moreover, there is a growing emphasis on the use of peptides in vaccine design as insights into tissue-specific processing of the immunogenic epitopes of proteins and the discovery of unusually long cytotoxic T-lymphocyte epitopes broaden the range of targets and give clues to enhancing peptide immunogenicity ^[26].

In this study, an immunoinformatics-driven genome-wide screening strategy of vaccine targets was adopted to identify a multi-epitope vaccine candidate against whole S glycoprotein of MERS-CoV that could be suitable to trigger a significant humoral and cellular immune response.

2. Materials and Methods

A total of 255 Spike glycoprotein of MERS CoV were retrieved from NCBI (https://www.ncbi.nlm.nih.gov/protein/) database in April 2016, and collected from different parts of the world; 184 isolates were collected in Saudi Arabia, twenty-three in United Arab Emirates, twenty-two in South Korea, and the rest were isolated in different countries; Oman, China, USA, United Kingdom, Greece, Qatar, Egypt, Tunisia, France, Thailand and Nigeria. Retrieved strains, their accession numbers and date of collection are listed in supplementary table (S1).

The retrieved sequences were used as a platform to obtain conserved regions using multiple sequence alignment (MSA). Sequences aligned with the aid of ClustalW as implemented in the BioEdit program, version 7.0.9.0. ^[27]

B cell epitope is characterized by being hydrophilic, accessible and in a beta turn region. Thus, the classical propensity scale methods and hidden Markov model programmed softwares from IEDB analysis resource (https://www.iedb.org/), were used for the following aspects: ^{[28, 29]}

Prediction of linear B-cell Epitopes: BepiPred from immune epitope database (https://toolsiedb.ofg/bcell/) ^[30] was used as linear B-cell epitope prediction from the conserved region with a default threshold value of 0.350.

Prediction of surface accessibility: by using Emini surface accessibility prediction tool of the immune epitope database (IEDB) ^[31], the surface accessible epitopes were predicted from the conserved regions holding the default threshold value 1.000 or higher.

Prediction of Epitopes antigenicity sites: the kolaskar and tongaonker antigenicity method ^[32] was used to determine the antigenic sites with a default threshold value of 1.045.

Prediction of epitopes hydrophilicity: parker hydrophilicity prediction tool ^[33] was used to determine the hydrophilicity of the conserved regions; the threshold default value was 1.286.

Prediction of beta turns sites: Chou and Fasman beta turn prediction method was used with the default threshold 1.009 to determine the sites contain beta turns.

Thresholds of all tool were provided by IEDB and it is mainly calculated by the software as the average score of the tested protein for each corresponding tool.

Analysis of peptide binding to MHC class I molecules was assessed by the IEDB MHC I prediction tool https://tools.iedb.org/mhci/n, for MHC-I binding predication, several alleles were used including HLA-A*02, HLA-B*51, HLA-B*50, HLA-B*08, HLA-C*07 , HLA-C*06 and HLA-C*15 that have been reported as frequent among Saudis.^{[34, 35, 36, 37, 38]} 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. Consensus method which combines ANN, SMM and comblib different methods was used ^{[39, 40, 41, 42, 43]}. Prior to prediction, all epitope lengths were set as 9mers, all internationally conserved epitopes that bind to alleles at score equal or less than 1.0 percentile rank were selected for further analysis. ^[44]

Analysis of peptide binding to MHC class II molecules was assessed by the IEDB MHC II prediction tool https://tools.immuneepitope.org/mhcii/ ^{[45, 46]}. For MHC-II binding predication, the reference set of alleles were used which include HLA-DQB1*02, HLA-DQB1*03, HLA-DQB1*06, HLA-DRB1*07, HLA-DRB1*04 and HLA-DRB1*03 that are reported to be frequent among Saudis ^{[34, 35, 36, 37, 38]}. 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 ^[47]. 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 predicts the IC50 values of peptides. The consensus approach was used which combines 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 ^[48]. All internationally conserved epitopes that bind to alleles at score equal or less than 10 percentile rank were selected for further analysis.

All potential MHC I and MHC II binders from Spike glycoprotein were assessed for population coverage against the whole world population and Saudi Arabia, and other populations that had been reported MERS CoV cases. Calculations achieved using the selected MHC-I and MHC-II interacted alleles by the IEDB population coverage calculation tool https://tools.iedb.org/tools/population/iedb_input ^[49].

The complete 3D structure of Spike glycoprotein was obtained by phyre2, (https://www.sbg.bio.ic.ac.uk/phyre2) which uses advanced remote homology detection methods to build 3D models. UCSF Chimera (version 1.8) was used to visualize the 3D structure, which is 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 visualization of all predicted T cell epitopes in the structural level ^{[50, 51]}.

Download as

• PPT
  PowerPoint Slide
• PNG
  Larger image(png format)

Veiw figureFigures index

•  NEW
  View larger figure in new window

View next figure

Figure 1. Prediction of B-cell epitopes by different scales

3. Results

Spike glycoprotein was subjected to Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity, Parker hydrophobicity and Chou and Fasman beta turn prediction methods in IEDB, Figure 1.

In Bepipred Linear Epitope Prediction method; the average binders score of Spike glycoprotein to B cell was 0.35, all values equal or greater than the default threshold 0.35 were predicted to be potential B cell binders.

In Emini surface accessibility prediction; the average surface accessibility areas of the protein was scored as 1.000, all values equal or greater than the default threshold 1.0 were regarded potentially in the surface.

The default threshold of antigenicity of the protein was 1.045; all values greater than 1.045 were considered as potential antigenic determinants.

In Parker hydrophilicity prediction; the average hydrophilicity score of the protein was 1.286, all values equal or greater than the default threshold 1.286 were potentially hydrophilic.

Two internationally conserved epitopes had succeeded all prediction methods, epitope GTPPQVY from 391 to 397 was found to have the highest score, followed by LTPRSVRSVP from 745 to 754. The result is summarized in Table 1 and proposed epitopes are shown in Figure 2 at the structural level of the spike protein.

Table 1. list of B- cell epitopes predicted by different scales

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View next table

Download as

• PPT
  PowerPoint Slide
• PNG
  Larger image(png format)

Veiw figureFigures index

•  NEW
  View larger figure in new window
• PREV
  View previous figure

View next figure

Figure 2. proposed B-Cell Epitopes

The Chou and Fasman beta turn prediction method was used with the default threshold 1.009, for more confirmation of the prediction.

Based on Consensus (ann/smm/comblib_sidney2008) with percentile rank ≤1, 123 epitopes were predicted to interact with different MHC I alleles. The epitope FSFGVTQEY showed high affinity to interact with 9 alleles. All epitopes and their corresponding MHC-1 alleles are shown in Table 2. Figure 3 displays epitopes in the structural level.

Table 2. list of epitopes that have binding affinity with the MHC Class I alleles

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Download as

• PPT
  PowerPoint Slide
• PNG
  Larger image(png format)

Veiw figureFigures index

•  NEW
  View larger figure in new window
• PREV
  View previous figure

View next figure

Figure 3. proposed T-Cell epitopes that interact with MHC Class I

Based on Consensus (smm/nn/sturniolo) with percentile rank ≤10, there were 374 predicted epitopes found to interact with MHC-II alleles [data not shown] from which the peptide (core) FNLTLLEPV shows very high binding affinity to 26 alleles. The promiscuous epitopes and those which bind more than twelve different alleles are summarized in Table 3. Figure 4 displays epitopes in the structural level.

Table 3. list of epitopes that have binding affinity with ≥12 Class II alleles

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Download as

• PPT
  PowerPoint Slide
• PNG
  Larger image(png format)

Veiw figureFigures index

•  NEW
  View larger figure in new window
• PREV
  View previous figure

Figure 4. proposed T-Cell epitopes that interact with MHC Class II

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 proposed epitopes are listed in Table 4.

In MHC class I, three epitopes that interact with most frequent MHC class I alleles (ITYQGLFPY, FSFGVTQEY and KLQPLTFLL) gave high percentage against the whole world population by IEDB population coverage tool. The population coverage of FSFGVTQEY and IAFNHPIQV show good coverage among Saudis (60.35% and 54.70%) respectively, Table 5 represents the population coverage for the proposed epitopes and shows their corresponding coverage percentage.

Also in MHC class II, four epitopes that interact with most frequent MHC class II alleles (FNLTLLEPV, FAAIPFAQS, SFAAIPFAQ and FYVYKLQPL) gave high percentage against the whole world population by IEDB population coverage tool. The population coverage for these proposed epitopes is excellent internationally as (99.99%) as well as Saudi Arabia (99.62%) and (100%) in England. Table 5 represents the proposed epitopes. FNLTLLEPV which binds to DQB1*02:01 and DQB1*03:02 that is reported in high frequency in Saudi Arabia was found to definitely increase the population coverage in Saudi Arabia as seen in tables (5A, B and C).

Table 4. population coverage of all epitopes in both MHC class I and II in the world

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Table (5A). population coverage for Class I proposed epitopes for selected regions

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Table (5B). population coverage for Class II proposed epitopes for selected regions

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Table (5C). population coverage for Class I proposed epitopes for selected regions

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

Table 6. population coverage for Class II proposed epitopes for selected regions

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table

View next table

4. Discussion

Vaccination has proven to be the mainstay in prevention of various deadly infectious diseases. Historically, live-attenuated or inactivated forms of microbial pathogens (viruses, bacteria, etc.) have been used for induction of immune responses that protect the host against the subsequent infections, such vaccine might contain unnecessary proteins for the induction of protective immunity and may lead to allergenic responses, This has created an interest in peptide vaccines containing only epitopes capable of inducing positive, desirable T cell and B cell mediated immune responses. There are many peptide vaccines under development, such as vaccine for human immunodeficiency virus (HIV), hepatitis C virus (HCV), malaria, foot and mouth disease, swine fever, influenza, anthrax, human papilloma virus (HPV), therapeutic anti-cancer vaccines pancreatic cancer, melanoma, non-small cell lung cancer, advanced hepatocellular carcinoma cutaneous T-cell lymphoma and B-Cell chronic lymphocytic leukemia. [52-69]^[52]

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 candidates for the highly lethal MERS coronaviral infection using the whole Spike glycoprotein as a target. Unlike previous studies and for better determination of the best candidate epitopes; all S protein was under investigation not only the receptor binding domain (RBD). Both complete S protein and RBD were in vitro and in vivo proven to elicit B lymphocyte to produce antibodies [12,70-75], therefore, we conducted this study to propose epitopes that would be enough to select out of these large non conserved domains to hopefully achieve the same result.

Conservancy in S protein in MERS CoV was found promising for peptide vaccine design, however, Tuhin ali et al found that a peptide region of 367-606 (which is the receptor binding domain) remained conserved, unlike our findings which showed that this region is no longer conserved, this region is no longer conserved, Tuhin ali et al,.2014 also found epitope CYSSLILDY interacting with 11 different MHC I alleles at threshold IC50 ≤ 100 and percentile rank ≤ 1, while we found this epitope only interacting with one allele as illustrated in Table 1, furthermore, this epitope was not predicted as B lymphocyte in our study ^[76].

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 hydrophobicity, Kolaskar and Tongaonkar antigenicity and Chou and Fasman beta turn prediction methods in IEDB. Epitopes illustrated in Table 1, are the only conserved regions from all retrieved strains of MERS coronavirus Spike glycoprotein that are available in NCBI database until 15^th April 2016 and have high probability of activating humoral immune response. In the RBD, the only conserved epitope that was found activating B lymphocyte immune response was the 7mer epitope 391 GTPPQVY 397. LTPRSVRSVP epitope is located in S2 domain, and was the only conserved epitope that was found passing the thresholds in S2 domain of Spike glycoprotein.

Wang, L. et al had found important residues in RBD, that if mutated will affect the binding of RBD with the neutralizing antibody; 535W and 536E. 535W is conserved residue, the following 536E residue is not conserved as only one Spike glycoprotein retrieved in this study (gb/ALJ76286), collected from Taif - Saudi Arabia in 2014, is showing novel mutation in this residue. Although, these residues are no longer located in conserved peptide, a focus needs to be targeted to this region as they show high score in bepipred prediction tool ^[77].

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]. Additionally, 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], and according to several studies in the allele frequencies among Saudis ( the most endemic area) the highest frequency of HLA-A was observed for A*02, followed by A*68, A*24, and A*26, for HLA-B locus, B*51, B*50, and B*08 represent very high frequency, the highest observed frequencies for HLA-C were C*07, C*06 , and C*15, whereas for MHC II, DQB1*02, DQB1*03, and DQB1*06 were representing the highly frequent alleles. The binding affinity of those alleles with all of the conserved epitopes of S protein was examined ^{[34, 35, 36, 37, 38]}.

Among 123 internationally conserved T cell epitopes predicted to interact with MHC Class I as shown in Table 2, ITYQGLFPY and FSFGVTQEY were found to interact with 9 alleles (HLA-A*29:02, HLA-B*46:01, HLA-A*30:02, HLA-A*01:01, HLA-A*11:01, HLA-A*26:01, HLA-B*15:01, HLA-B*58:01, HLA-A*25:01) and (HLA-B*46:01, HLA-A*01:01, HLA-B*35:01, HLA-A*29:02, HLA-A*26:01, HLA-C*06:02, HLA-A*25:01, HLA-B*15:01, HLA-B*58:01) respectively. IAFNHPIQV was found to bind with the HLA-B*51:01, HLA-C*06:02 that are highly frequent among Saudis as well as AAIPFAQSI which interact with the HLA-C*15:02. (FSFGVTQEY, MEAAYTSSL, SVRNLFASV, KLQPLTFLL, IAFNHPIQV and GLYFMHVGY) among the proposed MHCI binders are located in S2 domain, and only LSMKSDLSV is found to be located in RBD.

We found that 374 internationally conserved epitopes in S glycoprotein interact with MHC-II alleles as represented in Table 3. Epitope FNLTLLEPV is showing very high binding affinity to 26 MHC II alleles (HLA-DPA1*03:01, HLA-DPB1*04:02, HLA-DPA1*02:01, HLA-DPB1*01:01, HLA-DRB1*04:05, HLA-DRB1*09:01, HLA-DQA1*01:01, HLA-DQB1*05:01, HLA-DPA1*01:03, HLA-DPB1*02:01, HLA-DQA1*03:01, HLA-DQB1*03:02, HLA-DRB5*01:01, HLA-DRB1*03:01, HLA-DRB1*11:01, HLA-DRB1*01:01, HLA-DRB1*12:01, HLA-DPA1*01:03, HLA-DPB1*02:01, HLA-DPA1*01, HLA-DPB1*04:01, HLA-DQA1*05:01, HLA-DQB1*02:01, HLA-DRB1*07:01, HLA-DRB3*01:01, HLA-DRB1*13:02) among which highly frequent alleles in Saudis, this epitope alone showed 99.79% coverage in the world, 98.92% in Saudi Arabia and 99.82% in England. FYVYKLQPL, FAAIPFAQS and SFAAIPFAQ also showed very high binding affinity. FNLTLLEPV, FAAIPFAQS and SFAAIPFAQ are all located in S2 domain. All proposed MHC I and MHC II epitopes as illustrated in Table 5, Table 6 were better chosen to serve the best population coverage percentage as well as the lowest number of peptides to be used as multi epitope vaccines against Middle East Respiratory Syndrome.

5. Conclusion

Conventional peptide vaccine development methods are costly, and time consuming, insilico prediction is highly appreciated as it selects 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 conducted in countries with low population coverage to minimize the active transmission of the virus, especially among people contacting camels and other groups at risk. Ultimately, a vaccine needs to target a broad set of zoonotic CoVs, because the emergence of two zoonotic CoV infections in 10 years suggests that this will happen repeatedly.

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.

References

[1]	World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV). https://www.who.int/mediacentre/factsheets/mers-cov/en/.
	In article
[2]	Memish ZA, Cotten M, Watson SJ, Kellam P, Zumla A, Alhakeem RF, etal. Community Case Clusters of Middle East Respiratory Syndrome Coronavirus in Hafr Al-Batin, Kingdom of Saudi Arabia: A Descriptive Genomic study. Int J Infect Dis. 2014; 23: 63-68.
	In article		View Article  PubMed
[3]	Cotten M, Watson SJ, Zumla AI, Makhdoom HQ, Palser AL, Ong SH, etal. Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus. MBio. 2014; 5(1):e01062-13.
	In article		View Article  PubMed
[4]	Chastel C. Middle East respiratory syndrome (MERS): bats or dromedary, which of them is responsible?. Bull Soc Pathol Exot. 2014; 107(2):69-73.
	In article		View Article  PubMed
[5]	Al-Dorzi HM, Alsolamy S, Arabi YM. Critically ill patients with Middle East respiratory syndrome coronavirus infection. Crit Care. 2016; 20(1): 65.
	In article		View Article  PubMed
[6]	Centers for Disease Control and Prevention. Middle East Respiratory Syndrome (MERS). https://www.cdc.gov/coronavirus/mers/about/index.htm.
	In article
[7]	World Health Organization Regional Office for the Eastern Mediterranean. New coronavirus identified in two patients in the EMR. Weekly Epidemiologi-cal Monitor. 2012; 5(39) https://www.emro.who.int/images/stories/csr/documents/epi___issue_no__39.coronavirus.pdf.
	In article
[8]	World Health Organization.Middle East respiratory syndrome coronavirus (MERS-CoV) – Saudi Arabia. https://www.who.int/csr/don/4-december-2015-mers-saudi-arabia/en/.
	In article
[9]	Malczyk AH, Kupke A, Prüfer S, Scheuplein VA, Hutzler S, Kreuz D etal. A Highly Immunogenic and Protective Middle East Respiratory Syndrome Coronavirus Vaccine Based on a Recombinant Measles Virus Vaccine Platform. J Virol. 2015; 89(22):11654-67.
	In article		View Article  PubMed
[10]	Scobey T, Yount BL, Sims AC, Donaldson EF, Agnihothram SS, Menachery VD, etal. Reverse genetics with a full-length infectious cDNA of the Middle East respiratory syndrome coronavirus. Proc Natl Acad Sci. 2013; 110(40):16157-62.
	In article		View Article  PubMed
[11]	Public Health England. Risk assessment of Middle East respiratory syndrome coronavirus (MERS-CoV). https://www.gov.uk/government/uploads/system/uploads/attachment_data 505701/MERS-COV_RA_Mar2016_240216_RP__3_.pdf.
	In article
[12]	Song F, Fux R, Provacia LB, Volz A, Eickmann M, Becker S, etal. Middle East respiratory syndrome coronavirus spike protein delivered by modified vaccinia virus Ankara efficiently induces virus-neutralizing antibodies. J Virol. 2013;87(21):11950-4.
	In article		View Article  PubMed
[13]	Memish ZA, Cotten M, Meyer B, Watson SJ, Alsahafi AJ, Al Rabeeah AA, etal. Human infection with MERS coronavirus after exposure to infected camels, Saudi Arabia, 2013. Emerg Infect Dis. 2014 Jun; 20(6):1012-5.
	In article		View Article  PubMed
[14]	Reusken C, Messadi L, Feyisa A, Ularamu H, Godeke G, Danmarwa A, etal. Geographic distribution of MERS coronavirus among dromedary camels, Africa .Emerging Infectious Diseases. 2014; 20(8):1370-1374.
	In article		View Article  PubMed
[15]	Levinson W. Medical Microbiology and Immunology (examination and board review). 8th edition. McGraw-Hill, San Francisco, 2004; p268.
	In article
[16]	van Boheemen S, de Graaf M, Lauber C, Bestebroer TM, Raj VS, Zaki AM, etal. Genomic characterization of a newly discovered coronavirus associated with acute respiratory distress syndrome in humans. mBio. 2012; 3(6):e00473-12.
	In article		View Article  PubMed
[17]	Qian Z, Dominguez SR, Holmes KV. Role of the Spike Glycoprotein of Human Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in Virus Entry and Syncytia Formation. PLoS One. 2013; 8(10): e76469.
	In article		View Article  PubMed
[18]	Gierer S, Bertram S, Kaup F, Wrensch F, Heurich A, Krämer-Kühl A, etal. The spike protein of the emerging betacoronavirus EMC uses a novel coronavirus receptor for entry, can be activated by TMPRSS2, and is targeted by neutralizing antibodies. J Virol. 2013 May; 87(10):5502-11.
	In article		View Article  PubMed
[19]	Shi J, Zhang J, Li S, Sun J, Teng Y, Wu M, etal. Epitope-Based Vaccine Target Screening against Highly Pathogenic MERS-CoV: An In Silico Approach Applied to Emerging Infectious Diseases. PLoS One. 2015; 10(12): e0144475.
	In article		View Article  PubMed
[20]	de Groot RJ, Baker SC, Baric RS, Brown CS, Drosten C, Enjuanes L, etal. Middle East respiratory syndrome coronavirus (MERS-CoV): announcement of the Coronavirus Study Group. J Virol. 2013;87(14):7790-2.
	In article		View Article  PubMed
[21]	World Health Organization. Middle East respiratory syndrome coronavirus (MERS-CoV) .https:// www.who.int/mediacentre/factsheets/mers-cov/en/.
	In article
[22]	Graham RL, Donaldson EF, Baric RS. A decade after SARS: strategies for controlling emerging coronaviruses. Nat Rev Microbiol. 2013 D; 11(12):836-48.
	In article
[23]	Ying T, Du L, Ju TW, Prabakaran P, Lau CC, Lu L, etal. Exceptionally potent neutralization of Middle East respiratory syndrome coronavirus by human monoclonal antibodies. J Virol. 2014; 88(14):7796-805.
	In article		View Article  PubMed
[24]	Wang L, Shi W, Joyce MG, Modjarrad K, Zhang Y, Leung K, etal. Evaluation of candidate vaccine approaches for MERS-CoV. Nat Commun. 2015; 6:7712.
	In article		View Article  PubMed
[25]	Rappuoli R. Reverse vaccinology. Curr Opin Microbiol. 2000; 3 (5):445-50.
	In article		View Article
[26]	Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov. 2007; 6(5):404-14.
	In article		View Article  PubMed
[27]	Hall, T.A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids. Symp. Ser. 41:95-98.
	In article
[28]	Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, Wheeler DK, Gabbard JL, Hix D, Sette A, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2014 Oct 9. pii: gku938. [Epub ahead of print] PubMed PMID: 25300482.
	In article		PubMed
[29]	Anayet Hasan, Mehjabeen Hossain and Md. Jibran Alam. A Computational Assay to Design an Epitope-Based Peptide Vaccine Against Saint Louis Encephalitis Virus. Bioinformatics and Biology Insights 2013:7 347-355.
	In article		PubMed
[30]	Jens Erik Pontoppidan Larsen, Ole Lund and Morten Nielsen. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006; 2: 2.
	In article		View Article  PubMed
[31]	Emini EA, Hughes JV, Perlow DS, Boger J. 1985. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 55:836-839.
	In article		PubMed
[32]	Kolaskar AS, Tongaonkar PC. 1990. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett276:172-174.
	In article		View Article
[33]	Parker JM, Guo D, Hodges RS. 1986. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25:5425-5432.PMID: 2430611.
	In article		View Article  PubMed
[34]	Osman et al. HLA-A, -B, -C, -DRB1, and -DQB1 allele Lineages and Haplotype Frequencies among Saudis. Immunology and Immunogenetics Insights 2014:6 1-6.
	In article		View Article
[35]	Hajeer AH, Sawidan FA, Bohlega S, et al. HLA class I and class II polymorphisms in Saudi patients with myasthenia gravis. Int J Immunogenet. 2009;36: 169-172.
	In article		View Article  PubMed
[36]	Hajeer AH, Al Balwi MA, Aytül Uyar F, et al. HLA-A, -B, -C, -DBR1 and -DQB1 allele and haplotype frequencies in Saudis using next generation sequencing technique. Tissue Antigens. 2013;82:2520258.
	In article		View Article  PubMed
[37]	Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR. Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res. 2011;39:D9130D919.
	In article
[38]	Valluri V, Mustafa M, Santhosh A, et al. Frequencies of HLA-A, HLA-B, HLA-DR, and HLA-DQ phenotypes in the United Arab Emirates population. Tissue Antigens. 2005;66:107-113.
	In article		View Article  PubMed
[39]	Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, Nielsen M, Peters B. 2012. Immune epitope database analysis resource. NAR.
	In article
[40]	Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S, Lund O. 2003. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007-1017.
	In article		View Article  PubMed
[41]	Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, and Nielsen M. 2008. NetMHC-3.0: Accurate web accessible predictions of Human, Mouse, and Monkey MHC class I affinities for peptides of length 8-11. NAR 36:W509-512.
	In article		View Article  PubMed
[42]	Peters B, Sette A. 2005. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics 6:132.
	In article		View Article  PubMed
[43]	Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A, Peters B. 2008. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res 4:2.
	In article		View Article  PubMed
[44]	Kim Y, Ponomarenko J, Zhu Z, et al. Immune epitope database analysis resource. Nucleic Acids Research. 2012;40(Web Server issue):W525-W530.
	In article
[45]	Wang P, Sidney J, Dow C, Mothé B, Sette A, Peters B. 2008. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol. 4(4):e1000048.
	In article		View Article  PubMed
[46]	Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, Peters B. 2010. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics. 11:568.
	In article		View Article  PubMed
[47]	Pratik Narain Srivastava, Richa Jain, Shyam Dhar Dubey, Sharad Bhatnagar, Nabeel Ahmad. Prediction of Epitope-Based Peptides for Vaccine Development from Coat Proteins GP2 and VP24 of Ebola Virus Using Immunoinformatics, International Journal of Peptide Research and Therapeutics (2016) 22:119-133.
	In article		View Article
[48]	Zhang, Q., Wang, P., Kim, Y., Haste-Andersen, P., Beaver, J., Bourne, P. E. et al. (2008). Immune epitope database analysis resource (IEDB-AR).Nucleic Acids Research, 36(Web Server issue), W513-W518.
	In article		View Article  PubMed
[49]	Bui HH,Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics. 2006 Mar 17;7:153.
	In article		View Article  PubMed
[50]	The Phyre2 web portal for protein modeling, prediction and analysis Kelley LA et al. Nature Protocols 10, 845-858 (2015).
	In article
[51]	UCSF Chimera--a visualization system for exploratory research and analysis. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. J Comput Chem. 2004 Oct;25(13):1605-12.
	In article		View Article  PubMed
[52]	Bachler, B.C.; Humbert, M.; Palikuqi, B.; Siddappa, N.B.; Lakhashe, S.K.; Rasmussen, R.A.; Ruprecht, R.M. Novel biopanning strategy to identify epitopes associated with vaccine protection. J. Virol. 2013, 87, 4403-4416.
	In article		View Article  PubMed
[53]	Perrie, Y.; Kirby, D.; Bramwell, V.W.; Mohammed, A.R. Recent developments in particulate-based vaccines. Recent Pat. Drug Deliv. Formul. 2007, 1, 117-129.
	In article		View Article  PubMed
[54]	Black, M.; Trent, A.; Tirrell, M.; Olive, C. Advances in the design and delivery of peptide subunit vaccines with a focus on toll-like receptor agonists. Expert Rev. Vaccines 2010, 9, 157-173.
	In article		View Article  PubMed
[55]	Sesardic, D. Synthetic peptide vaccines. J. Med. Microbiol. 1993, 39, 241-242.
	In article		View Article  PubMed
[56]	Liu, Y.; McNevin, J.; Zhao, H.; Tebit, D.M.; Troyer, R.M.; McSweyn, M.; Ghosh, A.K.; Shriner, D.; Arts, E.J.; McElrath, M.J.; et al. Evolution of human immunodeficiency virus type 1 cytotoxic T-lymphocyte epitopes: Fitness-balanced escape. J. Virol. 2007, 81, 12179-12188.
	In article		View Article  PubMed
[57]	Kolesanova, E.F.; Sanzhakov, M.A.; Kharybin, O.N. Development of the schedule for multiple parallel -difficult Peptide synthesis on pins. Int. J. Pept. 2013.
	In article		View Article  PubMed
[58]	Epstein, J.E.; Giersing, B.; Mullen, G.; Moorthy, V.; Richie, T.L. Malaria vaccines: Are we getting closer? Curr. Opin. Mol. Ther. 2007, 9, 12-24.
	In article		PubMed
[59]	Volpina, O.M.; Gelfanov, V.M.; Yarov, A.V.; Surovoy, A.Y.; Chepurkin, A.V.; Ivanov, V.T. New virus-specific T-helper epitopes of foot-and-mouth disease viral VP1 protein. FEBS Lett. 1993, 333, 175-178.
	In article		View Article
[60]	Tarradas, J.; Monso, M.; Munoz, M.; Rosell, R.; Fraile, L.; Frías, M.T.; Domingo, M.; Andreu, D.; Sobrino, F.; Ganges, L. Partial protection against classical swine fever virus elicited by dendrimeric vaccine-candidate peptides in domestic pigs. Vaccine 2011, 29, 4422-4429.
	In article		View Article  PubMed
[61]	Stanekova, Z.; Kiraly, J.; Stropkovska, A.; Mikušková, T.; Mucha, V.; Kostolanský, F.; Varečková, E. Heterosubtypic protective immunity against influenza a virus induced by fusion peptide of the hemagglutinin in comparison to ectodomain of M2 protein. Acta Virol. 2011, 55, 61-67.
	In article		View Article  PubMed
[62]	Oscherwitz, J.; Yu, F.; Cease, K.B. A synthetic peptide vaccine directed against the 2ss2–2ss3 loop of domain 2 of protective antigen protects rabbits from inhalation anthrax. J. Immunol. 2010, 185, 3661-3668.
	In article		View Article  PubMed
[63]	Solares, A.M.; Baladron, I.; Ramos, T.; Valenzuela, C.; Borbon, Z.; Fanjull, S.; Gonzalez, L.; Castillo, D.; Esmir, J.; Granadillo, M.; et al. Safety and immunogenicity of a human papillomavirus peptide vaccine (CIGB-228) in women with high-grade cervical intraepithelial neoplasia: first-in-human, proof-of-concept trial. ISRN Obstet. Gynecol. 2011.
	In article		View Article  PubMed
[64]	Bernhardt, S.L.; Gjertsen, M.K.; Trachsel, S.; Møller, M.; Eriksen, J.A.; Meo, M.; Buanes, T.; Gaudernack, G. Telomerase peptide vaccination of patients with non-resectable pancreatic cancer: A dose escalating phase I/II study. Br. J. Cancer 2006, 95, 1474-1482.
	In article		View Article  PubMed
[65]	Brunsvig, P.F.; Aamdal, S.; Gjertsen, M.K.; Kvalheim, G.; Markowski-Grimsrud, C.J.; Sve, I.; Dyrhaug, M.; Trachsel, S.; Møller, M.; Eriksen, J.A.; et al. Telomerase peptide vaccination: A phase I/II study in patients with non-small cell lung cancer. Cancer Immunol. Immunother. 2006, 55, 1553-1564.
	In article		View Article  PubMed
[66]	Brunsvig, P.F.; Kyte, J.A.; Kersten, C.; Sundstrøm, S.; Møller, M.; Nyakas, M.; Hansen, G.L.; Gaudernack, G.; Aamdal, S. Telomerase peptide vaccination in NSCLC: A phase II trial in stage III patients vaccinated after chemoradiotherapy and an 8-year update on a phase I/II trial. Clin. Cancer Res. 2011, 17, 6847-6857.
	In article		View Article  PubMed
[67]	Kyte, J.A.; Gaudernack, G.; Dueland, S.; Trachsel, S.; Julsrud, L.; Aamdal, S. Telomerase peptide vaccination combined with temozolomide: A clinical trial in stage IV melanoma patients. Clin. Cancer Res. 2011, 17, 4568-4580.
	In article		View Article  PubMed
[68]	Greten, T.F.; Forner, A.; Korangy, F.; N’Kontchou, G.; Barget, N.; Ayuso, C.; Ormandy, L.A.; Manns, M.P.; Beaugrand, M.; Bruix, J. A phase II open label trial evaluating safety and efficacy of a telomerase peptide vaccination in patients with advanced hepatocellular carcinoma. BMC Cancer 2010, 10, e209.
	In article		View Article  PubMed
[69]	Kyte, J.A.; Trachsel, S.; Risberg, B.; Thor, S.P.; Lislerud, K.; Gaudernack, G. Unconventional cytokine profiles and development of T cell memory in long-term survivors after cancer vaccination. Cancer Immunol. Immunother. 2009, 58, 1609-1626. 25.
	In article
[70]	Du L, Zhao G, Yang Y, Qiu H, Wang L, Kou Z, et al. A conformation-dependent neutralizing monoclonal antibody specifically targeting receptor-binding domain in Middle East respiratory syndrome coronavirus spike protein. J Virol2014;88:7045-53.
	In article		View Article  PubMed
[71]	Jiang L, Wang N, Zuo T, Shi X, Poon KM, Wu Y, et al. Potent neutralizationof MERS-CoV by human neutralizing monoclonal antibodies to the viral spikeglycoprotein. Sci Transl Med 2014;6:234ra59.
	In article		View Article
[72]	Ying T, Du L, Ju TW, Prabakaran P, Lau CC, Lu L, et al. Exceptionally potent neutralization of middle East respiratory syndrome coronavirus by human monoclonal antibodies. J Virol 2014;88:7796-805.
	In article		View Article  PubMed
[73]	Zhang N, Jiang S, Du L. Current advancements and potential strategies in thedevelopment of MERS-CoV vaccines. Expert Rev Vaccines 2014;13:761-74.
	In article		View Article  PubMed
[74]	Mou H, Raj VS, van Kuppeveld FJ, Rottier PJ, Haagmans BL, Bosch BJ. The receptor binding domain of the new Middle East respiratory syndrome coronavirus maps to a 231-residue region in the spike protein that efficiently elicits neutralizing antibodies. J Virol 2013;87:9379-83.
	In article		View Article  PubMed
[75]	Du L, Zhao G, Kou Z, Ma C, Sun S, Poon VK, et al. Identification of a receptor-binding domain in the S protein of the novel human coronavirus Middle Eastrespiratory syndrome coronavirus as an essential target for vaccine develop-ment. J Virol 2013;87:9939-42.
	In article		View Article  PubMed
[76]	Tuhin ali et al, Bioinformation 10(8): 533-538 (2014).
	In article
[77]	Wang, L. et al. Evaluation of candidate vaccine approaches for MERS-CoV. Nat. Commun. 6:7712.
	In article		View Article

**Supplementary material**

Table (S1). Spike Glycoprotein strains retrieved, accession numbers and date of collection

Download as

PowerPoint Slide

Larger image(png format)

• Tables index

Veiw figure

View current table in a new window

View previous table