alexa Immunoinformatics Predication and Modelling of a Cocktail of B- and T-cells Epitopes from Envelope Glycoprotein and Nucleocapsid Proteins of Sin Nombre Virus

ISSN: 1745-7580

Immunome Research

  • Research Article   
  • Immunome Res 2017, Vol 13(3): 141
  • DOI: 10.4172/1745-7580.1000141

Immunoinformatics Predication and Modelling of a Cocktail of B- and T-cells Epitopes from Envelope Glycoprotein and Nucleocapsid Proteins of Sin Nombre Virus

Idris AB1*, Mahmoud SM2, Mohamed Elamin S3, Mustafa YY1, Osman AA4, Adam ME1, Ali LB3, Abbas MH5, Abu-haraz AH6, Abdelrahman KA7# and Salih MA6#
1Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, University of Khartoum, Khartoum, Sudan
2National Public Health Laboratory, Khartoum, Sudan
3Faculty of Pharmacy, University of Khartoum, Khartoum, Sudan
4Faculty of Medicine and Health Sciences, Omdurman Islamic University, Khartoum, Sudan
5Ahfad University for Women School of Pharmacy, Khartoum, Sudan
6Department of Biotechnology, Africa City of Technology, Khartoum, Sudan
7University of Medical Science and Technology, Khartoum, Sudan
#Contributed equally to this work
*Corresponding Author: Idris AB, Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, University of Khartoum, Khartoum, Sudan, Tel: +00249963064355, Email: [email protected]

Received Date: Aug 21, 2016 / Accepted Date: Sep 30, 2017 / Published Date: Oct 03, 2017

Abstract

Sin Nombre virus is a category A pathogen with a reported mortality rate ranging from 30% to 50%. It was responsible for the 2012 Yosemite National Park outbreak. Until now, Specific therapy is not available for the treatment of HCPS caused by SNV. Despite many efforts to develop safe and effective vaccines against SNV, included conventional approaches as well as molecular vaccine approaches, to date there are no vaccines proven to be highly efficacious against SNV. In our study, we analyzed envelope glycoprotein and nucleocapsid of SNV by using immunoinformatics tools housed in IEDB resources; in order to determine the most conserved and immunogenic epitopes for B- and T-cells. Then the predicted epitopes were assessed for the population coverage against the whole world population with the MHC-I and MHC-II restricted alleles. Among predicted epitopes for B-cell, the best candidates for glycoprotein and nucleocapsid were the epitope 743CKKYAYPWQT752 and the epitope 271QVDESKVS278, respectively. For glycoprotein CD8+ T cell predicted epitopes, the epitopes 208MTLPVTCFL216 and 458YTFTSLFSL466 were selected. Interestingly, the best candidates epitopes for nucleocapsid were the epitopes 25YILSFALPI133 and 239FLAARCPFL247 which had high affinity to interact with both MHC classes, I and II, and they had an excellent population coverage for Class I and II alleles throughout the world. To the best of our knowledge, our study for the first time has predicted a cocktail of B- and T-cell epitopes for designing an effective vaccine against HCPS caused by SNV

Keywords: Sin Nombre virus; Epitope-based vaccines; Immune epitope database; In silico tools

Introduction

Hantaviruses (Bunyaviridas, Hantavirus) are rodent-borne emerging viruses [1] which cause over 100,000-200,000 cases per annually worldwide [2-5]. They are broadly classified into the New World Hantaviruses, which includes those causing hantavirus cardiopulmonary syndrome HCPS, and the Old World Hantaviruses, which are associated with another disease, hemorrhagic fever with renal syndrome (HFRS) [2-5]. This classification is determined by the geographic distribution of the rodent reservoirs [4].

HCPS is a severe respiratory disease, rare but often fatal, presents as a wide clinical spectrum ranging from brief febrile prodrome with headache, myalgia and thrombocytopenia, to rapidly progressive pulmonary edema characterized by increased vascular permeability due to dysregulation of the endothelial barrier function. The vascular leakage leads to non-cardiogenic pulmonary edema and in serious cases followed by respiratory failure, multi-organ failure, cardiogenic shock and death [5-10]. A different of pathogenic mechanisms have been suggested, including immune cell-mediated injury, cytokinemediated injury and enhanced VEGF responses from intercellular junctions resulting from highly specific virus–integrin interactions [8]. Since most deaths are caused by myocardial dysfunction and hypoperfusion rather than hypoxia, some investigators prefer using the term hantavirus cardiopulmonary syndrome rather than the previous term hantavirus pulmonary syndrome [3,10]. All of the etiologic agents associated with cardiopulmonary syndrome, at least 20 hantavirus genotypes, are found in the Western Hemisphere with the primary important etiologic agents include Sin Nombre Hantavirus (SNV) in the United States and Canada [11,12].

SNV is a Category A pathogen [13] that causes approximately 692 cases of HCPS according to the Centers for Disease Control and Prevention, from 1993-2016, [14] with a reported mortality rate ranging from 30% to 50%. [2,3,6]. SNV is an enveloped virus with a trisegmented, negative-sense RNA genome. The large (L) segment encodes an RNAdependent RNA polymerase; the medium (M) segment encodes a glycoprotein precursor, GPC, which is co-translationally cleaved into Gn and Gc transmembrane glycoproteins; the small (S) segment encodes a nucleocapsid protein (Np). Both glycoprotein and nucleocapsid have been shown to contribute to protective immunity [15-19]. SNV was first discovered within the 1993s outbreak, [2,3,15-17]. SNV-associated HPS is a severe and unpredictable public health threat, as affirmed by the 2012 outbreak at Yosemite National Park, California that resulted in 3 fatalities and more than 260,000 park visitors potentially exposed to a lethal virus [20]. SNV infections have been identified among persons with occupational exposure to North American deer mice (Peromyscus maniculatus) [21], which is the natural reservoir of SNV [10,16].

Similar to other viruses that cause HCPS, SNV has a serious potential applicability as a biological weapon because of its high lethality and known aerosol route of transmission [5]. Moreover, it causes a considerable economic harm and stigma to affected communities. Until now, Specific therapy is not available for the treatment of HCPS caused by SNV [2,3,5]. The main treatment of severe HCPS cases is completely supportive, often in intensive care unit surroundings [2]. Ribavirin, a broad-spectrum nucleoside analogue antiviral drug, is of some benefit as therapy in HCPS but it does not seem to have any clinical application in HCPS patients due to the lack of conclusive clinical data [3,4]. However, many efforts have been investigated in developing safe and effective vaccine against HCPS, included conventional approaches as well as molecular vaccine approaches using recombinant subunit vaccines utilizing vaccine delivery systems and plasmid DNA delivered by gene gun [2,3,12,13]. These approaches do not seem to have made much progress in this area, to date there is no vaccine proven to be highly efficacious against HCPS especially SNV [1-3,20]. Thus, epitopebased vaccines (EVs) have a priority for selection than other traditional vaccines. They offer many of advantages such as: biosafety, selecting of conserved or immunodominant epitopes for activating cellular or humoral responses and bio-processing of these epitopes are introduced economically and rapidly. In addition, the geographic distributions of the viruses are often well defined and the ethnic populations in need of vaccination can be determined [22] Indeed, many studies showed the immunological success of peptide-based vaccines against infectious diseases in experimental animals as well as in clinical trials, which demonstrated the responses to peptide vaccines against infectious diseases, for example malaria [23-27] hepatitis B, and HIV infections, but unfortunately, epitope mapping using experimental methods are costly and laborious.

More recently, immunoinformatics-based approaches in predicting most conserved and immunogenic epitopes from the viral genome sequence databases could give a significant amount of information for EVs developments. Several of recent publications explain in an excellently detail the values and benefits gained by the use of immunoinformatics and predictions in applied immunology and vaccinology [28]. Moreover, There are many studies used immunoinformatics-based approaches for designing EVs for different infectious and serious diseases [29,30]. In general, any active vaccine candidate must have to contain, at least, two antigenic epitopes; one to induce specific B cell or CTL responses while other induce specific Th cell response [19]. In this study, we aim to analyze envelope glycoprotein and nucleocapsid of SNV by using immunoinformatics tools housed in IEDB resources; in order to determine the most conserved and immunogenic epitopes for B cell and T cell which could trigger humoral as well as cell mediated immune response.

Materials And Methods

Retrieval of protein sequences

Sin Nombre virus’s glycoprotein and nucleocapsid sequences were obtained from National Centre for Biotechnology and Information (http://www.ncbi.nlm.nih.gov/protein/) databases in Nov 2016. Accession numbers of glycoprotein and nucleocapsid of retrieved strains and their date and area of collections are listed in Tables 1 and 2, respectively.

GenBank Protein Accession No. Country Date of collection
NP_941974* USA 20-OCT-2015
AFV71283 USA 05-JAN-2013
AFV71282 USA 05-JAN-2013
ALI59819 UK 13-OCT-2015
AAA75530 USA 03-JUN-2011
AAC42202 USA 21-NOV-1995
2124409B USA 16-JUL-1996

* Reference strain.

Table 1: Accession numbers of glycoprotein retrieved strains and their date and area of collection.

GenBank Protein Accession No. Country Date of collection
NP_941975* USA 20-OCT-2015
AIA08880 USA 26-MAY-2014
AIA08877 USA 26-MAY-2014
AFV71285 USA 05-JAN-2013
AFV71284 USA 05-JAN-2013
AFV71288 USA 02-NOV-2012
AFV71287 USA 02-NOV-2012
AFV71286 USA 02-NOV-2012
ALI59820 UK 13-OCT-2015
AAA75529 USA 03-JUN-2011
AAC42203 USA 21-NOV-1995
2124409C USA 16-JUL-1996

* Reference strain

Table 2: Accession numbers of nucleocapsid retrieved strains and their date and area of collection

Determinations of conserve regions

The obtained sequences were aligned using multiple sequence aliment (MSA) with the aid of Clustal was implemented in the BioEdit program, version 7.0.9.0; in order to find the conserved regions among glycoprotein and neuclocapsid variants of the strains [31].

B-cell epitopes prediction

B cell epitope is part of an immunogen which interacts with B-lymphocytes. This interaction leads to differentiation of B-lymphocyte into an antibody-secreting plasma cell and memory cell [32].

The characteristic features of B cell epitopes are hydrophilicity and accessibility [19]. Therefore, the classical propensity scale methods and hidden Markov model programmed softwares were used from the IEDB (http://www.iedb.org/) analysis resource for the following aspects [33].

• linear B-cell Epitopes Prediction

Using Bepipred tool [34] from the conserved regions with a default threshold value of 0.35.

• Prediction of surface accessibility

Using Emini surface accessibility prediction tool [35] from the conserved region holding the default threshold value 1.000 or higher.

• Prediction of epitopes antigenicity sites

Using kolaskar and tongaonker antigenicity method [36] for detection of the antigenic sites with a default threshold value of 1.042.

T Cell epitope prediction

CD8+ T-cell epitopes prediction: For the prediction of CD8+ T-cell epitopes that bind to MHC-1, we used an artificial neural network (ANN) prediction method [37] from Immune Epitope Database (IEDB) and the predicted IC50 value was less than 100 nm. Prior to the prediction, peptide length was set to 9 amino acids and only frequently occurring alleles were selected. Conserved epitopes having IC50 value less than 100 nm were used for further analysis.

CD4+ T-cell epitopes prediction: The NN-align method under MHC-II binding prediction tool in IEDB was used for MHC-II binding [38]. Human allele references set were selected. The conserved predicted CD4+ T-cell epitopes having higher binding affinity to interact with alleles at IC50 less than 1000 nm was chosen for further analysis.

Population coverage analysis

All CD8+ T-cell and CD4+ T-cell predicted epitopes were assessed for population coverage against the whole world population by using the IEDB population coverage calculation tool [39].

3D structure modeling

Sin Number virus’s glycoprotein and nucleocapsid 3D structures were obtained by RaptorX Property (http://raptorx2.uchicago.edu/ StructurePropertyPred/predict/), which is a web server predicting structure property of a protein sequence without using any template information [40]. For visualization of the 3D structure, we used UCSF Chimera (version 1.8). Chimera currently available within the Chimera package and available from the chimera web site (http://www.cgl.ucsf. edu/cimera) [41]. The 3D structure verifies the service accessibility and hydrophilicity of predicted B lymphocyte epitopes, and identifies all predicted T cell epitopes at the structural level [29].

Results

Prediction of B-cell epitope

The glycoprotein and Nucleocapsid protein was exhibited to Bepipred linear epitope prediction, Kolaskar and Tongaonkar antigenicity and Emini surface accessibility prediction methods in IEDB, to predict the probability of specific regions in the protein to bind B cell receptor, being on the surface, being immunogenic in each. The results are illustrated in Figures 1-3 and also in Tables 3 and 4.

immunome-research-glyco-protein

Figure 1: Prediction of B-cell epitopes for glyc oprotein by different scales.

immunome-research-nuclocapsid-protein

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

immunome-research-cell-epitopes

Figure 3: B-cell epitopes proposed for glycoprotein (visualization by UCSF chimera).

Peptide  Start End Length Antigencity Emini
TAGL 16 19 4 1.024 0.505
TVGLGQGYVTGSVE 33 46 14 1.054 0.194
TCNIPPTTFEAAYKSR 112 127 16 1.015 1.554
IALTQPGHTYDTMT 196 209 14 1.001 1.079
SCTENSF 239 245 7 1.009 0.733
SEPLFVPTMEDYRS 257 270 14 1.015 2.279
LNPRGEDHDPDQNGQGLM 279 296 18 0.949 4.552
GPVTAKVPSTETTETMQGIAFAGAPMYS 300 327 28 1.004 0.39
VRKADPEYVFS 333 343 11 1.033 0.625
GIIAESNHSVCDKKTVPLT 345 363 19 1.054 0.241
GEIEKI 372 377 6 0.968 0.797
AGPGASCEAYSETGIFNIS 387 405 19 1.014 0.127
KFRGSEQ 416 422 7 0.949 2.953
LHGW 483 486 4 1.03 0.466
HYSTESK 522 528 7 0.997 4.189
VEYQKTM 538 544 7 2.181 1.011
CETAKELETHKKSCPEGQCP 556 575 20 1.035 1.558
ITESTESAL 580 588 9 1.001 0.928
QEN 602 604 3 0.881 2.547
ASADTPLMESGWSDTAHGV 650 668 19 0.995 0.451
ASSSSYSY 684 691 8 1.879 1.056
KLVNPANQEETLP 694 706 13 1.014 2.742
*CKKYAYPWQT 743 752 10 1.054 2.348
DYQYETSWGCNPPDCPGVGTGCTA 760 783 24 1.029 0.248
SVGK 793 796 4 1.05 0.836
GTEQTCKHI 814 822 9 1.017 0.896
LVT 828 830 3 1.181 0.466
SKLQPG 842 847 6 1.024 1.6
LEQ 856 858 3 1.039 1.306
CVFGDPGDIMSTTSGMRCPEHTGSF 871 895 25 1.006 0.081
ATTPTCEYQGNTVSG 902 916 15 1.008 0.962
RDSFQSFNVTEPHITSN 924 940 17 0.994 2.346
LEWIDPDSSIKD 942 953 12 0.993 1.382
VSFQDLSDNPCKVDLHTQSIDGAWGSG 963 989 27 1.031 0.203
LRGSNTVKVVGKGGHSG 1027 1043 17 1.019 0.32
DTDCTEEGLAASPPHLDRVTGYNQIDSDKVYDDGAPPCT 1051 1089 39 1.019 1.916

*Selected epitopes.

Table 3: List of predicted B cell epitopes for glycoprotein

Peptide Start End Length Antigencity Emini
KEVQ 5 8 4 1.045 1.52
QKLKDAERAVELDPDDVNKSTLQSRRA 23 49 27 1.003 5.71
LASKPVDPTGIEPDDHLKEK 74 93 20 1.014 1,732
SLR 95 97 3 1.045 0.965
IDLEEPSGQTADWK 107 120 14 0.971 1.263
RGRQTIKENKGTRIRFKDDSSYEEVNGIRK 144 173 30 0.95 11.914
PTAQSTMKADEITPGRF 182 198 17 0.969 1.197
FLPEQKDPRDAALA 246 259 14 1.022 1.141
*QVDESKVSD 271 279 9 1.035 1.48
ADARAESAT 285 293 9 0.974 1.02
ATP 299 301 3 1.012 1.005
APDRCPPTALY 310 320 11 1.072 0.665
MPE 324 326 3 0.914 1.181
KSVGTSEEKLKK 345 356 12 0.988 3.126
GDDMDPELRE 398 407 10 0.919 2.363
EISNQEPLK 419 427 9 0.989 1,789

* Peptide from (271-279) gives higher score if it is shorten (271 to 278) in all tools.

Table 4: List of predicted B cell epitopes for nucleocapsid protein.

Prediction of cytotoxic T-lymphocyte epitopes and interaction with MHC Class I

The sequences of glycoprotein and nucleocapsid analyzed by IEDB MHC-1 binding prediction tool to predict T cell epitopes submit interacting with different types of MHC Class I alleles. Based on Artificial neural network (ANN) with half-maximal inhibitory concentration (IC50) ≤ 100. The results of top epitopes are shown in Figures 3-5 and also in Tables 5 and 6.

immunome-research-cell-epitope

Figure 4: B cell epitope proposed for nucleocapsid (visualization by UCSF chimera).

immunome-research-cell-cytotoxic

Figure 5: Glycoprotein epitope 208MTLPVTCFL216 suggested for cytotoxic T cell interaction (MHC 1) (visualization by UCSF chimera).

Peptide Start End Allele ic50 Percentile
MTLPVTCFL 208 216 HLA-A*02:01 41.88 0.5
      HLA-A*02:06 7.21 0.1
      HLA-A*30:01 52.78 0.5
      HLA-A*68:02 5.58 0.2
      HLA-B*57:01 76.91 0.3
      HLA-B*58:01 15.35 0.3
      HLA-C*03:03 74.45 0.4
      HLA-C*14:02 34.97 0.2
YTFTSLFSL 458 466 HLA-A*02:01 6.44 0.1
      HLA-A*02:06 4.57 0.1
      HLA-A*68:02 9.22 0.2
      HLA-B*39:01 80.18 0.3
      HLA-C*03:03 32.15 0.4
      HLA-C*12:03 22.83 0.2
      HLA-C*14:02 39.92 0.2

Table 5: The most potential 2 CD8+ T-cell epitopes of glycoprotein with interacting MHC-1 alleles.

Peptide Start End Allele ic50 Percentile
YILSFALPI 125 133 HLA-A*02:01 6.98 0.1
      HLA-A*02:06 10.49 0.2
      HLA-A*32:01 45.63 0.3
FLAARCPFL 239 247 HLA-A*02:01 3.63 0.1
      HLA-A*02:06 8.16 0.1
      HLA-B*08:01 82.87 0.2
      HLA-C*03:03 44.74 0.4

Table 6: The most potential 2 CD8+ T-cell epitopes of nucleocapsid with interacting MHC-1 alleles.

Prediction of T helper cell epitopes and interaction with MHC Class II

The sequences of glycoprotein and nucleocapsid analyzed by IEDB MHC-II binding prediction tool based on NN-align with (IC50) ≤ 1000. The results of two top epitopes are shown in Figure 6 and in Tables 7 and 8.

immunome-research-cell-interaction

Figure 6: Glycoprotein epitope 458YTFTSLFSL466 suggested for cytotoxic T cell interaction (MHC 1) (visualization by UCSF chimera).

Core sequence Start End Peptide sequence Allele IC50 Rank
ILLTQVADL 50 58 EITPILLTQVADLKI HLA-DPA1*01:03/DPB1*02:01 442.9 20.15
        HLA-DPA1*02:01/DPB1*01:01 46 4.8
        HLA-DPA1*03:01/DPB1*04:02 37.3 4.42
        HLA-DQA1*01:02/DQB1*06:02 142.5 10.3
        HLA-DQA1*04:01/DQB1*04:02 495.9 7.75
        HLA-DQA1*05:01/DQB1*02:01 344.5 7.78
        HLA-DRB1*01:01 58.9 23.25
        HLA-DRB1*03:01 357.2 11.51
        HLA-DRB1*04:01 242.5 17.41
        HLA-DRB1*04:04 36.3 3.74
        HLA-DRB1*04:05 436.3 27.16
        HLA-DRB1*13:02 38.6 2.76
        HLA-DRB1*15:01 639.9 32.87
      ILLTQVADLKIESSC HLA-DPA1*02:01/DPB1*01:01 82.3 8.76
        HLA-DPA1*03:01/DPB1*04:02 71.5 7.65
        HLA-DQA1*05:01/DQB1*02:01 696.4 15.12
        HLA-DRB1*04:04 141.3 15.79
        HLA-DRB1*04:05 897 39.36
        HLA-DRB1*13:02 244.6 11.19
        HLA-DRB4*01:01 84.8 6.61
      ITPILLTQVADLKIE HLA-DPA1*01:03/DPB1*02:01 469.3 20.77
        HLA-DPA1*03:01/DPB1*04:02 37.5 4.44
        HLA-DQA1*04:01/DQB1*04:02 556.8 8.77
        HLA-DQA1*05:01/DQB1*02:01 415.4 9.37
        HLA-DRB1*01:01 55.8 22.59
        HLA-DRB1*03:01 214.6 8.37
        HLA-DRB1*04:04 34.3 3.45
        HLA-DRB1*04:05 367.8 24.6
        HLA-DRB1*09:01 298.6 17.64
        HLA-DRB1*13:02 40.1 2.86
        HLA-DRB1*15:01 656 33.25
      PILLTQVADLKIESS HLA-DPA1*01:03/DPB1*02:01 776.7 26.88
        HLA-DPA1*02:01/DPB1*01:01 71.4 7.65
        HLA-DPA1*03:01/DPB1*04:02 48 5.56
        HLA-DQA1*05:01/DQB1*02:01 575.5 12.74
        HLA-DRB1*04:04 51.2 5.85
        HLA-DRB1*04:05 602.4 32.3
        HLA-DRB1*13:02 95.2 5.86
      SVEITPILLTQVADL HLA-DPA1*01:03/DPB1*02:01 377.8 18.5
        HLA-DQA1*04:01/DQB1*04:02 298.3 4.26
        HLA-DQA1*05:01/DQB1*02:01 409.6 9.24
        HLA-DRB1*01:01 89.3 28.63
        HLA-DRB1*04:01 688.5 34.92
        HLA-DRB1*04:04 49.5 5.62
        HLA-DRB1*04:05 628.6 33.02
        HLA-DRB1*07:01 55.6 9.43
        HLA-DRB1*13:02 73.2 4.8
      TPILLTQVADLKIES HLA-DPA1*01:03/DPB1*02:01 603.3 23.68
        HLA-DPA1*03:01/DPB1*04:02 40.6 4.79
        HLA-DQA1*04:01/DQB1*04:02 673.6 10.73
        HLA-DQA1*05:01/DQB1*02:01 519.3 11.57
        HLA-DRB1*01:01 73.3 26.02
        HLA-DRB1*03:01 206.9 8.2
        HLA-DRB1*04:04 35.5 3.62
        HLA-DRB1*04:05 430.1 26.94
        HLA-DRB1*09:01 644.1 30
        HLA-DRB1*13:02 59.1 4.03
        HLA-DRB1*15:01 825.1 36.92
      VEITPILLTQVADLK HLA-DPA1*01:03/DPB1*02:01 439.7 20.07
        HLA-DQA1*04:01/DQB1*04:02 392.3 5.94
        HLA-DQA1*05:01/DQB1*02:01 335.1 7.57
        HLA-DRB1*01:01 71.3 25.66
        HLA-DRB1*04:01 304.5 20.64
        HLA-DRB1*04:04 42.3 4.6
        HLA-DRB1*04:05 585.1 31.81
        HLA-DRB1*07:01 83.4 12.53
        HLA-DRB1*13:02 64.2 4.3
LLSSRIQVI 165 173 CMIGLLSSRIQVIYE HLA-DPA1*01/DPB1*04:01 244 9.67
        HLA-DPA1*01:03/DPB1*02:01 98.7 8.29
        HLA-DPA1*02:01/DPB1*01:01 98.7 10.28
        HLA-DQA1*01:02/DQB1*06:02 82 5.66
        HLA-DQA1*05:01/DQB1*03:01 177.6 21.11
        HLA-DRB1*03:01 62.4 3.44
        HLA-DRB1*07:01 23.3 4.5
        HLA-DRB1*13:02 96 5.89
        HLA-DRB4*01:01 63.2 4.85
      GLLSSRIQVIYEKTY HLA-DPA1*01/DPB1*04:01 403.7 13.14
        HLA-DPA1*01:03/DPB1*02:01 202.8 12.99
        HLA-DPA1*02:01/DPB1*01:01 232 19.59
        HLA-DPA1*03:01/DPB1*04:02 35 4.16
        HLA-DQA1*01:02/DQB1*06:02 120.6 8.72
        HLA-DQA1*05:01/DQB1*03:01 480.5 35.13
        HLA-DRB1*01:01 163.3 37.53
        HLA-DRB1*03:01 218.1 8.46
        HLA-DRB1*04:05 446.7 27.52
        HLA-DRB1*07:01 111.5 15.21
        HLA-DRB1*08:02 576.2 13.97
        HLA-DRB1*13:02 247.9 11.28
        HLA-DRB4*01:01 46.7 3.42
      IGLLSSRIQVIYEKT HLA-DPA1*01/DPB1*04:01 306.5 11.14
        HLA-DPA1*01:03/DPB1*02:01 116.7 9.22
        HLA-DPA1*02:01/DPB1*01:01 100.2 10.42
        HLA-DPA1*03:01/DPB1*04:02 19.1 2.17
        HLA-DQA1*01:02/DQB1*06:02 83.1 5.75
        HLA-DQA1*05:01/DQB1*03:01 269.8 26.51
        HLA-DRB1*01:01 89 28.59
        HLA-DRB1*03:01 105.2 5.21
        HLA-DRB1*04:05 384.8 25.27
        HLA-DRB1*07:01 82.6 12.46
        HLA-DRB1*08:02 523.1 12.72
        HLA-DRB1*13:02 165.5 8.68
        HLA-DRB4*01:01 42 2.99
      LLSSRIQVIYEKTYC HLA-DPA1*01/DPB1*04:01 573.6 16.1
        HLA-DPA1*01:03/DPB1*02:01 291.3 15.99
        HLA-DPA1*02:01/DPB1*01:01 253.5 20.78
        HLA-DPA1*03:01/DPB1*04:02 178.4 13.86
        HLA-DQA1*01:02/DQB1*06:02 216.1 14.94
        HLA-DQA1*05:01/DQB1*03:01 577.7 38.19
        HLA-DRB1*01:01 227.2 43.13
        HLA-DRB1*03:01 475.1 13.68
        HLA-DRB1*04:05 529.2 30.2
        HLA-DRB1*07:01 163.8 19.2
        HLA-DRB1*13:02 386.8 14.91
        HLA-DRB4*01:01 48.4 3.57
      MIGLLSSRIQVIYEK HLA-DPA1*01/DPB1*04:01 235.5 9.46
        HLA-DPA1*01:03/DPB1*02:01 104.8 8.61
        HLA-DPA1*02:01/DPB1*01:01 95.3 9.98
        HLA-DQA1*01:02/DQB1*06:02 62.7 4.03
        HLA-DQA1*05:01/DQB1*03:01 186.5 21.71
        HLA-DRB1*03:01 50 2.86
        HLA-DRB1*07:01 42.5 7.59
        HLA-DRB1*08:02 351.6 8.44
        HLA-DRB1*13:02 99.2 6.03
        HLA-DRB4*01:01 50.1 3.71
      RSCMIGLLSSRIQVI HLA-DPA1*01/DPB1*04:01 151.4 7.08
        HLA-DPA1*01:03/DPB1*02:01 99.6 8.33
        HLA-DPA1*02:01/DPB1*01:01 86.4 9.15
        HLA-DPA1*03:01/DPB1*04:02 30.6 3.65
        HLA-DQA1*01:02/DQB1*06:02 75 5.08
        HLA-DQA1*04:01/DQB1*04:02 993.9 15.71
        HLA-DQA1*05:01/DQB1*03:01 234 24.59
        HLA-DRB1*03:01 202.2 8.09
        HLA-DRB1*07:01 9.7 1.57
        HLA-DRB1*13:02 110.7 6.53
        HLA-DRB4*01:01 81.4 6.35
      SCMIGLLSSRIQVIY HLA-DPA1*01/DPB1*04:01 172.7 7.75
        HLA-DPA1*01:03/DPB1*02:01 89.2 7.75
        HLA-DPA1*02:01/DPB1*01:01 91 9.59
        HLA-DQA1*04:01/DQB1*04:02 977 15.47
        HLA-DQA1*05:01/DQB1*03:01 181.2 21.36
        HLA-DRB1*03:01 93.6 4.78
        HLA-DRB1*07:01 13.6 2.49
        HLA-DRB1*13:02 98 5.98
        HLA-DRB4*01:01 71.7 5.57

Table 7: The most potential 2 CD4+ T-cell epitopes of glycoprotein with interacting MHC-II alleles.

Core sequence Start End Peptide sequence Allele IC50 Rank
YILSFALPI 125 133 GLYILSFALPIILKA HLA-DPA1*01/DPB1*04:01 47.3 2.93
        HLA-DQA1*05:01/DQB1*02:01 543 12.07
        HLA-DRB1*01:01 6.3 1.99
        HLA-DRB1*03:01 98.4 4.96
        HLA-DRB1*04:01 106.4 8.61
        HLA-DRB1*04:04 109.9 12.9
        HLA-DRB1*04:05 162.3 14.22
        HLA-DRB1*07:01 10.9 1.85
        HLA-DRB1*09:01 38 2.24
        HLA-DRB1*11:01 69.3 10.81
        HLA-DRB1*13:02 18.8 1.34
        HLA-DRB1*15:01 45.4 4.56
        HLA-DRB3*01:01 267.2 8.14
        HLA-DRB5*01:01 66.4 12
      IGLYILSFALPIILK HLA-DPA1*01/DPB1*04:01 40 2.55
        HLA-DPA1*01:03/DPB1*02:01 33.8 3.78
        HLA-DPA1*02:01/DPB1*01:01 35 3.48
        HLA-DPA1*03:01/DPB1*04:02 34.9 4.15
        HLA-DQA1*05:01/DQB1*02:01 414 9.34
        HLA-DRB1*01:01 5.2 0.97
        HLA-DRB1*03:01 41.5 2.45
        HLA-DRB1*04:01 103.4 8.38
        HLA-DRB1*04:04 107.7 12.67
        HLA-DRB1*04:05 130 11.99
        HLA-DRB1*07:01 8.9 1.38
        HLA-DRB1*09:01 43.2 2.68
        HLA-DRB1*11:01 63.7 10.18
        HLA-DRB1*13:02 15.1 1.04
        HLA-DRB1*15:01 22.4 1.82
        HLA-DRB3*01:01 118.8 5
        HLA-DRB5*01:01 56.5 10.81
      KSIGLYILSFALPII HLA-DPA1*01/DPB1*04:01 62.7 3.7
        HLA-DPA1*01:03/DPB1*02:01 34.3 3.83
        HLA-DPA1*03:01/DPB1*04:02 96.6 9.44
        HLA-DQA1*05:01/DQB1*03:01 345.9 30.11
        HLA-DRB1*01:01 6.2 1.9
        HLA-DRB1*03:01 117 5.6
        HLA-DRB1*04:01 162.1 12.61
        HLA-DRB1*04:04 75.9 9.15
        HLA-DRB1*04:05 106.2 10.14
        HLA-DRB1*07:01 6.5 0.83
        HLA-DRB1*09:01 58.2 3.87
        HLA-DRB1*11:01 253.2 22.84
        HLA-DRB1*13:02 26.7 1.94
        HLA-DRB3*01:01 76.1 3.76
        HLA-DRB5*01:01 141.8 18.5
      LYILSFALPIILKAL HLA-DPA1*01/DPB1*04:01 50.2 3.08
        HLA-DQA1*05:01/DQB1*02:01 543.2 12.08
        HLA-DRB1*01:01 8.3 3.8
        HLA-DRB1*03:01 264.9 9.56
        HLA-DRB1*04:01 137.5 10.92
        HLA-DRB1*04:04 112.4 13.15
        HLA-DRB1*04:05 225.2 17.98
        HLA-DRB1*07:01 12.5 2.22
        HLA-DRB1*09:01 38.7 2.29
        HLA-DRB1*11:01 95.4 13.34
        HLA-DRB1*13:02 24 1.73
        HLA-DRB1*15:01 89.8 9.03
        HLA-DRB3*01:01 609.2 13.2
        HLA-DRB5*01:01 84.2 13.85
      SIGLYILSFALPIIL HLA-DPA1*01/DPB1*04:01 49.9 3.06
        HLA-DPA1*03:01/DPB1*04:02 72.6 7.73
        HLA-DQA1*05:01/DQB1*02:01 310.4 6.98
        HLA-DRB1*01:01 5.6 1.33
        HLA-DRB1*03:01 56.1 3.15
        HLA-DRB1*04:01 128 10.24
        HLA-DRB1*04:05 113.6 10.73
        HLA-DRB1*07:01 6.8 0.9
        HLA-DRB1*09:01 44 2.74
        HLA-DRB1*11:01 124.3 15.64
        HLA-DRB1*13:02 18.5 1.32
        HLA-DRB3*01:01 86.4 4.08
        HLA-DRB5*01:01 90.1 14.42
      WKSIGLYILSFALPI HLA-DPA1*01/DPB1*04:01 69.1 3.99
        HLA-DPA1*01:03/DPB1*02:01 46.7 4.87
        HLA-DPA1*03:01/DPB1*04:02 113.5 10.51
        HLA-DQA1*05:01/DQB1*02:01 476.2 10.68
        HLA-DQA1*05:01/DQB1*03:01 369.7 31.08
        HLA-DRB1*01:01 6.7 2.37
        HLA-DRB1*03:01 277.3 9.85
        HLA-DRB1*04:01 223.8 16.38
        HLA-DRB1*04:05 124.8 11.6
        HLA-DRB1*07:01 7 0.94
        HLA-DRB1*09:01 67.7 4.58
        HLA-DRB1*11:01 474.9 30.37
        HLA-DRB1*13:02 49.1 3.42
        HLA-DRB3*01:01 70.7 3.57
        HLA-DRB5*01:01 199.8 21.97
      YILSFALPIILKALY HLA-DPA1*01/DPB1*04:01 117.7 5.94
        HLA-DQA1*05:01/DQB1*02:01 608.2 13.4
        HLA-DRB1*01:01 12.3 6.87
        HLA-DRB1*03:01 732.1 17.41
        HLA-DRB1*04:01 181.9 13.87
        HLA-DRB1*04:04 243.9 23.25
        HLA-DRB1*04:05 369.7 24.68
        HLA-DRB1*07:01 16 3.01
        HLA-DRB1*11:01 117.3 15.11
        HLA-DRB1*13:02 33.2 2.39
        HLA-DRB1*15:01 81.1 8.25
        HLA-DRB5*01:01 103.4 15.59
FLAARCPFL 239 247 DDFLAARCPFLPEQK HLA-DPA1*01/DPB1*04:01 508.6 15.04
        HLA-DPA1*01:03/DPB1*02:01 342.9 17.53
        HLA-DPA1*02:01/DPB1*01:01 840.2 39.47
        HLA-DPA1*03:01/DPB1*04:02 905.3 30.6
        HLA-DQA1*05:01/DQB1*03:01 376.1 31.34
        HLA-DRB1*01:01 234.4 43.68
        HLA-DRB1*04:01 952.1 41.86
        HLA-DRB1*04:04 516.7 35.27
        HLA-DRB1*04:05 729.2 35.62
        HLA-DRB1*07:01 449.4 31.75
        HLA-DRB1*09:01 301.7 17.77
        HLA-DRB1*11:01 783.9 37.19
        HLA-DRB1*15:01 645.2 33
        HLA-DRB3*01:01 597.1 13.04
        HLA-DRB5*01:01 927.5 42.07
      DFLAARCPFLPEQKD HLA-DPA1*01:03/DPB1*02:01 440 20.08
        HLA-DQA1*05:01/DQB1*03:01 667 40.67
        HLA-DRB1*01:01 241.7 44.23
        HLA-DRB1*04:04 487.3 34.26
        HLA-DRB1*04:05 752.1 36.16
        HLA-DRB1*07:01 626.6 36.66
        HLA-DRB1*09:01 450.8 23.8
        HLA-DRB1*15:01 657.7 33.29
      ERIDDFLAARCPFLP HLA-DPA1*01/DPB1*04:01 569.6 16.03
        HLA-DPA1*01:03/DPB1*02:01 276 15.53
        HLA-DPA1*03:01/DPB1*04:02 845 29.72
        HLA-DQA1*05:01/DQB1*02:01 470.5 10.56
        HLA-DQA1*05:01/DQB1*03:01 269.8 26.51
        HLA-DRB1*01:01 204.6 41.28
        HLA-DRB1*04:01 564.1 31.06
        HLA-DRB1*04:04 422.3 31.9
        HLA-DRB1*04:05 570.4 31.39
        HLA-DRB1*07:01 104.3 14.56
        HLA-DRB1*09:01 297 17.57
        HLA-DRB3*01:01 260.7 8.03
        HLA-DRB5*01:01 617.7 35.98
      FLAARCPFLPEQKDP HLA-DPA1*01:03/DPB1*02:01 753.7 26.5
        HLA-DRB1*01:01 248.5 44.75
        HLA-DRB1*04:04 747.1 41.84
        HLA-DRB1*07:01 958.8 43.51
        HLA-DRB1*09:01 860.8 35.73
        HLA-DRB1*15:01 756.8 35.52
      IDDFLAARCPFLPEQ HLA-DPA1*01/DPB1*04:01 385.7 12.79
        HLA-DPA1*01:03/DPB1*02:01 267.1 15.24
        HLA-DPA1*02:01/DPB1*01:01 767.5 37.9
        HLA-DPA1*03:01/DPB1*04:02 760.4 28.43
        HLA-DQA1*05:01/DQB1*02:01 735.8 15.86
        HLA-DQA1*05:01/DQB1*03:01 261.8 26.08
        HLA-DRB1*01:01 155.2 36.71
        HLA-DRB1*03:01 508.1 14.2
        HLA-DRB1*04:01 693.5 35.08
        HLA-DRB1*04:04 519.6 35.36
        HLA-DRB1*04:05 675.5 34.26
        HLA-DRB1*07:01 268.4 24.85
        HLA-DRB1*09:01 236.6 14.79
        HLA-DRB1*11:01 483.5 30.61
        HLA-DRB1*15:01 417.2 26.43
        HLA-DRB3*01:01 297.8 8.67
        HLA-DRB5*01:01 786.8 39.54
      MERIDDFLAARCPFL HLA-DPA1*01/DPB1*04:01 574.6 16.11
        HLA-DPA1*01:03/DPB1*02:01 235.2 14.16
        HLA-DPA1*02:01/DPB1*01:01 667.3 35.49
        HLA-DQA1*05:01/DQB1*02:01 581.7 12.86
        HLA-DQA1*05:01/DQB1*03:01 316.7 28.81
        HLA-DRB1*01:01 173.5 38.51
        HLA-DRB1*04:01 476.1 27.94
        HLA-DRB1*04:04 476.6 33.88
        HLA-DRB1*04:05 388 25.39
        HLA-DRB1*07:01 70.4 11.2
        HLA-DRB1*09:01 321 18.67
        HLA-DRB3*01:01 255.4 7.92
        HLA-DRB5*01:01 463 32.02
      RIDDFLAARCPFLPE HLA-DPA1*01/DPB1*04:01 435 13.74
        HLA-DPA1*01:03/DPB1*02:01 245.2 14.51
        HLA-DPA1*02:01/DPB1*01:01 960 41.81
        HLA-DPA1*03:01/DPB1*04:02 820.3 29.36
        HLA-DQA1*05:01/DQB1*02:01 515.7 11.5
        HLA-DQA1*05:01/DQB1*03:01 257.4 25.85
        HLA-DRB1*01:01 191.8 40.18
        HLA-DRB1*03:01 340.5 11.16
        HLA-DRB1*04:01 574.8 31.42
        HLA-DRB1*04:04 423.4 31.94
        HLA-DRB1*04:05 670.6 34.13
        HLA-DRB1*07:01 165.2 19.29
        HLA-DRB1*09:01 258.6 15.83
        HLA-DRB1*15:01 333.1 23.32
        HLA-DRB3*01:01 263.7 8.08
        HLA-DRB5*01:01 705.2 37.92

Table 8: The most potential 2 CD4+ T-cell epitopes of nucleocapsid with interacting MHC-II alleles.

Analysis of the population coverage

Epitopes with high affinity to interact with MHC-I and II international alleles were applied to population coverage analysis in IEDB. There were 22 and 70 epitopes in glycoprotein, and 10 and 44 in nucleocapsid in MHC-I and II have given a high percentage of population coverage. The results were shown in Tables 9 and 10 and proposed epitopes in both MHC class I and II were shown in Figures 7 and 8 and also in Tables 11 and 12.

immunome-research-cell-epitopes

Figure 7: Nucleocapsid protein epitopes suggested for both MHC-I and MHC-II interaction (visualization by UCSF chimera).

immunome-research-glyco-protein

Figure 8: Glycoprotein epitopes suggested for T helper cell interaction (MHC-II) (visualized by USCF chimera).

Epitope Coverage Total HLA hits Epitope Coverage Total HLA hits
Class I Class II
VIYEKTYCV 49.21% 4 ILLTQVADL 99.65% 22
QLIEGLCFI 40.60% 2 VHLIAPVQT 93.85% 18
FIPTHTIAL 42.41% 3 LLSSRIQVI 99.65% 21
MTLPVTCFL 55.00% 8 VIYEKTYCV 96.71% 13
TLPVTCFLV 40.60% 2 IYEKTYCVT 96.21% 8
FQGYYICFI 40.60% 2 YCVTGQLIE 97.55% 10
GLMRIAGPV 40.60% 2 VTGQLIEGL 98.99% 13
SSFSTLVRK 40.03% 4 QLIEGLCFI 97.48% 9
IIAESNHSV 42.53% 3 IEGLCFIPT 97.64% 10
LTWTGFLAV 40.60% 2 LCFIPTHTI 92.89% 17
YTFTSLFSL 55.70% 7 CFIPTHTIA 90.56% 6
SLFSLIPGV 40.60% 2 FIPTHTIAL 93.13% 16
ITFCFGWLL 44.77% 4 TLPVTCFLV 93.82% 7
FRYKSRCYV 43.99% 4 LPVTCFLVA 91.01% 7
YAYPWQTAK 47.31% 5 CFLVAKKLG 91.56% 7
KAYKIVSLK 43.03% 5 FLVAKKLGT 94.54% 15
KQWCTTSCV 40.60% 2 KKLGTQLKL 95.97% 9
GLTECANFI 40.60% 2 KLAVELEKL 98.96% 13
NLLRGSNTV 40.60% 2 LAVELEKLI 97.70% 12
GILNGNWVV 40.60% 2 FQGYYICFI 99.28% 18
ILNGNWVVV 42.66% 2 FIGKHSEPL 98.81% 17
LLFSFFCPV 49.90% 4 LFVPTMEDY 96.79% 8
      IAFAGAPMY 91.31% 13
      FAGAPMYSS 97.78% 16
      PMYSSFSTL 96.39% 9
      MYSSFSTLV 95.28% 15
      YVFSPGIIA 93.67% 14
      VPLTWTGFL 92.38% 5
      LTWTGFLAV 99.26% 14
      FLAVSGEIE 99.06% 17
      KFRGSEQRI 92.17% 7
      VIGQCIYTF 95.33% 12
      CIYTFTSLF 99.08% 18
      IYTFTSLFS 97.86% 13
      YTFTSLFSL 99.24% 13
      TFTSLFSLI 96.21% 8
      FTSLFSLIP 89.54% 14
      VAHSLAVEL 98.54% 19
      GWATTALLI 99.31% 14
      LITFCFGWL 96.39% 9
      FCFGWLLIP 97.43% 10
      LLTFSCSHY 96.15% 11
      YSTESKFKV 98.16% 13
      KFKVILERV 98.46% 14
      VILERVKVE 96.23% 10
      ILERVKVEY 90.67% 4
      YQKTMGSMV 93.92% 15
      TLGVFRYKS 90.19% 5
      FRYKSRCYV 96.40% 13
      CYVGLVWGI 97.29% 10
      YVGLVWGIL 97.14% 13
      LVWGILLTT 95.81% 8
      ILLTTELII 99.45% 15
      LLTTELIIW 97.48% 9
      IIWAASADT 90.00% 13
      WAASADTPL 91.43% 12
      FALASSSSY 98.78% 17
      FHFQLDKQV 99.50% 19
      FQLDKQVVH 93.65% 11
      VVHAEIQNL 93.73% 15
      YPWQTAKCF 93.57% 10
      CFFEKDYQY 92.71% 8
      FFEKDYQYE 93.01% 6
      VYLDKLRSV 96.48% 13
      YKIVSLKYT 95.55% 15
      LKYTRKVCI 99.27% 19
      QPGDTLLFL 95.83% 9
      LFLGPLEQG 98.37% 10
      YGATVTNLL 94.51% 16
      FTKSGEWLL 99.24% 17
      ILILSILLF 93.35% 7

Table 9: Glycoprotein epitopes with highest population coverage percentage.

Epitope Coverage Total HLA hits Epitope Coverage Total HLA hits
Class I Class II
GLYILSFAL 39.08% 1 ITLHEQQLV 83.18% 9
YILSFALPI 44.14% 3 DAERAVELD 79.44% 6
ILSFALPII 39.08% 1 SRRAAVSAL 92.35% 9
IILKALYML 40.60% 2 RRAAVSALE 86.21% 9
IILKALYML 40.60% 2 LKRELADLI 94.55% 18
GVIGFSFFV 42.53% 3 ADLIAAQKL 84.17% 8
FLAARCPFL 51.18% 4 LIAAQKLAS 96.44% 17
HLYVSMPTA 39.08% 1 LRYGNVLDV 99.07% 16
LRYGNVLDV 33.31% 2 WKSIGLYIL 94.46% 15
IRKPRHLYV 33.31% 2 LYILSFALP 93.34% 7
      YILSFALPI 99.77% 23
      ILKALYMLS 99.61% 18
      LKALYMLST 98.72% 16
      LRRTQSMGI 81.74% 12
      IIILYMSHW 91.28% 11
      LYMSHWGRE 95.37% 10
      LRELAQTLV 84.55% 11
      LRRTQSMGI 81.74% 12
      AFYQSYLRR 96.21% 9
      FYQSYLRRT 98.79% 16
      KSAFYQSYL 93.67% 9
      IMASKSVGT 91.14% 13
      ELGAFFAIL 96.65% 10
      LGAFFAILQ 96.05% 15
      GAFFAILQD 94.82% 10
      AFFAILQDM 89.50% 9
      FFAILQDMR 99.50% 20
      IFADIATPH 89.33% 14
      AESATIFAD 96.21% 12
      ESATIFADI 97.74% 10
      ARAESATIF 92.52% 8
      LATNRAYFI 97.70% 14
      FVKDWMERI 99.52% 19
      FLAARCPFL 99.74% 22
      VMGVIGFSF 97.33% 10
      VIGFSFFVK 97.77% 11
      IGFSFFVKD 93.55% 8
      FRTIACGLF 91.66% 10
      ILKALYMLS 99.61% 18
      LKALYMLST 98.72% 16
      LYILSFALP 93.34% 7
      YILSFALPI 99.77% 23
      WKSIGLYIL 94.46% 15
      LKRELADLI 94.55% 18

Table 10: Nucleocapsid epitopes with high population coverage percentage.

Epitope Coverage Total HLA hits Epitope Coverage Total HLA hits
Class I Class II
YILSFALPI 44.14% 3 YILSFALPI 99.77% 23
FLAARCPFL 51.18% 4 FLAARCPFL 99.74% 22
Epitope set 54.09%   Epitope set 99.77%  

Table 11: Proposed epitopes in both MHC class I and II of nucleocapsid.

Epitope Coverage Total HLA hits Epitope Coverage Total HLA hits
Class I Class II
MTLPVTCFL 55.00% 8 ILLTQVADL 99.65% 22
YTFTSLFSL 55.70% 7 LLSSRIQVI 99.65% 21
Epitope set 61.06%   Epitope set 99.89%  

Table 12: Proposed epitopes in both MHC class I and II of glycoprotein.

Discussion

Vaccination, which is a form of preventative medicine, has an extremely powerful impact on the defense against infectious agents that cause disease and death, and sometimes vaccines have the ability to completely eradicate disease from the globe (e.g. smallpox) [42,43]. None the less, there are still many human diseases have no effective vaccines [42]. One of these diseases is HCPS especially caused by SNV. Despite SNV is not transmitted from person-to-person; SNV is highly pathogenic (case fatality rate 30 to 50%) [44] regardless of age, [45] health status, [46] or access to advanced medical care, and was responsible for the 2012 Yosemite National Park outbreak [21,47]. The absence of effective vaccines, post exposure prophylactics, or therapeutics to prevent HCPS caused by SNV participated in the worry experienced by about 270,000 Yosemite visitors who got notice of possible exposure [46]. This brings us to the urgent need for the nondevelopment of an effective vaccine against SNV. In our study, we analyzed envelope glycoprotein and nucleocapsid of SNV, and predicted most conserved and immunogenic B- and T-cell epitopes which could trigger humoral as well as cellular mediated immune responses in order to develop an effective epitope-based vaccine. This concept of epitopebased vaccine, in which a cocktail of B- and T-cell epitopes are used to enhance protection against viral infection, was applied by Steward et al. in a study on the Respiratory syncytial virus (RSV), where RSV-specific humoral and cellular immunities were induced after immunization with a cocktail of peptides. Following challenge infection, a 190-fold reduction in RSV titer was observed in the lungs of immunized mice [48]. Thus the combination of humoral and cellular immunity is more promising at clearing viral infection than humoral or cellular immunity alone. A number of studies have demonstrated the strong antigenicity of both the membrane glycoprotein and nucleocapsid protein. However, neutralizing antibodies have been shown to be sufficient to confer protection in passive immunization of experiments using glycoproteins-specific monoclonal and polyclonal antibodies [2,15,49]. The nucleocapsid is a multifunctional protein that plays several roles in virus replication and assembly and is the major antigen to which the host mounts the humoral immune response. It is highly conserved among various Hantavirus genotypes and is expressed early with huge amounts in the cell during infection. The antibody to the nucleocapsid protein is demonstrated early in the immune response [50,51].

The conserved predicted epitopes for B- and T-cell were retrieved from all declared strains of SNV glycoprotein and nucleocapsid in NCBI databases until Nov 2016. As obviously clear from the result, the conserved regions cross retrieved strains accompanied with a good level of confidence. To design an effective peptide antigen for B cell, epitopes were predicted from the conserved sequences by Bepipred prediction tools should be passed both Emini surface accessibility and Kolaskar and Tongaonkar antigenicity thresholds, and peptide sequences should best be within 8-22 amino acids in length [19]. However, 20 predicted epitopes of glycoprotein were satisfied both Emini surface accessibility and Kolaskar and Tongaonkar antigenicity tests in IEDB. The linear epitope 743CKKYAYPWQT752 showed high succeeded score in both tests as illustrated in Table 3. For nucleocapsid, 9 predicted epitopes for B cell were succeeded both Emini surface accessibility and Kolaskar and Tongaonkar antigenicity tests. The linear epitope 271QVDESKVSD279 was 9 amino acids residue long, when we decreased the epitope length to 8 amino acid 271QVDESKVS278 we got the highest score in accessibility with a succeeded score in antigenicity tests, as illustrated in Table 4. Sagadevan et al. predicted IMASKSVGS/TAEEKLKKKSAF from nucleocapsid protein of Hantaviruses causing HCPS, based on the mean percent prediction probability score, to be the best candidate B-cell epitope for developing immunoassays in the detection of antibodies to hantaviruses causing HCPS [51]. But this peptide has a low value of antigenicity and does not pass the threshold of Kolaskar and Tongaonkar antigenicity test in IEDB. Thus it may be useful for the development of immunodiagnostic tools towards pan-detection of Hantavirus antibodies causing HCPS but not effective vaccines.

T-cell mediated immunity plays a crucial role in the defense against viral infections [52]. The inclusion of T-cell epitopes in the vaccine development induces a strong and long lasting immune response and antigenic drift where antigen can easily escape the antibody memory response [19,22]. While CD8+ cytotoxic T-cells generally recognize intracellular peptides displayed by HLA class I molecules, CD4+ T-helper cells generally recognize peptides from the extracellular space, displayed by HLA class II molecules (CD4+ T-cell epitopes) [22]. A potential problem in the development of CTL epitope-based vaccines is the large degree of MHC polymorphism and the need for the understanding of HLA restrictions in the target population for vaccines [53]. In our study, we chose the most frequently occurring alleles in IEDB for MHC binding prediction. For glycoprotein, among 168 CD8+ T cell conserved predicted epitopes, we found the epitope 208MTLPVTCFL216 had higher affinity to interact with 8 alleles (HLA-A*02:06, HLA-A*02:01, HLA-A*30:01, HLA-A*68:02, HLA-B*58:01, HLA-B*57:01, HLA-C*03:03, HLA-C*14:02) and the epitope 458YTFTSLFSL466 had the affinity to interact with 7 alleles (HLA-A*02:06, HLA-A*02:01, HLA-A*68:02, HLA-B*39:01, HLA-C*03:03, HLA-C*12:03, HLA-C*14:02). These two epitopes had very good population coverage for class I alleles throughout the world, as shown in Table 5 and Table 12. Also we found 674 CD4+ T cell predicted conserved epitopes interacting with MHC-II alleles, as shown in Table 7. Epitopes 50ILLTQVADL58 and 165LLSSRIQVI173 had high affinity to interact with 22 and 21 alleles, respectively. They interacted with HLA-DPA1*01/2/3, -DPA1*02, -DQB1*02/3/4/5/6, -DRB1*03/4/7, -DRB3*01, -DRB4*01, -DRB5*01. Interestingly, these two epitopes covered 99.65% of the world population. For nucleocapsid, we found 66 CD8+ T cell and 228 CD4+ T cell conserved predicted epitopes, as shown in Tables 6 and 7. Most importantly, the epitopes 125YILSFALPI133 and 239FLAARCPFL247 had high affinity to interact with both MHC classes, I and II, and also they had excellent population coverage for Class I and II alleles throughout the world (Table 11). Epitope FLAARCPFL was the same epitope predicted by Sathish et al. in 2017 as a suitable epitope for vaccine specific to the SNV genotype [54].

The HLA-A*02 supertype is expressed in all main ethnicities in the 39-46% range. Since many peptides that bind A*02:01 also display degenerate binding (binding to multiple alleles), an A2 supertype multiepitope vaccine could be developed to allow broad, nondevelopment ethnically biased population coverage [53] Of greater significance is the finding that all CD8+ T cell predicted epitopes showed interactions with HLA-A*02 and most of HLA-A*30, -A*68, -B*08, -B*39, -B*57 and C*04:01 which are more predominant HLA alleles in the US and North America where infection and outbreak of SNV have occurred [54]. On the other hand, it was found that all the CD4+ T cell epitopes interacted with HLA-DRB1*03:02, -DRB1*03:01 and -DRB1*07:01 which are among most common haplotype observed within American populations, that reported by Martin et al. in 2007 [55].

Conclusion

Bioinformatics approaches are a fundamental component of a high throughput pipeline for in silico mapping of thousands of potential epitopes, minimizing the time and cost involved in the experimental testing of such peptides [53]. Target populations for the vaccine are inhabitants of SNV-endemic geographic areas and people with occupational risks of infection [1]. Analysis for the proteasomes and transporter associated with antigen processing (TAP) efficiency is required; in order to predict the candidate epitopes based on the processing of the peptides in vivo. Along with in silico study, further in vivo and in vitro experiments are needed to prove the effectiveness of triggering and mounting an immune response. Our study for the first time has predicted a cocktail of B- and T-cell epitopes for designing an effective vaccine against HCPS caused by SNV.

References

Citation: Idris AB, Mahmoud SM, Mohamedelamin S, Mustafa YY, Osman AA, et al. (2017) Immunoinformatics Predication and Modelling of a Cocktail of B- and T-cells Epitopes from Envelope Glycoprotein and Nucleocapsid Proteins of Sin Nombre Virus. Immunome Res 13: 141. Doi: 10.4172/1745-7580.1000141

Copyright: © 2017 Idris AB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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