alexa Prediction and Conservancy Analysis of Multiepitope Based Peptide Vaccine Against Merkel Cell Polyomavirus: An Immunoinformatics Approach | Open Access Journals
ISSN: 1745-7580
Immunome Research
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Prediction and Conservancy Analysis of Multiepitope Based Peptide Vaccine Against Merkel Cell Polyomavirus: An Immunoinformatics Approach

Mawadda Abd-Elraheem Awad-Elkareem1*, Soada Ahmed Osman1, Hanaa Abdalla Mohamed2, Hadeel Abd-Elrahman Hassan3, Ahmed Hamdi Abu-haraz3, Khoubieb Ali Abd-elrahman4 and Mohamed Ahmed Salih3

1Department of Biotechnology, Ahfad University for Women, Khartoum, Sudan

2Department of Microbiology, Sudan University of Sciences and Technology, Khartoum, Sudan

3Department of Biotechnology, Africa City of Technology, Khartoum, Sudan

4University of Medical Science and Technology, Khartoum, Sudan

*Corresponding Author:
Mawadda Abd Elraheem Awad Elkareem
Department of Biotechnology, Ahfad University for Women, Khartoum, Sudan
Tel: 0917313523
E-mail: Mawadda_ph@hotmail.com

Received date: March 23, 2016; Accepted date: May 18, 2017; Published date: May 23, 2017

Citation: Awad-Elkareem MAE, Osman SA, Mohamed HA, Hassan HAE, Abu-haraz AH, et al. (2017) Prediction and Conservancy Analysis of Multiepitope Based Peptide Vaccine Against Merkel Cell Polyomavirus: An Immunoinformatics Approach. Immunome Res 13:134. doi: 10.4172/17457580.1000134

Copyright: © 2017 Awad-Elkareem MAE, 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.

Visit for more related articles at Immunome Research

Abstract

Merkel cell Polyomavirus is non-enveloped, dsDNA virus belonging to Polyomaviridae family linked to an uncommon aggressive skin malignancy. The poor prognosis and limited understanding of disease pathogenesis warrants innovative treatment. In this current study we aim to predict TB cell immunogenic epitopes from the VP1 protein of all merkel cell polyomavirus strain which will aid in effective epitope based vaccine design using immuoinformatics approaches. We retrieved 423 full-length VP1 protein sequences of merkel cell polyomaviruse virus species from the NCBI database. These sequences were analyzed to determine the conserved region and were used to predict the epitopes using the IEDB immunoinformatics algorithms. For B cell three epitope were predicted as peptide vaccine (QEKTVY, KTVYPK, and QEKTVYP). For T cell the predicted Class-I peptides (SLFSNLMPK, LQMWEAISV and LLVKGGVEV) were found to cover the maximum number of MHC I alleles. The highest scoring Class II MHC binding peptides were (IELYLNPRM, ISSLINVHY and INSLFSNLM). Further experiments will need to be undertaken to confirm the potential of these predicted epitopes in a future efficacious vaccine development.

Keyword

Merkel cell polyomavirus (MCPYV); Epitope; Peptide vaccine; Immune epitope database IEDB

Introduction

Merkel cell polyomavirus is a recently discovered small non enveloped circular double-stranded DNA virus etiologically linked to an uncommon but highly lethal form of skin malignancy, Merkel cell carcinoma (MCC). MCPYV belongs to the Orthopolyomavirus genus of the Polyomaviridae family, which include mammalian polyomaviruses such as murine PyV (MPyV), simian virus (SV40) and the human polyomaviruses JC (JCPyV) and BK (BKPyV) [1-11]. The genome is 5.4 kb which constitute two regions; the early region which encodes the large tumour (T) and small T antigens, and the late region which comprise the structural viral proteins VP1, VP2 and VP3, which form the viral capsid. [2-5], However, VP1 makes up more than 70% of the total protein content of virus particles and is also called the major structural protein which is responsible for immunogenic response inside the host body [4,10]. Antibodies against vp1 protein is likely to be expressed in 90% of MCC tumors [11], thus it represent an ideal therapeutic candidate for designing immunoprophylactic vaccine [10,12,13].

Merkel cell carcinoma is an aggressive lethal neuroectodermal malignancy arising from mechanoreceptor Merkel cells [3,6,11,14,15]. MCC was first described by Cyril Toker in 1972, who noted a colored painless solid nodule within five different areas of two older men, who later died as a result of this tumour, and three older women, yet the pathogenesis and etiology of MCC remains poorly understood [3,6]. MCC is rare, but its incidence has tripled over the past two decades in the United States to 1500 cases per year and 2,500 new cases diagnosed in the E.U [11,14,16]. Epidemiological studies revealed that older, lighter-skinned, and immunosuppressed individuals, such as those infected with HIV and/or diagnosed with AIDS are more susceptible to infection [1,14,17-19]. In 2008, a novel merkel cell polyomavirus was discovered and found to be integrated and associated with 80% of MCC tumors [1,6,20,21], thus it has been confirmed to be the etiological agent behind six other viruses now known to be either directly or indirectly causes human cancer [7,11,22].

Developing an advanced vaccine for MCC, that specifically targets the immunogenic proteins, is of vital significance to overcome the devastating disease [23]. In the previous study a DNA vaccine which encoding large and small T virus antigen was developed and has shown that it is possible to induce both CD4+ and CD8+ T lymphocyte response [24]. However an epitope based peptide vaccine could be another possible candidate. The aim of this present study is to predict a promiscuous epitopes that bind to B cell as well as both classes of MHC molecules with a maximum number of HLA molecules in a given set of population in MCPYV protein using an immunoinformatics approach which is a prerequisite in the development of an epitope based vaccine design.

Epitopes based subunit vaccines offer a much stronger and measured immune response as well as avoid the possible fatal consequences of employing entire viral proteins and peptides [23,25,26]. The poor prognosis of MCC patients as well as the limited understanding of disease pathogenesis warrants innovative treatments to control MCC [24]. This is the first study concerning Merkel cell polyomavirus vp1 protein vaccine design using immunoinformatics tools.

Materials and Methods

Protein sequence retrieval

A set of available 97 virulent strains of Merkel Cell Polyomavirus (MCPYV) from different geographic regions were retrieved from the NCBI database. (https://www.ncbi.nlm.nih.gov/protein/?term=Merkel +cell+polyomavirus+VP1) .

These sequences were retrieved in October 2016 and selected for immunobioinformtic analysis. These sequences were isolated from different geographical areas (USA, Japan, China, Germany, France and Lithuania) from 1995-2011. The retrieved VP1 97 strains with length of 423 a.a and their accession number and collection area are listed in Table 1.

Bepipred epitope StartEnd Length Emini surface threshold(1.00) Antigenicity threshold (1.031)
MAPKRKASSTCKT 1 13 13 1.294 0.995
KRQC 15 18 4 1.217 1.057
GCCPN 23 27 5 0.179 1.108
GEDSI 48 52 5 0.679 0.951
VNSPDLPT 65 72 8 0.802 1.04
DLQPKGSSPDQPIKENLP 82 99 18 3.112 1.003
GAGIPVS 153 159 7 0.151 1.06
EPL 171 173 3 0.969 1.055
TTNGGPIT 189 196 8 0.542 0.933
MTPKNQGLDPQAKAKLDKDGNYP 205 227 23 4.957 0.972
PSKNENSRYYGSIQTGSQTP 235 254 20 5.346 0.973
GVGPLC 272 277 6 0.094 1.143
KVSGQPMEG 338 346 9 0.71 0.981
DNQ 348 350 3 2.04 0.886
LPG 363 365 3 0.554 1.063
EGSE 358 361 4 1.331 0.897
GQEKTVYPK * 377 385 9 2.448 1.013
QEKTVYP* 378 384 7 2.208 1.045
QEKTVY* 378 383 6 1.912 1.042
EKTVYP 379 384 6 1.707 1.05
EKTVYPK 379 385 7 2.549 1.033
KTVYPK* 380 385 6 1.971 1.063
SVAPA 387 391 5 0.397 1.117

Table 1: List of B-cell epitopes predicted by different scales fromVP1 protein in Merkle cell Polyomavirus;*Peptide from 377 to 385 gives higher score in Kolaskar and Tongaonkar antigenicity if it is shorten to 7 amino acids (378 to 384) or to 6 amino acids (378 to 383) & (380 to 385).

Phylogenetic and alignment

The retrieved sequences were subjected to Phylogenetic and alignment study in order to determine the origin of each strain and the conservancy using different tools from (http://www.phylogeny. fr) [27]. The phylogenetic tree and alignment were presented in Figures 1 and 2.

immunome-research-immunoinformatics-prediction

Figure 1: Flowchart representing the immunoinformatics prediction of potential B and T lymphocyte epitopes for the development of peptide vaccine targeting MCPYV.

immunome-research-Cladogram-relationship

Figure 2: Cladogram shows the relationship between different MCPYV strains. The phylogenetic tree showed that YP-009111420 strain collected from USA (2009) could be related to the recent strains collected from Germany 2014, Brazil 2016.

Conserved regions determination

BioEdit sequence alignment editor (v7.0.9) were used to align the retrieved sequences to obtain conserved regions with the aid of ClustalW (Hall, 1999) by comparing the whole length amino acid of 97 VP1 strains against MCPYV reference sequence under gene bank accession number YP_009111420.1. 100% of identical and similar amino acid sequences were selected as a conserved region [28].

Prediction of B-cell epitopes

As the Immunogenic B cell epitopes interacts with B-lymphocytes, the B-lymphocyte is differentiated into antibody-secreting plasma cell and memory cell. B cell epitope is characterized by being accessible and antigenic [29]. B cell epitopes were predicted using tools from immune epitope data base analysis resource (IEDB-AR) (http://tools. iedb.org/bcell/) by Bepipred linear epitope prediction analysis [30,31]. The reference sequence was subjected to Bepipred linear epitope prediction tool to predict the binding probability of specific regions in the protein to B cell receptor with a default threshold value of (.0393). The predicted epitopes were subjected to Bioedit tool and only 100% conserved epitopes were selected. Then IEDB tools were used to predict surface accessible epitopes by Emini surface accessibility prediction [32] and antigenicity by Kolaskar and Tongaonkar antigenicity method [33] with thresholds of 1.000 and 1.031 respectively.

Binding predictions for MHC class I

For prediction of peptides bind to MHC class I; the reference sequence was submitted in MHC-I Binding prediction tool http://tools.iedb.org/ mhci/n in IEDB. In MHC-I peptide complex presentation to T lymphocytes several steps are involved. The cellular attachment of cleaved peptides to MHC molecules step was predicted. Prediction methods include Artificial Neural Network (ANN), Stabilized Matrix Method (SMM), or Scoring Matrices derived from Combinatorial Peptide Libraries (Comblib_ Sidney2008), ANN method was used [34-38]. Epitopes lengths were set as 9 mers prior to prediction. The conserved epitopes which bind to alleles at score equal or less than 100 half-maximal inhibitory concentrations (IC50) were selected for further analysis [34].

Binding predictions for MHC class II

Peptide binding Analysis of MHC class II molecules was assessed by the IEDB MHCII prediction tool at http://tools.immuneepitope. org/mhcii/ [39,40]. Certain HLA-DR, HLA-DP, HLA-DQ alleles were analyzed. MHC class II groove has the ability to bind to peptides with different lengths. This binding variability makes the prediction difficult and less accurate [41]. MHC II binding prediction can be achieved using five different IEDB tools; SMM_align, NN- align, Compinatorial Libraries, Sturniolo’s method and NetMHCIIpan in addition to the consensus method. NN-align method was used to predict MHC class II epitopes [35]. All conserved epitopes that bind to many alleles at score equal or less than 1000 half-maximal inhibitory concentration (IC50) is selected for further analysis.

Population coverage calculation

All MHC I and MHC II potential binders from Merkel Cell Polyomavirus VP1 capsid protein were assessed for population coverage analysis against the whole world population with the selected MHC I and MHC II interacted alleles using IEDB population coverage calculation tool at http://tools.iedb.org/tools/population/iedb_input [42]. Population coverage calculation is based on total HLA hits score that is obtained from IEDB, these data derived from the relative frequency of an allele at a particular locus in a population.

Assessment of epitope allergenicity

For allergenicity prediction AllerTOP (http://www.pharmfac.net/ allertop) was used [43]. So the predicted B cell epitopes and epitopes bind to MHC I & II are subjected to AllerTOP giving result either “probable allergen” or “probable non-allergen”.

Homology modeling

Merkel Cell Polyomavirus VP1capsid protein 3D structure was obtained by RaptorX, (http/www.raptor.uch icago.edu) which uses advanced homology detection techniques to build protein 3D structures. UCSF Chimera (version 1.8) was used to visualize the 3D structure, Chimera currently available at the chimera web site (http://www.cgl.ucsf.edu/cimera). Further verification of the surface accessibility and hydrophilicity of predicted B lymphocyte epitopes was achieved, visualization of all predicted T cell epitopes in the structural level were also assessed [44,45].

Result

Prediction of B-cell epitope

VP1 capsid protein was subjected to Bepipred linear epitope prediction that predicts linear epitope, Kolaskar and Tongaonkar antigenicity and Emini surface accessibility prediction methods in IEDB, Figure 1. In Bepipred Linear Epitope Prediction method; the average binders score of the protein to B cell was 0.393, with a maximum of 2.546 and a minimum of -1.464, all values equal or greater than the default threshold 0.393 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 5.749 and a minimum of 0.060, all values equal or greater than the default threshold 1.000 were potentially in the surface. The Kolasar and Tongaonkar antigenicity prediction; the average of the antigenicity was 1.033, with a maximum of 1.235 and minimum of 0.877; all values greater than 1.026 are potential antigenic determinants. The result of all conserved predicted B cell epitopes are shown in Table 1 and Figures 3-6.

immunome-research-representative-multiple

Figure 3: representative multiple sequence alignment of whole length amino acid of 96 VP1 strains against the reference protein using (Bioedit tool v7.0.9); The highlighted sequence represents the most mutated region; Dot represents the conservancy between sequences.

immunome-research-Bepipred-Linear

Figure 4: Bepipred Linear Epitope Prediction; Yellow areas above the red line( threshold) are proposed to be a part of B cell epitopes. While green areas are not.

immunome-research-Tongaonkar-antigenicity

Figure 5: Kolaskar and Tongaonkar antigenicity prediction; Yellow areas above the red line (threshold) are proposed to be a part of B cell epitopes. While green areas are not.

immunome-research-Emini-surface

Figure 6: Emini surface accessibility prediction. Yellow areas above the red line (threshold) are proposed to be a part of B cell epitopes. While green areas are not.

MHC class 1 binding prediction

The VP1capsid protein was subjected to IEDB MHC-1 binding prediction tool. 29 peptides were predicted to interact with different MHC class 1 alleles using artificial neural network (ANN) method. The peptide SLFSNLMPK from 330 to 338 had higher affinity to interact with 4 alleles (HLA-A*03:01, HLA-A*11:01, HLA-A*30:01 & HLA-A*68:01). The predicted epitopes with their corresponding MHC1 alleles are listed in the Table 2, Figures 7 and 8.

Peptide Start End Length Allele ANN_ic50* percentile rank
APKRKASST 2 10 9 HLA-B*07:02 97.43 0.3
ASVPKLLVK 29 37 9 HLA-A*11:01 21.32 0.3
AYSVARVSL 100 108 9 HLA-C*14:02 54.49 0.2
DSITQIELY 50 58 9 HLA-A*26:01 49.81 0.1
DTLQMWEAI 118 126 9 HLA-A*32:01 48.77 0.3
EAISVKTEV 124 132 9 HLA-A*68:02 5.32 0.2
EVVGISSLI 131 139 9 HLA-A*26:01 31.33 0.1
        HLA-A*68:02 2.95 0.2
FSNTLTTVL 259 267 9 HLA-B*39:01 55.09 0.3
        HLA-C*15:02 77.15 0.1
GVNYHMFAI 160 168 9 HLA-A*02:06 59.59 0.7
        HLA-A*32:01 88.82 0.3
ISSLINVHY 135 143 9 HLA-B*58:01 54.83 0.3
ITCDTLQMW 115 123 9 HLA-B*57:01 73.45 0.3
        HLA-B*58:01 18.43 0.3
KENLPAYSV 95 103 9 HLA-B*40:02 11.87 0.1
        HLA-B*44:02 77.75 0.1
KRKASSTCK 4 12 9 HLA-A*30:01 27.32 0.4
LLVKGGVEV* 34 42 9 HLA-A*02:01 83.35 0.5
        HLA-A*02:06 31.73 0.6
LPRYFNVTL 305 313 9 HLA-B*07:02 5.97 0.1
        HLA-B*35:01 66.6 0.4
LQMWEAISV* 120 128 9 HLA-A*02:01 21.99 0.4
        HLA-A*02:06 4.27 0.1
        HLA-B*39:01 74.99 0.3
MPKVSGQPM 336 344 9 HLA-B*07:02 6.8 0.1
        HLA-B*08:01 59.3 0.2
         HLA-B*35:01 18.27 0.2
NEDITCDTL 112 120 9 HLA-B*40:01 25.59 0.2
NPYPVVNLI 320 328 9 HLA-B*51:01 68.27 0.1
        HLA-B*53:01 78.09 0.3
NVHYWDMKR 140 148 9 HLA-A*31:01 93.09 0.5
        HLA-A*68:01 11.03 0.1
QMWEAISVK 121 129 9 HLA-A*03:01 72.45 0.2
RVHDYGAGI 148 156 9 HLA-A*30:01 28.75 0.4
RYFNVTLRK 307 315 9 HLA-A*11:01 39.91 0.4
        HLA-A*30:01 11.58 0.2
         HLA-A*31:01 52.51 0.4
RYYGSIQTG 242 250 9 HLA-C*14:02 77.25 0.3
SKNENSRYY 236 244 9 HLA-C*06:02 90.48 0.1
SLFSNLMPK* 330 338 9 HLA-A*03:01 8.36 0.1
        HLA-A*11:01 5.05 0.2
        HLA-A*30:01 51.63 0.5
        HLA-A*68:01 43.73 0.6
SSLINVHYW 136 144 9 HLA-B*57:01 9.42 0.1
        HLA-B*58:01 4.82 0.1
SVARVSLPM 102 110 9 HLA-A*68:02 52.81 0.7
        HLA-B*07:02 75.63 0.2
        HLA-B*15:01 53.52 0.2
        HLA-B*35:01 73.5 0.4
TEVVGISSL 130 138 9 HLA-B*40:01 10.2 0.1
        HLA-B*40:02 46.89 0.2

Table 2: list of epitopes that had binding affinity to MHC Class I alleles;*Proposed epitopes. ANN_ic50*the half maximal inhibitory concentration (IC50) is a measure of the effectiveness for successful binding of peptide to MHC molecule by the Artificial Neural Network method.

immunome-research-Position-interactive

Figure 7: Position and interactive visualization of proposed conserved B cell epitopes in structural level of vp1 major capsid protein of MCPYV; Modeling was done using (chimera 1.8) software.

immunome-research-interactive-visualization

Figure 8: Position and interactive visualization of proposed conserved T cell epitopes that interact with MHC I in structural level of vp1 of MCPYV. Modeling was done using (chimera 1.8) software.

MHC class II binding prediction

As in MHC I, the protein subjected to MHC- II binding prediction tool using NN-align method.156 predicted epitopes were found to interact with MHC II different alleles. The peptides that have higher affinity are listed below in the Table 3 and Figure 9.

Epitope Start End Allele Peptide IC50 Percentile Rank
IELYLNPRM 55 63 HLA-DPA1*02:01/DPB1*01:01 TQIELYLNPRMGVNS 512.2 31.11
        IELYLNPRMGVNSPD 722 36.84
      HLA-DQA1*01:01/DQB1*05:01 ITQIELYLNPRMGVN 188.6 4.11
        SITQIELYLNPRMGV 202 4.37
        TQIELYLNPRMGVNS 220.7 4.72
        QIELYLNPRMGVNSP 254.1 5.32
        DSITQIELYLNPRMG 284.2 5.83
        EDSITQIELYLNPRM 352.1 6.93
        IELYLNPRMGVNSPD 501.6 9.06
      HLA-DRB1*01:01 DSITQIELYLNPRMG 154 36.59
        EDSITQIELYLNPRM 276.9 46.71
      HLA-DRB1*04:01 SITQIELYLNPRMGV 67.6 5.42
        DSITQIELYLNPRMG 73.9 5.97
        EDSITQIELYLNPRM 75.6 6.11
        ITQIELYLNPRMGVN 76.8 6.22
        TQIELYLNPRMGVNS 160.6 12.51
        QIELYLNPRMGVNSP 230.4 16.76
        IELYLNPRMGVNSPD 361.6 23.28
      HLA-DRB1*04:05 SITQIELYLNPRMGV 52.7 5.15
        EDSITQIELYLNPRM 56.4 5.55
        ITQIELYLNPRMGVN 56.9 5.6
        DSITQIELYLNPRMG 59 5.82
        TQIELYLNPRMGVNS 94.9 9.19
        QIELYLNPRMGVNSP 258.6 19.74
        IELYLNPRMGVNSPD 344.3 23.65
      HLA-DRB1*04:04 QIELYLNPRMGVNSP 507.2 34.95
      HLA-DRB1*07:01 SITQIELYLNPRMGV 845.5 41.52
      HLA-DRB1*09:01 ITQIELYLNPRMGVN 178.3 11.77
        TQIELYLNPRMGVNS 187.3 12.25
        SITQIELYLNPRMGV 232.8 14.6
        DSITQIELYLNPRMG 558 27.41
        EDSITQIELYLNPRM 674.9 30.92
      HLA-DRB1*13:02 DSITQIELYLNPRMG 290.8 12.48
        EDSITQIELYLNPRM 296.5 12.63
      HLA-DRB1*15:01 SITQIELYLNPRMGV 12.5 0.66
        ITQIELYLNPRMGVN 12.6 0.67
        TQIELYLNPRMGVNS 14.4 0.85
        EDSITQIELYLNPRM 15.4 0.97
        DSITQIELYLNPRMG 15.6 1
        QIELYLNPRMGVNSP 18.8 1.37
        IELYLNPRMGVNSPD 74.9 7.66
      HLA-DRB4*01:01 ITQIELYLNPRMGVN 304.7 19.69
        SITQIELYLNPRMGV 328.5 20.75
        DSITQIELYLNPRMG 347.6 21.58
        TQIELYLNPRMGVNS 373 22.64
        QIELYLNPRMGVNSP 757.7 35.01
        IELYLNPRMGVNSPD 847.4 37.22
      HLA-DRB5*01:01 ITQIELYLNPRMGVN 444.5 31.48
        SITQIELYLNPRMGV 548.9 34.33
        DSITQIELYLNPRMG 803 39.84
ISSLINVHY 135 143 HLA-DRB1*01:01 VVGISSLINVHYWDM 36.6 17.63
        TEVVGISSLINVHYW 50.5 21.37
        EVVGISSLINVHYWD 61.2 23.74
        VGISSLINVHYWDMK 86.3 28.17
        GISSLINVHYWDMKR 165.3 37.72
      HLA-DRB1*04:01 VVGISSLINVHYWDM 91.8 7.45
        EVVGISSLINVHYWD 102 8.27
        TEVVGISSLINVHYW 104.1 8.44
        KTEVVGISSLINVHY 108.9 8.81
        VGISSLINVHYWDMK 144.7 11.42
        GISSLINVHYWDMKR 223.4 16.36
        ISSLINVHYWDMKRV 448.1 26.85
      HLA-DRB1*04:05 TEVVGISSLINVHYW 161 14.13
        EVVGISSLINVHYWD 178.7 15.28
        VVGISSLINVHYWDM 179.3 15.32
        KTEVVGISSLINVHY 190.4 16.01
        VGISSLINVHYWDMK 226.5 18.05
        GISSLINVHYWDMKR 354.6 24.09
        ISSLINVHYWDMKRV 717.1 35.32
      HLA-DRB1*04:04 GISSLINVHYWDMKR 109.6 12.87
        ISSLINVHYWDMKRV 298.3 26.2
      HLA-DRB1*07:01 KTEVVGISSLINVHY 55.7 9.44
        TEVVGISSLINVHYW 75.8 11.77
        EVVGISSLINVHYWD 124.7 16.31
      HLA-DRB1*08:02 VVGISSLINVHYWDM 549.4 13.36
      HLA-DRB1*09:01 VVGISSLINVHYWDM 647 30.09
        EVVGISSLINVHYWD 682.2 31.13
        KTEVVGISSLINVHY 699.1 31.57
        TEVVGISSLINVHYW 753.9 33.05
        VGISSLINVHYWDMK 774.1 33.53
      HLA-DRB1*11:01 VVGISSLINVHYWDM 77.7 11.7
        VGISSLINVHYWDMK 123 15.55
        EVVGISSLINVHYWD 139.6 16.72
        TEVVGISSLINVHYW 206.7 20.64
        GISSLINVHYWDMKR 208 20.71
        ISSLINVHYWDMKRV 282 24.04
        KTEVVGISSLINVHY 328.4 25.82
      HLA-DRB1*15:01 GISSLINVHYWDMKR 154.7 14.1
        KTEVVGISSLINVHY 178 15.63
        TEVVGISSLINVHYW 182.7 15.93
        EVVGISSLINVHYWD 188.3 16.28
        VGISSLINVHYWDMK 214.9 17.78
        VVGISSLINVHYWDM 222 18.16
      HLA-DRB4*01:01 TEVVGISSLINVHYW 198.6 14.23
        KTEVVGISSLINVHY 203.3 14.49
        EVVGISSLINVHYWD 352.5 21.78
      HLA-DRB5*01:01 VVGISSLINVHYWDM 342.9 28.19
        GISSLINVHYWDMKR 370 29.12
        VGISSLINVHYWDMK 389.5 29.77
        EVVGISSLINVHYWD 477.6 32.44
        TEVVGISSLINVHYW 496.9 32.98
INSLFSNLM 328 336 HLA-DPA1*02:01/DPB1*05:01 LINSLFSNLMPKVSG 986.1 17.6
      HLA-DQA1*05:01/DQB1*02:01 PVVNLINSLFSNLMP 605.8 13.35
        VNLINSLFSNLMPKV 684.9 14.9
        VVNLINSLFSNLMPK 687 14.94
        YPVVNLINSLFSNLM 803.7 17.1
      HLA-DQA1*05:01/DQB1*02:01 NLINSLFSNLMPKVS 985.7 20.22
      HLA-DRB1*01:01 VNLINSLFSNLMPKV 10.1 5.27
        NLINSLFSNLMPKVS 10.8 5.8
        YPVVNLINSLFSNLM 11.1 6.03
        VVNLINSLFSNLMPK 12.2 6.81
        LINSLFSNLMPKVSG 12.5 7.01
        PVVNLINSLFSNLMP 15.1 8.66
      HLA-DRB1*04:01 VNLINSLFSNLMPKV 12.6 0.37
        VVNLINSLFSNLMPK 15.1 0.56
        NLINSLFSNLMPKVS 15.7 0.6
        PVVNLINSLFSNLMP 20.8 1.04
        LINSLFSNLMPKVSG 21.2 1.08
        YPVVNLINSLFSNLM 26.8 1.6
        INSLFSNLMPKVSGQ 37.8 2.64
      HLA-DRB1*04:05 YPVVNLINSLFSNLM 13 0.53
        PVVNLINSLFSNLMP 17.1 0.95
        VVNLINSLFSNLMPK 19.1 1.18
        VNLINSLFSNLMPKV 19.7 1.25
        NLINSLFSNLMPKVS 27.3 2.16
        LINSLFSNLMPKVSG 45.3 4.33
        INSLFSNLMPKVSGQ 72 7.09
      HLA-DRB1*07:01 YPVVNLINSLFSNLM 11.4 1.98
        PVVNLINSLFSNLMP 16 3.01
        VVNLINSLFSNLMPK 23.4 4.52
        VNLINSLFSNLMPKV 26.9 5.16
        NLINSLFSNLMPKVS 41.7 7.47
        LINSLFSNLMPKVSG 62.6 10.25
        INSLFSNLMPKVSGQ 102.1 14.38
      HLA-DRB1*09:01 NLINSLFSNLMPKVS 68.6 4.65
        LINSLFSNLMPKVSG 78.9 5.43
        VNLINSLFSNLMPKV 79.8 5.5
        VVNLINSLFSNLMPK 104.3 7.19
        PVVNLINSLFSNLMP 148 9.98
        YPVVNLINSLFSNLM 159.9 10.66
      HLA-DRB1*11:01 VNLINSLFSNLMPKV 139.4 16.7
        VVNLINSLFSNLMPK 256.6 22.99
      HLA-DRB1*15:01 PVVNLINSLFSNLMP 164.5 14.76
        VVNLINSLFSNLMPK 177.2 15.59
      HLA-DRB4*01:01 YPVVNLINSLFSNLM 72.5 5.63
        PVVNLINSLFSNLMP 73.2 5.69
        VVNLINSLFSNLMPK 83.5 6.51
        VNLINSLFSNLMPKV 92.3 7.19
        NLINSLFSNLMPKVS 149.5 11.22
        LINSLFSNLMPKVSG 179.5 13.11
        INSLFSNLMPKVSGQ 203.6 14.51
      HLA-DRB5*01:01 VNLINSLFSNLMPKV 12.4 3.03
        VVNLINSLFSNLMPK 16.5 4.08
        PVVNLINSLFSNLMP 21.8 5.28
        NLINSLFSNLMPKVS 21.8 5.28
        LINSLFSNLMPKVSG 36 7.92
        INSLFSNLMPKVSGQ 63.6 11.69

Table 3: List of the proposed epitopes that had binding affinity to MHC Class II alleles.

immunome-research-proposed-conserved

Figure 9: Position and visualization of proposed conserved T cell epitopes that interact with MHC 1I in structural level of vp1 protein of MCPYV. (Modeling was done using (chimera 1.8) software.

Population coverage analysis

Epitopes that are predicted to interact with MHC-I and II alleles were selected for population coverage analysis. The results of population coverage of all epitopes that bind to MHC I & II in the world are listed in (Tables 4 and 5) respectively. The proposed epitopes with their coverage results are shown in Table 6.

Epitope Coverageclass I Total HLA hits
 APKRKASST 12.78% 1
ASVPKLLVK 15.53% 1
AYSVARVSL 3.04% 1
DSITQIELY 5.82% 1
DTLQMWEAI 4.61% 1
EAISVKTEV 2.50% 1
EVVGISSLI 8.25% 2
FSNTLTTVL 7.04% 2
GVNYHMFAI 6.51% 2
ISSLINVHY 3.42% 1
ITCDTLQMW 7.26% 2
KENLPAYSV 10.93% 2
KRKASSTCK 3.89% 1
LLVKGGVEV 40.60% 2
LPRYFNVTL 20.62% 2
LQMWEAISV 42.23% 3
MPKVSGQPM 29.99% 3
NEDITCDTL 7.81% 1
NPYPVVNLI 9.87% 2
NVHYWDMKR 11.03% 2
QMWEAISVK 16.81% 1
RVHDYGAGI 3.89% 1
RYFNVTLRK 23.91% 3
RYYGSIQTG 3.04% 1
SKNENSRYY 15.52% 1
SLFSNLMPK 38.86% 4
SSLINVHYW 7.26% 2
SVARVSLPM 29.93% 4
TEVVGISSL 11.13% 2
Epitope set 94.16%  

Table 4: Population coverage of all epitopes in MHC class I.

Epitope Coverage class II Total HLA hits Epitope Coverage class II Total HLA hits
KRKASSTCK 0.00% 1 GAGIPVSGV 0.00% 2
VPKLLVKGG 10.54% 2 AGIPVSGVN 0.00% 1
PKLLVKGGV 0.00% 1 IPVSGVNYH 18.41% 2
KLLVKGGVE 18.23% 1 PVSGVNYHM 17.82% 2
LLVKGGVEV 28.79% 3 VSGVNYHMF 18.23% 3
LVKGGVEVL 34.26% 7 SGVNYHMFA 27.90% 3
VKGGVEVLS 6.40% 3 VNYHMFAIG 0.00% 1
KGGVEVLSV 0.00% 1 NYHMFAIGG 4.77% 2
GGVEVLSVV 0.00% 1 YHMFAIGGE 24.10% 4
GVEVLSVVT 18.23% 1 HMFAIGGEP 9.32% 5
VEVLSVVTG 18.15% 4 MFAIGGEPL 0.00% 1
EVLSVVTGE 0.00% 2 FAIGGEPLD 56.92% 10
VLSVVTGED 3.02% 1 AIGGEPLDL 0.00% 1
LSVVTGEDS 18.23% 3 IGGEPLDLQ 0.00% 3
VVTGEDSIT 0.00% 1 GEPLDLQGL 0.00% 2
VTGEDSITQ 27.97% 2 PLDLQGLVL 0.00% 2
GEDSITQIE 0.00% 1 QGLVLDYQT 11.53% 1
EDSITQIEL 0.00% 2 TTNGGPITI 0.00% 1
DSITQIELY 0.00% 4 TNGGPITIE 0.00% 2
SITQIELYL 28.79% 5 LGRKMTPKN 4.77% 1
ITQIELYLN 20.57% 4 GRKMTPKNQ 21.43% 2
TQIELYLNP 4.77% 1 NQGLDPQAK 11.53% 1
IELYLNPRM* 65.84% 12 LDPQAKAKL 18.41% 1
WYTYTYDLQ 22.06% 6 NSRYYGSIQ 18.41% 1
YTYTYDLQP 18.47% 5 SRYYGSIQT 4.77% 1
YTYDLQPKG 26.80% 5 RYYGSIQTG 0.00% 1
TYDLQPKGS 11.53% 1 YYGSIQTGS 29.38% 6
YDLQPKGSS 10.54% 1 YGSIQTGSQ 22.06% 3
PKGSSPDQP 0.00% 1 VLQFSNTLT 59.12% 11
KGSSPDQPI 18.23% 1 LQFSNTLTT 42.33% 10
IKENLPAYS 57.34% 10 QFSNTLTTV 0.00% 1
KENLPAYSV 11.53% 1 FSNTLTTVL 50.51% 11
NLPAYSVAR 18.23% 2 SNTLTTVLL 11.53% 1
LPAYSVARV 30.29% 6 NTLTTVLLD 0.00% 2
PAYSVARVS 0.00% 1 TLTTVLLDE 0.00% 5
AYSVARVSL 27.73% 5 LTTVLLDEN 7.71% 4
YSVARVSLP 28.85% 5 TVLLDENGV 11.53% 1
SVARVSLPM 34.78% 2 VLLDENGVG 27.97% 3
VARVSLPML 19.66% 8 LDENGVGPL 6.69% 1
ARVSLPMLN 10.54% 4 ENGVGPLCK 0.00% 2
RVSLPMLNE 3.02% 1 LCKGDGLFI 61.09% 6
VSLPMLNED 4.77% 2 CKGDGLFIS 0.00% 1
SLPMLNEDI 14.37% 4 GDGLFISCA 0.00% 2
LPMLNEDIT 4.77% 2 IVGFLFKTS 17.84% 6
LNEDITCDT 17.84% 2 VGFLFKTSG 36.31% 6
EDITCDTLQ 0.00% 2 GFLFKTSGK 11.21% 2
DITCDTLQM 0.00% 3 ALHGLPRYF 15.05% 4
ITCDTLQMW 27.97% 8 LHGLPRYFN 24.10% 3
TCDTLQMWE 0.00% 1 HGLPRYFNV 11.53% 1
CDTLQMWEA 11.53% 2 LPRYFNVTL 43.71% 8
DTLQMWEAI 0.00% 2 PRYFNVTLR 4.77% 2
TLQMWEAIS 7.04% 3 RYFNVTLRK 0.00% 2
LQMWEAISV 52.19% 8 WVKNPYPVV 49.39% 7
MWEAISVKT 43.78% 4 KNPYPVVNL 20.95% 2
WEAISVKTE 16.52% 7 PYPVVNLIN 3.02% 1
AISVKTEVV 33.10% 5 YPVVNLINS 7.04% 3
SVKTEVVGI 34.26% 3 PVVNLINSL 11.30% 2
VKTEVVGIS 2.33% 2 VVNLINSLF 41.67% 8
TEVVGISSL 0.00% 3 VNLINSLFS 38.62% 7
EVVGISSLI 44.03% 4 NLINSLFSN 0.00% 4
VVGISSLIN 45.82% 10 LINSLFSNL 35.36% 9
VGISSLINV 11.30% 4 INSLFSNLM* 65.37% 11
GISSLINVH 0.00% 1 NSLFSNLMP 4.77% 2
ISSLINVHY* 69.46% 11 SLFSNLMPK 0.00% 1
SSLINVHYW 0.00% 1 LFSNLMPKV 41.13% 10
SLINVHYWD 0.00% 2 FSNLMPKVS 25.65% 4
LINVHYWDM 34.26% 8 NLMPKVSGQ 0.00% 1
INVHYWDMK 4.77% 4 LMPKVSGQP 0.00% 1
NVHYWDMKR 18.41% 2 MPKVSGQPM 13.72% 3
VHYWDMKRV 28.79% 5 KVSGQPMEG 0.00% 1
HYWDMKRVH 10.54% 1 EEVRIYEGS 0.00% 1
YWDMKRVHD 4.77% 2 PDIVRFLDK 0.00% 1
WDMKRVHDY 11.53% 1 IVRFLDKFG 20.57% 6
RVHDYGAGI 29.99% 4 VRFLDKFGQ 10.54% 3
VHDYGAGIP 0.00% 1 RFLDKFGQE 0.00% 2
HDYGAGIPV 44.03% 4 FLDKFGQEK 11.53% 2
DYGAGIPVS 0.00% 4 LDKFGQEKT 18.41% 1
YGAGIPVSG 27.70% 4 FGQEKTVYP 26.27% 3
Epitope set 81.94%        

Table 5: Population coverage of all epitopes in MHC class II.

 
Epitope Coverage Class I Total HLA hits Epitope Coverage Class II Total HLA hits
LLVKGGVEV 40.60% 2 IELYLNPRM 65.84% 12
LQMWEAISV 42.23% 3 ISSLINVHY 69.46% 11
SLFSNLMPK 38.86% 4 INSLFSNLM 65.37% 11
Epitope set 70.30%   Epitope set 73.11%  

Table 6: Population coverage of proposed epitopes for both MHC class I and II in the world.

Allergenicity test

The proposed B cell epitopes & those bind with different set of MHC I and II alleles were subjected to AllerTOP 2.0 software to avoid production of IgE antibodies as possible. The results are listed in Table 7.

B cell epitopes Result MHC class I epitopes Result MHC class II epitopes Result
QEKTVYP probable allergen LLVKGGVEV Probable allergen IELYLNPM Probable Non allergen
KTVYPK probable allergen LQMWEAISV Probable allergen ISSLINVHY Probable allergen
QEKTVY probable non-allergen SLFSNLMPK Probable Non-allergen INSLFSNM Probable allergen

Table 7: Result of Allergenicity Test of predicted B cell and MHC class I & II epitopes.

Discussion

In the current study we have successfully predicted a promiscuous epitopes for designing subunit based vaccine. The immune system appears to be playing a critical role in MCC biology with increasing evidence of virus-specific cellular and humoral immune responses that influence the prognosis of MCC patients. Newer strategies are currently being used to treat cancer, among these peptide vaccines which serve as a promising anticancer candidates as they target tumor cell and induce specific T cell response to tumor cell [1,13,46-48].

To best of our knowledge there is no effective approved vaccine against this virus, however previously a DNA vaccine encoding large or small T antigen as well as VP1 virus like particles have been developed. These type of vaccine have been shown to possess a protective specific CD4+\CD8+ T cell response in vaccinated mice, despite that subunit vaccine production which target a specific immunogenic protein would be helpful in generating adequate immune response inside the host body. Furthermore, a murine model expressing tumor cell line from B 16 mouse melanoma was created by Gomez et al. would be useful in clinical setting to address the efficacy of our predicted vaccine [17,24,49-51].

Peptide Vaccination produces profound and long lasting modifications in the adaptive immune system comprising T and B cells. Peptide vaccines are intrinsically safer than alternative vaccine formulations. Moreover, they will allow focusing solely on relevant epitopes, avoiding those that lead to non-protective responses [48]. Currently, there is an increasing interest in developing vaccines based on synthetic peptides. Peptide vaccines under various phases of trial and development, the vast majority of them related to cancers. [52-64].

In the present study we choose our predicted epitopes to be effective peptide antigens for both B and T cells. We selected 100% conserved sequence identity to VP1 major capsid protein. Several studies revealed its ability to induce potential immune response in MCC positive tumors [11,13].

In our case we choose our predicted B cell epitopes to be potential and and strong immunogenic peptide antigens for B cell, the length of the predicted epitopes ranged from 3 to 23 amino acids. According to Linear B cell epitope prediction tool available from IEDB these epitopes were found to be above the threshold scores in Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity, were analyzed based on methods of the IEDB. Epitopes illustrated in Table 1, are the only conserved regions among all retrieved strains of MCPYV vp1 protein that have been reported in NCBI database until 20th October 2016 and have high probability of activating humoral immune response. However, epitope QEKTVYP* from 377 to 384 was found to have the highest score, followed by KTVYPK from 380 to 385 and QEKTVY from 379 to 383 as summarized in Table 1. These findings indicated that these epitopes are surface accessible and antigenic.

Studies have shown T-cells to be important mediators of MCPyV-specific immune Surveillance thus, T cell epitope prediction was performed based on the probability of MHC-peptide ligand formation and presentation to different T cell populations [65,66]. In the development of universal vaccine, capable of inducing adequate immune response against all circulating strains, alleles binding affinity and accurate characterization of population coverage are highly recommended. A total of 29 conserved peptides in MHC class 1 were selected to bind to multiple HLA alleles as shown in Table 2, among these LLVKGGVEV, LQMWEAISV and SLFSNLMPK have high binding affinity as well as high percentage coverage (HLA-A*02:01, HLA-A*02:06), (HLA-A*02:01, HLA-A*02:06 and HLA-B*39:01), (HLA-A*03:01, HLA-A*11:01, HLA-A*30:01 and HLA-A*68:01) respectively. While the highest scoring MHC class II were (IELYLNPRM), (ISSLINVHY) and (INSLFSNLM) as shown in Table 3. Moreover, epitope LLVKGGVEV and LQMWEAISV has successfully predicted to interact with HLA-A*02:01 the most prevalent major histocompatibility complex (MHC) class I allele family in humans, presenting at high frequencies in all ethnic populations. Interestingly, MHC I epitope SLFSNLMPK has succeded to elicit MHC II response as seen in Table 3. These epitopes were found to successfully bind to several HLA-D, P, and Q alleles indicating that further attention need to be targeted to this region. Furthermore 225NYPIEVWCPDPSK237 and 245GSIKTGSQTPTVL257 were suggested before by Iyer et al. [67]. The later (GSIQTGSQ) has successfully interacted with HLADRB1* 01:01 which provides instructions for making a protein that plays a critical role in the immune system.

Allergic reactions are triggered when allergens cross-link preformed IgE bound to the high-affinity receptor FcεRI on mast cells. So mast cells act as alert the immune system to local infection [68]. Responses to allergens in humans are very heterogeneous and involve recognition of a large number of epitopes [69]. Thus; we subjected predicted B and cells to allergenicity test, among the 9 predicted epitopes it was concluded that two of them (SLFSNLMPK and IELYLNPRM) have the potential to be real epitopes, in MHC1 and MHCII respectively as their probable non allergic effect. On the other hand epitopes that are predicted to activate B cell KTVYPK and QEKTVYP were found to have low potential to be a real epitope, as their probable allergic effect which needs further experimental investigation.

Conclusion

The increasing incidence of human viral infections warrants the design of innovative treatment. With the recent advances in the field of bioinformatics, newer strategies are being devised to control and fight infectious diseases [70].

Merkel cell carcinoma is an aggressive devastating disease that warrants the need of developing effective protective vaccine. Several epitopes were proposed in this study especially SLFSNLMPK that successfully bind with high affinity to both MHC classes. In addition to (GSIQTGSQ) that is suggested before by lyer et al. as adoptive immunotherapy [67]. Further in vitro and in vivo studies will need to be undertaken in order to confirm the effectiveness of these predicted epitopes as peptide vaccine.

Acknowledgment

We would like to thank the member of waves for medical research and training center.

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

Article Usage

  • Total views: 285
  • [From(publication date):
    June-2017 - Jul 22, 2017]
  • Breakdown by view type
  • HTML page views : 222
  • PDF downloads :63
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

agrifoodaquavet@omicsonline.com

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

clinical_biochem@omicsonline.com

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

business@omicsonline.com

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

chemicaleng_chemistry@omicsonline.com

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

environmentalsci@omicsonline.com

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

engineering@omicsonline.com

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

generalsci_healthcare@omicsonline.com

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

genetics_molbio@omicsonline.com

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

immuno_microbio@omicsonline.com

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

omics@omicsonline.com

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

materialsci@omicsonline.com

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

mathematics_physics@omicsonline.com

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

medical@omicsonline.com

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

neuro_psychology@omicsonline.com

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

pharma@omicsonline.com

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

social_politicalsci@omicsonline.com

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version