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Advances in Bioinformatics logoLink to Advances in Bioinformatics
. 2019 Mar 18;2019:1270485. doi: 10.1155/2019/1270485

Immunoinformatics Approach for Multiepitopes Vaccine Prediction against Glycoprotein B of Avian Infectious Laryngotracheitis Virus

Sumaia A Ali 1,2,, Yassir A Almofti 1, Khoubieb A Abd-elrahman 3
PMCID: PMC6442309  PMID: 31011331

Abstract

Infectious laryngotracheitis virus (ILTV) is a gallid herpesvirus type 1, a member of the genus Iltovirus. It causes an infection in the upper respiratory tract mainly trachea which results in significant economic losses in the poultry industry worldwide. Vaccination against ILTV produced latent infected carriers' birds, which become a source of virus transmission to nonvaccinated flocks. Thus this study aimed to design safe multiepitopes vaccine against glycoprotein B of ILT virus using immunoinformatic tools. Forty-four sequences of complete envelope glycoprotein B were retrieved from GenBank of National Center for Biotechnology Information (NCBI) and aligned for conservancy by multiple sequence alignment (MSA). Immune Epitope Database (IEDB) analysis resources were used to predict and analyze candidate epitopes that could act as a promising peptide vaccine. For B cell epitopes, thirty-one linear epitopes were predicted using Bepipred. However eight epitopes were found to be on both surface and antigenic epitopes using Emini surface accessibility and antigenicity, respectively. Three epitopes (190KKLP193, 386YSSTHVRS393, and 317KESV320) were proposed as B cell epitopes. For T cells several epitopes were interacted with MHC class I with high affinity and specificity, but the best recognized epitopes were 118YVFNVTLYY126, 335VSYKNSYHF343, and 622YLLYEDYTF630. MHC-II binding epitopes, 301FLTDEQFTI309,277FLEIANYQV285, and 743IASFLSNPF751, were proposed as promising epitopes due to their high affinity for MHC-II molecules. Moreover the docked ligand epitopes from MHC-1 molecule exhibited high binding affinity with the receptors; BF chicken alleles (BF2 2101 and 0401) expressed by the lower global energy of the molecules. In this study nine epitopes were predicted as promising vaccine candidate against ILTV. In vivo and in vitro studies are required to support the effectiveness of these predicted epitopes as a multipeptide vaccine through clinical trials.

1. Introduction

Infectious laryngotracheitis (ILT) is classified as a gallid herpesvirus 1 which belongs to the fay Herpesviridae, genus Iltovirus [13]. The virus is included within List E of the Office International des Epizooties (OIE). It causes a major viral respiratory disease of chicken [2]. The disease causes marked economic losses of poultry industry with mortality reaching 70%, especially in high- density poultry-producing regions [3]. High mortality, demonstrated by the severe form of the disease, is a result of severe tracheal lesions in the respiratory tract, significant respiratory distress, expectoration of bloody sputum, sneezing and persistent nasal discharge decreased egg production, weight loss, and susceptibility to infections with other pathogens [4]. The mild form exhibited low mortality, mucoid tracheitis, and sinusitis [5, 6].

Vaccination against the viral diseases is very important for protection, due to the lack of appropriate antiviral drugs, high cost, and time consuming of development of new antiviral drugs. Different types of vaccines are available for ILTV such as vaccines produced in chicken-embryo-origin (CEO), tissue-culture-origin (TCO), and recombinant vaccines [5]. However, vaccination against ILTV is recommended only in endemic areas to prevent transmission of the virus by latent infected carriers' birds to nonvaccinated healthy flocks [1, 7, 8]. Moreover current vaccines are themselves mildly pathogenic and modified live ILT vaccines increase the virulence of the disease by mutation during bird-to-bird passage in the field [2, 9, 10]. DNA encoding glycoprotein B vaccine was found to give levels of protection when given intramuscularly comparable to traditional live-attenuated ILTV vaccines. Glycoprotein B and D genes of ILTV have been used to produce immunogenic proteins to elicit protective immune response. These glycoproteins which are located on the viral envelope and the surface of infected cells are required for viral attachment [1]. Developing of new drugs to treat viral diseases is very expensive and time consuming. Therefore, vaccines remain the best choice to protect animals and humans from viruses and other pathogens. In addition traditional techniques of live-attenuated or inactivated vaccines have the risk of allergic reactions. Peptide vaccines are economically reasonable, require less time for development, and hold the promise of multivalent dosages [1113]. Recently, bioinformatics software has been used largely to design synthetic peptide vaccines, based on B and T cell responses [14]. The design of multipeptide vaccines using computational model that links various immunoinformatic prediction tools is known to produce satisfactory results [15, 16]. The safety, accuracy, feasibility, and speed of these vaccines were well discussed through various computational studies [17, 18]. Thus, it is essential to design safe effective vaccine against ILTV that prevents birds from being carriers of the disease using bioinformatics tools. The aim of the present study was to design a vaccine for ILT virus using peptides predicted from glycoproteins especially type B as an immunogen to stimulate protective immune response. The reason of selecting glycoprotein B as a target is due to its function in host attachment and in stimulating immune response in the host.

2. Materials and Methods

2.1. Protein Sequence Retrieval and Phylogeny

Forty-four envelope glycoprotein B (GB) sequences of virulent isolates of ILTV were retrieved from GenBank of National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) in August 2017. The strains of the virus were isolated from chicken from different geographic regions. Complete sequences of all gene subtypes were selected for various immune-bioinformatics analysis. Retrieved strains and their accession numbers and geographical regions are listed in Table 1.

Table 1.

Retrieved strains of ILTV with their date of collection, accession numbers, and geographical regions.

Accession No country Year Accession No country Year
YP_182356.1 USA 2005 AEW67850.1 USA 2012
AFV79628.1 China 2011 AEW67771.1 USA 2011
ABX59525.1 ''USA 2007 AEB97319.1 Australia 2010
ABX59524.1 USA 2007 ABX59533.1 USA 2007
ANB43607.1 Russia 2000 ABX59532.1 USA 2007
ANF04484.1 Australia 2015 ABX59531.1 USA 2007
ANN24991.1 USA 2017 ABX59530.1 USA 2007
ANN24921.1 USA 2016 ABX59529.1 USA 2007
AJR27653.1 Italy 2007 ABX59528.1 USA 2007
AJR27811.1 Italy 1980 ABX59527.1 USA 2007
AJR27732.1 Italy 2011 ABX59526.1 USA 2007
AJR27574.1 Italy 2015 ABX59523.1 USA 2007
AJR27495.1 Italy 2015 ABX59522.1 USA 2007
AER28131.1 Australia 2011 ABX59521.1 USA 2007
AER28052.1 Australia 2011 ABX59520.1 USA 2007
AGN48336.1 China 2012 ABX59519.1 USA 2007
AGN48256.1 China 2012 ABX59518.1 USA 2007
AGN48178.1 China 2009 ABX59517.1 USA 2007
AGC23137.1 Australia 1970 ABX59516.1 USA 2007
AGC23058.1 Australia 1999 ABX59515.1 USA 2007
AFN02008.1 Australia 2011 ABX59514.1 USA 2007
AFN01929.1 Australia 2011 ABX59513.1 USA 2007

Refseq of ILTV envelope glycoprotein B.

2.2. Phylogenetic Evolution

Phylogenetic tree of the retrieved sequences of glycoprotein B of ILTV was created using phylogeny.fr online software (http://phylogeny.lirmm.fr/phylo_cgi/index.cgi) [19].

2.3. Multiple Sequence Alignment

The retrieved sequences of ILTV glycoprotein B (GB) were subjected to multiple sequence alignments (MSA) to obtain the conserve regions. This was performed using BioEdit software version 7.2.5 with the aid of ClustalW as applied in the BioEdit program to construct the alignment [20].

2.4. Sequence-Based Method

The reference sequence (YP_182356.1) of ILT virus glycoprotein B (GB) was submitted to different prediction tools at the Immune Epitope Database analysis resource (http://www.iedb.org/). Epitope analysis resources were used to predict B and T cell epitopes [21]. Predicted epitopes were then investigated in aligned retrieved GB sequences after MSA for conservancy. Conserved epitopes would be considered as candidate epitopes for B and T cells.

2.4.1. B Cell Epitope Prediction

Identification of the surface accessibility, hydrophobicity, flexibility, and antigenicity was performed by analyzing candidate epitopes using several B cell prediction methods from Immune Epitope Database (http://tools.iedb.org/bcell/). BepiPred linear epitope prediction from Immune Epitope Database (http://tools.iedb.org/bcell/result/) was used to predict linear B cell epitopes with default threshold -.012 [22]. Emini surface accessibility prediction tool of IEDB was performed to detect the surface accessible epitopes with default threshold 1.000 [23], while the prediction of epitopes antigenicity sites of candidate epitopes was achieved to identify the antigenic sites using Kolaskar and Tongaonker antigenicity method (http://tools.immuneepitope.org/bcell/) with default threshold 1.027 [24]. The thresholds of these methods are demonstrated in Figure 1.

Figure 1.

Figure 1

Prediction of B cell epitopes using (a) Bepipred linear epitope, (b) Emini surface accessibility, and (c) Kolaskar and Tongaonkar Antigenicity methods. Yellow areas above the threshold (red line) are suggested to be a part of B cell epitope, while green areas are not.

2.4.2. T Cell Epitope Prediction

(1) Cytotoxic T-Lymphocyte Epitopes Prediction and Interaction with MHC- I. The major histocompatibility complex-1 (MHC class I) binding prediction tool (http://tools.iedb.org/mhci/) was used to predict Cytotoxic T cell epitopes [25]. Analysis was achieved using human HLA alleles, due to the lack of chicken alleles in IEDB data set. Artificial neural network (ANN) was used to predict the binding affinity [26, 27]. Peptide length for all selected epitopes was set to 9 amino acids (mers). The half-maximal inhibitory concentration (IC50) values required for the peptide's binding to the specific MHC-I molecules were set less than or equal to 300 nM.

(2) Prediction of T Cell Epitopes and Interaction with MHC Class II. The MHC class II binding prediction tool (http://tools.iedb.org/mhcii/) was used to predict T cell epitopes. IC50 for strong binding peptides was set less than 1000 to determine the interaction potentials of T cell epitopes and MHC class II allele (HLA DR, DP and DQ). Human MHC class I and II alleles were used in this study due to the difficulty to determine MHC B complex alleles in poultry. NN-align method was also used with IC50 less than or equal to 1000 nM [28]. Peptides with low IC50 value were proposed to be promising MHC-II epitopes.

2.5. Homology Modeling

2.5.1. Structural Prediction of the Reference Sequence of ILTV Glycoprotein B

Homology modeling was used for constructing the three-dimensional (3D) structure of the reference sequence of ILTV glycoprotein B. Raptor X structure prediction server (http://raptorx.uchicago.edu/StructurePrediction/predict/) was used for this purpose. The 3D structure was then treated with Chimera software 1.8 to display the position of proposed epitopes [2932].

2.5.2. Structure of BF Chicken Alleles

Protein sequence and PDB ID of chicken alleles (BF22101 & BF20401) were retrieved from the NCBI database/ (PDB: 4D0C, CAK54661.1 and PDB: 4D0C CAK54660.1) and submitted to Raptor X server (http://raptorx.uchicago.edu/) for homology modeling. Chimera software was used to display 3D structure of BF alleles [2932].

2.5.3. Structure of Predicted Epitopes

The homology modeling of the MHCI predicted peptides was performed with PEP FOLD3 (http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3/) to predict the linear structures from amino acid sequences [3335].

2.6. Molecular Docking

Molecular docking was performed according to peptide-binding groove affinity, between chicken BF alleles (BF22101 & BF20401) and the proposed peptides from MHCI. Chicken BF alleles were set as receptors and the proposed peptides were set as ligands. Molecular docking technique of 3D structure of BF alleles and 3D modeled epitopes was performed using PatchDock online autodock tools; an automatic server for molecular docking (https://bioinfo3d.cs.tau.ac.il/PatchDock/) by submitting PDB of ligands and receptors after homology modeling by Raptor X server and PEP FOLD3 [36, 37]. Firedock was used to select the best models [38]. Visualization of the result was performed off-line using UCSF-Chimera visualization tool 1.8. [29].

3. Results and Discussion

In the vaccine industry, presenting a specific antigen or a host of antigens to the immune system is necessary to increase immunity against viral diseases. The functional component of the vaccine should be able to stimulate the immune system, by using vaccines containing intact inactive components (attenuated viruses; purified immunogenic parts of the pathogen) to trigger immune response [27, 39]. It is known that the use of whole viral proteins to induce an immune response is not necessarily but small portions of protein called antigenic determinants or adhesive epitopes can be used to stimulate the desired immunity [40].

The use of bioinformatics analyses is an applicable method for predicting and designing new multiepitope vaccines against animals' infectious diseases as well as chickens [17, 41, 42]. This is the first in silico study to design peptide vaccine against avian ILTV through humoral and cell mediated immune responses. The expected epitopes in this study could help in prevention of latent infection caused by the use of attenuated vaccines and developing more effective and trustable prophylactic and therapeutic vaccines than conventional methods.

3.1. Sequences Alignment

Alignment of all retrieved sequences using ClustalW through BioEdit software showed high conservancy between the aligned sequences. As shown in Figure 2, the conserved regions were recognized by identity and similarity of amino acid sequences.

Figure 2.

Figure 2

Multiple sequence alignment (MSA) of the retrieved strains using BioEdit software and ClustalW. Dots indicated the conservancy and letters in cubes showed the alteration in amino acid.

3.2. Phylogenetic Evolution

Phylogenetic tree was created using (http://www.phylogeny.fr). The evolutionary divergence analysis of the enveloped glycoprotein B of the different strains of ILTV is presented in Figure 3.

Figure 3.

Figure 3

Evolutionary divergence analysis of enveloped glycoprotein B (GB) of different strains of ILTV.

3.3. Prediction of B Cell Epitopes

Surface accessibility, hydrophilicity, flexibility, and antigenicity are important B cell antigenic indexes to design peptide vaccine. Investigation of ILTV glycoprotein B using different prediction methods of B cell at the Immune Epitope Database (IEDB) revealed varying threshold for different scales (see Figure 1). Thirty-one unique linear epitopes with 4 peptides or more in length were predicted using Bepipred Linear Epitope Prediction method depending on binding affinity to B lymphocytes. Analysis of these epitopes for surface accessibility and antigenicity proposed seventeen and thirteen peptides works as surface and antigenic epitopes, respectively (see Table 2). The predicted epitopes were found of high conservancy when tested in aligned sequences. Of these, only eight epitopes successfully covered all the antigenic indexes of B cell prediction tests. The best B cell predicted epitopes that overlap all B cell prediction methods were 190KKLP193, 386YSSTHVRS393, and 317KESV320. The 3D structure of these predicted epitopes is shown in Figure 4.

Table 2.

List of B cell epitopes predicted by different B cell scales.

No. Peptide Start End Length Emini 1000 Antigenicity
1.027
1 QFTI 306 309 4 0.514 1.042
2 GQPVS 518 522 5 0.678 1.07
3 KLNPNS 506 511 6 1.715 0.968
4 NASEIE 635 640 6 0.888 0.951
5 LGEVGKA 718 724 7 0.303 1.032
6 DAMEEKESV 312 320 9 1.355 0.959
7 KESV 317 320 4 1.168 1.044
8 EVPEAVRVS 328 336 9 0.395 0.959
9 VIRGDRGDA 697 705 9 0.433 0.981
10 PQITNEYVTR 141 150 10 1.433 1.009
11 EYVTR 146 150 5 1.465 1.035
12 TFSSGKQPFN 351 360 10 0.957 0.977
13 RSGECSSKATY 163 173 11 0.837 1.01
14 YDNDEAEKKLP 183 193 11 4.592 0.964
15 KKLP 190 193 4 1.729 1.044
16 KDEQKARRQKA 834 844 11 11.486 0.946
17 EAIGSGAPKEPQI 58 70 13 0.374 0.997
18 HCHRHADSTNMTE 93 105 13 0.786 0.986
19 CSSPTGASVARLAQ 77 90 14 0.103 1.072
20 YSSTHVRSGDIEYYL 386 400 15 0.537 1.052
21 YSSTHVRS 386 393 8 1.152 1.058
22 NFTKRHQTLGYRTSTS 211 226 16 2.246 0.976
23 RHQTLGY 215 221 7 1.232 1.027
24 SSSPESQFSANSTENH 569 584 16 1.851 0.973
25 VYTREELRDTGTLNYD 663 678 16 1.703 0.985
26 VYTREEL 663 669 7 1.176 1.04
27 VRDLETGQIRPPKKRNFL 285 302 18 1.191 1.001
28 QIRPP 292 296 5 1.463 1.034
29 FGMATGDTVEISPFYTKNTTGPRRHSV 245 271 27 0.185 0.991
30 EEAQRQNHLPRGRERRQAAGRRTASLQSGPQGDRITTHSS 423 462 40 8.232 0.969
31 QNHLP 428 432 5 1.243 1.042

Shortened peptide that has high score in both Emini and Kolaskar.

Figure 4.

Figure 4

(a) The reference glycoprotein B of ILTV. (b) The position of proposed B cell epitopes in the 3D structure of reference glycoprotein B of ILTV.

3.4. Prediction of T Cell Epitopes

CD8+ and CD4+ T cells have principal role in stimulation of immune response as well as antigen mediated clonal expression of B cell [14]. Several technical problems challenged the design peptide vaccine against ILTV based on T cytotoxic and T helper epitopes prediction, most importantly, the lack of online bioinformatics database for chicken MHC alleles. For this reason human MHC class I alleles (HLA-A and HLA-B) were used in this study as an alternate alleles to investigate the interaction of epitopes with MHCI using epitope prediction software [43]. Studies have shown that the MHC genes in chickens are classified into MHCI associated genes (B-F) and MHCII (B-L) associated B-G genes [44]. The B-F alleles in chicken were found to be similar in stimulation of immune system to mammalian class I homologs especially in presenting the antigen of T-lymphocyte [45, 46]. MHC class I molecule in the chicken especially BF22101 from the B21 haplotype is highly expressed, leading to strong genetic links with infectious pathogens. In addition, BF22101 from the B21 haplotype has principal role in provoking resistance to Marek's disease caused by an oncogenic herpesvirus [43], to which ILTV belongs.

3.4.1. Prediction of Epitopes Interacted with MHC Class I

MHC-1 binding prediction tool using IEDB database predicted sixteen epitopes that interacted with the cytotoxic T cell as they strongly linked with multiple alleles. As shown in Table 3, MHCI results expected several CTL epitopes. The top epitope was 118YVFNVTLYY126 which interacted and linked with 16 human alleles, followed by 335VSYKNSYHF343 and 622YLLYEDYTF630 as they linked with 9 human MHCI alleles. The 3D structure of the proposed epitopes is shown in Figure 5.

Table 3.

Position of most promising epitopes in the glycoprotein B of ILTV that bind with high affinity with the human MHC class I alleles.

Peptide Start End Allele ic50
MLICVCVAI 16 24 HLA-A02:01 19.5
HLA-A02:06 27.85
HLA-A32:01 181.4
HLA-A68:02 50.12
HLA-B15:01 209.67

YVFNVTLYY 118 126 HLA-A01:01 52.47
HLA-A03:01 31.62
HLA-A11:01 8.01
HLA-A25:01 245.85
HLA-A26:01 7.03
HLA-A29:02 2.26
HLA-A30:02 29.28
HLA-A68:01 10.69
HLA-B15:01 41.17
HLA-B35:01 8.03
HLA-B46:01 109.43
HLA-B58:01 103.63

LYYKHITTV 124 132 HLA-A23:01 176.88
HLA-A24:02 255.62
HLA-C06:02 228.43
HLA-C07:01 281.81
HLA-C12:03 40.39
HLA-C14:02 20.88

TTVTTWALF 130 138 HLA-A23:01 186.44
HLA-A24:02 291.18
HLA-A26:01 43.17
HLA-A29:02 140.22
HLA-B57:01 135.77
HLA-B58:01 126.76

MATGDTVEI 247 255 HLA-A02:06 298.31
HLA-A68:02 27.18
HLA-B35:01 70.95
HLA-B53:01 91.98
HLA-C03:03 22.67
HLA-C12:03 107.28

DTVEISPFY 251 259 HLA-A25:01 219.57
HLA-A26:01 2.97
HLA-A29:02 28.64
HLA-A68:01 41.47
HLA-B35:01 269.44

YRFLEIANY 275 283 HLA-B27:05 183.15
HLA-C06:02 157.29
HLA-C07:01 169.97
HLA-C07:02 281.69
HLA-C12:03 252.46
HLA-C14:02 290.98

VSYKNSYHF 335 343 HLA-A23:01 26.91
HLA-A24:02 283.17
HLA-A32:01 256.64
HLA-B15:01 45.93
HLA-B57:01 69.13
HLA-B58:01 18.43
HLA-C03:03 254.73
HLA-C12:03 111.22
HLA-C15:02 261.59

YKNSYHFSL 337 345 HLA-B08:01 212.76
HLA-B39:01 7.84
HLA-C03:03 17.48
HLA-C07:02 41.18
HLA-C14:02 249.85

HVRSGDIEY 390 398 HLA-A29:02 250.74
HLA-A30:01 43.1
HLA-B15:01 199.81
HLA-B15:02 295.16
HLA-B35:01 9.19

MSHGLAEMY 413 421 HLA-A29:02 33.03
HLA-A30:02 95.04
HLA-B15:01 263.61
HLA-B35:01 49.68
HLA-B57:01 132.85
HLA-B58:01 228.7

FAYDKIQAH 470 478 HLA-B35:01 56.25
HLA-B46:01 189.03
HLA-C03:03 5.42
HLA-C12:03 9.1
HLA-C14:02 231.14

YLLYEDYTF 622 630 HLA-A02:01 215.94
HLA-A02:06 77.51
HLA-A23:01 31.7
HLA-A24:02 237.46
HLA-A29:02 60.11
HLA-A32:01 132.77
HLA-B15:01 163.87
HLA-B15:02 48.14
HLA-B35:01 62.28

VVMTAAAAV 728 736 HLA-A02:01 80.99
HLA-A02:06 10.14
HLA-A68:02 42.19
HLA-C14:02 60.62
HLA-C15:02 231.69

IASFLSNPF 743 751 HLA-A32:01 81.65
HLA-B15:01 94.19
HLA-B35:01 11.95
HLA-B58:01 219.61
HLA-C03:03 83.79
HLA-C12:03 189.81

FLSNPFAAL 746 754 HLA-A02:01 16.28
HLA-A02:06 8.88
HLA-A68:02 172.75
HLA-B15:01 128.57
HLA-B15:02 136.86
HLA-C03:03 3.25
HLA-C12:03 140.95
HLA-C14:02 85.38

KSNPVQVLF 778 786 HLA-A32:01 76.58
HLA-B15:01 283.12
HLA-B57:01 19.71
HLA-B58:01 2.62
HLA-C15:02 155.84

Proposed peptides.

Figure 5.

Figure 5

The 3D structure of reference glycoprotein B of ILTV and the position of proposed cytotoxic T cell epitopes suggested to interact with MHC-I virus illustrated by UCSF-Chimera visualization tool.

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

The reference strain of glycoprotein B was subjected to different analyzing methods using IEDB MHC-II binding prediction tool based on NN-align with half-maximal inhibitory concentration (IC50) ≤ 1000. The peptide (core) 301FLTDEQFTI309 exhibited high affinity to MHCII alleles due to great binding with 76 MHC-II alleles followed by 277FLEIANYQV285 and 743IASFLSNPF751 as they linked with 67 human alleles (Table 4 and Figure 6).

Table 4.

List of best six epitopes that bind with high affinity with the human MHC class II alleles.

Core Sequence Peptide Sequence Start End Allele IC50
LLRSTVSKA LPLVPSLLRSTVSKA 192 206 HLA-DQA101:02/DQB106:02 824.7
HLA-DRB101:01 99.4
HLA-DRB103:01 421.6
HLA-DRB104:01 300.9
HLA-DRB104:05 768.9
HLA-DRB107:01 155.6
HLA-DRB108:02 143.9
HLA-DRB109:01 783.8
HLA-DRB115:01 888.9
PLVPSLLRSTVSKAF 193 207 HLA-DPA102:01/DPB101:01 436
HLA-DQA101:02/DQB106:02 538.2
HLA-DQA105:01/DQB103:01 413.8
HLA-DRB101:01 42.1
HLA-DRB103:01 123.5
HLA-DRB104:01 185.5
HLA-DRB104:04 166.7
HLA-DRB104:05 478
HLA-DRB108:02 103
HLA-DRB109:01 394.9
HLA-DRB113:02 774.7
HLA-DRB115:01 978
LVPSLLRSTVSKAFH 194 208 HLA-DPA102:01/DPB101:01 464.3
HLA-DQA101:02/DQB106:02 457.4
HLA-DQA105:01/DQB103:01 378.5
HLA-DRB101:01 26.9
HLA-DRB103:01 57.1
HLA-DRB104:01 147.6
HLA-DRB104:05 360
HLA-DRB108:02 71.5
HLA-DRB109:01 336.5
HLA-DRB111:01 148.8
HLA-DRB113:02 652.3
HLA-DRB115:01 824.6
VPSLLRSTVSKAFHT 195 209 HLA-DPA102:01/DPB101:01 682.2
HLA-DQA101:02/DQB106:02 397.2
HLA-DRB101:01 18
HLA-DRB103:01 32.8
HLA-DRB104:01 108.8
HLA-DRB104:05 303.1
HLA-DRB108:02 52
HLA-DRB109:01 219.9
HLA-DRB113:02 568.1
HLA-DRB115:01 745
PSLLRSTVSKAFHTT 196 210 HLA-DPA102:01/DPB101:01 871.8
HLA-DQA101:02/DQB106:02 506.4
HLA-DRB101:01 26.3
HLA-DRB103:01 49.1
HLA-DRB104:01 126.7
HLA-DRB104:04 178.1
HLA-DRB104:05 369.2
HLA-DRB108:02 61.5
HLA-DRB115:01 809.3
SLLRSTVSKAFHTTN 197 211 HLA-DQA101:02/DQB106:02 609.2
HLA-DRB101:01 36.6
HLA-DRB103:01 89.2
HLA-DRB104:01 130.9
HLA-DRB104:05 467.4
HLA-DRB108:02 75.6
LLRSTVSKAFHTTNF 198 212 HLA-DQA101:02/DQB106:02 836.1
HLA-DRB103:01 218.7
HLA-DRB104:01 184.1
HLA-DRB104:04 384.5
HLA-DRB104:05 643.2
HLA-DRB108:02 106

FLEIANYQV VYRDYRFLEIANYQV
271
285
HLA-DRB107:01 9.1
HLA-DRB108:02 752
HLA-DRB113:02 288.3
HLA-DRB501:01 67.4
YRDYRFLEIANYQVR
272
286
HLA-DRB107:01 10.9
HLA-DRB108:02 565
HLA-DRB113:02 237.5
HLA-DRB501:01 29.8
RDYRFLEIANYQVRD 273 287 HLA-DPA101/DPB104:01 284.1
HLA-DPA101:03/DPB102:01 90.8
HLA-DRB101:01 8.9
HLA-DRB104:01 41.8
HLA-DRB107:01 15.6
HLA-DRB108:02 585.2
HLA-DRB111:01 54.1
HLA-DRB113:02 216.2
HLA-DRB301:01 875
HLA-DRB501:01 27.2
HLA-DPA102:01/DPB105:01 218.5
HLA-DQA101:01/DQB105:01 637.4
HLA-DQA101:02/DQB106:02 331.2
HLA-DRB104:04 81.1
HLA-DRB104:05 32.1
HLA-DRB109:01 273.4
DYRFLEIANYQVRDL 274 288 HLA-DPA101/DPB104:01 475
HLA-DPA101:03/DPB102:01 72.5
HLA-DPA102:01/DPB101:01 42.9
HLA-DPA102:01/DPB105:01 294.4
HLA-DPA103:01/DPB104:02 462.5
HLA-DRB101:01 8.3
HLA-DRB104:01 58.2
HLA-DRB104:05 39.5
HLA-DRB107:01 15.9
HLA-DRB108:02 704.3
HLA-DRB111:01 43.5
HLA-DRB113:02 190.3
HLA-DRB301:01 979.2
HLA-DRB501:01 18.3
RFLEIANYQVRDLET 276 290 HLA-DPA101/DPB104:01 594
HLA-DPA101:03/DPB102:01 113.7
HLA-DPA102:01/DPB101:01 66
HLA-DPA102:01/DPB105:01 726.4
HLA-DRB101:01 15.5
HLA-DRB104:01 261.2
HLA-DRB104:04 174.2
HLA-DRB104:05 174.3
HLA-DRB107:01 31
HLA-DRB109:01 931.6
HLA-DRB111:01 140.6
HLA-DRB113:02 486.4
HLA-DRB501:01 39
FLEIANYQVRDLETG 277 291 HLA-DPA101:03/DPB102:01 336.8
HLA-DPA102:01/DPB101:01 146.9
HLA-DRB101:01 28.9
HLA-DRB104:04 273.3
HLA-DRB104:05 325.2
HLA-DRB107:01 57.2
HLA-DRB111:01 340.9
HLA-DRB113:02 852.9
HLA-DRB501:01 101.7

FLTDEQFTI PPKKRNFLTDEQFTI 295 309 HLA-DPA101/DPB104:01 246.4
HLA-DPA101:03/DPB102:01 35.5
HLA-DPA102:01/DPB101:01 93.6
HLA-DPA102:01/DPB105:01 541
HLA-DPA103:01/DPB104:02 506.6
HLA-DRB103:01 76.1
HLA-DRB104:01 198.7
HLA-DRB104:05 274.9
HLA-DRB107:01 98.6
HLA-DRB301:01 4.9
HLA-DRB501:01 948.8
PKKRNFLTDEQFTIG 296 310 HLA-DPA101/DPB104:01 172.7
HLA-DPA101:03/DPB102:01 24.5
HLA-DPA102:01/DPB101:01 82.3
HLA-DPA102:01/DPB105:01 475.5
HLA-DPA103:01/DPB104:02 341.5
HLA-DRB101:01 850.7
HLA-DRB103:01 59.9
HLA-DRB104:01 136.1
HLA-DRB104:04 869.9
HLA-DRB104:05 325
HLA-DRB107:01 162
HLA-DRB301:01 4.8
HLA-DRB501:01 769
KKRNFLTDEQFTIGW 297 311 HLA-DPA101/DPB104:01 123
HLA-DPA101:03/DPB102:01 20.1
HLA-DPA102:01/DPB101:01 66.4
HLA-DPA102:01/DPB105:01 394.6
HLA-DPA103:01/DPB104:02 199
HLA-DRB101:01 342.9
HLA-DRB103:01 37.7
HLA-DRB104:01 103.7
HLA-DRB104:04 805.9
HLA-DRB104:05 330.3
HLA-DRB107:01 203.9
HLA-DRB301:01 4.6
HLA-DRB501:01 527
KRNFLTDEQFTIGWD 298 312 HLA-DPA101/DPB104:01 110.8
HLA-DPA101:03/DPB102:01 21.6
HLA-DPA102:01/DPB101:01 65
HLA-DPA102:01/DPB105:01 405.2
HLA-DPA103:01/DPB104:02 148.2
HLA-DRB101:01 354.3
HLA-DRB103:01 31.7
HLA-DRB104:01 98.2
HLA-DRB104:05 398.7
HLA-DRB107:01 325.7
HLA-DRB301:01 4.4
HLA-DRB501:01 505.9
RNFLTDEQFTIGWDA 299 313 HLA-DPA101/DPB104:01 115.3
HLA-DPA101:03/DPB102:01 26.7
HLA-DPA102:01/DPB101:01 88.5
HLA-DPA102:01/DPB105:01 532.9
HLA-DPA103:01/DPB104:02 182.5
HLA-DRB101:01 687.7
HLA-DRB103:01 64.7
HLA-DRB104:01 178.3
HLA-DRB104:05 700.1
HLA-DRB301:01 5.9
HLA-DRB501:01 948.3
NFLTDEQFTIGWDAM 300 314 HLA-DPA101/DPB104:01 244
HLA-DPA101:03/DPB102:01 44.6
HLA-DPA102:01/DPB101:01 173.3
HLA-DPA103:01/DPB104:02 250.2
HLA-DQA105:01/DQB102:01 475.1
HLA-DRB103:01 164.3
HLA-DRB104:01 278.3
HLA-DRB301:01 9
FLTDEQFTIGWDAME 301 315 HLA-DPA101/DPB104:01 640
HLA-DPA101:03/DPB102:01 154.8
HLA-DPA102:01/DPB101:01 395.6
HLA-DPA103:01/DPB104:02 437.8
HLA-DQA105:01/DQB102:01 459.5
HLA-DRB103:01 433.9
HLA-DRB104:01 271.4
HLA-DRB301:01 15.5

LLGDIVAVS QPVSARLLGDIVAVS 519 533 HLA-DPA102:01/DPB101:01 922.5
HLA-DPA103:01/DPB104:02 822.3
HLA-DQA101:02/DQB106:02 726
HLA-DQA105:01/DQB103:01 42.2
HLA-DRB104:01 494.1
HLA-DRB108:02 914.5
HLA-DRB301:01 211.5
PVSARLLGDIVAVSK 520 534 HLA-DPA102:01/DPB101:01 752.7
HLA-DPA103:01/DPB104:02 853.4
HLA-DQA101:02/DQB106:02 715.4
HLA-DQA105:01/DQB103:01 34
HLA-DRB103:01 380.3
HLA-DRB104:01 298
HLA-DRB104:05 458.8
HLA-DRB108:02 571.8
HLA-DRB301:01 196.5
VSARLLGDIVAVSKC 521 535 HLA-DPA102:01/DPB101:01 723.1
HLA-DPA103:01/DPB104:02 733.4
HLA-DQA101:02/DQB106:02 502.8
HLA-DQA105:01/DQB103:01 29.9
HLA-DRB103:01 214.3
HLA-DRB104:01 237
HLA-DRB104:05 429.4
HLA-DRB108:02 541.8
HLA-DRB301:01 172.3
SARLLGDIVAVSKCI 522 536 HLA-DPA102:01/DPB101:01 677.6
HLA-DPA103:01/DPB104:02 650.5
HLA-DQA101:02/DQB106:02 555.2
HLA-DQA105:01/DQB103:01 26.4
HLA-DRB101:01 175.3
HLA-DRB103:01 124.9
HLA-DRB104:01 187.9
HLA-DRB104:05 396.3
HLA-DRB108:02 396.7
HLA-DRB301:01 143.4
ARLLGDIVAVSKCIE 523 537 HLA-DPA102:01/DPB101:01 791.2
HLA-DPA103:01/DPB104:02 787.4
HLA-DQA101:02/DQB106:02 590.2
HLA-DQA105:01/DQB103:01 31.5
HLA-DRB101:01 270.9
HLA-DRB103:01 199.3
HLA-DRB104:01 255.3
HLA-DRB104:05 643.6
HLA-DRB108:02 360.5
HLA-DRB301:01 379.2
RLLGDIVAVSKCIEI 524 538 HLA-DPA102:01/DPB101:01 807.2
HLA-DPA103:01/DPB104:02 911.1
HLA-DQA101:02/DQB106:02 621.6
HLA-DQA105:01/DQB103:01 35.4
HLA-DRB103:01 347.3
HLA-DRB104:01 375.6
HLA-DRB104:05 977.9
HLA-DRB108:02 414.9
HLA-DRB301:01 974.2
LLGDIVAVSKCIEIP 525 539 HLA-DPA102:01/DPB101:01 941
HLA-DQA101:02/DQB106:02 748.6
HLA-DQA105:01/DQB103:01 37.1
HLA-DRB103:01 688
HLA-DRB104:01 848.5
HLA-DRB108:02 521

IASFLSNPF ISTVSGIASFLSNPF 737 751 HLA-DPA102:01/DPB101:01 264.9
HLA-DQA105:01/DQB102:01 384.2
HLA-DRB104:01 53.5
HLA-DRB104:04 92.5
HLA-DRB104:05 16.2
HLA-DRB107:01 25
HLA-DRB109:01 104.9
HLA-DRB115:01 24.4
HLA-DRB501:01 103.5
STVSGIASFLSNPFA 738 752 HLA-DPA101/DPB104:01 559.3
HLA-DPA101:03/DPB102:01 320.6
HLA-DPA102:01/DPB101:01 240.5
HLA-DQA101:01/DQB105:01 976.4
HLA-DQA105:01/DQB102:01 474.6
HLA-DRB101:01 101.3
HLA-DRB104:01 41.8
HLA-DRB104:05 15.5
HLA-DRB107:01 31.3
HLA-DRB108:02 589.8
HLA-DRB109:01 74.3
HLA-DRB115:01 18.9
HLA-DRB501:01 77.6
TVSGIASFLSNPFAA 739 753 HLA-DPA101:03/DPB102:01 179.9
HLA-DPA102:01/DPB101:01 219.5
HLA-DQA101:01/DQB105:01 915.8
HLA-DQA105:01/DQB102:01 611.8
HLA-DRB101:01 40.6
HLA-DRB104:01 27.7
HLA-DRB104:05 15.5
HLA-DRB107:01 44
HLA-DRB108:02 538.8
HLA-DRB109:01 72.2
HLA-DRB115:01 15.9
HLA-DRB501:01 57.1
VSGIASFLSNPFAAL 740 754 HLA-DQA101:01/DQB105:01 710
HLA-DQA105:01/DQB102:01 755.5
HLA-DRB101:01 14.2
HLA-DRB103:01 427.9
HLA-DRB104:01 22
HLA-DRB104:05 14.6
HLA-DRB107:01 43.3
HLA-DRB108:02 486.4
HLA-DRB109:01 52.2
HLA-DRB111:01 377.7
HLA-DRB115:01 12.8
HLA-DRB501:01 29.4
SGIASFLSNPFAALG 741 755 HLA-DQA101:01/DQB105:01 936.1
HLA-DRB103:01 629.7
HLA-DRB104:01 27.6
HLA-DRB104:05 18
HLA-DRB107:01 67.9
HLA-DRB108:02 582.5
HLA-DRB109:01 56.2
HLA-DRB115:01 14.6
HLA-DRB501:01 50.2
GIASFLSNPFAALGI 742 756 HLA-DRB101:01 27.1
HLA-DRB104:01 38.1
HLA-DRB104:05 27.3
HLA-DRB108:02 594.2
HLA-DRB109:01 58.6
HLA-DRB115:01 17.4
HLA-DRB501:01 73.5
IASFLSNPFAALGIG 743 757 HLA-DRB104:01 67
HLA-DRB104:05 41.9
HLA-DRB115:01 28.6
HLA-DRB401:01 735.5
HLA-DRB501:01 119.8

LLGDIVAVS QPVSARLLGDIVAVS 519 533 HLA-DPA102:01/DPB101:01 922.5
HLA-DPA103:01/DPB104:02 822.3
HLA-DQA101:02/DQB106:02 726
HLA-DQA105:01/DQB103:01 42.2
HLA-DRB104:01 494.1
HLA-DRB108:02 914.5
HLA-DRB301:01 211.5
PVSARLLGDIVAVSK 520 534 HLA-DPA102:01/DPB101:01 752.7
HLA-DPA103:01/DPB104:02 853.4
HLA-DQA101:02/DQB106:02 715.4
HLA-DQA105:01/DQB103:01 34
HLA-DRB103:01 380.3
HLA-DRB104:01 298
HLA-DRB104:05 458.8
HLA-DRB108:02 571.8
HLA-DRB301:01 196.5
VSARLLGDIVAVSKC 521 535 HLA-DPA102:01/DPB101:01 723.1
HLA-DPA103:01/DPB104:02 733.4
HLA-DQA101:02/DQB106:02 502.8
HLA-DQA105:01/DQB103:01 29.9
HLA-DRB103:01 214.3
HLA-DRB104:01 237
HLA-DRB104:05 429.4
HLA-DRB108:02 541.8
HLA-DRB301:01 172.3
SARLLGDIVAVSKCI 522 536 HLA-DPA102:01/DPB101:01 677.6
HLA-DPA103:01/DPB104:02 650.5
HLA-DQA101:02/DQB106:02 555.2
HLA-DQA105:01/DQB103:01 26.4
HLA-DRB101:01 175.3
HLA-DRB103:01 124.9
HLA-DRB104:01 187.9
HLA-DRB104:05 396.3
HLA-DRB108:02 396.7
HLA-DRB301:01 143.4
ARLLGDIVAVSKCIE 523 537 HLA-DPA102:01/DPB101:01 791.2
HLA-DPA103:01/DPB104:02 787.4
HLA-DQA101:02/DQB106:02 590.2
HLA-DQA105:01/DQB103:01 31.5
HLA-DRB101:01 270.9
HLA-DRB103:01 199.3
HLA-DRB104:01 255.3
HLA-DRB104:05 643.6
HLA-DRB108:02 360.5
HLA-DRB301:01 379.2
RLLGDIVAVSKCIEI 524 538 HLA-DPA102:01/DPB101:01 807.2
HLA-DPA103:01/DPB104:02 911.1
HLA-DQA101:02/DQB106:02 621.6
HLA-DQA105:01/DQB103:01 35.4
HLA-DRB103:01 347.3
HLA-DRB104:01 375.6
HLA-DRB104:05 977.9
HLA-DRB108:02 414.9
HLA-DRB301:01 974.2
LLGDIVAVSKCIEIP 525 539 HLA-DPA102:01/DPB101:01 941
HLA-DQA101:02/DQB106:02 748.6
HLA-DQA105:01/DQB103:01 37.1
HLA-DRB103:01 688
HLA-DRB104:01 848.5
HLA-DRB108:02 521

Inhibitory concentration needed for binding MHC II to the IEDB NN-align method; the lower value is better.

Figure 6.

Figure 6

3D structure of reference glycoprotein B of ILTV and the position of proposed helper T cell epitopes suggested to interact with MHC-II virus illustrated by UCSF-Chimera visualization tool.

3.5. Overlapping of T Cell Epitopes Residues in MHC Classes I and II

Twelve epitopes from top five proposed MHC class I were associated with at least to 15 alleles from MHC class II epitopes (see Table 5). It was observed that top proposed epitope from MHCII (FLTDEQFTI), which achieved the highest linkages with 76 alleles from MHC class II, was linked with 3 alleles only from MHCI. While the second proposed epitopes (FLEIANYQV and IASFLSNPF) that bound to 67 MHCII alleles were associated with 2 and 6 alleles from MHC1, respectively. However, of these top epitopes, only four of top MHC1 epitopes and two of best epitopes from MHCII were not linked to any alleles from MHCII and MHCI, respectively.

Table 5.

Comparison between the numbers of alleles linked with top proposed epitopes in MHCI and MHCII.

Peptide MHCI MHCII
MLICVCVAI 5 0
YVFNVTLYY 12 49
LYYKHITTV 6 21
TTVTTWALF 6 16
MATGDTVEI 6 0
DTVEISPFY 5 5
YRFLEIANY 6 49
VSYKNSYHF 9 15
YKNSYHFSL 5 45
HVRSGDIEY 5 0
MSHGLAEMY 6 22
VVMTAAAAV 5 42
FAYDKIQAH 5 49
YLLYEDYTF 9 48
IASFLSNPF 6 67#
FLSNPFAAL 8 58
KSNPVQVLF 5 0
LLGDIVAVS 0 60
FLTDEQFTI 3 76#
FLEIANYQV 2 67#
LLRSTVSKA 0 64

Proposed MHCI docked epitopes; #top proposed MHCII epitopes.

3.6. Molecular Docking of B-F Alleles and Predicted CTL Epitopes

The top ranked CTL proposed epitopes were selected for molecular docking to predict and symbolize the image of real CTL epitopes interaction with chicken alleles. For this purpose, two types of chicken BF alleles (BF22101; BF20401) were selected. The docked epitopes (301FLTDEQFTI309, 277FLEIANYQV285, and 743IASFLSNPF751) using peptide-binding groove affinity were used to evaluate the ability of predicted epitopes to bind with chicken BF alleles/receptors to chicken alleles BF2 (BF22101 and BF20401). Results indicated that the docked epitopes achieved strong binding affinity to Chicken BF2 alleles based on global energy and attractive VDW in kcal/mol unit. The lowest binding energy (kcal/mol) was selected to predict probable CTL epitopes. Docked ligand epitopes (118YVFNVTLYY126, 622YLLYEDYTF630, and 335VSYKNSYHF343) with BF2 2101 alleles (receptor) showed higher binding affinity which expressed by the lower global energy (-91.78, -89.53, and -66.41, respectively). However, BF 2 0401 allele as a receptor produced less binding affinity with docked ligands (-45.65, -51.56, and -61.68, respectively). These results indicated that the binding affinity of ligands is higher with the receptor BF22101 allele compared with the other allele (BF2 0401) which produced less binding affinity. In addition, the docked molecules showed different groove binding site for both BF alleles. Figure 7 presents the 3D structure of chicken BF2 alleles and the proposed binding sites of docked epitopes. The binding energy scores in both BF2 alleles for the suggested epitopes using Patch Dock server for molecular docking are shown in Table 6. The visualization of the binding interactions between chicken BF2 receptor and MHCI epitopes in the structural level was performed using UCSF-Chimera visualization tool 1.8 (see Figures 8 and 9).

Figure 7.

Figure 7

The 3D structure of BF2 alleles of chicken using Chimera visualization tool. Red circle indicated the binding site of epitopes.

Table 6.

The binding energy and attractive VDW scores for the suggested epitopes with chicken BF2 alleles using PatchDock server.

Ligand Receptor Global energy kcal/mol Attractive VDW kcal/mol
YVFNVTLYY BF2 2101 -91.78 -32.23
BF2 0401 - 45.65 -27.43

YLLYEDYTF BF2 2101 -89.53 -34.81
BF2 0401 -61.68 -27.77

VSYKNSYHF BF2 2101 -66.41 -28.95
BF2 0401 -51.56 -30.71

Figure 8.

Figure 8

Visualization of PatchDock Molecular docking of MHC-I proposed epitopes and chicken BF2 alleles receptors using UCSF-Chimera visualization tool. Receptors (BF alleles) are represented by pink colour while CTL epitopes are represented by green one.

Figure 9.

Figure 9

Visualization of PatchDock Molecular docking of MHCI proposed epitopes and chicken BF2 alleles receptors using UCSF-Chimera visualization tool. Receptors (BF alleles) are represented by rounded ribbon structure hot pink colour while CTL epitopes are represented by green one.

4. Conclusion

Smart computational techniques which provide tremendous predictive and analytical information facilitate the prediction of novel epitopes that may act as a powerful vaccine through immunoinformatic technology.

This is the first in silico study to design peptide vaccine against avian ILTV through humoral and cell mediated immune responses. The expected epitopes in this study could help in prevention of latent infection caused by the use of attenuated vaccines and developing more effective and trustable prophylactic and therapeutic vaccines than conventional methods.

In this study new epitopes were proposed as promising multiepitopes vaccine for ILTV. CTL epitopes were selected as vaccine candidates due to their high binding affinity with different alleles. The result should be supported by designing the peptide vaccine in the lab and through clinical trials.

Acknowledgments

The authors would like to thank the staff members of College of Veterinary Medicine, University of Bahri, Sudan, for their cooperation and support.

Data Availability

The sequences of envelope glycoprotein B (GB) of ILTV were retrieved from GenBank of National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) in August 2017. Retrieved strains and their accession numbers and geographical regions were listed in Table 1.

Conflicts of Interest

The authors declared that they have no conflicts of interest regarding the publication of this paper.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The sequences of envelope glycoprotein B (GB) of ILTV were retrieved from GenBank of National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) in August 2017. Retrieved strains and their accession numbers and geographical regions were listed in Table 1.


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