Advances in Bioinformatics
Volume 2019 (2019), Article ID 1270485, 23 pages
https://doi.org/10.1155/2019/1270485

Research Article

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

Sumaia A. Ali,1,2 Yassir A. Almofti,¹ and Khoubieb A. Abd-elrahman³

¹Department of Molecular Biology and Bioinformatics, College of Veterinary Medicine, University of Bahri, Khartoum, Sudan
²Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, Sudan University of Science and Technology, Sudan
³Department of Pharmaceutical Technology, College of Pharmacy, University of Medical Science and Technology (MUST), Khartoum, Sudan

Correspondence should be addressed to Sumaia A. Ali; somiahadlool@yahoo.com

Received 7 November 2018; Accepted 17 February 2019; Published 18 March 2019

Academic Editor: Nurit Haspel

Copyright © 2019 Sumaia A. Ali et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 [1–3]. 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 [11–13]. 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.

(a)

(b)

(c)

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 [29–32].

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 [29–32].

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 [33–35].

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: 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: 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 , , and . 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.

(a)

(b)

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 BF2∗2101 from the B21 haplotype is highly expressed, leading to strong genetic links with infectious pathogens. In addition, BF2∗2101 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 which interacted and linked with 16 human alleles, followed by and 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: 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) exhibited high affinity to MHCII alleles due to great binding with 76 MHC-II alleles followed by and 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: 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 (, , and ) 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 (, , and ) 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).

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

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: 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.

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.

Acknowledgments

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

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