alexa In silico Prediction of Peptide based Vaccine against Fowlpox Virus (FPV)

ISSN: 1745-7580

Immunome Research

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In silico Prediction of Peptide based Vaccine against Fowlpox Virus (FPV)

Idris ST1*, Salih S2, Basheir M3, Elhadi A4, Kamel S3, Abd-elrahman KA3, Hamdi A4 and Hassan MA4
1Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, University of Khartoum, Khartoum, Sudan
2Department of Biotechnology, Africa City of Technology, Khartoum, Sudan
3Department of Pharmacology, Faculty of Pharmacy, International University of Africa, Khartoum, Sudan
4Department of Microbiology, Africa City of Technology, Sudan
*Corresponding Author: Idris ST, Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, University of Khartoum, Khartoum, Sudan, Tel: 229-958-532-79, Email: [email protected]

Received Date: Nov 17, 2017 / Accepted Date: Feb 28, 2018 / Published Date: Aug 15, 2018

Abstract

Fowlpox virus (FPV) is double stranded DNA virus and a member of Poxviridae family which transmitted via aerosols and insect bite and causes cutaneous and diphtheritic infection in poultry population. This study aimed to design peptide vaccine by selecting all possible epitopes after analyzing of all FPV140 protein sequence reported in NCBI database using in silico approaches. After alignment of retrieved sequence the conserved region applied into IEDB analysis tool to predict B and T cell epitopes, then testing the affinity of predicted epitopes to bind to (BF2*2101) (BF2*0401) chicken receptor for MHC1 molecule, peptides low energy when docked against receptor were suggested as epitopes based vaccine. Peptides (50 PPSPKP 55, 51 PSPKPL 56, 52 SPKPLP 57, 53 PKPLPK 58, 54 KPLPKS 59, 55 PLPKSK 60, 56 LPKSKQ 61 and 18 RPSSTV 23) were most potential B cell epitopes while (110 YIMDNAEKL 118, 274 FYHRMYYPL 282, 278 MYYPLFSVF 286 231 YVVDNDRYV 239 and 317 LLSGVFLAY 325) docked epitopes suggested to be T cell epitopes because of their good binding affinity especially this overlapped one 110 YIMDNAEKL 118. This study concluded that those predicted epitopes might use to produce good vaccine against FPV after in vitro and in vivo studies to evaluate its efficiency.

Keywords: Fowlpox virus; Epitopes; Vaccine; Insect bite

Introduction

Fowlpox virus (FPV) is a worldwide spread virus and high prevalent in tropical and subtropical countries. It’s highly infectious but slow in spreading. The occurrence of infection is variable according to climates, hygiene and vaccination. FPV infects chickens, turkeys and other type of birds mindless of differences in sex, age and breed it transmitted directly from infected birds by inhalation or indirectly by insect bites. It causes two type of infection dry pox (mild) or wet pox (severe) infection. The dry type also known as cutaneous infection is featured by lesions or nodules on unfeathered areas of the bird body. This form has high currency but it’s mild. The severe form is the wet type known as diphtheritic infection which infects mucus membrane of respiratory and gastrointestinal tract especially (larynx, pharynx and mouth) is featured by necrotic lesions, this type cause death more than dry type due to suffocation [1-16].

FPV related to genus Avipox (APVs). APVs belong to subfamily Chordopoxvirinae which is the part of Poxviridae family. APVs are large, oval shaped enveloped viruses with double strand DNA. APVs differ from other DNA viruses, they replicate simply in cytoplasm. The mature FPV is brick like shape, with dimension 330 × 280 × 200 nm. The outer membrane contain random package of surface tubules. The virion composed of biconcave nucleoid in the center with two bodies in sides DNA of FPV consists of 288-300 kilo base pair. FPV140 is one of membrane associate protein of FPV the protein functions in attachment of intracellular mature virus particles (IMVP) to cell. It’s used to differentiate FPV from other APVs because it’s highly conserved. FPV140 is highly antigenic and immunodominent [1-4,6,8-15,17,18].

FPV survives for long time in poultry environment in contrast to other viruses because its genome contains genes which protect it from environment (photolyase and A type inclusion body genes). FPV disease lead to severe economic crash globally which result from plunge in egg production, reduction in growth of young birds, blindness and in some cases death [1,5,6,9,12-15].

Vaccines activate body resistance to specific diseases by starting the immunological reaction and decrease clinical signs and downturn virus shedding and transmission. Vaccines for chickens are usually inactivated vaccines which are time consuming, labor intensive, expensive and inaccurate or live vaccines which are widely used but it can cause disease depending on the environmental factors and immunity status it’s recently improved by genetic modification but the high cost is obstacles. For FPV live vaccine is mainly used [8,19].

Epitope based vaccine depend on identification of specific epitopes from pathogen. These epitopes are capable of inducing B cell and T cell mediated immunity. Many studies show the effectiveness of peptide based vaccine against infectious disease such as malaria, HIV, TB and Hepatitis B. The insilico tools make the epitope prediction simple and easy, minimize the cost of construction and production of vaccine so that prevent infection hazards and eliminate the allergic and reactogenic response though it seems promising in next vaccine technology [19-22].

This study aimed to design peptide vaccine against FPV by using FPV 140 protein as target. No previous reports found in FPV epitopes based vaccine so this may considered the first study using insilico approach to design epitope vaccine against FPV which its outbreaks cause severe economic loss in poultry population.

Materials and Methods

Protein sequence retrieval

A total of 20 virulent strain of Fowl pox virus FPV140 protein were retrieved from NCBI (http://www.ncbi.nlm.nih.gov/protein/) database in Septemeber 2016. These 20 strains retrieved were selected from different parts of the world for immunobioinformtics analysis; retrieved protein strains and their accession numbers as well as date and area of collection are listed in Table 1.

Accession Number Date of Collection Country
NP-039103 NA NA
AAF44484 1999 USA
AEB40184 2009 India
AEB40181 2008 India
AEB40178 2008 India
AEB40175 2008 India
AEB40172 2008 India
AEB40169 2008 India
AFS52252 2011 Egypt
AFS52251 2011 Egypt
AFS52250 2011 Egypt
AFS52249 2011 Egypt
CAJ21219 NA United Kingdom
CAJ21216 NA United Kingdom
CAJ21213 NA United Kingdom
CAJ21210 NA United Kingdom
CCA65952 NA Austria
CCA65949 NA Austria
Q9J590 NA NA
ADP92335 NA China

Table 1: Virus Strains retrieved and their Accession numbers and area of collection; *NA: not available.

Determination of conserved regions

The retrieved sequences were aligned to obtain conserved regions using multiple sequence alignment (MSA). Sequences aligned with the aid of ClustalW as implemented in the BioEdit program for finding the conserved regions among international virulent variants. Later on, the candidate epitopes were analyzed by different prediction tools from Immune Epitope Database IEDB analysis resource (http://www.iedb.org/) [23,24].

Sequence based methods

B-cell epitope prediction

B cell epitope is the portion of an immunogen, which interacts with B-lymphocytes. As a result, the B-lymphocyte is differentiated into antibody-secreting plasma cell and memory cell. B cell epitope is characterized by being hydrophilic and accessible [25] .Thus, the classical propensity scale methods and hidden Markov model programmed softwares from IEDB analysis resource were used for:

Prediction of linear B-cell epitopes: Depening on the following aspects: BepiPred from immune epitope database [26] was used for linear B-cell epitopes prediction from the conserved region with a default threshold value of 0.35.

Prediction of surface accessibility: By using Emini surface accessibility prediction tool of the immune epitope data base (IEDB) [27] the surface accessible epitopes were predicted from the conserved region holding the default threshold value 1.000 or higher.

Prediction of epitopes antigenicity sites: The kolaskar and tongaonker antigenicity method were used to determine the antigenic sites with a default threshold value of 1.042 [28].

Prediction of epitopes hydrophilicity: Parker hydrophilicity prediction tool was used to determine the hydrophilicity of the conserved regions; and the threshold default value was 1.286 [29].

T cell epitope prediction

It was done by online immune-informatics tool IEDB (http://tools.iedb.org). Prediction for several organisms is supported by this tool as chicken is not among them. However, several studies suggest some similarities between HLA alleles and chicken MHC, [30-34], So for MHC class-I and MHC class-II the man HLA A, B and C and HLA DR, DP and DQ were used respectively.

MHC class I binding predictions: The major histocompatibility complex MHC class-I binding prediction tool (http://tools.iedb.org/mhci/) [35] was used to predict Cytotoxic T cell epitopes. Prediction methods achieved by artificial neural network (ANN). Prior to prediction, all epitope lengths were set as 9 m, conserved epitopes that bind to many HLA alleles at score equal or less than 1.0 percentile rank and 100 IC50 were selected for further analysis [36].

MHC class II binding predictions

The MHC class-II binding prediction tool (http://tools.iedb.org/mhcii/) [37] was used to predict helper T-cell epitopes. The prediction achieved by NN- align that uses the artificial neural networks that allows for simultaneous identification of the MHC class II binding core epitopes and binding affinity. The percentile rank for strong binding peptides was set at ≤ 10 with IC50 ≤ 500 to determine the interaction potentials of helper T-cell epitope peptide and MHC class II allele (HLA DR, DP and DQ) [38]. All conserved epitopes that bind to many alleles at score equal or less than 10 percentile rank with IC50 ≤ 500 is selected for further analysis.

Structure-based methods

Homology modeling and visualization

FPV140 protein 3D structure obtained by phyre2, (http://www.sbg.bio.ic.ac.uk/phyre2) which uses advanced remote homology detection methods to build 3D models not as chicken alleles BF2 *2101 and BF2*0401 were retrieved from the NCBI database/structure (MMDB ID: 61647/PDB ID: 3BEW and MMDB ID: 105232/PDB ID 4G42, respectively) [39]. UCSF Chimera (version 1.8) was used to visualize the 3D structures, Chimera currently available within the Chimera package and available from the chimera web site (http://www.cgl.ucsf.edu/cimera). Homology modeling was achieved to establish docking, and for further verification of the service accessibility and hydrophilicity of B lymphocyte epitopes predicted, as well as to visualize all predicted T cell epitopes in the structural level [40,41].

Docking

Top epitopes of MHC I alleles that predicted to bind with IC50 below 100 and percentile rank less than 1.00 were selected as the ligands, which are modeled using PEP- FOLD online peptide modeling tool. Two chicken BF alleles /receptors (BF2 *2101, BF2*0401) have been evaluated according to peptide-binding groove affinity which reported by Kokh et al. [42] and Zhang et al. [43]. Protein sequence and PDB ID of BF2 *2101, BF2*0401 were retrieved from the NCBI database/structure (MMDB ID: 61647/PDB ID: 3BEW and MMDB ID: 105232/PDB ID 4G42, respectively) [44]. Molecular Docking technique applied by PatchDock (http://bioinfo3d.cs.tau.ac.il/PatchDock/) online auto-dock tools [44]. Then the visualization had done by UCSF-Chimera visualization tool 1.8 [45-48].

Results

B cell prediction and modelling

Sequences of FPV140 protein were applied to Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity and Parker hydrophobicity prediction tools in IEDB.   Eight B cell epitopes were predicted by Bepipred linear epitope prediction (Table 2).

No. Start End Peptide Length Emini surface accessibility/ Threshold 1.000 Kolaskar and Tongaonkar antigenicity/ Threshold 1.026 Parker hydrophobicity prediction/ Threshold 1.000
1 1 6 MAPGDK 6 1.002   0.937 3.567
2 17 24 GRPSSTVV 8 0.48 1.064 2.85
3 34 73 WSYKKGIKNGYDDYRDPPSPKPLPKSKQEPNADDKVGDIE 40 10.331 0.982 3.66
4 83 84 GY 2 1.002 0.937 3.567
5 116 118 EKL 3 1.273 1.01 1.433
6 128 132 DNTIT 5 1.008 0.922 3.88
7 207 213 TNNKPSF 7 1.989 0.937 3.471
8 238 238 Y 1 1.207 1.161 -1.9

Table 2: The predicted epitopes by Bepipred linear epitope prediction.

There was eight epitopes succeeded the three test from those predicted epitopes 34 WSYKKGIKNGYDDYRDPPSPKPLPKSKQEPNADDKVGDIE 73 and 17 GRPSSTVV 24 (Table 3 and Figure 1).

immunome-research-structure-Predicted

Figure 1: 3D structure of Predicted B cell epitopes of FPV140 protein in FPV virus illustrated by UCSF Chimera visualization tool.

No. Start End Peptide Emini surface accessibility score/ threshold 1.000 antigenicity score/ threshold 1.026 hydrophobicity score/ threshold 1.000
1 18 23 RPSSTV 1.142 1.042 3.467
2 50 55 PPSPKP 3.004 1.033 3.433
3 51 56 PSPKPL 1.602 1.064 1.550
4 52 57 SPKPLP 1.602 1.064 1.550
5 53 58 PKPLPK 2.391 1.050 1.417
6 54 59 KPLPKS 2.072 1.042 2.150
7 55 60 PLPKSK 2.072 1.042 2.150
8 56 61 LPKSKQ 2.320 1.033 2.800

Table 3: Peptides predicted as epitopes (pass Emini surface accessibility, Kolaskar and Tongaonkar antigenicity and Parker hydrophobicity prediction tools).

Prediction of cytotoxic T cell epitopes and modelling

The reference FPV140 protein sequence was analyzed using IEDB MHC-1 binding prediction tool to predict cytotoxic T cell epitopes which interacted with different types of MHC Class I alleles in Man. Based on ANN with percentile rank ≤ 1 and ANN IC-50 ≤ 100. The top five were 110 YIMDNAEKL 118, 274 FYHRMYYPL 282, 278 MYYPLFSVF 286 and 231 YVVDNDRYV 239, 317 LLSGVFLAY 325 (Table 4 and Figure 2). Epitopes and their corresponding alleles were shown in Table 5.

immunome-research-structure-cytotoxic

Figure 2: 3D structure of cytotoxic T cell top five epitopes.

Start End Peptide Allele Length ic50 Percentile
8 16 QIIFVITTI HLA-A*32:01 9 82 0.7
18 26 RPSSTVVPF HLA-B*07:02 9 13 0.3
  HLA-B*35:01 9 9 0.4
  HLA-B*53:01 9 51 0.3
30 38 EVSEWSYKK HLA-A*68:01 9 16 0.4
72 80 IEYDEMVSV HLA-B*40:02 9 40 0.6
  HLA-C*12:03 9 19 0.8
77 85 MVSVRDGYY HLA-A*29:02 9 17 0.4
  HLA-A*30:02 9 18 0.3
83 91 GYYSDVCRL HLA-C*07:02 9 68 0.3
  HLA-C*14:02 9 4 0.2
99 107 IFIADHISL HLA-C*14:02 9 25 0.9
100 108 FIADHISLW HLA-A*26:01 9 63 0.3
101 109 IADHISLWR HLA-C*05:01 9 50 0.7
110 118 *YIMDNAEKL HLA-A*02:01 9 20 0.8
  HLA-C*03:03 9 3 0.2
  HLA-C*07:01 9 83 0.8
  HLA-C*12:03 9 16 0.7
  HLA-C*14:02 9 10 0.4
  HLA-C*15:02 9 72 0.7
114 122 NAEKLPNYV HLA-C*12:03 9 26 1
115 123 AEKLPNYVV HLA-B*40:02 9 34 0.5
137 145 ITNLDNITK HLA-A*11:01 9 36 0.8
154 162 ILQLVTHTK HLA-A*03:01 9 92 0.7
  HLA-A*11:01 9 44 1
166 174 DRNSQHLML HLA-C*06:02 9 79 0.4
  HLA-C*07:01 9 28 0.3
173 181 MLLPDLEAF HLA-B*15:01 9 45 0.7
191 199 AYIIRQEAV HLA-C*14:02 9 17 0.6
192 200 YIIRQEAVR HLA-A*68:01 9 21 0.6
194 202 IRQEAVRKL HLA-C*06:02 9 17 0.2
  HLA-C*07:01 9 14 0.2
197 205 EAVRKLYSY HLA-A*26:01 9 40 0.3
205 213 YFTNNKPSF HLA-C*07:02 9 82 0.3
  HLA-C*14:02 9 6 0.3
211 219 PSFDISLEI HLA-C*15:02 9 78 0.8
220 228 LRIENTLGI HLA-C*06:02 9 26 0.2
  HLA-C*07:01 9 23 0.3
224 232 NTLGITRYV HLA-A*68:02 9 6 0.3
  HLA-C*15:02 9 53 0.5
230 238 RYVVDNDRY HLA-A*30:02 9 94 0.8
231 239 *YVVDNDRYV HLA-A*02:06 9 15 1
  HLA-A*68:02 9 25 1
  HLA-C*07:01 9 94 0.8
  HLA-C*12:03 9 14 0.6
  HLA-C*15:02 9 71 0.7
232 240 VVDNDRYVY HLA-A*01:01 9 80 0.3
  HLA-A*30:02 9 47 0.5
  HLA-C*05:01 9 7 0.3
237 245 RYVYHDYKL HLA-A*23:01 9 78 0.5
241 249 HDYKLANEF HLA-B*40:02 9 73 0.8
249 257 FMKNKKNRL HLA-B*08:01 9 10 0.2
260 268 KSRIDGWIM HLA-B*57:01 9 90 0.5
266 274 WIMDNWPSF HLA-B*15:01 9 39 0.5
  HLA-B*35:01 9 17 0.6
267 275 *IMDNWPSFY HLA-A*01:01 9 7 0.2
  HLA-A*29:02 9 9 0.4
  HLA-A*30:02 9 18 0.3
  HLA-C*05:01 9 26 0.6
271 279 WPSFYHRMY HLA-B*35:01 9 11 0.4
272 280 PSFYHRMYY HLA-A*29:02 9 27 0.6
274 282 *FYHRMYYPL HLA-A*23:01 9 26 0.3
  HLA-A*24:02 9 19 0.2
  HLA-B*08:01 9 87 0.6
  HLA-B*39:01 9 18 0.3
  HLA-C*07:02 9 18 0.1
  HLA-C*14:02 9 3 0.1
275 283 YHRMYYPLF HLA-C*07:01 9 71 0.7
  HLA-C*14:02 9 21 0.7
277 285 RMYYPLFSV HLA-A*02:01 9 5 0.3
  HLA-A*02:06 9 7 0.6
  HLA-A*32:01 9 21 0.3
278 286 *MYYPLFSVF HLA-A*23:01 9 9 0.1
  HLA-A*24:02 9 27 0.3
  HLA-A*29:02 9 79 0.9
  HLA-B*15:01 9 65 0.9
  HLA-C*07:02 9 25 0.2
  HLA-C*14:02 9 3 0.1
281 289 PLFSVFGKY HLA-A*29:02 9 55 0.8
  HLA-A*30:02 9 37 0.5
289 297 YDITMMFLI HLA-B*40:02 9 73 0.8
291 299 ITMMFLIAI HLA-A*32:01 9 19 0.3
292 300 TMMFLIAIV HLA-A*02:01 9 8 0.4
293 301 MMFLIAIVI HLA-A*32:01 9 9 0.2
  HLA-B*39:01 9 49 0.7
294 302 MFLIAIVII HLA-A*23:01 9 85 0.5
295 303 FLIAIVIII HLA-A*02:01 9 9 0.4
297 305 IAIVIIIGL HLA-C*03:03 9 9 0.9
313 321 KLLWLLSGV HLA-A*02:01 9 6 0.3
  HLA-A*02:06 9 4 0.3
314 322 LLWLLSGVF HLA-B*15:01 9 28 0.3
316 324 WLLSGVFLA HLA-A*02:01 9 5 0.3
  HLA-A*02:06 9 8 0.6
317 325 *LLSGVFLAY HLA-A*01:01 9 69 0.3
  HLA-A*03:01 9 78 0.6
  HLA-A*29:02 9 4 0.2
  HLA-A*30:02 9 71 0.7
  HLA-B*15:01 9 21 0.2

Table 4: The cytotoxic T cell epitopes and their corresponding alleles *Top five epitopes suggested for docking.

Core Sequence Start End Allele Peptide Sequence IC50 Rank
FIADHISLW 100 108 HLA-DRB1*03:01 TKIFIADHISLWRYI 16.7 0.92
DTKIFIADHISLWRY 18 1.01
KIFIADHISLWRYIM 28.5 1.67
EDTKIFIADHISLWR 34 2.02
IFIADHISLWRYIMD 60 3.32
TEDTKIFIADHISLW 67.4 3.68
FIADHISLWRYIMDN 236.8 8.92
HLA-DRB1*04:01 TKIFIADHISLWRYI 20.7 1.03
DTKIFIADHISLWRY 23.3 1.27
EDTKIFIADHISLWR 29.4 1.84
KIFIADHISLWRYIM 29.6 1.86
TEDTKIFIADHISLW 42 3.05
IFIADHISLWRYIMD 52.1 4.02
HLA-DRB1*07:01 TEDTKIFIADHISLW 45.3 7.99
HLA-DRB3*01:01 DTKIFIADHISLWRY 4.1 0.05
TKIFIADHISLWRYI 4.1 0.05
EDTKIFIADHISLWR 4.2 0.06
TEDTKIFIADHISLW 4.3 0.07
KIFIADHISLWRYIM 5 0.12
IFIADHISLWRYIMD 6.7 0.25
FIADHISLWRYIMDN 9.5 0.47
HLA-DQA1*05:01/DQB1*02:01 EDTKIFIADHISLWR 331 7.47
DTKIFIADHISLWRY 371.3 8.4
TEDTKIFIADHISLW 414.9 9.36
HLA-DPA1*01/DPB1*04:01 KIFIADHISLWRYIM 186.7 8.16
TKIFIADHISLWRYI 216.5 8.98
HLA-DPA1*01:03/DPB1*02:01 TKIFIADHISLWRYI 37.1 4.08
KIFIADHISLWRYIM 40.7 4.39
DTKIFIADHISLWRY 44.8 4.72
EDTKIFIADHISLWR 56.4 5.62
IFIADHISLWRYIMD 60.1 5.88
TEDTKIFIADHISLW 64.4 6.17
FIADHISLWRYIMDN 89.3 7.76
HLA-DPA1*03:01/DPB1*04:02 TKIFIADHISLWRYI 18.9 2.14
KIFIADHISLWRYIM 21.9 2.55
DTKIFIADHISLWRY 23.1 2.72
IFIADHISLWRYIMD 30.7 3.67
EDTKIFIADHISLWR 37.6 4.45
TEDTKIFIADHISLW 70.3 7.55
*YIMDNAEKL 110 118 HLA-DRB1*01:01 LWRYIMDNAEKLPNY 10 5.19
HLA-DRB1*03:01 LWRYIMDNAEKLPNY 30.2 1.77
WRYIMDNAEKLPNYV 46.4 2.7
SLWRYIMDNAEKLPN 60 3.32
RYIMDNAEKLPNYVV 75.7 4.02
ISLWRYIMDNAEKLP 114.3 5.52
YIMDNAEKLPNYVVI 149.2 6.64
HISLWRYIMDNAEKL 188.3 7.73
HLA-DRB1*04:01 SLWRYIMDNAEKLPN 37.6 2.63
LWRYIMDNAEKLPNY 39.3 2.79
ISLWRYIMDNAEKLP 45.4 3.36
HISLWRYIMDNAEKL 50 3.81
WRYIMDNAEKLPNYV 81.3 6.58
HLA-DRB1*07:01 HISLWRYIMDNAEKL 32.7 6.16
ISLWRYIMDNAEKLP 35.5 6.56
SLWRYIMDNAEKLPN 41.6 7.45
HLA-DRB1*07:01 LWRYIMDNAEKLPNY 44.3 7.85
LWRYIMDNAEKLPNY 38.8 2.3
SLWRYIMDNAEKLPN 48.1 3.08
ISLWRYIMDNAEKLP 56.6 3.76
HISLWRYIMDNAEKL 64 4.31
WRYIMDNAEKLPNYV 65.6 4.44
RYIMDNAEKLPNYVV 102.1 7.06
HLA-DRB1*13:02 LWRYIMDNAEKLPNY 20.2 1.45
SLWRYIMDNAEKLPN 21.2 1.52
ISLWRYIMDNAEKLP 22.6 1.62
WRYIMDNAEKLPNYV 23.6 1.7
HISLWRYIMDNAEKL 25.8 1.87
RYIMDNAEKLPNYVV 37.3 2.67
YIMDNAEKLPNYVVI 61.1 4.15
HLA-DRB3*01:01 LWRYIMDNAEKLPNY 8 0.35
SLWRYIMDNAEKLPN 8.6 0.4
ISLWRYIMDNAEKLP 9.4 0.46
WRYIMDNAEKLPNYV 10.4 0.55
HISLWRYIMDNAEKL 10.5 0.56
RYIMDNAEKLPNYVV 15.1 0.9
YIMDNAEKLPNYVVI 25 1.55
HLA-DRB5*01:01 HISLWRYIMDNAEKL 26.5 6.23
LWRYIMDNAEKLPNY 27.1 6.34
SLWRYIMDNAEKLPN 28.8 6.65
ISLWRYIMDNAEKLP 30.8 7.01
WRYIMDNAEKLPNYV 49.7 9.95
HLA-DQA1*05:01/DQB1*02:01 HISLWRYIMDNAEKL 164.7 3.42
ISLWRYIMDNAEKLP 180 3.81
SLWRYIMDNAEKLPN 247.1 5.47
LWRYIMDNAEKLPNY 311.1 7
WRYIMDNAEKLPNYV 415 9.36
FITNLDNIT 136 144 HLA-DRB1*04:01 GEGFITNLDNITKVL 46.1 3.43
TGEGFITNLDNITKV 65.7 5.25
GFITNLDNITKVLND 72 5.8
ITGEGFITNLDNITK 95.7 7.76
FITNLDNITKVLNDN 119.1 9.58
HLA-DRB1*04:04 ITGEGFITNLDNITK 50.1 5.71
HLA-DRB1*08:02 EGFITNLDNITKVLN 247.2 5.68
GEGFITNLDNITKVL 387.2 9.35
HLA-DRB1*13:02 GEGFITNLDNITKVL 43.8 3.09
EGFITNLDNITKVLN 48.5 3.38
TGEGFITNLDNITKV 69.2 4.58
GFITNLDNITKVLND 76.3 4.97
FITNLDNITKVLNDN 117 6.79
ITGEGFITNLDNITK 144 7.86
HLA-DRB3*01:01 GEGFITNLDNITKVL 289.6 8.53
TGEGFITNLDNITKV 290.8 8.55
TITGEGFITNLDNIT 294.8 8.62
ITGEGFITNLDNITK 314 8.93
LQLVTHTKL 155 163 HLA-DRB1*07:01 NNVDILQLVTHTKLL 3.9 0.25
NVDILQLVTHTKLLK 4.1 0.29
VDILQLVTHTKLLKD 4.7 0.43
DNNVDILQLVTHTKL 4.8 0.45
DILQLVTHTKLLKDR 5.7 0.66
ILQLVTHTKLLKDRN 6.4 0.81
LQLVTHTKLLKDRNS 8.9 1.38
HLA-DRB1*09:01 VDILQLVTHTKLLKD 119.8 8.22
NVDILQLVTHTKLLK 147.6 9.97
HLA-DRB1*11:01 VDILQLVTHTKLLKD 25.7 4.73
DILQLVTHTKLLKDR 29 5.34
ILQLVTHTKLLKDRN 30.1 5.53
NVDILQLVTHTKLLK 30.7 5.64
LQLVTHTKLLKDRNS 50.7 8.6
HLA-DRB1*15:01 NVDILQLVTHTKLLK 77.5 7.9
NNVDILQLVTHTKLL 87.8 8.85
VDILQLVTHTKLLKD 95.5 9.54
HLA-DRB4*01:01 VDILQLVTHTKLLKD 32.7 2.16
DILQLVTHTKLLKDR 33.2 2.21
NNVDILQLVTHTKLL 34.2 2.3
NVDILQLVTHTKLLK 34.2 2.3
ILQLVTHTKLLKDRN 34.4 2.31
DNNVDILQLVTHTKL 39.2 2.74
LQLVTHTKLLKDRNS 51.1 3.81
HLA-DPA1*01/DPB1*04:01 ILQLVTHTKLLKDRN 138 6.65
DILQLVTHTKLLKDR 139 6.68
VDILQLVTHTKLLKD 156.5 7.25
NVDILQLVTHTKLLK 243.6 9.66
HLA-DPA1*01:03/DPB1*02:01 DILQLVTHTKLLKDR 92.8 7.95
VDILQLVTHTKLLKD 93.2 7.97
ILQLVTHTKLLKDRN 97.6 8.23
HLA-DPA1*02:01/DPB1*01:01 VDILQLVTHTKLLKD 32.6 3.18
DILQLVTHTKLLKDR 33.4 3.28
ILQLVTHTKLLKDRN 35.4 3.53
NVDILQLVTHTKLLK 41 4.22
LQLVTHTKLLKDRNS 54.9 5.85
NNVDILQLVTHTKLL 55.6 5.92
DNNVDILQLVTHTKL 90.6 9.55
HLA-DPA1*02:01/DPB1*05:01 VDILQLVTHTKLLKD 192.2 4.24
LVTHTKLLK 157 165 HLA-DRB1*03:01 ILQLVTHTKLLKDRN 87.1 4.48
DILQLVTHTKLLKDR 90.2 4.62
VDILQLVTHTKLLKD 104.3 5.17
NVDILQLVTHTKLLK 127.5 5.94
LQLVTHTKLLKDRNS 207.5 8.21
HLA-DRB5*01:01 ILQLVTHTKLLKDRN 36.1 7.93
HLA-DPA1*01/DPB1*04:01 LQLVTHTKLLKDRNS 165 7.51
HLA-DPA1*02:01/DPB1*05:01 ILQLVTHTKLLKDRN 160.2 3.48
DILQLVTHTKLLKDR 178.3 3.91
HLA-DPA1*03:01/DPB1*04:02 ILQLVTHTKLLKDRN 14.3 1.48
DILQLVTHTKLLKDR 16.6 1.81
LQLVTHTKLLKDRNS 17.3 1.91
VDILQLVTHTKLLKD 21.9 2.55
NVDILQLVTHTKLLK 31.5 3.76
QLVTHTKLLKDRNSQ 43.6 5.11
LVTHTKLLKDRNSQH 88.2 8.89

Table 5: Top T helper cell epitopes and interaction with MHC-II alleles.

Prediction of T helper cell epitopes and modelling

There were five T helper cell conserved epitopes resulted when applied FPV140 protein reference sequence to IEDB MHC-II binding prediction tool to interact with Man MHC II alleles based on nn-align with percentile rank ≤ 10 and nn IC50 ≤ 500, the top five were 110 YIMDNAEKL 118, 155 LQLVTHTKL163, 100 FIADHISLW 108, 136 FITNLDNIT 144, and 157 LVTHTKLLK 165 interacted with five epitopes (Table 6 and Figure 3).

immunome-research-cell-epitopes

Figure 3: Top five T helper cell epitopes interacted with MHC-II alleles.

Peptide Start End BF2*2101
binding energy (kcal/mol)
BF2*0401
binding energy (kcal/mol)
YIMDNAEKL 110 118 -38.57 -25.85
FYHRMYYPL 274 282 -52.08 -49.43
MYYPLFSVF 278 286 -62.58 -*
YVVDNDRYV 231 239 -37.57 -39.41
LLSGVFLAY 317 325 -63.79 -68.52

Table 6: The docking energy Kcal/mol of BF alleles and CTL epitopes *not bind in ideal way with this receptor.

There is overlapping in this epitope 110 YIMDNAEKL 118 between MHC-I epitopes and MHC-II epitopes (Table 6).

Molecular docking of B-F alleles and predicted CTL epitope

The five suggested CTL peptides that interacted with selected man’s MHC-1 alleles: 110 YIMDNAEKL 118, 274 FYHRMYYPL 282, 278 MYYPLFSVF 286 and 231 YVVDNDRYV 239, 317 LLSGVFLAY 325 were used as ligands to detect their interaction with selected BF alleles /receptors (BF2*2101, BF2*0401) Figure  4 by docking Techniques using on-line software. Based on the binding energy in kcal/mol unit, the lowest binding energy (kcal/mol) was selected to obtain best binding and to predict real CTL epitopes as possible, (Figures 5a and 5b).

immunome-research-BF-alleles

Figure 4: BF alleles (BF2*2101, BF2*0401).

immunome-research-Chimera-visualization

Figures 5a and b: The interaction between epitopes and receptors (BF2*2101, BF2*0401) using UCSF-Chimera visualization tool after online docking. A: YIMDNAEKL, B: YVVDNDRYV C: FYHRMYYPL, D: LLSGVFLAY, E: MYYPLFSVF This epitope (MYYPLFSVF) not interacted with receptor BF2*0401 in ideal way.

Discussion

Vaccination is a method to protect and minimize the possibility of infection. In the past there are many type of vaccines used, the most common one is a live attenuated vaccine though it provides the needed immunity but it may cause infection or allergy because it contains the necessary and much unnecessary proteins, in the other hand epitopes based vaccine is just include epitopes which responsible for inducing B and T cell mediated immunity. Nowadays it’s used for many serious diseases such as HIV, Hepatitis B, cancer and for zoonotic viruses like Newcastle disease and avian influenza. In this study FPV 140 used as a target in the designing of peptide based vaccine against Fowlpox virus which is wide spread and had outbreaks in Brazil 1997 and China 2009 which led to severe economic plunge [2,5,19,44].

For good B cell epitope prediction the selected peptide should pass the threshold scores in Bepipred linear epitope prediction, Emini surface accessibility, Kolaskar and Tongaonkar antigenicity and Parker hydrophilicity prediction methods. Eight B cell epitopes were predicted by Bepipred linear epitope prediction. Seven epitopes (50 PPSPKP 55, 51 PSPKPL 56, 52 SPKPLP 57, 53 PKPLPK 58, 54 KPLPKS 59, 55 PLPKSK 60, 56 LPKSKQ 61) from 34 WSYKKGIKNGYDDYRDPPSPKPLPKSKQEPNADDKVGDIE 73  in addition to 18 RPSSTV 23 from 17 GRPSSTVV 24 succeed the Emini surface accessibility, Kolaskar and Tongaonkar antigenicity and Parker hydrophobicity prediction tools. Sometimes may no peptide pass specific test like in Zika virus study Badawi et al. has no peptide passed antigenicity test (44), or as in Newcastle study there was no conserved peptide passed the three test (surface accessibility, antigenicity and hydrophilicity) [49].

The B cell immunity stands for short time so that T cell immunity is required and important because it s long lasting and the CD4 and CD8 has main role in antiviral immunity. Therefore designing of peptide vaccine against T cell is more promising and effective. The T cell predicted epitopes is measured by binding affinity between the peptide and MHC alleles but unfortunately there is no database for chicken allele so the human allele is used as model due to similarity between human and chicken alleles (B-F and B-L alleles) [50,51] therefore HLA A, HLA B and HLA C is used for MHC I while HLA DR, HLA DQ and HLA DP is used for MHC II.

For CTL epitopes prediction ANN method was used with percentile rank ≤ 1 and IC-50 ≤ 100; fifty one conserved epitopes were predicted to interact with Man MHC-1 alleles, eighteen peptides interacted with 2-4 alleles, the top five epitopes 110 YIMDNAEKL 118, 274 FYHRMYYPL 282, 278 MYYPLFSVF 286 and 231 YVVDNDRYV 239, 317 LLSGVFLAY 325 interacted with six and five epitopes respectively (Figure 2).

T helper cell five conserved epitopes resulted when applied FPV140 protein reference sequence to IEDB MHC-II binding prediction tool to interact with Man MHC II alleles, based on nn-align with percentile rank ≤ 10 and IC50 ≤ 500, 110 YIMDNAEKL 118, 155 LQLVTHTKL 163 interacted with nine epitopes followed by 100 FIADHISLW108 with eight epitopes and lastly 136 FITNLDNIT 144, 157 LVTHTKLLK 165 with five epitopes (Table 6 and Figure 3).

There is overlapping in 110 YIMDNAEKL 118 epitope between MHC-I epitopes and MHC-II epitopes (Table 6). Its interacted with (HLA-A*02:01, HLA-C*03:03, HLA-C*07:01, HLA-C*12:03, HLA-C*14:02, HLA-C*15:02)MHC-I alleles and (HLA-DRB1*01:01, HLA-DRB1*03:01, HLA-DRB1*04:01, HLA-DRB1*07:01, HLA-DRB1*07:01, HLA-DRB1*13:02, HLA-DRB3*01:01, HLA-DRB5*01:01, HLA-DQA1*05:01/DQB1*02:01)MHC-II alleles.

The CTL epitopes (110 YIMDNAEKL 118, 274 FYHRMYYPL 282, 278 MYYPLFSVF 286 231 YVVDNDRYV 239 and 317 LLSGVFLAY 325) docked and interacted with BF2*2101, BF2*0401 to detect the presence of real CTL epitopes, theselection of those alleles depend on Kokh et al. study who reported the presence of the first structures of an MHC molecule (BF2*2101) in chicken MHC haplotype B21, not in mammals, Zhang J et al. study who reported the crystal structure of BF2*0401 from the B4 haplotype, Osman et al. used those alleles for docking [41,42,47]. The lowest binding energy (k cal/mol) for (BF2*2101) (BF2*0401) alleles shown by 317 LLSGVFLAY 325 followed by 278 MYYPLFSVF 286 which is not bind with BF2*0401 in ideal way, 274 FYHRMYYPL 282, 231 YVVDNDRYV 239 and 110 YIMDNAEKL 118. Those docked epitopes suggested to be peptide vaccine.

Concisely the five docked epitopes suggested to be peptide vaccine especially 110 YIMDNAEKL 118 it overlapped between CTL epitopes and T helper cell according to these result it will give good vaccine if applied in vivo and in vitro and it will short the time and cost for vaccine production but also we recommend more studies for FPV peptide vaccine due to small sample size in this study and the importance of this vaccine for poultry population.

Conclusion

In this study we tried out to design epitope based vaccine against FPV, which could be test for efficacy in activation of humoral and cell mediated immunity. This study gave a computational data which help in vaccine identification and designing with safety and less cost, thus led to prevention of infection through poultry population. Our result based on sequence analysis and in silico prediction though in vitro and in vivo studies required as long with in silico study to prove the effectiveness of vaccine.

References

Citation: Idris ST, Salih S, Basheir M, Elhadi A, Kamel S, et al. (2018) In silico Prediction of Peptide based Vaccine against Fowlpox Virus (FPV). Immunome Res 14: 154. DOI: 10.4172/1745-7580.1000154

Copyright: © 2018 Idris ST, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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