# Immunoinformatics Approach for Multiepitope Vaccine Prediction from H, M, F, and N Proteins of Peste des Petits Ruminants Virus

Authors: Bothina B. M. Gaafar; Sumaia A. Ali; Khoubieb Ali Abd-elrahman; Yassir A. Almofti
Journal: Journal of Immunology Research
 (2019)

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## Abstract

Background. Small ruminant morbillivirus or peste des petits ruminants virus (PPRV) is an acute and highly contagious viral disease of goats, sheep, and other livestock. This study aimed at predicting an effective multiepitope vaccine against PPRV from the immunogenic proteins haemagglutinin (H), matrix (M), fusion (F), and nucleoprotein (N) using immunoinformatics tools. Materials and Methods. The sequences of the immunogenic proteins were retrieved from GenBank of the National Center for Biotechnology Information (NCBI). BioEdit software was used to align each protein from the retrieved sequences for conservancy. Immune Epitope Database (IEDB) analysis resources were used to predict B and T cell epitopes. For B cells, the criteria for electing epitopes depend on the epitope linearity, surface accessibility, and antigenicity. Results. Nine epitopes from the H protein, eight epitopes from the M protein, and ten epitopes from each of the F and N proteins were predicted as linear epitopes. The surface accessibility method proposed seven surface epitopes from each of the H and F proteins in addition to six and four epitopes from the M and N proteins, respectively. For antigenicity, only two epitopes 142PPERV146 and 63DPLSP67 were predicted as antigenic from H and M, respectively. For T cells, MHC-I binding prediction tools showed multiple epitopes that interacted strongly with BoLA alleles. For instance, the epitope 45MFLSLIGLL53 from the H protein interacted with four BoLA alleles, while 276FKKILCYPL284 predicted from the M protein interacted with two alleles. Although F and N proteins demonstrated no favorable interaction with B cells, they strongly interacted with T cells. For instance, 358STKSCARTL366 from the F protein interacted with five alleles, followed by 340SQNALYPMS348 and 442IDLGPAISL450 that interacted with three alleles each. The epitopes from the N protein displayed strong interaction with BoLA alleles such as 490RSAEALFRL498 that interacted with five alleles, followed by two epitopes 2ATLLKSLAL10 and 304QQLGEVAPY312 that interacted with four alleles each. In addition to that, four epitopes 3TLLKSLALF11, 356YFDPAYFRL364, 360AYFRLGQEM368, and 412PRQAQVSFL420 interacted with three alleles each. Conclusion. Fourteen epitopes were predicted as promising vaccine candidates against PPRV from four immunogenic proteins. These epitopes should be validated experimentally through in vitro and in vivo studies.


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## Body

## 1. Introduction

Small ruminant morbillivirus (previously called peste des petits ruminants virus (PPRV)) is one of the most damaging ruminant diseases. It is among the priority diseases indicated in the FAO-OIE Global Framework for the Progressive Control of Transboundary Animal Diseases (GF-TADs) in the 5-year Action Plan [1, 2]. PPRV is one of the top ten diseases in sheep and goats that are having a high impact on the poor rural small ruminant farmers [3]. The disease is considered an acute and highly contagious viral disease with a high morbidity and mortality rate in small ruminants, such as goats and sheep and related wild animals [4, 5]. The disease is characterized by high fever, depression, anorexia, ocular and nasal discharge, pneumonia, necrosis and ulceration of mucous membranes, and inflammation of the gastrointestinal tract leading to severe diarrhea [6, 7]. It causes high death rates in goats and sheep up to 100% and 90%, respectively. However, sheep can be subclinically infected and play a major role in the silent spread of PPRV over large distances and across borders [1]. The disease is widely distributed in Africa, on the Arabian Peninsula, and in the Middle East and Asia [5, 8, 9]. Morbilliviruses are rapidly inactivated at environmental temperature by solar radiation and desiccation. This indicated that the transmission occurred by direct contact with infected animals or their excretions. Transmission of PPRV occurs primarily by droplet infection but may also occur by ingestion of contaminated feed or water [6].PPRV is an enveloped single strand of negative sense RNA virus, belonging to the genus Morbillivirus, in the family Paramyxoviridae which is closely related torinderpest virus (RPV), canine distemper virus (CDV), and measles virus (MeV) [5, 10, 11]. The genome of morbilliviruses is organized into six transcriptional units encoding six structural proteins. These structural proteins include the nucleoprotein (N protein), matrix protein (M protein), polymerase or large protein (L protein), phosphoprotein (P protein), and two envelope glycoproteins, the haemagglutinin protein (H protein) and the fusion protein (F protein) [12–14]. The N protein played an important role in the viral life cycle, interacting with both viral and cellular proteins. It also interacted with the viral RNA to form the nucleocapsid structures seen in both the virions and infected cells [13]. The viral L and P proteins interact with the nucleocapsids to form the functional transcription/replication unit of the virion [13]. The C-termini of morbillivirus N proteins also interacted with cellular regulatory proteins such as heat shock protein Hsp72, interferon regulator factor- (IRF-) 3, and a novel cell surface receptor (genetically engineered receptor) [13]. The F protein facilitated the virus penetration of the host cell membrane. This protein is also critical for the induction of an effective protective immune response [15]. The M protein of paramyxoviruses forms an inner coat to the viral envelope and thus serves as a bridge between the surface viral glycoproteins and the ribonucleoprotein core. By virtue of its position, M appeared to play a central role in viral assembly by formation of new virions which were liberated from the infected cell by budding [16, 17]. Interaction of the PPRV H and F proteins with the host plasma membrane led to viral entry by binding of the H protein to receptors [17]. Generally, the protective cell-mediated and humoral immune responses against morbilliviruses are directed mainly against H, F, M, and N proteins. Moreover, PPRV is genetically grouped into four distinct lineages (I, II, III, and IV) based on the analysis of the fusion (F) gene. This classification of PPRV into lineages has broadened the understanding of the molecular epidemiology and worldwide movement of PPR viruses [7, 18–20].Vaccination is the main tool for controlling and eradicating the PPR virus [12]. Despite the fact that live attenuated vaccines have been widely used to protect small ruminants against circulating PPRV [1, 3, 7], the continuous spread of PPR disease indicated two possible hypotheses. The first is the emergence of new PPRV strains with new genetic makeup and greater fitness in the face of vaccine-elicited protection. The second is the lapses in regulatory control that ultimately lead to movement of diseased/infected individuals across the region/state/country without proper monitoring and surveillance [1].The advances made in the field of immunoinformatics tools coinciding with the knowledge on the host immune response lead to new disciplines in vaccine design against diseases via computer in silico epitope predictions. The epitope-driven vaccine is a new concept that is being successfully applied in multiple studies, particularly to the development of vaccines targeting conserved epitopes in variable or rapidly mutating pathogens [21–23]. The identification of specific epitopes derived from infectious disease has significantly advanced the development of peptide-based vaccines. Peptides elicited more desirable manipulation of immune response through the use of the B cell epitopes. These epitopes mainly induce antibody production from B cells and cellular response and cytokine secretion from T cells. The approach regarding the molecular basis of antigen recognition and HLA binding motifs to host class I and class II MHC proteins is highly supported by the immunoinformatics which aids in designing epitope-based vaccine motifs that serve as therapeutic candidates for many infectious diseases [24].The main objective of this study was to analyze multiple immunogenic proteins from the PPR genome for designing a safe multiepitope vaccine using immunoinformatics tools present in the Immune Epitope Database (IEDB). These proteins include haemagglutinin protein (H), matrix protein (M), fusion protein (F), and nucleoprotein (N) sequences of PPRV strains reported in the (NCBI) database.


## 2. Materials and Methods

### 2.1. Sequence Retrieval

Four immunogenic protein sequences of PPRV (updated August 2018) were retrieved from GenBank of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) in Oct. 2018. These included 82 sequences from the haemagglutinin protein (H protein), 67 sequences from the matrix protein (M protein), 94 sequences from the fusion protein (F protein), and 80 sequences from the nucleoprotein (N protein). All sequences were retrieved in FASTA format. The retrieved sequences, their accession numbers, and geographical locations are listed in Tables 1–4.Table 1
Retrieved strains of the H protein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
AEH25644
China
2011
29
ATS17278
Sierra Leone
2017
57
ASN64042
China
2017
2
ABY71271
China
2008
30
AMX28327
India
2017
58
ASN64036
China
2017
3
AAS68031
India
2009
31
AMX28319
India
2017
59
ASN64030
China
2017
4
ABX75304
Cote d’Ivoire
2008
32
AMX28311
India
2017
60
ASN64024
China
2017
5
ABX75312
Nigeria
2008
33
ANS54233
Liberia
2016
61
ASN64018
China
2017
6
ADM32488
India
2012
34
AKT04315
Benin
2016
62
ASN64012
China
2017
7
AEX61013
India
2012
35
AKT04307
Benin
2016
63
ASN64006
China
2017
8
ASY05923
Georgia
2017
36
AKR81281
India
2015
64
ASN64000
China
2017
9
ARB50221
China
2017
37
AKT04323
Cote d’Ivoire
2015
65
ASN63994
China
2017
10
ANS59483
India
2016
38
AJT59441
Senegal
2015
66
ASN63988
China
2017
11
AKQ09544
India
2015
39
AID07002
Ghana
2015
67
ASN63982
China
2017
12
AIL54036
UAE
2014
40
AIN40492
Kenya
2014
68
ASN63976
China
2017
13
AIL54028
Oman
2014
41
AHG50444
India
2014
69
ASN63970
China
2017
14
AIL54020
Uganda
2014
42
ACQ44671
China
2011
70
ASN63964
China
2017
15
AIL53996
Ethiopia
2014
43
ABY61988
India
2008
71
ASN63867
China
2017
16
AIL54012
India
2014
44
ABY61986
India
2008
72
ASN63861
China
2017
17
AIL54004
Ethiopia
2014
45
CAH61258
Turkey
2005
73
ASN63855
China
2017
18
CAD54790
India
2004
46
ANG60369
Nigeria
2016
74
ASN63849
China
2017
19
AHA58209
Iraq
2018
47
ANG60361
Nigeria
2016
75
ART66998
Algeria
2017
20
ARP51875
Mongolia
2018
48
AJE30413
China
2015
76
ALM55670
China
2016
21
AOO35467
China
2018
49
AJE30404
China
2015
77
ALA65398
China
2015
22
AIL29370
Turkey
2014
50
AUP34040
Nigeria
2018
78
AKN58853
China
2015
23
AGG09146
Morocco
2014
51
ASN64078
China
2015
79
AJA39814
China
2015
24
ADN03213
India
2013
52
ASN64072
China
2017
80
AIK97759
China
2014
25
ACN62119
India
2015
53
ASN64066
China
2017
81
AIK19904
Senegal
2014
26
CAJ01700
Nigeria
2005
54
ASN64060
China
2017
82
ADX95995
Nigeria
2011
27
YP_133827
Turkey
2018
55
ASN64054
China
2017
28
AUO30190
Bangladesh
2018
56
ASN64048
China
2017Table 2
Retrieved strains of the matrix protein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
YP_133825
Turkey
2018
24
APX56396
Mali
2013
47
AIL54018
Uganda
2014
2
ANS54231
Liberia
2016
25
APX56395
Senegal
2015
48
AIL54010
India
2014
3
AKT04313
Benin
2016
26
APX56394
Comoros
2005
49
AIL53994
Ethiopia
2014
4
AKT04305
Benin
2016
27
APX56393
Mali
2018
50
AIL54002
Ethiopia
2014
5
AMX28325
India
2017
28
APX56392
Mali
2018
51
AGO28147
India
2013
6
AMX28317
India
2017
29
APX56391
Mali
2018
47
AIL54018
Uganda
2014
7
ANS59481
India
2016
30
APX56390
Algeria
2018
52
AGG09144
Morocco
2013
8
AKR81279
India
2015
31
APX56389
Guinea
2018
53
AEH25642
China
2011
9
CAJ01698
Nigeria
2005
32
APX56388
Mauritania
2018
54
ACQ44669
China
2011
10
ADN03215
India
2015
33
APX56387
Senegal
2018
55
ABY61987
India
2008
11
ADN03212
India
2012
34
APX56386
Senegal
2018
56
ABY61985
India
2008
12
ADM32486
India
2012
35
AUO30188
Bangladesh
2018
57
CAH61256
Turkey
2005
13
ACN62117
India
2012
36
ATS17276
Sierra Leone
2017
58
AOO35465
China
2016
14
AEX61011
India
2012
37
ASY05921
Georgia
2017
59
ABX75310
Nigeria
2008
15
AWD71674
Pakistan
2018
38
ARP51873
Mongolia
2017
60
ABX75302
Cote d’Ivoire
2008
16
AWD71668
Pakistan
2018
39
AKQ09542
India
2015
61
AUP34038
Nigeria
2018
17
AWD71662
Pakistan
2018
40
AKT04321
Cote d’Ivoire
2015
62
ART66996
Algeria
2017
18
AMX28309
India
2017
41
AJT59439
Senegal
2015
63
AIK19902
Senegal
2014
19
APX56401
Senegal
2018
42
AKG94167
India
2015
64
ADX95993
Nigeria
2011
20
APX56400
Senegal
2018
43
AID07000
Ghana
2015
65
AAS68029
India
2009
21
APX56399
Senegal
2018
44
AIN40490
Kenya
2014
66
ANG60367
Nigeria
2016
22
APX56398
Senegal
2014
45
AIL54034
UAE
2014
67
ANG60359
Nigeria
2016
23
APX56397
Senegal
2014
46
AIL54026
Oman
2014Table 3
Retrieved strains of the fusion protein of PPRV with their date of collection, accession numbers, and geographical region.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
YP_133821
Turkey
2018
28
AMQ48343
China
2016
55
AFC87747
Nigeria
2012
2
AIN40487
Kenya
2014
29
AMQ48342
China
2016
56
AFC87741
Nigeria
2012
3
AYA72170
India
2018
30
AKQ09546
India
2015
57
AFC87740
Nigeria
2012
4
AYA72169
India
2018
31
AKG94165
India
2015
58
AFC87739
Nigeria
2012
5
AYA72168
India
2018
32
AIL54030
UAE
2014
59
ABZ81035
China
2008
6
AYA72167
India
2018
33
AIL54022
Oman
2014
60
AEX61010
India
2012
7
ACN62116
India
2012
34
AIL54014
Uganda
2014
61
AEH25639
China
2011
8
ACN62115
India
2012
35
AIL53990
Ethiopia
2014
62
ACQ44667
China
2011
9
AUO30184
Bangladesh
2018
36
AIL54006
India
2014
63
CAH61252
Turkey
2005
10
AUB45018
China
2017
37
AIL53998
Ethiopia
2014
64
CAD91555
Turkey
2003
11
ATS17282
Sierra Leone
2017
38
AGJ84027
Morocco
2013
65
CAA52454
Nigeria
2005
12
ANS54228
Liberia
2016
39
AFC87764
Nigeria
2012
66
AMX28321
India
2017
13
AKT04301
Benin
2016
40
AFC87763
Nigeria
2012
67
AMX28313
India
2017
14
AKT04317
Cote d’Ivoire
2015
41
AFC87762
Nigeria
2012
68
AMX28305
India
2017
15
AJT59435
Senegal
2015
42
AFC87761
Nigeria
2012
69
ANS59477
India
2016
16
ABX75299
Cote d’Ivoire
2008
43
AFC87760
Nigeria
2012
70
AKT04309
Benin
2016
17
ABY61984
India
2008
44
AFC87759
Nigeria
2012
71
AKR81275
India
2015
18
ABX75307
Nigeria
2008
45
AFC87758
Nigeria
2012
72
AGG09141
Morocco
2013
19
ART66992
Algeria
2017
46
AFC87757
Nigeria
2012
73
ADJ05523
China
2010
20
APD77391
China
2016
47
AFC87756
Nigeria
2012
74
AAS68026
India
2009
21
ADN03214
India
2012
48
AFC87755
Nigeria
2012
75
ANG60363
Nigeria
2016
22
ADN03211
India
2012
49
AFC87754
Nigeria
2012
76
ANG60355
Nigeria
2016
23
ADM32485
India
2012
50
AFC87753
Nigeria
2012
77
AUP34034
Nigeria
2018
24
ASY05917
Georgia
2017
51
AFC87752
Nigeria
2012
78
AIK19898
Senegal
2014
25
ARP51869
India
2017
52
AFC87750
Nigeria
2012
79
ADX95989
Nigeria
2011
26
AOO35463
China
2017
53
AFC87749
Nigeria
2012
80
AID07004
Ghana
2015
27
AMQ48344
China
2016
54
AFC87748
Nigeria
2012Table 4
Retrieved strains of the nucleoprotein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
AHA58208
Iraq
2018
33
ASN63969
China
2017
65
AIL54019
Uganda
2014
2
AOO35466
China
2016
34
ASN63963
China
2017
66
AIL53995
Ethiopia
2014
3
AGJ84028
Morocco
2013
35
ASN63872
China
2017
67
AIK97758
China
2014
4
CAJ01699
Nigeria
2005
36
ASN63866
China
2017
68
AIK19903
Senegal
2014
5
AHN53450
India
2014
37
ASN63860
China
2017
69
AIL54011
India
2014
6
ADN03216
India
2012
38
ASN63854
China
2017
70
AIL54003
Ethiopia
2014
7
ADM32487
India
2012
39
ASN63848
China
2017
71
AHG50445
India
2014
8
ACN62118
India
2012
40
ARP51874
Mongolia
2017
72
AGP04219
Bangladesh
2013
9
AEX61012
India
2012
41
AMX28326
India
2017
73
AGG09145
Morocco
2013
10
ADX95994
Nigeria
2011
42
AMX28318
India
2017
74
AFR66765
China
2012
11
YP_133826
Turkey
2017
43
AMX28310
India
2017
75
ACV31220
India
2012
12
ATS17277
Sierra
2017
44
APD77392
China
2016
76
ACV31219
India
2012
13
ASY05922
Georgia
2017
45
ANS59482
India
2016
77
ACQ44670
China
2011
14
ASV72322
China
2017
46
AHF58487
India
2016
78
ADJ05518
China
2010
15
ASN64077
China
2017
47
ALM55669
China
2016
79
CAH61257
Turkey
2005
16
ASN64071
China
2017
48
AKT04314
Benin
2016
80
AXE28383
Israel
2018
17
ASN64065
China
2017
49
AKT04306
Benin
2016
81
AWD71675
Pakistan
2018
18
ASN64059
China
2017
50
ALA65397
China
2015
82
AWD71669
Pakistan
2018
19
ASN64053
China
2017
51
AKN58852
China
2015
83
AWD71663
Pakistan
2018
20
ASN64047
China
2017
52
AKR81280
India
2015
84
AUP34039
Nigeria
2018
21
ASN64041
China
2017
53
AKQ09543
India
2015
85
AUO30189
Bangladesh
2018
22
ASN64035
China
2017
54
AKT04322
Cote d’Ivoire
2015
86
ANG60368
Nigeria
2016
23
ASN64029
China
2017
55
AJT59440
Senegal
2015
87
ANG60360
Nigeria
2016
24
ASN64023
China
2017
56
AKG94168
India
2015
88
ANS54232
Liberia
2016
25
ASN64017
China
2017
57
AJA39813
China
2015
89
ART66997
Algeria
2017
26
ASN64011
China
2017
58
AID07001
Ghana
2015
90
ABX75303
Cote d’Ivoire
2008
27
ASN64005
China
2017
59
AJE30412
China
2015
91
ABX75311
Nigeria
2008
28
ASN63999
China
2017
60
AJE30403
China
2015
92
ABY71270
China
2008
29
ASN63993
China
2017
61
AJE30396
China
2015
93
AAS68030
India
2009
30
ASN63987
China
2017
62
AIN40491
Kenya
2014
94
AEH25643
China
2011
31
ASN63981
China
2017
63
AIL54035
UAE
2014
32
ASN63975
China
2017
64
AIL54027
Oman
2014


### 2.2. Phylogenetic Evolution

A phylogenetic tree of the retrieved sequences of each immunogenic protein was constricted using MEGA7.0.26 (7170509) software [25]. Each protein tree was constructed using the maximum likelihood parameter in the software.


### 2.3. Multiple Sequence Alignment

The complete protein sequences of each immunogenic protein of PPRV were aligned via BioEdit software (version 7.2.5) to generate a multiple sequence alignment (MSA) with the ClustalW tool [26].


### 2.4. Epitope Prediction

Several immunobioinformatics tools were used for prediction of multiple epitopes from the four immunogenic proteins of PPRV. Tools from the Immune Epitope Database analysis resource (http://www.iedb.org/) [27] were used to analyze the immunogenic proteins. The input was the reference sequences of H protein (YP_133827.2), M protein (YP_133825.1), F protein (YP_133826.1), and N protein (YP_133821.1). They were submitted to Epitope Analysis Resources to predict B and T cell epitopes. The predicted epitopes were further investigated in aligned retrieved sequences for conservancy to identify the proposed candidate epitopes.


#### 2.4.1. B Cell Epitope Prediction

Epitopes that interacted with the B lymphocytes are a discrete part from the antigenic molecule that is recognized by the B cell receptor and elicited immunoglobulin production. These predicted epitopes are characterized by their surface accessibility and their antigenic reactivity with the immunoglobulins of the humoral immunity [24]. Epitope prediction tools of the Immune Epitope Database (IEDB) at http://tools.iedb.org/bcell/ [27] were used for this purpose. Linear B cell epitopes were predicted by BepiPred linear epitope prediction (http://tools.iedb.org/bcell/result/) [28]. The Emini surface accessibility prediction tool was performed to detect the surface accessible epitopes (http://tools.iedb.org/bcell/) [29], while prediction of antigenic epitopes was performed to identify the antigenic determinants on proteins based on the physicochemical properties of amino acid residues using the Kolaskar and Tongaonkar antigenicity method (http://tools.immuneepitope.org/bcell/) [30].


#### 2.4.2. Cytotoxic T Lymphocyte Epitope Prediction

IEDB tools (http://tools.iedb.org/mhci/) were used to predict different cytotoxic T cell (CTL) epitopes that bind to the major histocompatibility complex class I alleles (MHC class I) [31]. Analysis was done using cow alleles (BoLA-D18.4, BoLA-HD6, BoLA-JSP.1, BoLA-T2a, BoLA-T2b, and BoLA-T2c). An artificial neural network (ANN) was used to predict the binding affinity [32, 33]. The peptide length for all selected epitopes was set to 9 amino acids (9mers). Percentile rank required for the peptide’s binding to the specific MHC-I molecules was set in the range from 1 to 3.


### 2.5. Homology Modeling

#### 2.5.1. The Three-Dimensional (3D) Structures of the Reference Sequences of PPRV

The prediction of the three-dimensional (3D) structure of H, M, and F protein reference sequences of PPRV was performed using the RaptorX structure prediction server (http://raptorx.uchicago.edu/StructurePrediction/predict/) [34–36], while the N protein sequence was submitted to the SPARKS-X server (http://sparks-lab.org/yueyang/server/SPARKS-X/) [37]. The 3D structure of each protein reference sequence was later treated with Chimera software 1.8 to show the position of proposed epitopes [38].


## 2.1. Sequence Retrieval

Four immunogenic protein sequences of PPRV (updated August 2018) were retrieved from GenBank of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/protein) in Oct. 2018. These included 82 sequences from the haemagglutinin protein (H protein), 67 sequences from the matrix protein (M protein), 94 sequences from the fusion protein (F protein), and 80 sequences from the nucleoprotein (N protein). All sequences were retrieved in FASTA format. The retrieved sequences, their accession numbers, and geographical locations are listed in Tables 1–4.Table 1
Retrieved strains of the H protein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
AEH25644
China
2011
29
ATS17278
Sierra Leone
2017
57
ASN64042
China
2017
2
ABY71271
China
2008
30
AMX28327
India
2017
58
ASN64036
China
2017
3
AAS68031
India
2009
31
AMX28319
India
2017
59
ASN64030
China
2017
4
ABX75304
Cote d’Ivoire
2008
32
AMX28311
India
2017
60
ASN64024
China
2017
5
ABX75312
Nigeria
2008
33
ANS54233
Liberia
2016
61
ASN64018
China
2017
6
ADM32488
India
2012
34
AKT04315
Benin
2016
62
ASN64012
China
2017
7
AEX61013
India
2012
35
AKT04307
Benin
2016
63
ASN64006
China
2017
8
ASY05923
Georgia
2017
36
AKR81281
India
2015
64
ASN64000
China
2017
9
ARB50221
China
2017
37
AKT04323
Cote d’Ivoire
2015
65
ASN63994
China
2017
10
ANS59483
India
2016
38
AJT59441
Senegal
2015
66
ASN63988
China
2017
11
AKQ09544
India
2015
39
AID07002
Ghana
2015
67
ASN63982
China
2017
12
AIL54036
UAE
2014
40
AIN40492
Kenya
2014
68
ASN63976
China
2017
13
AIL54028
Oman
2014
41
AHG50444
India
2014
69
ASN63970
China
2017
14
AIL54020
Uganda
2014
42
ACQ44671
China
2011
70
ASN63964
China
2017
15
AIL53996
Ethiopia
2014
43
ABY61988
India
2008
71
ASN63867
China
2017
16
AIL54012
India
2014
44
ABY61986
India
2008
72
ASN63861
China
2017
17
AIL54004
Ethiopia
2014
45
CAH61258
Turkey
2005
73
ASN63855
China
2017
18
CAD54790
India
2004
46
ANG60369
Nigeria
2016
74
ASN63849
China
2017
19
AHA58209
Iraq
2018
47
ANG60361
Nigeria
2016
75
ART66998
Algeria
2017
20
ARP51875
Mongolia
2018
48
AJE30413
China
2015
76
ALM55670
China
2016
21
AOO35467
China
2018
49
AJE30404
China
2015
77
ALA65398
China
2015
22
AIL29370
Turkey
2014
50
AUP34040
Nigeria
2018
78
AKN58853
China
2015
23
AGG09146
Morocco
2014
51
ASN64078
China
2015
79
AJA39814
China
2015
24
ADN03213
India
2013
52
ASN64072
China
2017
80
AIK97759
China
2014
25
ACN62119
India
2015
53
ASN64066
China
2017
81
AIK19904
Senegal
2014
26
CAJ01700
Nigeria
2005
54
ASN64060
China
2017
82
ADX95995
Nigeria
2011
27
YP_133827
Turkey
2018
55
ASN64054
China
2017
28
AUO30190
Bangladesh
2018
56
ASN64048
China
2017Table 2
Retrieved strains of the matrix protein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
YP_133825
Turkey
2018
24
APX56396
Mali
2013
47
AIL54018
Uganda
2014
2
ANS54231
Liberia
2016
25
APX56395
Senegal
2015
48
AIL54010
India
2014
3
AKT04313
Benin
2016
26
APX56394
Comoros
2005
49
AIL53994
Ethiopia
2014
4
AKT04305
Benin
2016
27
APX56393
Mali
2018
50
AIL54002
Ethiopia
2014
5
AMX28325
India
2017
28
APX56392
Mali
2018
51
AGO28147
India
2013
6
AMX28317
India
2017
29
APX56391
Mali
2018
47
AIL54018
Uganda
2014
7
ANS59481
India
2016
30
APX56390
Algeria
2018
52
AGG09144
Morocco
2013
8
AKR81279
India
2015
31
APX56389
Guinea
2018
53
AEH25642
China
2011
9
CAJ01698
Nigeria
2005
32
APX56388
Mauritania
2018
54
ACQ44669
China
2011
10
ADN03215
India
2015
33
APX56387
Senegal
2018
55
ABY61987
India
2008
11
ADN03212
India
2012
34
APX56386
Senegal
2018
56
ABY61985
India
2008
12
ADM32486
India
2012
35
AUO30188
Bangladesh
2018
57
CAH61256
Turkey
2005
13
ACN62117
India
2012
36
ATS17276
Sierra Leone
2017
58
AOO35465
China
2016
14
AEX61011
India
2012
37
ASY05921
Georgia
2017
59
ABX75310
Nigeria
2008
15
AWD71674
Pakistan
2018
38
ARP51873
Mongolia
2017
60
ABX75302
Cote d’Ivoire
2008
16
AWD71668
Pakistan
2018
39
AKQ09542
India
2015
61
AUP34038
Nigeria
2018
17
AWD71662
Pakistan
2018
40
AKT04321
Cote d’Ivoire
2015
62
ART66996
Algeria
2017
18
AMX28309
India
2017
41
AJT59439
Senegal
2015
63
AIK19902
Senegal
2014
19
APX56401
Senegal
2018
42
AKG94167
India
2015
64
ADX95993
Nigeria
2011
20
APX56400
Senegal
2018
43
AID07000
Ghana
2015
65
AAS68029
India
2009
21
APX56399
Senegal
2018
44
AIN40490
Kenya
2014
66
ANG60367
Nigeria
2016
22
APX56398
Senegal
2014
45
AIL54034
UAE
2014
67
ANG60359
Nigeria
2016
23
APX56397
Senegal
2014
46
AIL54026
Oman
2014Table 3
Retrieved strains of the fusion protein of PPRV with their date of collection, accession numbers, and geographical region.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
YP_133821
Turkey
2018
28
AMQ48343
China
2016
55
AFC87747
Nigeria
2012
2
AIN40487
Kenya
2014
29
AMQ48342
China
2016
56
AFC87741
Nigeria
2012
3
AYA72170
India
2018
30
AKQ09546
India
2015
57
AFC87740
Nigeria
2012
4
AYA72169
India
2018
31
AKG94165
India
2015
58
AFC87739
Nigeria
2012
5
AYA72168
India
2018
32
AIL54030
UAE
2014
59
ABZ81035
China
2008
6
AYA72167
India
2018
33
AIL54022
Oman
2014
60
AEX61010
India
2012
7
ACN62116
India
2012
34
AIL54014
Uganda
2014
61
AEH25639
China
2011
8
ACN62115
India
2012
35
AIL53990
Ethiopia
2014
62
ACQ44667
China
2011
9
AUO30184
Bangladesh
2018
36
AIL54006
India
2014
63
CAH61252
Turkey
2005
10
AUB45018
China
2017
37
AIL53998
Ethiopia
2014
64
CAD91555
Turkey
2003
11
ATS17282
Sierra Leone
2017
38
AGJ84027
Morocco
2013
65
CAA52454
Nigeria
2005
12
ANS54228
Liberia
2016
39
AFC87764
Nigeria
2012
66
AMX28321
India
2017
13
AKT04301
Benin
2016
40
AFC87763
Nigeria
2012
67
AMX28313
India
2017
14
AKT04317
Cote d’Ivoire
2015
41
AFC87762
Nigeria
2012
68
AMX28305
India
2017
15
AJT59435
Senegal
2015
42
AFC87761
Nigeria
2012
69
ANS59477
India
2016
16
ABX75299
Cote d’Ivoire
2008
43
AFC87760
Nigeria
2012
70
AKT04309
Benin
2016
17
ABY61984
India
2008
44
AFC87759
Nigeria
2012
71
AKR81275
India
2015
18
ABX75307
Nigeria
2008
45
AFC87758
Nigeria
2012
72
AGG09141
Morocco
2013
19
ART66992
Algeria
2017
46
AFC87757
Nigeria
2012
73
ADJ05523
China
2010
20
APD77391
China
2016
47
AFC87756
Nigeria
2012
74
AAS68026
India
2009
21
ADN03214
India
2012
48
AFC87755
Nigeria
2012
75
ANG60363
Nigeria
2016
22
ADN03211
India
2012
49
AFC87754
Nigeria
2012
76
ANG60355
Nigeria
2016
23
ADM32485
India
2012
50
AFC87753
Nigeria
2012
77
AUP34034
Nigeria
2018
24
ASY05917
Georgia
2017
51
AFC87752
Nigeria
2012
78
AIK19898
Senegal
2014
25
ARP51869
India
2017
52
AFC87750
Nigeria
2012
79
ADX95989
Nigeria
2011
26
AOO35463
China
2017
53
AFC87749
Nigeria
2012
80
AID07004
Ghana
2015
27
AMQ48344
China
2016
54
AFC87748
Nigeria
2012Table 4
Retrieved strains of the nucleoprotein of PPRV with their date of collection, accession numbers, and geographical regions.
No.
Accession number
Country
Year
No.
Accession number
Country
Year
No.
Accession number
Country
Year
1
AHA58208
Iraq
2018
33
ASN63969
China
2017
65
AIL54019
Uganda
2014
2
AOO35466
China
2016
34
ASN63963
China
2017
66
AIL53995
Ethiopia
2014
3
AGJ84028
Morocco
2013
35
ASN63872
China
2017
67
AIK97758
China
2014
4
CAJ01699
Nigeria
2005
36
ASN63866
China
2017
68
AIK19903
Senegal
2014
5
AHN53450
India
2014
37
ASN63860
China
2017
69
AIL54011
India
2014
6
ADN03216
India
2012
38
ASN63854
China
2017
70
AIL54003
Ethiopia
2014
7
ADM32487
India
2012
39
ASN63848
China
2017
71
AHG50445
India
2014
8
ACN62118
India
2012
40
ARP51874
Mongolia
2017
72
AGP04219
Bangladesh
2013
9
AEX61012
India
2012
41
AMX28326
India
2017
73
AGG09145
Morocco
2013
10
ADX95994
Nigeria
2011
42
AMX28318
India
2017
74
AFR66765
China
2012
11
YP_133826
Turkey
2017
43
AMX28310
India
2017
75
ACV31220
India
2012
12
ATS17277
Sierra
2017
44
APD77392
China
2016
76
ACV31219
India
2012
13
ASY05922
Georgia
2017
45
ANS59482
India
2016
77
ACQ44670
China
2011
14
ASV72322
China
2017
46
AHF58487
India
2016
78
ADJ05518
China
2010
15
ASN64077
China
2017
47
ALM55669
China
2016
79
CAH61257
Turkey
2005
16
ASN64071
China
2017
48
AKT04314
Benin
2016
80
AXE28383
Israel
2018
17
ASN64065
China
2017
49
AKT04306
Benin
2016
81
AWD71675
Pakistan
2018
18
ASN64059
China
2017
50
ALA65397
China
2015
82
AWD71669
Pakistan
2018
19
ASN64053
China
2017
51
AKN58852
China
2015
83
AWD71663
Pakistan
2018
20
ASN64047
China
2017
52
AKR81280
India
2015
84
AUP34039
Nigeria
2018
21
ASN64041
China
2017
53
AKQ09543
India
2015
85
AUO30189
Bangladesh
2018
22
ASN64035
China
2017
54
AKT04322
Cote d’Ivoire
2015
86
ANG60368
Nigeria
2016
23
ASN64029
China
2017
55
AJT59440
Senegal
2015
87
ANG60360
Nigeria
2016
24
ASN64023
China
2017
56
AKG94168
India
2015
88
ANS54232
Liberia
2016
25
ASN64017
China
2017
57
AJA39813
China
2015
89
ART66997
Algeria
2017
26
ASN64011
China
2017
58
AID07001
Ghana
2015
90
ABX75303
Cote d’Ivoire
2008
27
ASN64005
China
2017
59
AJE30412
China
2015
91
ABX75311
Nigeria
2008
28
ASN63999
China
2017
60
AJE30403
China
2015
92
ABY71270
China
2008
29
ASN63993
China
2017
61
AJE30396
China
2015
93
AAS68030
India
2009
30
ASN63987
China
2017
62
AIN40491
Kenya
2014
94
AEH25643
China
2011
31
ASN63981
China
2017
63
AIL54035
UAE
2014
32
ASN63975
China
2017
64
AIL54027
Oman
2014


## 2.2. Phylogenetic Evolution

A phylogenetic tree of the retrieved sequences of each immunogenic protein was constricted using MEGA7.0.26 (7170509) software [25]. Each protein tree was constructed using the maximum likelihood parameter in the software.


## 2.3. Multiple Sequence Alignment

The complete protein sequences of each immunogenic protein of PPRV were aligned via BioEdit software (version 7.2.5) to generate a multiple sequence alignment (MSA) with the ClustalW tool [26].


## 2.4. Epitope Prediction

Several immunobioinformatics tools were used for prediction of multiple epitopes from the four immunogenic proteins of PPRV. Tools from the Immune Epitope Database analysis resource (http://www.iedb.org/) [27] were used to analyze the immunogenic proteins. The input was the reference sequences of H protein (YP_133827.2), M protein (YP_133825.1), F protein (YP_133826.1), and N protein (YP_133821.1). They were submitted to Epitope Analysis Resources to predict B and T cell epitopes. The predicted epitopes were further investigated in aligned retrieved sequences for conservancy to identify the proposed candidate epitopes.


### 2.4.1. B Cell Epitope Prediction

Epitopes that interacted with the B lymphocytes are a discrete part from the antigenic molecule that is recognized by the B cell receptor and elicited immunoglobulin production. These predicted epitopes are characterized by their surface accessibility and their antigenic reactivity with the immunoglobulins of the humoral immunity [24]. Epitope prediction tools of the Immune Epitope Database (IEDB) at http://tools.iedb.org/bcell/ [27] were used for this purpose. Linear B cell epitopes were predicted by BepiPred linear epitope prediction (http://tools.iedb.org/bcell/result/) [28]. The Emini surface accessibility prediction tool was performed to detect the surface accessible epitopes (http://tools.iedb.org/bcell/) [29], while prediction of antigenic epitopes was performed to identify the antigenic determinants on proteins based on the physicochemical properties of amino acid residues using the Kolaskar and Tongaonkar antigenicity method (http://tools.immuneepitope.org/bcell/) [30].


### 2.4.2. Cytotoxic T Lymphocyte Epitope Prediction

IEDB tools (http://tools.iedb.org/mhci/) were used to predict different cytotoxic T cell (CTL) epitopes that bind to the major histocompatibility complex class I alleles (MHC class I) [31]. Analysis was done using cow alleles (BoLA-D18.4, BoLA-HD6, BoLA-JSP.1, BoLA-T2a, BoLA-T2b, and BoLA-T2c). An artificial neural network (ANN) was used to predict the binding affinity [32, 33]. The peptide length for all selected epitopes was set to 9 amino acids (9mers). Percentile rank required for the peptide’s binding to the specific MHC-I molecules was set in the range from 1 to 3.


## 2.4.1. B Cell Epitope Prediction

Epitopes that interacted with the B lymphocytes are a discrete part from the antigenic molecule that is recognized by the B cell receptor and elicited immunoglobulin production. These predicted epitopes are characterized by their surface accessibility and their antigenic reactivity with the immunoglobulins of the humoral immunity [24]. Epitope prediction tools of the Immune Epitope Database (IEDB) at http://tools.iedb.org/bcell/ [27] were used for this purpose. Linear B cell epitopes were predicted by BepiPred linear epitope prediction (http://tools.iedb.org/bcell/result/) [28]. The Emini surface accessibility prediction tool was performed to detect the surface accessible epitopes (http://tools.iedb.org/bcell/) [29], while prediction of antigenic epitopes was performed to identify the antigenic determinants on proteins based on the physicochemical properties of amino acid residues using the Kolaskar and Tongaonkar antigenicity method (http://tools.immuneepitope.org/bcell/) [30].


## 2.4.2. Cytotoxic T Lymphocyte Epitope Prediction

IEDB tools (http://tools.iedb.org/mhci/) were used to predict different cytotoxic T cell (CTL) epitopes that bind to the major histocompatibility complex class I alleles (MHC class I) [31]. Analysis was done using cow alleles (BoLA-D18.4, BoLA-HD6, BoLA-JSP.1, BoLA-T2a, BoLA-T2b, and BoLA-T2c). An artificial neural network (ANN) was used to predict the binding affinity [32, 33]. The peptide length for all selected epitopes was set to 9 amino acids (9mers). Percentile rank required for the peptide’s binding to the specific MHC-I molecules was set in the range from 1 to 3.


## 2.5. Homology Modeling

### 2.5.1. The Three-Dimensional (3D) Structures of the Reference Sequences of PPRV

The prediction of the three-dimensional (3D) structure of H, M, and F protein reference sequences of PPRV was performed using the RaptorX structure prediction server (http://raptorx.uchicago.edu/StructurePrediction/predict/) [34–36], while the N protein sequence was submitted to the SPARKS-X server (http://sparks-lab.org/yueyang/server/SPARKS-X/) [37]. The 3D structure of each protein reference sequence was later treated with Chimera software 1.8 to show the position of proposed epitopes [38].


## 2.5.1. The Three-Dimensional (3D) Structures of the Reference Sequences of PPRV

The prediction of the three-dimensional (3D) structure of H, M, and F protein reference sequences of PPRV was performed using the RaptorX structure prediction server (http://raptorx.uchicago.edu/StructurePrediction/predict/) [34–36], while the N protein sequence was submitted to the SPARKS-X server (http://sparks-lab.org/yueyang/server/SPARKS-X/) [37]. The 3D structure of each protein reference sequence was later treated with Chimera software 1.8 to show the position of proposed epitopes [38].


## 3. Results and Discussion

The validity and benefits of peptide vaccines designed by bioinformatics tools had been verified by appreciable research [24]. The availability of the complete genome, proteome sequences, and pathogenesis of many pathogenic microorganisms contributed to the production of a vaccine through bioinformatics [24, 39]. In this study, the predicted epitopes from B and T lymphocytes would help in the development of a more effective, reliable, preventive, and therapeutic vaccine against the PPRV than the conventional methods.


### 3.1. Phylogenetic Evolution

A phylogenetic tree was constructed using MEGA7.0.26 (7170509). The evolutionary divergence among each protein was analyzed. As shown in Figure1, the retrieved strains of the H protein revealed that Asian strains were clustered together as well as the European and African strains. However, strains from the United Arab Emirates and Oman were closely related to African strains (namely to Ethiopian strains). With regard to the phylogeny of the M protein strains, the African strains were also clustered together, but among them, the Oman and United Arab Emirates strains were observed to be close to the Ethiopian strains same as those of the H protein. This result may indicate the transfer of the H and M strain segments between these countries. Also, some European and Turkish strains were clustered together. As shown in Figure 2, the retrieved strains of F and N proteins from the Asian strains were clustered together with molecular divergence among them as well as the strains retrieved from the African countries. Also, the Omanis and Emiratis strains showed close relationship to the African strains. These results indicated that these strain segments were widely distributed in Africa, Asia, Europe, and the Arab region.Figure 1
Phylogenetic tree of retrieved strains of H and M proteins. The retrieved strains demonstrated divergence in their common ancestors.Figure 2
Phylogenetic tree of retrieved strains of F and N proteins. The retrieved strains demonstrated divergence in their common ancestors.


### 3.2. Sequence Alignment

Multiple sequence alignment was performed using ClustalW in BioEdit software. As shown in Figure3, the aligned sequences of each of the four analyzed proteins (H, M, F, and N proteins) showed considerable conservancy among the retrieved strains. However, some regions exhibited differences (mutations) in some amino acids in various sequences.Figure 3
Multiple sequence alignment (MSA) of the retrieved strains of H, M, F, and N proteins using BioEdit software and ClustalW. Dots indicate the conservancy of the retrieved strains, and letters within the aligned sequences indicate no conservancy (mutation) in the amino acid.


### 3.3. Prediction of B Cell Epitopes

B cell epitope prediction methods aimed are at identifying the antigens recognized by B lymphocytes to initiate humoral immunity [24]. The important criteria for selecting a potential epitope for vaccine development are surface accessibility, hydrophobicity, flexibility, and antigenicity [40]. The predicted epitopes should be located on the surface of the cells so that it is more accessible for both the humoral and the cellular immune systems. Antigenicity also is one of the important features of an antigen for vaccine development [40]. Depending on binding affinity to B lymphocytes, the BepiPred linear epitope prediction method predicted nine linear epitopes from the H protein, eight epitopes from M proteins, and ten epitopes for each of the F and N proteins. Analysis of these linear epitopes for surface accessibility proposed seven surface epitopes from each of the H and F proteins, six epitopes from the M protein, and four epitopes from the N protein.As shown in Figure4, the threshold values were 0.350 and 1.000 for all epitopes predicted through the BepiPred linear epitope (conserved epitopes) and Emini prediction methods (surface accessibility), respectively. The antigenicity prediction method proposed only two epitopes for all test immunogenic proteins of PPRV. Also, Figure 4 shows that the antigenic epitopes were predicted from H, M, F, and N proteins using the Kolaskar and Tongaonkar antigenicity method under threshold values of 1.014, 1.037, 1.054, and 1.014, respectively. However, no epitopes successfully passed the threshold for the F and N proteins.Figure 4
Prediction of B cell epitopes by different IEDB scales (BepiPred linear epitope prediction, Emini surface accessibility, and Kolaskar and Tongaonkar antigenicity prediction) for H, M, F, and N proteins. Regions above the threshold (red line) were proposed as a part of the B cell epitope while regions below the threshold (red line) were not.Only one epitope from each of the H and M proteins successfully overlapped all the B cell antigenic index prediction methods. Namely, these epitopes were142PPERV146 from the H protein and 63DPLSP67 from the M protein. The 3D structure of the four proteins (H, M, F, and N) is shown in Figure 5. The positions of the best B cells that predicted epitopes from the H and M proteins are demonstrated in Figure 6. The overall predicted epitopes from the four proteins are illustrated in Table 5.Figure 5
The prediction of the three-dimensional (3D) structure of H, M, and F protein reference sequences of PPRV was performed using the RaptorX structure prediction server, while the N protein sequence was submitted to the SPARKS-X server.Figure 6
The positions of the proposed B cell epitopes in the 3D structure of the reference sequences of PPRV H and M proteins.Table 5
B cell epitope prediction from H, M, F, and N proteins; the position of peptides is according to the position of amino acids in the protein of the PPR virus.
H protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.041
1
PHNK
16
19
4
2.683
0.969
2
SIDHQ
83
87
5
1.169
1.03
3
PPERV#
142
146
5
1.904
1.047
4
TVTL
305
308
4
0.505
1.113
5
TLGG
330
333
4
0.462
0.977
6
EANWVVPSTDVRDLQ
362
376
15
1.022
1.039
7
KTRPPSFCNGTG
387
398
12
1.314
0.982
8
GPWSEGRIP
400
408
9
1.023
0.962
9
DVSR
530
533
4
1.29
1.034
M protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.037
1
SAWDV
10
14
5
0.533
1.044
2
GDRK
43
46
4
2.478
0.886
3
EDNDPLSP∗
60
67
8
3.167
0.969
4
DPLSP∗#
63
67
5
1.332
1.051
5
VGRT
69
72
4
0.795
1.01
6
PEEL
87
90
4
1.464
1.004
7
DNGYYS∗
167
172
6
2.116
0.975
8
INDD
325
328
4
1.203
0.915
F protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.054
1
TGSA
34
37
4
0.867
0.965
2
SNQA
153
156
4
1.691
0.967
3
SLRDP
216
220
5
2.058
1.013
4
QEWYT
305
309
5
2.625
0.966
5
VFTP
331
334
4
0.643
1.112
6
GTVC
336
339
4
0.255
1.144
7
GSTKS
357
361
5
1.888
0.947
8
QDPDK
402
406
5
5.498
0.948
9
VGSREYPD
428
435
8
2.837
1.01
10
LKPDLTGTSKS
531
541
11
3.269
0.996
N protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.014
1
DKAPTASGSGGAI∗
16
28
13
0.249
0.981
2
IPGDSSI
39
45
7
0.345
1.019
3
GDPDINGS
60
67
8
0.735
0.935
4
TDDPDV
92
97
6
1.569
0.992
5
STRSQS
107
112
6
2.397
0.972
6
GADLD∗
120
124
5
0.619
0.984
7
VTAPDTAADS
182
191
10
0.594
1.02
8
RTPGNKPR
242
249
8
4.853
0.92
9
KFSA
323
326
4
0.83
1.024
10
RGTGPRQA∗
408
415
8
1.69
0.943
∗Peptides revealed a higher score if they were shortened in all tools. #Epitopes that passed all the B cell prediction methods and were proposed as B cell epitopes.


### 3.4. Prediction of CTL Epitopes That Interacted with MHC Class I (BoLA Alleles)

CD8+ and CD4+ T cells have a principal role in the stimulation of immune response as well as antigen-mediated clonal expression of the B cell [14]. Unfortunately, the bovine genome project did not assemble a complete sequence of the bovine MHC-II locus [41–43]. Thus, the analysis was completed with BoLA MHC-I alleles only. Cell-mediated immunity induced by cytotoxic T lymphocytes (CTLs) is vital for the defense against viral diseases. CTLs are responsible for the immune elimination of intracellular pathogens such as viruses because these cells recognize the presented endogenous antigenic peptides by the MHC class I molecules [44].In this study, MHC-I binding prediction methods using the IEDB database predicted different CTL epitopes that strongly interacted with various BoLA alleles. The fusion (F) protein proposed a higher number of predicted epitopes with strong interaction with BoLA alleles. Ten epitopes were proposed based on the number of the interacted alleles. The best one was358STKSCARTL366 that associated with five alleles, followed by 442IDLGPAISL450 and 340SQNALYPMS348 as they linked to three alleles each. However, seven epitopes, namely, 339CSQNALYPMS347, 336GTVCSQNAL344, 279IAYPTLSEI287, 230LSYALGGDI238, 136SLMNSQAIE144, 283TLSEIKGVI291, and 314YVATQGYLI322 were predicted to interact with two alleles.The nucleoprotein (N) also displayed strong interaction activity with BoLA alleles. Seven epitopes were proposed with strong interaction with BoLA alleles. The top N protein epitope was490RSAEALFRL498 which was associated with five alleles, followed by two epitopes, namely, 2ATLLKSLAL10, and 304QQLGEVAPY312 that linked to four alleles each. In addition to that, four epitopes 3TLLKSLALF11, 356YFDPAYFRL364, 360AYFRLGQEM368, and 412PRQAQVSFL420 interacted with three bovine alleles each. Surprisingly, these two proteins (F and N) achieved promising results in CTL prediction methods, although they failed to predict any epitope carrying all the ideal traits in B cells.The haemagglutinin (H) protein predicted five CTL epitopes, but one epitope was predicted as the best peptide,45MFLSLIGLL53, as it linked to four BoLA alleles, followed by four peptides that interacted with two alleles each. They were 113DLVKFISDK121, 405GRIPAYGVI413, 52LLAIAGIRL60, and 44VMFLSLIGL52. However, this protein showed a somewhat satisfactory result in B and T cell prediction methods. The M protein showed unsatisfactory results in CTL prediction methods different from that predicted by B cell methods. The results suggested only one epitope; 276FKKILCYPL284 interacted with only two alleles. The overall epitopes that were proposed to interact with CTL alleles are illustrated in Table 6 for all proteins. The positions of the best CTL-predicted epitopes in their immunogenic protein structure are shown in Figure 7.Table 6
Position of CTL epitopes in the H protein, M protein, F protein, and N protein of PPRV that bind with high affinity with the BoLA class I alleles.
Peptide
Start
End
Allele
Percentile rank
H protein
DLVKFISDK
113
121
BoLA-T2a
2
BoLA-T2C
1.9
FLRVFEIGL
251
259
BoLA-HD6
1
GRIPAYGVI
405
413
BoLA-D18.4
2.3
BoLA-T2b
2.1
LLAIAGIRL
52
60
BoLA-HD6
1.5
BoLA-T2b
2.3
LSLIGLLAI
47
55
BoLA-T2a
2.9
LVKFISDKI
114
122
BoLA-HD6
1.3
MFLSLIGLL
45
53
BoLA-HD6
2.1
BoLA-JSP.1
1.7
BoLA-T2b
1.4
BoLA-T2C
1.7
VMFLSLIGL
44
52
BoLA-JSP.1
2.6
BoLA-T2C
1.7
WCYHDCLIY
578
586
BoLA-T2a
1.2
WSEGRIPAY
402
410
BoLA-JSP.1
2.2
M protein
SAWDVKGSI
10
18
BoLA-HD6
1.8
EELLREATE
88
96
BoLA-T2b
1
ELLREATEL
89
97
BoLA-HD6
1.3
PQRFRVVYM
152
160
BoLA-JSP.1
1.7
HVGNFRRKK
220
228
BoLA-T2a
2.4
GGIGGTSLH
251
259
BoLA-T2a
2
LHAQLGFKK
270
278
BoLA-T2a
1.2
AQLGFKKIL
272
280
BoLA-T2C
2.4
FKKILCYPL
276
284
BoLA-D18.4
1.2
BoLA-HD6
1.3
EFRVYDDVI
316
324
BoLA-HD6
2.6
F protein
AGVALHQSL
129
137
BoLA-T2C
1.7
ASVLCKCYT
387
395
BoLA-T2a
1.4
AYPTLSEIK
280
288
BoLA-T2a
2.9
CSQNALYPM
339
347
BoLA-JSP.1
2.3
BoLA-T2a
2.5
DETSCVFTP
326
334
BoLA-T2b
2.7
DLGPAISLE
443
451
BoLA-T2C
1.3
EKLDVGTNL
451
459
BoLA-T2b
2.7
GSTKSCART
357
365
BoLA-T2a
2.6
GTVCSQNAL
336
344
BoLA-JSP.1
2.5
BoLA-T2b
1.6
GVALHQSLM
130
138
BoLA-HD6
2.1
IAYPTLSEI
279
287
BoLA-HD6
2.9
BoLA-JSP.1
2.3
IDLGPAISL
442
450
BoLA-D18.4
2.1
BoLA-T2b
1.4
BoLA-T2C
1.2
IQALSYALG
227
235
BoLA-T2b
2.7
IQVGSREYP
426
434
BoLA-D18.4
2.8
KGIKARVTY
259
267
BoLA-D18.4
1.2
KPDLTGTSK
532
540
BoLA-T2a
2.6
LEKLDVGTN
450
458
BoLA-T2b
3
LIANCASVL
382
390
BoLA-HD6
1.6
LSKGNLIAN
377
385
BoLA-T2a
3
LSYALGGDI
230
238
BoLA-HD6
1.8
BoLA-JSP.1
1.2
NALYPMSPL
342
350
BoLA-T2b
1.5
PMSPLLQEC
346
354
BoLA-T2C
3
RFILSKGNL
374
382
BoLA-T2b
2
SIQALSYAL
226
234
BoLA-T2b
1
SLMNSQAIE
136
144
BoLA-D18.4
3
BoLA-T2C
2.5
SQNALYPMS
340
348
BoLA-D18.4
2.7
BoLA-HD6
1.7
BoLA-D18.4
2.3
STKSCARTL
358
366
BoLA-D18.4
3
BoLA-HD6
2.7
BoLA-JSP.1
1.6
BoLA-T2b
2.9
BoLA-T2C
2.7
TGTSKSYVR
536
544
BoLA-T2a
2.9
TKSCARTLV
359
367
BoLA-D18.4
2.3
TLSEIKGVI
283
291
BoLA-D18.4
2.4
BoLA-T2C
1.8
YVATQGYLI
314
322
BoLA-HD6
2
BoLA-T2C
2.8
N protein
ATLLKSLAL
2
10
BoLA-D18.4
1.6
BoLA-JSP.1
1.3
BoLA-T2a
2.6
BoLA-T2b
1.9
TLLKSLALF
3
11
BoLA-D18.4
2.6
BoLA-HD6
3
BoLA-T2C
1.3
QQLGEVAPY
304
312
BoLA-HD6
2.2
BoLA-JSP.1
3
BoLA-T2a
1.4
BoLA-T2b
2.9
YFDPAYFRL
356
364
BoLA-JSP.1
1.3
BoLA-T2b
2.4
BoLA-T2C
1.5
AYFRLGQEM
360
368
BoLA-HD6
1.6
BoLA-JSP.1
2.1
BoLA-T2C
2.9
PRQAQVSFL
412
420
BoLA-JSP.1
1.7
BoLA-T2b
2.2
BoLA-T2C
2.6
RSAEALFRL
490
498
BoLA-D18.4
1.2
BoLA-HD6
1.2
BoLA-T2a
1.9
BoLA-T2b
2.9
BoLA-T2C
2.7Figure 7
The positions of the predicted T cell epitopes in the 3D structure of the reference sequences of PPRV H, M, F, and N proteins.Vaccination is considered the most effective way of controlling PPR. The infection by morbillivirus is associated with severe immunosuppression that is characterized by a massive virus-specific immune response. Protection is mediated by cell-mediated and humoral immune responses directed mainly against particular proteins in the viral structure. These proteins included H, F, and N proteins [45–47]. It was reported that the envelope glycoproteins H and F of PPRV demonstrated a protective and neutralizing antibody response [3, 48–50]. In this study, using the immunoinformatics prediction methods, the H protein demonstrated affinity to interact with B cells that was characterized by antibody production. This result coincided with the previously published reports [3, 48–50], while the F protein failed to interact with B cells; i.e., no epitopes from the F protein had passed the threshold of the B cell prediction methods. However, this protein revealed multiple predicted epitopes that demonstrated high affinity to the alleles of CTLs. The M protein which is believed to play a very significant role in morbillivirus assembly and budding by concentrating the F, H, and N proteins at the virus-assembly site [16, 17] showed moderate affinity to B cells. One epitope from the M protein as well as the H protein was predicted as a B cell epitope. Moreover, the M protein revealed multiple epitopes that interacted with CTLs of the cell-mediated immunity. This result indicated that the M protein besides its role in the virus assembly may also contain antigenic determinants that could be elected as vaccine candidates.In addition to that, cell-mediated immunity plays a role in protection against the viral infection. Despite the N protein being the most frequent viral protein in PPRV, it does not induce a neutralizing antibody response in the host [50]. However, it has been found to induce a strong cell-mediated immune response, which is believed to contribute to protection. Here, in this report, the same result was obtained. The N protein demonstrated no affinity to elicit the humoral immune response. However, it showed favorable affinity to interact with a cell-mediated response. It is noteworthy that five out of seven epitopes predicted from the nucleoprotein of PPRV in this study were found to be proposed by another in silico study using mouse alleles and NetMHCI methods [51]. The proposed epitopes from that study were ATLLKSLAL, TLLKSLALF, YFDPAYFRL, AYFRLGQEM, and RSAEALFRL. Thus, the predictions for the different epitopes that bound to different alleles particularly from the N protein of PPRV were somewhat in agreement regardless of the alleles (cow and mouse alleles) and algorithm used (ANN, NetMHCI).In general, epitope-based vaccines that are chemically well-characterized have become desirable candidate vaccines due to their relative ease of production and construction, chemical stability, and lack of infectious potential [52]. Many in silico studies have shown the value of using prediction programs to evaluate the efficiency of binding of putative epitopes to various human and animal alleles [33, 52–55].


## 3.1. Phylogenetic Evolution

A phylogenetic tree was constructed using MEGA7.0.26 (7170509). The evolutionary divergence among each protein was analyzed. As shown in Figure1, the retrieved strains of the H protein revealed that Asian strains were clustered together as well as the European and African strains. However, strains from the United Arab Emirates and Oman were closely related to African strains (namely to Ethiopian strains). With regard to the phylogeny of the M protein strains, the African strains were also clustered together, but among them, the Oman and United Arab Emirates strains were observed to be close to the Ethiopian strains same as those of the H protein. This result may indicate the transfer of the H and M strain segments between these countries. Also, some European and Turkish strains were clustered together. As shown in Figure 2, the retrieved strains of F and N proteins from the Asian strains were clustered together with molecular divergence among them as well as the strains retrieved from the African countries. Also, the Omanis and Emiratis strains showed close relationship to the African strains. These results indicated that these strain segments were widely distributed in Africa, Asia, Europe, and the Arab region.Figure 1
Phylogenetic tree of retrieved strains of H and M proteins. The retrieved strains demonstrated divergence in their common ancestors.Figure 2
Phylogenetic tree of retrieved strains of F and N proteins. The retrieved strains demonstrated divergence in their common ancestors.


## 3.2. Sequence Alignment

Multiple sequence alignment was performed using ClustalW in BioEdit software. As shown in Figure3, the aligned sequences of each of the four analyzed proteins (H, M, F, and N proteins) showed considerable conservancy among the retrieved strains. However, some regions exhibited differences (mutations) in some amino acids in various sequences.Figure 3
Multiple sequence alignment (MSA) of the retrieved strains of H, M, F, and N proteins using BioEdit software and ClustalW. Dots indicate the conservancy of the retrieved strains, and letters within the aligned sequences indicate no conservancy (mutation) in the amino acid.


## 3.3. Prediction of B Cell Epitopes

B cell epitope prediction methods aimed are at identifying the antigens recognized by B lymphocytes to initiate humoral immunity [24]. The important criteria for selecting a potential epitope for vaccine development are surface accessibility, hydrophobicity, flexibility, and antigenicity [40]. The predicted epitopes should be located on the surface of the cells so that it is more accessible for both the humoral and the cellular immune systems. Antigenicity also is one of the important features of an antigen for vaccine development [40]. Depending on binding affinity to B lymphocytes, the BepiPred linear epitope prediction method predicted nine linear epitopes from the H protein, eight epitopes from M proteins, and ten epitopes for each of the F and N proteins. Analysis of these linear epitopes for surface accessibility proposed seven surface epitopes from each of the H and F proteins, six epitopes from the M protein, and four epitopes from the N protein.As shown in Figure4, the threshold values were 0.350 and 1.000 for all epitopes predicted through the BepiPred linear epitope (conserved epitopes) and Emini prediction methods (surface accessibility), respectively. The antigenicity prediction method proposed only two epitopes for all test immunogenic proteins of PPRV. Also, Figure 4 shows that the antigenic epitopes were predicted from H, M, F, and N proteins using the Kolaskar and Tongaonkar antigenicity method under threshold values of 1.014, 1.037, 1.054, and 1.014, respectively. However, no epitopes successfully passed the threshold for the F and N proteins.Figure 4
Prediction of B cell epitopes by different IEDB scales (BepiPred linear epitope prediction, Emini surface accessibility, and Kolaskar and Tongaonkar antigenicity prediction) for H, M, F, and N proteins. Regions above the threshold (red line) were proposed as a part of the B cell epitope while regions below the threshold (red line) were not.Only one epitope from each of the H and M proteins successfully overlapped all the B cell antigenic index prediction methods. Namely, these epitopes were142PPERV146 from the H protein and 63DPLSP67 from the M protein. The 3D structure of the four proteins (H, M, F, and N) is shown in Figure 5. The positions of the best B cells that predicted epitopes from the H and M proteins are demonstrated in Figure 6. The overall predicted epitopes from the four proteins are illustrated in Table 5.Figure 5
The prediction of the three-dimensional (3D) structure of H, M, and F protein reference sequences of PPRV was performed using the RaptorX structure prediction server, while the N protein sequence was submitted to the SPARKS-X server.Figure 6
The positions of the proposed B cell epitopes in the 3D structure of the reference sequences of PPRV H and M proteins.Table 5
B cell epitope prediction from H, M, F, and N proteins; the position of peptides is according to the position of amino acids in the protein of the PPR virus.
H protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.041
1
PHNK
16
19
4
2.683
0.969
2
SIDHQ
83
87
5
1.169
1.03
3
PPERV#
142
146
5
1.904
1.047
4
TVTL
305
308
4
0.505
1.113
5
TLGG
330
333
4
0.462
0.977
6
EANWVVPSTDVRDLQ
362
376
15
1.022
1.039
7
KTRPPSFCNGTG
387
398
12
1.314
0.982
8
GPWSEGRIP
400
408
9
1.023
0.962
9
DVSR
530
533
4
1.29
1.034
M protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.037
1
SAWDV
10
14
5
0.533
1.044
2
GDRK
43
46
4
2.478
0.886
3
EDNDPLSP∗
60
67
8
3.167
0.969
4
DPLSP∗#
63
67
5
1.332
1.051
5
VGRT
69
72
4
0.795
1.01
6
PEEL
87
90
4
1.464
1.004
7
DNGYYS∗
167
172
6
2.116
0.975
8
INDD
325
328
4
1.203
0.915
F protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.054
1
TGSA
34
37
4
0.867
0.965
2
SNQA
153
156
4
1.691
0.967
3
SLRDP
216
220
5
2.058
1.013
4
QEWYT
305
309
5
2.625
0.966
5
VFTP
331
334
4
0.643
1.112
6
GTVC
336
339
4
0.255
1.144
7
GSTKS
357
361
5
1.888
0.947
8
QDPDK
402
406
5
5.498
0.948
9
VGSREYPD
428
435
8
2.837
1.01
10
LKPDLTGTSKS
531
541
11
3.269
0.996
N protein
Peptide
Start
End
Length
Emini 1.000
Kolaskar 1.014
1
DKAPTASGSGGAI∗
16
28
13
0.249
0.981
2
IPGDSSI
39
45
7
0.345
1.019
3
GDPDINGS
60
67
8
0.735
0.935
4
TDDPDV
92
97
6
1.569
0.992
5
STRSQS
107
112
6
2.397
0.972
6
GADLD∗
120
124
5
0.619
0.984
7
VTAPDTAADS
182
191
10
0.594
1.02
8
RTPGNKPR
242
249
8
4.853
0.92
9
KFSA
323
326
4
0.83
1.024
10
RGTGPRQA∗
408
415
8
1.69
0.943
∗Peptides revealed a higher score if they were shortened in all tools. #Epitopes that passed all the B cell prediction methods and were proposed as B cell epitopes.


## 3.4. Prediction of CTL Epitopes That Interacted with MHC Class I (BoLA Alleles)

CD8+ and CD4+ T cells have a principal role in the stimulation of immune response as well as antigen-mediated clonal expression of the B cell [14]. Unfortunately, the bovine genome project did not assemble a complete sequence of the bovine MHC-II locus [41–43]. Thus, the analysis was completed with BoLA MHC-I alleles only. Cell-mediated immunity induced by cytotoxic T lymphocytes (CTLs) is vital for the defense against viral diseases. CTLs are responsible for the immune elimination of intracellular pathogens such as viruses because these cells recognize the presented endogenous antigenic peptides by the MHC class I molecules [44].In this study, MHC-I binding prediction methods using the IEDB database predicted different CTL epitopes that strongly interacted with various BoLA alleles. The fusion (F) protein proposed a higher number of predicted epitopes with strong interaction with BoLA alleles. Ten epitopes were proposed based on the number of the interacted alleles. The best one was358STKSCARTL366 that associated with five alleles, followed by 442IDLGPAISL450 and 340SQNALYPMS348 as they linked to three alleles each. However, seven epitopes, namely, 339CSQNALYPMS347, 336GTVCSQNAL344, 279IAYPTLSEI287, 230LSYALGGDI238, 136SLMNSQAIE144, 283TLSEIKGVI291, and 314YVATQGYLI322 were predicted to interact with two alleles.The nucleoprotein (N) also displayed strong interaction activity with BoLA alleles. Seven epitopes were proposed with strong interaction with BoLA alleles. The top N protein epitope was490RSAEALFRL498 which was associated with five alleles, followed by two epitopes, namely, 2ATLLKSLAL10, and 304QQLGEVAPY312 that linked to four alleles each. In addition to that, four epitopes 3TLLKSLALF11, 356YFDPAYFRL364, 360AYFRLGQEM368, and 412PRQAQVSFL420 interacted with three bovine alleles each. Surprisingly, these two proteins (F and N) achieved promising results in CTL prediction methods, although they failed to predict any epitope carrying all the ideal traits in B cells.The haemagglutinin (H) protein predicted five CTL epitopes, but one epitope was predicted as the best peptide,45MFLSLIGLL53, as it linked to four BoLA alleles, followed by four peptides that interacted with two alleles each. They were 113DLVKFISDK121, 405GRIPAYGVI413, 52LLAIAGIRL60, and 44VMFLSLIGL52. However, this protein showed a somewhat satisfactory result in B and T cell prediction methods. The M protein showed unsatisfactory results in CTL prediction methods different from that predicted by B cell methods. The results suggested only one epitope; 276FKKILCYPL284 interacted with only two alleles. The overall epitopes that were proposed to interact with CTL alleles are illustrated in Table 6 for all proteins. The positions of the best CTL-predicted epitopes in their immunogenic protein structure are shown in Figure 7.Table 6
Position of CTL epitopes in the H protein, M protein, F protein, and N protein of PPRV that bind with high affinity with the BoLA class I alleles.
Peptide
Start
End
Allele
Percentile rank
H protein
DLVKFISDK
113
121
BoLA-T2a
2
BoLA-T2C
1.9
FLRVFEIGL
251
259
BoLA-HD6
1
GRIPAYGVI
405
413
BoLA-D18.4
2.3
BoLA-T2b
2.1
LLAIAGIRL
52
60
BoLA-HD6
1.5
BoLA-T2b
2.3
LSLIGLLAI
47
55
BoLA-T2a
2.9
LVKFISDKI
114
122
BoLA-HD6
1.3
MFLSLIGLL
45
53
BoLA-HD6
2.1
BoLA-JSP.1
1.7
BoLA-T2b
1.4
BoLA-T2C
1.7
VMFLSLIGL
44
52
BoLA-JSP.1
2.6
BoLA-T2C
1.7
WCYHDCLIY
578
586
BoLA-T2a
1.2
WSEGRIPAY
402
410
BoLA-JSP.1
2.2
M protein
SAWDVKGSI
10
18
BoLA-HD6
1.8
EELLREATE
88
96
BoLA-T2b
1
ELLREATEL
89
97
BoLA-HD6
1.3
PQRFRVVYM
152
160
BoLA-JSP.1
1.7
HVGNFRRKK
220
228
BoLA-T2a
2.4
GGIGGTSLH
251
259
BoLA-T2a
2
LHAQLGFKK
270
278
BoLA-T2a
1.2
AQLGFKKIL
272
280
BoLA-T2C
2.4
FKKILCYPL
276
284
BoLA-D18.4
1.2
BoLA-HD6
1.3
EFRVYDDVI
316
324
BoLA-HD6
2.6
F protein
AGVALHQSL
129
137
BoLA-T2C
1.7
ASVLCKCYT
387
395
BoLA-T2a
1.4
AYPTLSEIK
280
288
BoLA-T2a
2.9
CSQNALYPM
339
347
BoLA-JSP.1
2.3
BoLA-T2a
2.5
DETSCVFTP
326
334
BoLA-T2b
2.7
DLGPAISLE
443
451
BoLA-T2C
1.3
EKLDVGTNL
451
459
BoLA-T2b
2.7
GSTKSCART
357
365
BoLA-T2a
2.6
GTVCSQNAL
336
344
BoLA-JSP.1
2.5
BoLA-T2b
1.6
GVALHQSLM
130
138
BoLA-HD6
2.1
IAYPTLSEI
279
287
BoLA-HD6
2.9
BoLA-JSP.1
2.3
IDLGPAISL
442
450
BoLA-D18.4
2.1
BoLA-T2b
1.4
BoLA-T2C
1.2
IQALSYALG
227
235
BoLA-T2b
2.7
IQVGSREYP
426
434
BoLA-D18.4
2.8
KGIKARVTY
259
267
BoLA-D18.4
1.2
KPDLTGTSK
532
540
BoLA-T2a
2.6
LEKLDVGTN
450
458
BoLA-T2b
3
LIANCASVL
382
390
BoLA-HD6
1.6
LSKGNLIAN
377
385
BoLA-T2a
3
LSYALGGDI
230
238
BoLA-HD6
1.8
BoLA-JSP.1
1.2
NALYPMSPL
342
350
BoLA-T2b
1.5
PMSPLLQEC
346
354
BoLA-T2C
3
RFILSKGNL
374
382
BoLA-T2b
2
SIQALSYAL
226
234
BoLA-T2b
1
SLMNSQAIE
136
144
BoLA-D18.4
3
BoLA-T2C
2.5
SQNALYPMS
340
348
BoLA-D18.4
2.7
BoLA-HD6
1.7
BoLA-D18.4
2.3
STKSCARTL
358
366
BoLA-D18.4
3
BoLA-HD6
2.7
BoLA-JSP.1
1.6
BoLA-T2b
2.9
BoLA-T2C
2.7
TGTSKSYVR
536
544
BoLA-T2a
2.9
TKSCARTLV
359
367
BoLA-D18.4
2.3
TLSEIKGVI
283
291
BoLA-D18.4
2.4
BoLA-T2C
1.8
YVATQGYLI
314
322
BoLA-HD6
2
BoLA-T2C
2.8
N protein
ATLLKSLAL
2
10
BoLA-D18.4
1.6
BoLA-JSP.1
1.3
BoLA-T2a
2.6
BoLA-T2b
1.9
TLLKSLALF
3
11
BoLA-D18.4
2.6
BoLA-HD6
3
BoLA-T2C
1.3
QQLGEVAPY
304
312
BoLA-HD6
2.2
BoLA-JSP.1
3
BoLA-T2a
1.4
BoLA-T2b
2.9
YFDPAYFRL
356
364
BoLA-JSP.1
1.3
BoLA-T2b
2.4
BoLA-T2C
1.5
AYFRLGQEM
360
368
BoLA-HD6
1.6
BoLA-JSP.1
2.1
BoLA-T2C
2.9
PRQAQVSFL
412
420
BoLA-JSP.1
1.7
BoLA-T2b
2.2
BoLA-T2C
2.6
RSAEALFRL
490
498
BoLA-D18.4
1.2
BoLA-HD6
1.2
BoLA-T2a
1.9
BoLA-T2b
2.9
BoLA-T2C
2.7Figure 7
The positions of the predicted T cell epitopes in the 3D structure of the reference sequences of PPRV H, M, F, and N proteins.Vaccination is considered the most effective way of controlling PPR. The infection by morbillivirus is associated with severe immunosuppression that is characterized by a massive virus-specific immune response. Protection is mediated by cell-mediated and humoral immune responses directed mainly against particular proteins in the viral structure. These proteins included H, F, and N proteins [45–47]. It was reported that the envelope glycoproteins H and F of PPRV demonstrated a protective and neutralizing antibody response [3, 48–50]. In this study, using the immunoinformatics prediction methods, the H protein demonstrated affinity to interact with B cells that was characterized by antibody production. This result coincided with the previously published reports [3, 48–50], while the F protein failed to interact with B cells; i.e., no epitopes from the F protein had passed the threshold of the B cell prediction methods. However, this protein revealed multiple predicted epitopes that demonstrated high affinity to the alleles of CTLs. The M protein which is believed to play a very significant role in morbillivirus assembly and budding by concentrating the F, H, and N proteins at the virus-assembly site [16, 17] showed moderate affinity to B cells. One epitope from the M protein as well as the H protein was predicted as a B cell epitope. Moreover, the M protein revealed multiple epitopes that interacted with CTLs of the cell-mediated immunity. This result indicated that the M protein besides its role in the virus assembly may also contain antigenic determinants that could be elected as vaccine candidates.In addition to that, cell-mediated immunity plays a role in protection against the viral infection. Despite the N protein being the most frequent viral protein in PPRV, it does not induce a neutralizing antibody response in the host [50]. However, it has been found to induce a strong cell-mediated immune response, which is believed to contribute to protection. Here, in this report, the same result was obtained. The N protein demonstrated no affinity to elicit the humoral immune response. However, it showed favorable affinity to interact with a cell-mediated response. It is noteworthy that five out of seven epitopes predicted from the nucleoprotein of PPRV in this study were found to be proposed by another in silico study using mouse alleles and NetMHCI methods [51]. The proposed epitopes from that study were ATLLKSLAL, TLLKSLALF, YFDPAYFRL, AYFRLGQEM, and RSAEALFRL. Thus, the predictions for the different epitopes that bound to different alleles particularly from the N protein of PPRV were somewhat in agreement regardless of the alleles (cow and mouse alleles) and algorithm used (ANN, NetMHCI).In general, epitope-based vaccines that are chemically well-characterized have become desirable candidate vaccines due to their relative ease of production and construction, chemical stability, and lack of infectious potential [52]. Many in silico studies have shown the value of using prediction programs to evaluate the efficiency of binding of putative epitopes to various human and animal alleles [33, 52–55].


## 4. Conclusion

This study focused mainly on the production of a peptide vaccine against H, M, F, and N proteins of PPRV using immunoinformatics tools. Epitopes that showed conservancy and high binding affinities to many MHC alleles are considered the best candidates forin vitro and in vivo testing. Epitopes that were predicted from B cell prediction methods like 142PPERV146 and 305TVTL308 from the H protein and 63DPLSP67 and 64PLSP67 from the M protein could act as good B cell epitopes to induce humoral immunity. While the F and N proteins failed to fulfill all B cell indexes used in this study for the prediction of promising epitopes, however, these proteins predicted epitopes that interacted with various BoLA MHC-I alleles. For instance, the best epitopes were predicted from F (358STKSCARTL366) and N (490RSAEALFRL498) proteins as they interacted with five MHC-I BoLA alleles, followed by 45MFLSLIGLL53 proposed from the H protein and linked with four alleles, while the 276FKKILCYPL284 epitope was predicted from the M protein linked with only two alleles. Although bioinformatics studies have been established to facilitate the peptide design, not all peptides that are predicted in silico are optimally immunogenic in vivo and it remains necessary to test the expected peptides in vivo to ensure that the T cell responses are elicited.



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*Source: 6124030-2019-10-30.xml*