Designing a
Multiepitope Vaccine (MEV) using In Silico Approach against Newcastle
Disease (ND) Virus
Sunaina Shahzad1,
Muhammad Sarwar Khan1*, Faiz Ahmad Joyia1 and Muhammad
Anjum Zia2
1Centre of Agricultural
Biochemistry and Biotechnology (CABB), University of Agriculture,
Faisalabad-38040, Pakistan
2Department of Biochemistry,
University of Agriculture Faisalabad-38040, Pakistan
*For correspondence: sarwarkhan_40@hotmail.com
Received 27 April 2022;
Accepted 26 May 2022; Published 15 June 2022
Abstract
Virulent strains of the Newcastle disease virus (NDV)
cause a highly contagious disease in domestic poultry that has resulted in
major economic losses worldwide. Recurrent mutations in the viral genome and
emersion of new strains are attributed to disease constant spread. These
repeated episodes of infection have been questioning the efficacy of vaccines
in use. However, recent advancement in molecular biology has enabled scientists
to design recombinant vaccines which will be safer and provide long-lasting
immunity. Thus, this study aims to analyze the Fusion glycoprotein (F0) of the
NDV using a computational approach to identify highly antigenic and conserved
epitopes that can be used for the development of a therapeutic peptide vaccine.
The sequence of F0 glycoprotein of a virulent strain was subjected to software
for the prediction of T cell and B cell epitopes. Epitopes were scrutinized
based on their antigenicity, toxicity, conservancy, and topology. Cytotoxic
T-lymphocytes (CTL) epitopes were checked for their ability to interact with
chicken BF alleles through molecular docking. A 308 amino acid long MEV was
constructed by joining cytotoxic T cell, helper T cell and interferon-gamma
(IFN-γ) epitopes along with an adjuvant. The construct was found highly antigenic,
stable, non-allergenic and stable. The ability of MEV to elicit host immune
response is demonstrated by its strong binding affinity with chicken toll-like
receptor (TLR-4). Our findings suggest that the designed MEV can act as a
potent vaccine candidate against variant virulent NDV strains and can help in
reducing the number of outbreaks. © 2022 Friends Science Publishers
Keywords: Newcastle
disease virus; In silico; Fusion glycoprotein;
Epitopes; Multiepitope vaccine; Docking
Introduction
The agricultural sector plays a pivotal role
in Pakistan’s economy with a major contribution of almost 21% to the Gross domestic production (GDP) (Abdisa and Tagesu
2017). Its subsector includes crops, livestock, fishery and forestry,
where only the first two are considered crucial. Livestock is the most
productive subsector of agriculture with a fair share of 11.9% of GDP and is a
source of income for thousands of rural families (Shahid et al. 2020). Of all livestock products, poultry meat is considered
the most important as it offers low-cost animal protein. The poultry industry
in Pakistan has been playing an important role in narrowing down the gap
between the supply and demand of protein. However, the main risk to this
industry is low biosecurity measures and persistent occurrence of infectious
diseases which ultimately lead to low production (Absalón et al. 2019).
Among all
poultry diseases, Newcastle disease (ND) exhibits devastating economic effects
on commercial as well as domestic produce (Mayers et al. 2017). Not only its outbreaks are expensive but the
preventive measures taken against the disease are also very costly. The disease
was reported as early as 1926 when two main outbreaks occurred at two different
poles of the world, i.e., New-Castle
upon Tyne, England and the island of Java, Indonesia (Alexander 2009). Pakistan
witnessed a devastating outbreak in 2012, which killed almost 45 million
chickens in Punjab alone (Rehan et al.
2019).
ND, also
known as a pseudo-fowl pest, is a zoonotic disease that occurs after infection
with lethal virus avian paramyxovirus I
or also known as NDV (Alexander 2000). The virus contains a single-stranded and
negative sense ribonucleic acid which is almost 15,200 nucleotides in length.
Its genome encodes for six important proteins i.e., phosphoprotein (P), nucleoprotein (NP),
hemagglutinin-neuraminidase (HN), matrix (M), fusion (F) and RNA polymerase
(L). HN and F proteins are located outside the viral envelop and are mainly
responsible for host invasion and infectivity (Al-Garib et al. 2003).
Different
strains of virus display varying clinical pictures and based on mean death time
these are divided into three pathotypes, i.e.,
lentogenic, mesogenic, and velogenic (Songhua et al. 2003). This difference in the pathogenicity index is
credited to a peptide motif located in the F protein. Cleavage of this motif is
carried out by host proteases which in turn convert inactive precursor F0 (65
kD) to active F1 (55 kD) and F2 (10 kD) proteins. This step is crucial for the
initiation of infection and is linked to virulence as the cleavage site is
different among all NDV strains (Panda
et al. 2004).
Different vaccines have been used so far to protect chickens and other
exotic birds against ND, however, none of the live or killed vaccines has been
found effective in reducing the virus transmission rate. To cope with this
presenting problem, scientists have been working on developing recombinant
vaccines that can generate a better immune response with limited safety
concerns (Jorge and Dellagostin 2017). The advent of knowledge of antigen
recognition and the role of host major histocompatibility complex (MHC) I and
II receptors in immunity generation has resulted in the development of specific
motif or epitope based vaccines (Fleri et al.
2017).
To develop an
effective subunit vaccine, accurate antigenic determinants are selected and an
adjuvant is added to enhance its immune response in the host. The concept of
modern vaccines has enabled scientists to produce recombinant vaccines against
various infectious viruses such as fowlpox (Swayne et al. 2000), vaccinia virus (Sebastian and Gilbert 2016), turkey
herpesvirus (Liu et al. 2019) and
pigeon pox virus (Skinner et al.
2005). In silico analysis can help in
the development of safe, suitable and effective vaccines (Soria-Guerra et al. 2015). Given the current
prevailing outbreaks of ND in Pakistan, it is a basic necessity to develop a
recombinant vaccine against NDV to prevent any further loss to poultry birds.
This study aims to investigate NDV Fusion protein using the computerized
approach to identify all possible immunogenic epitopes which can be used as a
possible vaccine target.
Materials
and Methods
Sequence
retrieval and analyses
To evaluate the practicability of our approach for a
subunit vaccine against NDV, a sequence of target antigen F0 encompassing 1665
nucleotides was obtained in FASTA format from the NCBI database (https://www.ncbi.nlm.nih.gov/).
Few mutations were induced at the cleavage site to convert the protein into a
less virulent form.
Bioinformatic
analysis of fusion protein
F gene was translated using the JustBio tool and its
physicochemical properties were determined through Expasy’s Protparam (https://web.expasy.org/protparam/).
Secondary structure was analyzed using Chou and Fasman tool was used (http://www.biogem.org/tool/chou-fasman/).
The antigenicity
of synthetic F0 protein was evaluated via the VAXIJEN-v.20 online server which
predicts immunogenicity based on its physicochemical properties.
Epitope
prediction and evaluation
B-cell
epitope prediction: B
cell epitopes were predicted using ABCPred and then further evaluated using the
Bipepred linear epitope prediction tool of Immune epitope database analysis
(IEDB). Surface accessible epitopes were obtained through the Emini surface
accessibility prediction tool, whereas protein antigenic sites were determined
using Kolaskar and Tongaonkar antigenicity method. Ellipro from IEDB is used
for the identification of continuous B-cell epitopes with a threshold value set
at 0.5 (http://tools.iedb.org/ellipro/).
T-cell
epitope prediction: Cytotoxic T-cell (CTL) epitopes that are presented by
MHC-I alleles were obtained by NetMHC 4.0 server (http://www.cbs.dtu.dk/services/NetMHC/)
and Helper T-cell (HTL) epitopes were retrieved through NetMHCII 3.2 server (http://www.cbs.dtu.dk/services/NetMHCII/).
IFN-γ epitope prediction: To find out
regions in F0 protein that tend to induce IFN-γ, a web-based server
IFNepitope was used (http://crdd.osdd.net/raghava/ifnepitope/).
Evaluation of Epitopes
Antigenicity of B cell and T cell epitopes was evaluated
using Vaxijen v. 2.0 where a threshold value of 0.5 was used and only highly
immunogenic epitopes were selected. Epitope toxicity was found using ToxinPred
(http://crdd.osdd.net/raghava/toxinpred/), this online software predicts
toxicity based on physicochemical properties. Allergenicity was determined
using AllerTOP and Topology was predicted through TMHMM. To identify the degree
of the conservancy of epitopes within a set of protein sequences, the IEDB
conservancy analysis tool was used (http://tools.iedb.org/conservancy/).
Homology
modeling
For the construction of the
tertiary structure of ND F0 protein, RaptorX (http://raptorx.uchicago.edu/ContactMap/)
was used which is a distance-based protein structure prediction server. The
protein sequence of HLA alleles and chicken alleles (BF2 21:01
& BF2 04:01) were obtained from NCBI (accession no. NP_001026509.1 and CAK54660.1) and
submitted to PHYRE2 for homology modeling (http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index).
Whereas, 3D structures of MHC
epitopes were obtained through PEP-FOLD at RPBS MOBYLE portal for docking
analysis.
Molecular
docking
The molecular interaction of
proposed MHC peptides was analyzed with HLA alleles and chicken BF alleles,
respectively. Docking was conducted based on peptide-binding groove affinity using
Molecular Operating Environment (MOE) software which is an integrated
computer-aided molecular design platform.
Construction of MEV
For the construction of a highly immunogenic and stable
MEV, an adjuvant was linked with the first CTL epitope through an EAAAK linker.
The remaining epitopes were linked together using AAY and GPGPG spacers to
preserve their activity.
Evaluation of vaccine construct
Physicochemical properties of MEV were obtained using
Protparam and its sequence was evaluated for its antigenicity and allergenicity
using online software. The secondary structure of the chimeric vaccine was
predicted using Psipred (http://bioinf.cs.ucl.ac.uk/psipred/) and the 3D
tertiary structure was retrieved using Robetta (https://robetta.bakerlab.org/).
The stereochemical properties of the predicted 3D structure were done using
Procheck, ERRAT and Verify3D (https://saves.mbi.ucla.edu/). To refine MEV 3D
structure Galaxy Refine (http://galaxy.seoklab.org/cgi-bin/submit_REFINE.cgi) was
used.
Docking of vaccine construct
with chicken immune receptors
Molecular docking was conducted to analyze the binding
affinity of our final vaccine construct with a chicken toll-like receptor. For
this purpose, an automated protein docking server ClusPro 2.0 was used, and
results were visualized using Chimera.
Results
Target
protein sequence and structural analysis
The nucleotide sequence of NDV F0 gene was retrieved from Genbank and sequence of its cleavage motif was altered to revert velogenic strain into a less virulent form i.e., lentogenic. Cleavage motif is located at 336 to 354 bp where sequence for velogenic strain (AGGAGACAGAAACGCTTT) was converted into a less virulent lentogenic strain (GGAAGACAAGGTAGACTC).
Physicochemical
properties were obtained through Protparam, an Expasy-based online tool. F0
protein is found to be alkaline and the isoelectric point is 8.46. The
molecular weight of synthetic F0 protein is estimated to be 58864 D. The
instability index for fusion protein is computed as 36.99 which indicates its
stability in E. coli cells.
Several
online software were used to predict the secondary structure and the generated
files were compared for the identification of protein structure. In the
predicted secondary structure, helices lie in the peptide region of 75 to 108, 121
to 170 and 511 to 540, respectively.
The
antigenicity of the F0 protein was evaluated using the VAXIJEN_v2.0 tool at a
threshold level of 0.5. Overall prediction of antigenic protein was found to be
0.5306 which declared F0 as a potent immunogen.
A protein can only function properly in its final folded
form and any change in its shape will alter its function. Protein tertiary
structure was determined using RaptorX which is a template-based tertiary
structure modeller and provides high-quality structure models. The predicted
model for F0 has an RMSD value of 2.564 and an overall GDT value of 66, where a
model having a GDT value of above 50 is considered a good and stable protein.
Ramachandran plot analyses of the model show that 94% of amino acids fall under
the favorable region.
B-cell epitope
prediction
These epitopes are a portion of an antigenic protein
that interacts and activates B lymphocytes and is responsible for triggering an
immune response against virus-infected cells. Fusion glycoprotein linear B-cell
epitopes were retrieved using ABCPred which were further analyzed using
Bipepred, Kolaskar and Tongaonkar antigenicity, and Emini surface accessibility
of the IEDB online tool.
From all IEDB
predicted epitopes, fifteen were selected after screening according to their
allergenicity, antigenicity, topology, and conservancy. All B cell epitopes
were found to be highly conserved after testing them against aligned F0
sequences. However, epitopes that satisfy and overlap all prediction tools were
8KIPAPMMLTIRVALVL23, 29ANSIDGRPLAAAGIVV44,
221FGPQITSPALNKLTIQ236, 243GGNMDYLLTKLGIGNN258,
289LPSVGNLNNMRATYLE304, and 327GSVIEELDTSYCIETD342.
Selected epitopes are presented in Table 1 and graphical presentations of
peptide regions that are likely to be recognized as epitopes by a B cell
response are represented in Fig. 1.
Ellipro was
used to find out discontinuous B cell epitopes that allows the prediction of
epitopes based on a protein 3D structure. A total of 136 residues from 93 to
181 and 390 to 438 with a score of 0.82 and 0.76 respectively, were declared
conformational B cell epitopes.
T-cells
epitope analysis
CTL epitopes were retrieved through NetMHC 4.0 which
were generated to show interaction with different HLA alleles. A higher
interaction score between epitope and HLA indicates higher chances of the
epitope being presented to HTL. In this study human MHC-I alleles were selected
instead of chicken B-F alleles because of their unavailability Table 1: Selected
B cell epitopes based on their immunogenicity, allergenicity, topology and
conservancy
Peptide sequence |
ABCPred Score |
Antigenicity score |
Allergenicity |
Toxicity |
Topology |
Conservancy |
Minimum Identity % |
GSVIEELDTSYCIETD |
0.85 |
0.5014 |
Non-allergen |
Non-Toxic |
Outside |
97% |
93.75 |
LPSVGNLNNMRATYLE |
0.77 |
0.8710 |
Non-allergen |
Non-Toxic |
Outside |
98% |
93.75 |
ANSIDGRPLAAAGIVV |
0.76 |
0.6780 |
Non-allergen |
Non-Toxic |
Outside |
96% |
93.75 |
FGPQITSPALNKLTIQ |
0.72 |
0.5006 |
Non-allergen |
Non-Toxic |
Outside |
97% |
93.75 |
GGNMDYLLTKLGIGNN |
0.65 |
0.8280 |
Non-allergen |
Non-Toxic |
Outside |
96% |
87.5 |
KIPAPMMLTIRVALVL |
0.64 |
0.6073 |
Non-allergen |
Non-Toxic |
Outside |
92% |
81.25 |
Fig. 1: Prediction of B cell epitopes using (A) Kolaskar and Tongaonkar antigenicity
prediction (B) Emini surface
accessibility prediction (C)
Bipepred linear epitope prediction (D)
Karplus and Schulz flexibility prediction (E) Parker hydrophilicity prediction.
The yellow area above the threshold level is supposed to be potent epitopes
where peaks highlighted in a maroon box are the final B cell epitopes selected
in epitope prediction software; however, they both show
biochemical and functional similarity.
A total of 31
MHC-I epitopes were selected that showed the ability to interact with multiple
alleles and have strong defense capabilities. However, only five MHC I epitopes
were found to be antigenic, conserved and located on the protein's outer
surface.
CTL epitope showing high affinity with their respective
alleles are; 42VALVLSCICPANSI55 interact with five
alleles HLA-E*01:01 and HLA-B (51:01, 48:01, 07:02) alleles, 231VELNLYLTELTTVF244
interact with seven alleles HLA-B (35:03, 18:01, 46:01, 44:03) and HLA-A
(11:01, 30:02, 68:02), 258IQALYNLAGGNMDY271 interact with
six alleles HLA-A (29:02, 30:02, 11:01, 01:01) and HLA-B (15:02, 07:02), 312LPSVGNLNNMRATY325
show interaction with four alleles HLA-A*30:02 and HLA-B (15:01, 53:01,
35:01) and 524LITYIVLTIISLVF537 interact with five
alleles HLA-A (23:01, 24:02, 02:01, 68:02) and HLA-B*46:01.
On the other hand, HTL is involved in provoking adaptive
immune response as they help in the activation of B cells and CTL to kill
infected cells. As avian BL alleles are difficult to determine, we selected
human MHC class II alleles, HLA and DR, however, only two epitopes 201ALITYIVLTIISLVF225
and 210ELNLYLTELTTVFGP224 were selected after scrutiny.
Fig. 2: Docking interaction. (A) Interaction of CTL epitopes with their respective HLA alleles. (B) Interacting residues. (C) Binding of peptide and allele within
the pocket
IFN-γ epitope analysis
IFN-γ
plays an active role in provoking both innate and adaptive immune responses.
Nineteen epitopes were predicted using IFNepitope, out of which, only three 29GVALGVATAAQITAA44,
194LPSVGNLNNMRATYL209, and 398VKLTSTSALITYIVL413
showed positive results.
Interaction
analysis of epitopes with respective alleles
Interaction
between selected epitopes and their respective alleles was evaluated by
performing molecular docking using Molecular environment operating (MOE)
software. This software is designed to support molecular modeling and
structure-based designs. Interactions are classified according to their S-score
and RMSD value and the energy of HLA alleles was minimized before docking.
London DG tool was used for scoring and refinement was done using the force
field. Out of 10 predicted conformations, only those were selected that show
high scores and RMSD values between 2 to 3.
Fig. 3: Molecular docking of proposed CTL epitope and chicken
MHC class I alleles. (A) Interaction
of IQALYNLAGGNMDY with BF2 21:01. (B) Interaction of IQALYNLAGGNMDY with BF2 04:01
Fig. 4: Designing and structure validation of MEV construct. (A) An adjuvant linked final construct
of MEV where CTL, HTL, and IFN-γ epitopes along with their linkers are
shown. (B) A 308 amino acid long
sequence of MEV which consists of CTB adjuvant (blue) and epitopes are linked
together using AAY and GPGPG linkers (grey). (C) 3D structure of MEV vaccine. (D) Ramachandran plot analysis
Table 2: Selected MHC class I, II and Interferon-gamma epitopes
based on their interaction with MHC-I alleles, antigenicity, allergenicity,
topology and conservancy
Epitope sequence |
MHC I alleles |
Antigenicity score |
Toxicity |
Allergenicity |
Topology |
Conservancy |
Minimum identity |
|
|
MHC class I |
|
|
|
||
LITYIVLTIISLVF |
HLA-A*23:01, HLA-A*24:02,
HLA-B*46:01, HLA-A*02:01, HLA-A*68:02 |
0.7306 |
Non-toxic |
Non-Allergen |
Outside |
93.00%
(93/100) |
92.86 |
VELNLYLTELTTVF |
HLA-B*35:03, HLA-B*18:01,
HLA-A*11:01, HLA-A*30:02, HLA-A*68:02, HLA-B*46:01, HLA-B*44:03 |
0.6869 |
Non-toxic |
Non-Allergen |
Outside |
98.00%
(98/100) |
92.86 |
LPSVGNLNNMRATY |
HLA-B*35:01, HLA-A*30:02,
HLA-B*15:01, HLA-B*53:01 |
0.8204 |
Non-toxic |
Non-Allergen |
Outside |
98.00%
(98/100) |
92.86 |
IQALYNLAGGNMDY |
HLA-A*29:02, HLA-B*15:02,
HLA-A*30:02, HLA-B*07:02, HLA-A*11:01, HLA-A*01:01 |
0.7712 |
Non-toxic |
Non-Allergen |
Outside |
96.00%
(96/100) |
92.86 |
VALVLSCICPANSI |
HLA-E*01:01, HLA-B*51:01,
HLA-B*48:01, HLA-B*07:02, HLA-B*07:02 |
0.8500 |
Non-toxic |
Non-Allergen |
Outside |
94.00%
(94/100) |
92.86 |
|
|
MHC class
II |
|
|
|
||
ELNLYLTELTTVFGP |
HLA-DRB1*04:21,
HLA-DRB1*08:01 |
0.574 |
Non-Toxic |
Non-Allergen |
Outside |
98.00%
(98/100) |
93.33 |
ALITYIVLTIISLVF |
HLA-DRB1*15:06,
HLA-DRB1*08:13 |
0.75 |
Non-Toxic |
Non-Allergen |
Outside |
93.00%
(93/100) |
93.33 |
|
|
Interferon-gamma |
|
|
|
||
|
0.899 |
Non-Toxic |
Non-Allergen |
Outside |
97.00%
(97/100) |
86.67% |
|
LPSVGNLNNMRATYL |
|
0.8743 |
Non-Toxic |
Non-Allergen |
Outside |
98.00%
(97/100) |
93.33% |
VKLTSTSALITYIVL |
|
0.737 |
Non-Toxic |
Non-Allergen |
Outside |
95.00%
(97/100) |
93.33% |
Table 3: Docking results retrieved from
MOE by using three-dimensional structures of CTL epitopes against human and
avian alleles
Epitope |
MHC I Allele |
Docking score |
RMSD value |
Interacting Residues |
LITYIVLTIISLVF |
HLA-B*46:01 |
-19.36 |
2.5 |
Leu11, Leu13, Thr20, Asp54, Ala235 |
VELNLYLTELTTVF |
HLA-B*18:01 |
-15.32 |
2.2 |
Lys 121, Ala 136, Ser 116, Leu 126, Thr 134, Thr 143 |
LPSVGNLNNMRATY |
HLA-B*35:01 |
-28.45 |
2.68 |
Arg 5, Gly 99, Asp 101, Gly 111, Asp 113, Gln 114 |
IQALYNLAGGNMDY |
HLA-A*29:02 |
-33.85 |
2.5 |
Lys 200, Thr 202, Arg 205 |
VALVLSCICPANSI |
HLA-B*48:01 |
-23.36 |
2.4 |
Arg 86, Gly 186, Glu 187, Val 189, Leu 192, Arg 193 |
Fig. 5: Molecular docking. (A)
Docking complex of MEV in cyan and TLR4 in brown. (B) 3X magnification of docked complex, where interacting residues
of MEV are highlighted in purple and chicken immune receptors in red
Docking
scores and interacting residues are listed in Table 3. Epitope IQALYNLAGGNMDY showed the highest binding score of -33.85 and made
strong H bonds with the HLA allele using Lys200, Thr202
and Arg205 residues. The interaction of all selected CTL epitopes is
shown in Fig. 2. All epitopes showed efficient binding with their respective
HLA alleles indicating that predicted peptides are capable to bind and generate
immunity in the host body.
CTL
epitope IQALYNLAGGNMDY was then
tested for its ability to bind with chicken BF alleles. Docking results based
on RMSD value and binding energy scores showed that CTL epitope achieved
stronger binding affinity with BF2 04:01 with two hydrogen bonds at residues
Ser290 and Glu292. On the other hand, interaction with
BF2 21:01 showed only one hydrogen bond at Ser290. Interacting
residues and binding of epitope at BF2 allele binding site are shown in Fig. 3.
Construction
of MEV
The selected MHC-I, II, and IFN-γ epitopes were
used to construct an MEV. To increase vaccine immunogenicity, a 123 amino acid
long cholera toxin B adjuvant was linked at the N-terminal of MEV using an
EAAAK linker. Epitopes were then merged sequentially using AAY and GPGPG
spacers, respectively. These linkers help in increasing stability and avoiding
junctional epitopes. The final vaccine construct was 308 amino acids long. The
final vaccine construct is shown in Fig. 4.
Evaluation of MEV
Physicochemical properties evaluated using Protparam
indicate that the molecular weight of MEV protein is 32.8 kD which is slightly
alkaline with an isoelectric point of 7.62. GRAVY index of 0.488 shows that it
is hydrophobic, whereas, the instability index of 23.82 indicates protein as
stable. Antigenicity evaluated through Vaxijen shows the value of 0.6143
(threshold: 0.5) which confirms the immunogenic nature of the vaccine, and
AllerTOP confirms that it is non-allergen in nature.
Secondary structure predicted through
Psipred shows that MEV comprises 45% α-helices, 17% β-strands, and 38% coils. The 3D tertiary structure was
generated using Robetta which shows a confidence level of 0.63 over the
predicted structure, where a confidence level of 0.5–1.0 is considered good.
The tertiary structure was further evaluated using ERRAT and the overall
quality factor was achieved as 96.92, as a value of 80–100% shows that
constructed model is reliable.
Ramachandran plot built using PROCHECK indicates that 93% of
residues fall under the most favorable region and 6.7% under the allowed
region. The 3D structure was then refined using Galaxy Refine and the quality
analysis of the improved model evaluated using ERRAT, reached up to 99.29% and
almost 96% of residues fall under the favorable region.
Molecular docking of MEV with chicken TLR4
For an efficient immune response in the host body, a
strong association between antigen and immune receptors is required. Chicken TLR4
is well studied and broadly expressed in all body tissues. To check its proper
engagement with MEV, molecular
docking was conducted using ClusPro, which presented the 10 best models for
interaction. These models were then investigated using Chimera and only that
model was selected which showed efficient binding, least binding score and
maximum clustering members. The docked complex had binding energy of -1286.3
and 48 clustering members and a total of 9 hydrogen bonds were found between
MEV and TLR4 complex. Interacting residues of MEV and TLR4 complex are shown in
Fig. 5.
Discussion
Vaccination is considered the most effective method for
the prevention of an infectious disease that acts by presenting a foreign
antigen to the host immune system. Many attenuated and inactive forms of NDV
have been in use for eliciting antigen-antibody response but repetitive
outbreaks of ND are raising questions regarding their potential for protection
against infection and reduction of viral transmission (Milić et al. 2017). Pakistan has been exporting
Mukteshwar R2B, a mesogenic vaccine, which is reported to have adverse
reactions and is even lethal for immune-compromised chicks (Shahid 2017).
Desired immunity against NDV is achieved when the vaccine protects against
variant viral strains with minimum side effects. The recent knowledge on immune
receptors and their interaction with different viral antigens has led to the
concept of epitope based vaccines. These vaccines are the safest option as they
are highly antigenic but least virulent in nature. Raza et al. (2022)
and Mozafari et al. (2022) have previously designed multiepitope vaccine
against HN protein of NDV. However, there was a need to target fusion
glycoprotein as it is highly antigenic in nature, exhibits 16 conserved immune
epitopes on its outer surface, and is a major determinant of viral virulence.
So this study
aimed to design an MEV for NDV in particular for F0 protein with the help of
computational methods. The purpose was to identify new highly immunogenic T and
B cell epitopes that are supposed to provoke a long-lasting immunity against
infection. Another study by (Arora et al.
2010) demonstrates the comparison between the live vaccine and viral protein
fractions (F alone and HN-F), where the author stated that immunity generated
by F0 protein is similar to a whole vaccine or both proteins together. F0 was
found highly antigenic, also it is a membrane-bound protein and thus it is
involved in signaling and acts as a receptor.
Several
immunoinformatic tools were used for the prediction of B and T cell peptides at
primary, secondary, and tertiary structural levels of protein. To determine an
effective antigenic peptide against B cells, the epitope should show a score
above the threshold level when analyzed using Emini surface, Bipepred linear
and Tongaonkar prediction methods (Zhang et
al. 2008). IEDB server shows that a large portion of the F0 sequence is
immunogenic and acts as an epitopic region that can be identified by B cells
for the development of antibodies in the host body. Ellipro confirms that
selected epitopic regions are located at the outer surface of the protein.
Vaccines are mostly designed to elicit B cell immunity but it is now
studied that immunity generated through T cells is stronger and long-lasting
(Broere and Eden 2019). This modern strategy has been successfully used to
design a vaccine against malaria and cancer (Oyarzún and Kobe 2016). MHC class
I and II epitopes were finalized based on their conservancy, antigenicity,
topology, and their ability to interact with multiple alleles. Unfortunately,
no software can predict the binding interaction of a protein with chicken class
I and II MHC alleles (Milona et al. 2007),
however, some studies show similarities between chicken and human MHC alleles.
Table 2 presents epitopes that show interaction with different class MHC-I and
II alleles. These docked epitopes were then interacted with BF2 21:01 and BF2
04:01 alleles to confirm the presence of real CTL epitopes. These alleles were
selected based on a study by (Koch et al.
2007) which suggests that these alleles have novel peptide binding affinity and
accommodate a variety of peptides presented as epitopes to CTL.
Lately, MEV
constructs are displaying promising results for the control of different viral
infections. It requires accurate identification of epitopes and efficiently
combining those using linkers and adjuvant without disturbing their actual
structure and function. MEV is usually poorly antigenic and requires the
addition of an adjuvant which can overall improve the quality and quantity of
immune response (Khan et al. 2019).
Various
bacterial endotoxins are known to have adjuvant properties but CTB can
efficiently bind mucosal epithelial cells and elicit long-term immunological
memory. Designing an MEV without linkers can result in the formation of either
an entirely new protein with different features or a functionless abnormal
peptide. Thus, vaccine design can be improved by adding tandem repeats such as
EAAAK, KK, AAY and GPGPG (Meza et al.
2017). MEV designed in our study was found to be highly immunogenic, non-toxic to
cells and non-allergenic to the host body, thus, portraying its potential to
elicit a strong immune response.
While
designing a subunit vaccine, molecular docking is very important to check
whether the designed antigen is binding to specific host immune receptors or
not. Docking analysis was conducted to figure out the immune response of TLR4
against vaccine construct. Results of this study suggest that MEV can be a
potential candidate that can be further analyzed both in vitro and in vivo to
develop an effective vaccine against the Newcastle disease virus.
Conclusion
Recent studies show that highly antigenic epitopes of
some proteins can act as vaccine targets as they can successfully elicit an
immune response and protect the host organism from pathogen attack. Thus, in
this current study, in silico
approach was used to develop a vaccine that is based on stable epitopes which
are highly antigenic and conserved and show the capability to interact and make
bonds with host immune receptors. A combination of analyses was used for the
construction of an immunogenic MEV, however, it requires experimental
validation to ensure the efficacy of the vaccine. We hope that the presented
MEV construct will help design a safe and long-lasting vaccine against NDV.
Acknowledgments
The authors are grateful to Higher Education Commission
(HEC) for providing the funds to MSK.
Author
Contributions
The project was conceived and supervised by MSK,
however, SS being student carried out the work and prepared the manuscript. The
manuscript was critically reviewed by MSK, FAJ and MAZ.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability
Data presented in this study will be available on
a fair request to the corresponding author.
Ethics Approval
Not applicable to this paper.
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