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Chikungunya
is a reemerging disease caused by the CHIK virus of Togaviridae family. Aedes aegypti and Aedes albopictous are the carrier mosquitoes of this disease. The
disease is spreading globally with the increasing number of cases every day,
the first outbreak of which was noticed in the year 1952 in Tanzania, while the
latest outbreak found in 2016 in India. The symptoms may be chronic if disease
persists more than 2-3 weeks and cause severe joint pain, probably leading to
arthritis. There is no medicine or vaccine available for the treatment of this
disease. In this study, authors collected the African genotype of CHIK virus
and predicted T cell epitopes. The predicted epitopes were tested for the
sharing with B cell, TAP binding activity. The selected epitopes tertiary
structure was modeled and then docked with the cTAP1 (1JJ7) protein. Those
epitopes showed the interaction with the cTAP1, further docked with the
respective HLA allele. The epitope-HLA complex was tested for stability using
NAMD-VMD molecular dynamics simulation method. 128FLARNYPTV136 were found as the promiscuous epitopes that function
as the vaccine candidate for the designing of vaccine of chikungunya. Data
obtained from the present study provide new insights into development of novel
process of vaccine research of chikungunya and ultimately shedding off the
burden from the human society.
Keywords: Chikungunya,
Peptide vaccine, cTAP, HLA, Vaccine designing, Immunoinformatics
BACKGROUND
Chikungunya is a viral disease caused by the
CHIK virus, belongs to the genus alphavirus and family Togaviridae. The disease
is transmitted via the bite of female mosquito, i.e., Aedes aegypti
& Aedes albopictus of genus Aedes and family culicidae. The threat
of the disease is increasing with the increased number of cases around the
globe. The common symptoms of chikungunya are high grade fever, nausea,
headache and joint pain. The disease has both acute and chronic symptoms. The
chronic symptoms are characterized by the severe joints pain with arthralgia
[1]. Till date there is no direct treatment available for this disease. Medical
practitioners adopt the method for the lowering down the fever and
pain-relieving therapy. Due to this the disease is progressively expanding the
epidemic area.
Name Chikungunya derived from the place where
this disease first appeared that is Makonde Plateau. The meaning of Makonde is
the “Bends Up”, such symptoms appears in the disease [2]. The disease first
appeared in 1952 in Africa and first outbreak was found in the
Tanzania (Previously known as Tanganyika) in 1953. Till then the virus has been kept
circulating in the area and spreading globally and increasing the number of
registered cases. The disease shed off from most of the regions from long time
but re-emerged as a drastic one. In India disease got disappear in 1973 but
after the 32 years disease again reemerged in 2005 and affected nearly 1.4
million people in the country. The recent outbreak occurred in 2016in Capital region
of India [3].
CHIK virus contains a single stranded RNA as a genome with
the size of 11.8 Kb and encoding two polyproteins, Structural Polyprotein and
Non-Structural Polyprotein namely. The Structural Non-Structural polyprotein is
further subdivided into six and four proteins, respectively. Structural
polyproteins contain envelope protein (E1, E2 & E3), Nuclear capsid protein
and a 6K protein domain, while
The expansion of disease keeps increasing
around the world and there is no vaccine and medication available for the
treatment of this disease. Peptide based vaccine could be the fruitful solution
to cope up with this emerging disease. The peptide-based vaccine uses the small
fragment of microbial component, which are capable of inducing long lasting
effects against the microbial invader. Due to this powerful feature the
upcoming era is for peptide based synthetic vaccines. For synthesizing the
peptide-based vaccine, selection of epitopes are the crucial steps as this
identification of appropriate microbial protein content is needed. Designing a
peptide-based vaccine [5] is not an easy process, it is a challenge and the
major task is the identification of the short peptides as a promiscuous T cell
epitope. The small size of epitope is needed to get identified by the T cell,
if the size is larger the T cell would not recognize them. The identified
epitopes should be recognized by the MHC class I molecule before analyzed by
the T-cell.
The MHC molecules present on the surface of T
cell, i.e., Tc (CD8-Class-I MHC) and TH (CD4-Class-II
MHC). These CD8 & CD4 cells help in the processing of the epitopes bound with
T cell and produce the effective immunological response to degenerate the
microbial invader and provide the immunity to the host cell.The small antigenic
peptide should reach to the RER lumen where they will bind with the class I MHC
molecule and then the complex is processed by the T cell [6]. To achieve this
process the TAP (Transporter Associated Protein) provides the cavity through
the TAP1 and TAP2. This process required energy in form of ATP, ATP binds on
the site of TAP protein to continue the process [7].
Peptide based vaccine designing is still a challenge in
recent era due to the less available methods for identification of small
peptides, which could function as a promiscuous T cell epitope. Therefore, the present research
work was undertaken using the concept of immunoinformatic techniques to
identify and analyze the small peptides, which may function as a vaccine
candidate and become a valuable asset in designing the vaccine for the
treatment of chikungunya.
METHODOLOGY
Chikungunya African strain S-27 (Accession
No: AF369024) were retrieved from the NCBI (National Centre for Biotechnology
Information) database (https://www.ncbi.nlm.nih.gov/) [8]. The complete genome
of African genotype contains the structural (Accession No: AAN05102.2) and non-structural
(Accession No: AAN05101.1) proteins.
PREDICTION OF T
CELL & B CELL EPITOPES
All the selected structural and
non-structural proteins were analyzed by using IEDB (Immune Epitope Database
and Analysis Resource) database (http://tools.iedb.org/mhci/)
against the common HLA alleles frequent in the human population. The IEDB is a
machine learning based tool which used the concept of ANN and SMM method for
the prediction of MHC class I epitopes. The cleft of the MHC class I molecule
is suitable for the small size peptides, so in this study, authors used the
nanomeric peptides having the IC50 value less than 50 nm. Predicted
peptides have IC50 value less then 50 considered as good binders.
The selected epitopes were tested for the conservancy analysis using the IEDB
conservancy tool and only peptides having conservancy in the range of 88-100 %
were only selected in the study [9].
The retrieved structural and non-structural
sequences were tested again by the BCPred tool for the identification of B cell
epitopes. BCPred (http://ailab-projects1.ist.psu.edu:8080/bcpred/predict.html) is a machine learning algorithm uses the concept
of SVM and AAP method and predicts the linear B cell epitopes of the length of
20 amino acids. The antibodies approach to the T cell and B cell epitopes and
the epitopes become more achievable for the antibody if the T cell epitopes
shares part with the B cell epitopes. In this study only those T cell epitopes
were selected who shares the part of B cell epitopes [10].
THREE-DIMENSIONAL
MODELLING OF PREDICTED EPITOPES AND HLA ALLELES
The selected epitopes who satisfy all the
criteria of selection i.e. IC50<50, conservancy score in range
88-100% and shares the part with B cell linear epitopes, were modeled using the
PepStrMOD tool (https://webs.iiitd.edu.in/raghava/pepstrmod/)
[11]. The tool is able to model the peptide of length 7 to 25 amino acids. The
modelled tertiary structure of epitopes was tested for the stability using the
molecular dynamics simulation using the Amber 6.0. The HLA allele structure of
HLA-A-02:02 was modelled using the MODELLER 9.21 [12] (https://salilab.org/modeller/) by using the 6APN
as the template retrieved from the PDB database. The modelled HLA allele were
tested for various tools to check the stability of the modelled structure.
DOCKING STUDY
OF SELECTED EPITOPES WITH cTAP1 AND HLA ALLELE
The modelled epitopes were docked against the
cTAP1 (PDB ID: 1JJ7) protein using the AutoDock 4.2 [13] (http://autodock.scripps.edu/) and Cygwin
terminal. Those epitopes who shows the binding with the cTAP1 protein exposes
for the binding with the favorable HLA allele of epitopes. The binding poses of
docking study were analysed using the Discovery Studio Visualizer [14]. The
quality of binding was analysed by the various parameters such as, Binding
energy, H-Bonds, H-Bonds Distance, Ki value and RMSD value.
MOLECULAR
DYNAMICS & SIMULATION STUDIES OF EPITOPE-HLA ALLELE COMPLEX
Epitope-HLA allele complex were tested for
the stability by using the molecular dynamics & simulation analysis
methods. NAMD-VMD [15,16] tool is used for preparation of ATOM file and PSF
files and for running the molecular dynamics using the CHARMM force field at
310°K with 100000 runs. The RMSD value was calculated using the command line of
VMD and then rmsd.dat file is generated and using the Origin tool interactive
RMSD plot was generated.
RESULTS &
DISCUSSION
Identification of Epitopes
Chikungunya S-27 African strain was retrieved
from the NCBI database and the T cell and B cell epitopes were predicted using
the IEDB analysis resource and BCPred tool respectively. Twenty different HLA
alleles were used for the prediction of epitopes. The tool predicted 134
structural and 275 non-structural T cell epitopes. These predicted epitopes
were tested for the conservancy analysis and all epitopes were fall in the
range of 88-100 % conservancy. The identified epitopes were tested for the B
cell epitopes and only 26 structural and 50 non-structural epitopes were
identified that shares part with the B cell epitopes. These identified epitopes
were tested by TAPPred tool [17] to predict the binding affinity of the
predicted epitopes. Among these 76 epitopes 13 were found with the high binding
affinity towards the cTAP protein. So, these 13 epitopes (Table 1) were
selected for the study and further analysis.
Modelling of Selected Epitopes and HLA
Alleles
The selected epitopes were modelled using the
PepStrMOD tool, a small peptide modelling tool uses the NCAA and PTM forcefield
libraries to perform the molecular dynamics analysis to check the stability of
modelled epitope by using AMBER 6.0 [18]. HLA allele structure was modeled
using the Modeller 9v21. Tertiary structure of HLA alleles was retrieved from
the PDB (Protein Data Bank) database. The tertiary structure of HLA-A-68:01
(6PBH), HLA-A-02:03 (3OX8), HLA-A-02:06 (3OXR), and HLA-A-02:01 (4U6X) was
available in the RSCB-PDB database [19] and these structures were retrieved.
Structure of HLA-A-02:02 were not available in the PDB database, so tertiary
structure was modelled using the method of homology modelling by modeler tool
using the 6APN as a template. The modelled structure was analyzed by several
tool to check its quality factor (Table 2) and it was found that
modelled structure possess good quality and could be used further.
1. At
least 80% amino acid residue must have 3D score >= 0.2.
2. It
defines the quality factor of the modelled protein structure.
3. On the
basis of Voronai Radical planes predicts the quality of the structure
4. Defines
the available Error Warning and Pass of the modelled structure.
5. The quality
of the modelled structure is predicted in terms of Z Score.
6. The
quality of the modelled score is predicted in terms of LG score.
7. The
data represents the number of amino acids available in the favored region in
the Ramachandran Plot.
F: Favored Region, A: Allowed Region, DA: Disallowed Region.
Molecular
Docking Analysis
The molecular docking study was performed
between the modelled epitopes and cTAP1 (1JJ7) and the process was repeated
again between epitopes who was found interacting with cTAP1 and respective HLA
allele using the AutoDock 4.2. The study reveals that an epitope shows the
interaction with the cTAP1 and they also show the good binding energy with the
HLA allele. Epitope 128FLARNYPTV136
shows the binding energy of -2.38Kcal/mol with the 6 H-bond when docked with
the cTAP1 protein. Further same epitope was docked with the four different
alleles i.e. HLA-A (0201), (0202), (0203), and (0206) and it shows the best
binding energy -4.25 Kcal/mol & -3.53 Kcal/mol with HLA-A-02:02 and
HLA-A-02:01 allele (Table 3).
Molecular Dynamics & Simulation
Molecular dynamics aids to the understanding
the molecular behavior in the defined parameters [20]. The molecular dynamics
and simulation study of 2 predicted complex (Epitope-HLA allele) was performed
using the NAMD-VMD tool. The MD simulation was run for 100000-time step and the
energy minimization was performed for the 10000 steps at 310°K and default parameters and the
simulation was run for the 1 FS (femtosecond). The result analysis of
simulation study confirms that FLARNYPTV-HLA-A-02:02 complex (Figure
1) is more stable in comparison to the other complexes. The graph was
plotted between the RMSD and Time (PS), for building the graph VMD tool was
used. The protein’s file was loaded first and then the protein_md.dcd file was
uploaded for further analysis. The RMSD trajectory tool was used to plot the
RMSD vs Time graph. In the RMSD vs Time graph protein_wb.psf and
protein_wb_md.dcd files were used. The RMSD trajectory tool first performs the
alignment and then plot the RMSD vs Time graph using the time slot 0.0 to 1.0.
Among the 2 identified epitopes HLA complexes 128FLARNYPTV136
epitopefound stable in the MD simulation analysis.
CONCLUSION
The immunoinformatics approaches and the
advancement in the computational methods have shown the new path in the field
of vaccine technology [21]. The designing of short peptide vaccine was a time
taking and tedious process and the chances of success was very less but these methods
made possible to cut short the time with the high success rate [22]. In this
study, authors reported a T cell epitope which is interacting with the HLA
class I allele. The nanomeric epitope 128FLARNYPTV136 was
found matching with all the criterion and conditions to become as a vaccine
candidate. Hence, authors suggest that this epitope is most promiscuous and can
function as a vaccine candidate for the treatment of chikungunya. Authors are
looking forward to use this result in the process of vaccine designing for the
chikungunya.
ACKNOWLEDGMENTS
The authors are thankful to the Management of
IFTM University and KS Vira College of Engineering and Management for providing
all the necessary facilities and support in order to carry out this research
work and preparation of the manuscript.
CONFLICT
OF INTERESTS
The authors declare that they have no conflict of
interests.
ETHICAL
APPROVAL
The authors declare that there were no animal
or human objects involved in this present study.
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