Arafat Rahman Oany1, Abdullah-Al Emran2, Tahmina Pervin Jyoti3. 1. Department of Biotechnology and Genetic Engineering, Life Science Faculty, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. 2. Department of Biotechnology and Genetic Engineering, Life Science Faculty, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh ; Translational Research Institute, University of Queensland, Brisbane, Australia. 3. Biotechnology and Genetic Engineering Discipline, Life Science School, Khulna University, Khulna, Bangladesh.
Abstract
Human coronavirus (HCoV), a member of Coronaviridae family, is the causative agent of upper respiratory tract infections and "atypical pneumonia". Despite severe epidemic outbreaks on several occasions and lack of antiviral drug, not much progress has been made with regard to an epitope-based vaccine designed for HCoV. In this study, a computational approach was adopted to identify a multiepitope vaccine candidate against this virus that could be suitable to trigger a significant immune response. Sequences of the spike proteins were collected from a protein database and analyzed with an in silico tool, to identify the most immunogenic protein. Both T cell immunity and B cell immunity were checked for the peptides to ensure that they had the capacity to induce both humoral and cell-mediated immunity. The peptide sequence from 88-94 amino acids and the sequence KSSTGFVYF were found as the most potential B cell and T cell epitopes, respectively. Furthermore, conservancy analysis was also done using in silico tools and showed a conservancy of 64.29% for all epitopes. The peptide sequence could interact with as many as 16 human leukocyte antigens (HLAs) and showed high cumulative population coverage, ranging from 75.68% to 90.73%. The epitope was further tested for binding against the HLA molecules, using in silico docking techniques, to verify the binding cleft epitope interaction. The allergenicity of the epitopes was also evaluated. This computational study of design of an epitope-based peptide vaccine against HCoVs allows us to determine novel peptide antigen targets in spike proteins on intuitive grounds, albeit the preliminary results thereof require validation by in vitro and in vivo experiments.
Human coronavirus (HCoV), a member of Coronaviridae family, is the causative agent of upper respiratory tract infections and "atypical pneumonia". Despite severe epidemic outbreaks on several occasions and lack of antiviral drug, not much progress has been made with regard to an epitope-based vaccine designed for HCoV. In this study, a computational approach was adopted to identify a multiepitope vaccine candidate against this virus that could be suitable to trigger a significant immune response. Sequences of the spike proteins were collected from a protein database and analyzed with an in silico tool, to identify the most immunogenic protein. Both T cell immunity and B cell immunity were checked for the peptides to ensure that they had the capacity to induce both humoral and cell-mediated immunity. The peptide sequence from 88-94 amino acids and the sequence KSSTGFVYF were found as the most potential B cell and T cell epitopes, respectively. Furthermore, conservancy analysis was also done using in silico tools and showed a conservancy of 64.29% for all epitopes. The peptide sequence could interact with as many as 16 human leukocyte antigens (HLAs) and showed high cumulative population coverage, ranging from 75.68% to 90.73%. The epitope was further tested for binding against the HLA molecules, using in silico docking techniques, to verify the binding cleft epitope interaction. The allergenicity of the epitopes was also evaluated. This computational study of design of an epitope-based peptide vaccine against HCoVs allows us to determine novel peptide antigen targets in spike proteins on intuitive grounds, albeit the preliminary results thereof require validation by in vitro and in vivo experiments.
Human coronavirus (HCoV) belongs to the Coronaviridae family
(alphacoronavirus 1) and comprises a large group of enveloped, positive-sense,
single-stranded polyadenylated RNA virus.1,2 It consists of the largest
known viral RNA genomes, ranging from 27.6 to 31.6 kb. Usually, coronaviruses are classified
into three groups (group I to III), based on their serological cross-reactivity.3 Their classification is also supported by
evolutionary analysis.1 The group I
viruses are animal pathogens, including porcine epidemic diarrhea virus and feline
infectious peritonitis virus. The group II viruses are responsible for domestic animal
pathogenic infections, and the final group III viruses are responsible for avian species
infection.4 However both the group I and
group II viruses are considered HCoV. The protein molecules that usually contribute the
structure of all coronaviruses are the spike (S), envelope (E), membrane (M) and
nucleocapsid (N). HCoV is usually the causative agent of upper respiratory tract infections
and also the causative agent of “atypical pneumonia”, which was first
identified in the People’s Republic of China.5 As nowadays, an environmental resistance is shown by these
viruses,6 it is urgent to develop an
effective prevention for HCoV. Currently, there is no available treatment or vaccine to cure
HCoV infections. Due to the ever rising spread of this viral infection, the development of
vaccines or antiviral drugs against HCoVs infections is crucial.A novel approach integrating immunogenetics and immunogenomics with bioinformatics for the
development of vaccines is known as vaccinomics.7 This approach has been used to address the development of new vaccines. The
present conventional approach for vaccine development relies on antigen expression, in
sufficient amount, from in vitro culture models; however, many antigens, while expressed
sufficiently, may not be good candidates for vaccine. With these conventional approaches, it
has not been possible to control different types of outbreaks of viral pathogens, such as
recent avian and swine influenza strains, due to their time-consuming development process.
Hence, the rapid in silico informatics-based approach has gained much popularity with the
recent advancement in the sequencing of many pathogen genomes and protein sequence
databases.8 The
“vaccinomics” approach has already proven to be essential for combating
diseases such as multiple sclerosis,9
malaria,10 and tumors.11 However, these methods of vaccine
development usually work through the identification of human leukocyte antigens (HLA)
ligands and T cell epitopes,12 which
specify the selection of the potent vaccine candidates associated with the transporter of
antigen presentation (TAP) molecules.13–16 Allergenicity
assessment is one of the vital steps in the development of a peptide vaccine because when we
provide the vaccine into the human body, it is detected as a foreign substance. As a result,
inflammation occurs, demonstrating an allergic reaction. For the prediction of a B-cell
epitope, hydrophilicity is an important criterion which is usually in the beta turns region.
These assessments strengthen the possibility of the vaccine candidates. Therefore, our
present study was undertaken to design an epitope-based peptide vaccine against HCoVs (229E,
NL63, HKU1, EMC, and OC43) using the vaccinomics approach, with the wet lab researcher
expected to validate our prediction.
Materials and methods
The flow chart summarizing the protocols for the complete epitope prediction is depicted in
Figure 1.
Figure 1
Flow chart summarizing the protocols for the complete epitope prediction.
Abbreviations: 3D, three dimensional; IC50, half-maximal
inhibitory concentration; HCoV, human coronavirus; HLA, human leukocyte antigen; ;
HLA-B, the-major histocompatibility complex, class I, B; IEDB, Immune Epitope Database;
MHC, major histocompatibility complex; TAP, transporter of antigen presentation.
Viral strain selection
ViralZone, a database of the ExPASy Bioinformatics Resource Portal was used for the
selection of HCoVs and their associated information, including their genus, family, host,
transmission, disease, genome, and proteome.
Protein sequence retrieval
The outer membrane protein (spike protein) sequences of HCoV were retrieved from the
UniProtKB database.17 Then all the
sequences were stored as a FASTA format for further analysis.
Evolution analysis
For the analysis of the evolutionary divergence in the membrane proteins of HCoV, a
phylogenetic tree was constructed, using the ClustalW2 multiple sequence alignment
tool.18
Antigenic protein identification
VaxiJen v2.0,19 a server for the
prediction of protective antigens and subunit vaccines, was used for the determination of
the most potent antigenic protein. Here, we used the default parameter of this server for
the determination of the antigenic protein.
T Cell epitope identification
The NetCTL 1.2 server was used for the identification of the T cell epitope.20 The prediction method integrated peptide
major histocompatibility complex class I (MHC-I) binding; proteasomal C terminal cleavage,
and TAP transport efficiency. The epitope prediction was restricted to 12 MHC-I
supertypes. MHC-I binding and proteasomal cleavage were performed through artificial
neural networks, and the weight matrix was used for TAP transport efficiency. The
parameter we used for this analysis was set at threshold 0.5 to maintain sensitivity and
specificity of 0.89 and 0.94, respectively. This allowed us to recruit more epitopes for
further analysis. A combined algorithm of MHC-I binding, TAP transport efficiency, and
proteasomal cleavage efficiency were selected to predict overall scores.A tool from the Immune Epitope Database21 was used to predict the MHC-I binding. The stabilized matrix base method
(SMM)22 was used to calculate the
half-maximal inhibitory concentration (IC50) values of peptide binding to MHC-I
molecules from different prediction methods. For the binding analysis, all the alleles
were selected, and the length was set at 9.0 before prediction was done. For the selected
epitopes, a web-based tool was used to predict proteasomal cleavage, TAP transport, and
MHC-I.23 This tool combines
predictors of proteasomal processing, TAP transport, and MHC-I binding to produce an
overall score for each peptide’s intrinsic potential as a T cell epitope. SMM was
also implemented for this prediction.
Epitope conservancy analysis
For the analysis of the epitope conservancy, the web-based tool from IEDB24 analysis resource was used.
Prediction of population coverage
Population coverage for each individual epitope was selected by the IEDB population
coverage calculation tool analysis resource. Here we used the allelic frequency of the
interacting HLA alleles for the prediction of the population coverage for the
corresponding epitope.
Allergenicity assessment
The web-based AllerHunter server25 was
used to predict the allergenicity of our proposed epitope for vaccine development. This
server predicts allergenicity through a combinational prediction, by using both
integration of the Food and Agriculture Organization (FAO)/World Health Organization (WHO)
allergenicity evaluation scheme and support vector machines (SVM)-pairwise sequence
similarity. AllerHunter predicts allergens as well as nonallergens with high specificity.
This makes AllerHunter is a very useful program for allergen cross-reactivity
prediction.26,27
Molecular docking analysis and HLA allele interaction
Design of the three-dimensional (3D) epitope structure
For the docking analysis, the KSSTGFVYF epitope was subjected to PEP-FOLD web-based
server28 for 3D structure
conversion, in order to analyze the interactions with different HLAs. This server
modeled five 3D structures of the proposed epitope, and the best one was selected for
the docking analysis.
Docking analysis
To ensure the binding between HLA molecules and our predicted epitope, a docking study
was performed using Molegro Virtual Docker, version 6.0 (CLC bio, Aarhus, Denmark).29 The HLA-B*15:01 was selected
for docking on the basis of the available Protein Data Bank (PDB) structure deposited in
the database, which interacted with our proposed epitope. The Protein Data Bank
structure 1XR8, of Epstein – Barr virus EBNA-3 complexed with humanUbcH6
peptide, was retrieved from the Research Collaboratory for Structural Bioinformatics
(RCSB) protein database30 and
simplified to HLA-B*15:01. Finally the docking was established at a grid of X:
24.81, Y: 29.16, and Z: 40.59.
Identification of the B cell epitope
Prediction of potentially immunogenic epitopes in a given protein sequence may
significantly reduce wet lab effort needed to discover the epitopes required for the
design of vaccines and for immunodiagnostics. The aim of the prediction of the B cell
epitope was to find the potential antigen that would interact with B lymphocytes and
initiate an immunoresponse.31 Tools
from IEDB were used to identify the B cell antigenicity, including the Kolaskar and
Tongaonkar antigenicity scale,32 Emini
surface accessibility prediction,33
Karplus and Schulz flexibility prediction,34 and Bepipred linear epitope prediction analysis.35 The Chou and Fasman beta turn prediction tool36 was used because the antigenic parts of
a protein belong to the beta turn regions.37
Results
Divergence analysis of the retrieved sequences
A total of 56 outer membrane protein (spike protein) sequences from the different
variants belonging to five types (229E, NL63, HKU1, EMC, and OC43) of HCoVs were retrieved
from the UniProtKB database. Then, the sequences were subjected to multiple sequence
alignments in order to construct a phylogenetic tree (Figure S1). The phylogram
showed evolutionary divergence among the different strains of HCoV.
Antigenic protein prediction
The VaxiJen server assessed all of the retrieved protein sequences in order to find the
most potent antigenic protein. UniprotKB id: B2KKT9 was selected as the most potent
antigenic protein, with a highest total prediction score of 0.6016. Then, the protein was
used for further analysis.
T cell epitope identification
In a preselected environment, the NetCTL server predicted the potent T cell epitopes from
the selected protein sequence. Based on the high combinatorial score, the five best
epitopes (Table 1) were selected for
further analysis.
Table 1
The selected epitopes, on the basis of their overall score predicted by the NetCTL
server
Number
Epitopes
Overall score (nM)
1
GSDVNCNGY
2.9177
2
TLQYDVLFY
2.1285
3
YYCFINSTI
1.797
4
KSSTGFVYF
1.7667
5
KTLQYDVLF
1.5846
MHC-I binding prediction, which was run through SMM, predicted a wide range of MHC-I
allele interactions with the five T cell epitopes. The MHC-I alleles for which the
epitopes showed higher affinity (IC50 <200 nM) were selected for
further analysis (Table 2).
Table 2
The five potential T cell epitopes, along with their interacting MHC-I alleles and
total processing score, and epitope conservancy result
Epitope
Interacting MHC-I allele with an affinity of
>200 nM and the total score (proteasome score, TAP score, MHC-I score,
processing score)
Abbreviations: HLA, human leukocyte antigen; MHC-I, major
histocompatibility complex class I; TAP, transporter of antigen presentation.
MHC-I processing (proteasomal cleavage/TAP transport/MHC-I combined predictor) predicted
an overall score for each peptide’s intrinsic potential to be a T cell epitope
from the protein sequence. Proteasome complex, which cleaved the peptide bonds, thus
converted the proteins into peptides. The peptide molecules from proteasome cleavage
associated with class-I MHC molecules, and the peptide-MHC molecule then were transported
to the cell membrane where they were presented to T helper cells. Here, higher overall
score for each peptide denotes higher processing capabilities (Table 2).Among the five T cell epitopes, a 9 mer epitope, KSSTGFVYF, was found to interact with
most MHC-I alleles, including HLA-B*27:20; HLA-B*15:17;
HLA-B*15:03; HLA-B*40:13; HLA-A*32:07; HLA-B*58:01;
HLA-C*03:03; HLA-A*68:23; HLA-C*12:03; HLA-A*02:50;
HLA-A*32:01; HLA-A*32:15; HLA-C*05:01; HLA-C*15:02;
HLA-B*58:02; HLA-B*15:01 with higher affinity (Table 2).
Epitope conservancy and population coverage analysis
The IEDB conservancy analysis tool analyzed the conservancy of the predicted epitopes,
which are shown in Table 2. The
population coverage of the predicted epitopes is depicted in Figure 2.
Figure 2
Population coverage, based on MHC-I restriction data. Different HCoV-affected regions
were selected for evaluation of the population coverage of the proposed epitopes.
Notes: In the graphs, the line (-o-) represents the cumulative percentage
of population coverage of the epitopes; the bars represent the population coverage for
each epitope.
Abbreviations: HCoV, human coronavirus; HLA, human leukocyte antigen;
MHC-I, major histocompatibility complex class I; PC90, 90% population coverage.
The sequence-based allergenicity prediction was precisely calculated using the
AllerHunter tool, and the predicted queried epitope allergenicity score was 0.02
(sensitivity =93.0%, specificity =79.4%).
Molecular docking analysis
The predicted epitope bound in the groove of the HLA-B*15:01 with an energy of
-17.662 kcal/mol. The docking interaction was visualized with the PyMOL molecular graphics
system, version 1.5.0.4 (Schrödinger, LLC, Portland, OR, USA), shown in Figure 3.
Figure 3
HLA-B*15:01 and epitope KSSTGFVYF interaction analysis. (A) The
three dimensional structure of the epitope KSSTGFVYF. (B) The epitope
KSSTGFVYF binds in the groove of the HLA-B*15:01.
Abbreviation: HLA-B, the-major histocompatibility complex, class I, B.
B cell epitope identification
Here, we predicted amino acid scale-based methods for the identification of potential
B-cell epitopes. According to this procedure we used different analysis methods for the
prediction of a continuous B cell epitope.The Kolaskar and Tongaonkar32
antigenicity prediction method analyzed antigenicity on the basis of the physiochemical
properties of amino acids and abundances in experimentally known epitopes. The average
antigenic propensity of the protein was 1.058, with maximum of 1.240 and minimum of 0.920.
The antigenic determination threshold for the protein was 1.00; all values greater than
1.00 were potential antigenic determinants. We found that seven epitopes satisfied the
threshold value set prior to the analysis, and they had the potential to express the B
cell response. The results are summarized in Table 3 and Figure 4.
Table 3
Kolaskar and Tongaonkar antigenicity analysis
Number
Start position
End position
Peptide
Peptide length
1
4
12
CLCPVPGLK
9
2
14
21
STGFVYFN
8
3
26
32
DVNCNGY
7
4
34
40
HNSVADV
7
5
54
84
NLKSGVIVFKTLQYDVLFYCSNSSSGVLDTT
31
6
86
99
PFGPSSQPYYCFIN
14
7
104
126
TTHVSTFVGILPPTVREIVVART
23
Figure 4
Kolashkar and Tongaonkar antigenicity prediction of the most antigenic protein,
B2KKT9.
Notes: The x-axis and y-axis represent the sequence position and antigenic
propensity, respectively. The threshold value is 1.0. The regions above the threshold
are antigenic, shown in yellow.
To be a potent B cell epitope, it must have surface accessibility. Hence Emini surface
accessibility prediction was obtained. The region 88 to 94 amino acid residues were more
accessible. This is described in Figure 5
and Table 4.
Figure 5
Emini surface accessibility prediction of the most antigenic protein, B2KKT9.
Notes: The x-axis and y-axis represent the sequence position and surface
probability, respectively. The threshold value is 1.000. The regions above the threshold
are antigenic, shown in yellow.
Table 4
Emini surface accessibility prediction of the peptides
Number
Start position
End position
Peptide
Peptide length
1
30
36
NGYQHNS
7
2
50
55
NSVDNL
6
3
88
94
GPSSQPY
7
The beta turns are often accessible and considerably hydrophilic in nature. These are two
properties of antigenic regions of a protein.38 For this reason, Chou and Fasman beta-turn prediction was done. The region
73–95 (approximately 73–79 and 88–95) was considered as a
β-turns region (Figure 6).
Figure 6
Karplus and Schulz flexibility prediction of the most antigenic protein, B2KKT9.
Notes: The x-axis and y-axis represent the position and score,
respectively. The threshold is 1.0. The flexible regions of the protein are shown in
yellow color, above the threshold value.
From the experimental evidence, it has been found that the flexibility of the peptide is
correlated to antigenicity.39 Hence,
the Karplus and Schulz34 flexibility
prediction method was implemented. In this prediction method, the region of 75–95
was found to be the most flexible (Figure
7). Finally, we launched the Bepipred linear epitope prediction tool. This
program is based on a Hidden Markov model, the best single method for predicting linear
B-cell epitopes. The result of analysis with this method is summarized in Figure 8 and Table 5. By cross-referencing all the data, we predicted
that the peptide sequences from 88–94 amino acids are capable of inducing the
desired immune response as B cell epitopes.
Figure 7
Chou and Fasman beta-turn prediction of the most antigenic protein, B2KKT9.
Notes: The x-axis and y-axis represent the position and score,
respectively. The threshold is 1.041. The regions having beta turns in the protein are
shown in yellow color, above the threshold value.
Figure 8
Bepipred linear epitope prediction of the most antigenic protein, B2KKT9.
Notes: The x-axis and y-axis represent the position and score,
respectively. The threshold is 0.35. The regions having beta turns are shown in yellow.
The highest peak region indicates the most potent B cell epitope.
Table 5
Bepipred linear epitope prediction
Number
Start position
End position
Peptide
Peptide length
1
10
13
GLKS
4
2
23
35
TGSDVNCNGYQHN
13
3
50
50
N
1
4
52
54
VDN
3
5
76
81
SSSGVL
6
6
85
93
IPFGPSSQP
9
Discussion
The development of a new vaccine in a timely fashion is very crucial for defending the ever
rising global burden of disease.40–44 With the
advancement of sequence-based technology, now we have enough information about the genomics
and proteomics of different viruses. As a result, with the help of various bioinformatics
tools, we can design peptide vaccines based on a neutralizing epitope. For example, the
design of an epitope-based vaccine against rhinovirus,45 dengue virus,46 chikungunya virus,47 Saint
Louis encephalitis virus,48 etc has
already been suggested. Though epitope-based vaccine design has become a familiar concept,
in the case of HCoV there has not yet been much work done. The HCoV is an RNA virus, which
tends to mutate more frequently than the DNA viruses.49 These types of mutation mostly occur at the outer membrane
protein, ie, at the spike protein.50
These types of mutation increase the sustainability of the HCoVs, by ensuring their escape
from both the cell-mediated and humoral immune responses.51 Despite this, spike proteins have the most potential as a
target for vaccine design because of their ability to induce a faster and longer-term
mucosal immune response than that of the other proteins52 and for this reason, has gained much popularity with
researchers.53,54 From this aspect, a universal HCoV vaccine needs to be
designed, in order to overcome the adverse effects of this viral infection.At present, vaccines are mostly based on B cell immunity. But recently, vaccine based on T
cell epitope has been encouraged as the host can generate a strong immune response by CD8+ T
cell against the infected cell.55 With
time, due to antigenic drift, any foreign particle can escape the antibody memory response;
however, the T cell immune response often provides long-lasting immunity. Here, we predicted
both B cell and T cell epitopes for conferring immunity in different ways, but other recent
studies about HCoV represented the T cell epitope only, and we want to express our greater
findings here.56 There are several
criteria that need to be fulfilled by a vaccine candidate epitope, and our predicted epitope
fulfilled all the criteria. The initial criterion is the conservancy of the epitopes, which
was measured by the IEDB conservancy analysis tool. Among the five potential T cell
epitopes, all possessed the same conservancy, of 64.29%. We also found similar conservancy
of the B cell epitope, which was 64.29%. Having the same conservancy for all the epitopes,
the KSSTGFVYF epitope possessed the highest amount of interactions with the HLA alleles. A
very recent study showed a highly conserved sequence in RNA directed RNA polymerase of
HCoVs;56 nevertheless, our discovery of
a spike protein with 64.29% conservancy among the 56 spike proteins has drawn much
attention, and we consider this too as a epitope candidate for vaccine development.Population coverage is another important factor in the development of a vaccine. For the
all predicted epitopes, the cumulative percentage of population coverage was measured. We
found the highest population coverage in South Ireland, which was 90.73%, followed by Italy
and North America, with 87.13% and 75.68% coverage, respectively. The HCoV was first found
in Europe,57,58 hence, we also observed the overall coverage in Europe and
found this to be 82.59%. Oceania’s region covered 79.08%. We also recorded 80.31%
population coverage for the East Asian region, considered as one of the hot spots of this
viral infection. It has been reported that in Hong Kong and the People’s Republic of
China, during 2001–2002 there were about 587 cases (among these, 26 children) of
acute respiratory disease caused by different types of HCoV infection. Specifically, in Hong
Kong, each year this virus caused about 224 hospitalizations per 100,000 population aged
≤6 years.59However, allergenicity is one of the prominent obstacles in vaccine development. Today,
most vaccines stimulate the immune system into an “allergic” reaction,60 through induction of type 2 T helper T
(Th2) cells and immunoglobulin E (IgE). The AllerHunter score value is the probability that
a particular sequence is a cross-reactive allergen. However, the threshold for prediction of
allergen cross-reactivity is adjusted such that a sequence is predicted as a cross-reactive
allergen if its probability is >0.06. Here, our proposed epitope’s
allergenicity score was 0.02, and thus it was considered as a nonallergen. According to the
FAO/WHO evaluation scheme of allergenicity prediction, a sequence is potentially allergenic
if it either has an identity of at least six contiguous amino acids or >35 percent
sequence identity over a window of 80 amino acids when compared to known allergens. Hence,
our query epitopes did not fulfill the criteria for the FAO/WHO evaluation scheme of
allergenicity prediction and was classified by this scheme as a nonallergen.However, our predicted in silico results were based on diligent analysis of sequence and
various immune databases. This type of study has recently received experimental
validation,61 and for this reason, we
have suggested that the proposed epitope would be able to trigger an efficacious immune
response as a peptide vaccine in vivo.
Conclusion
Our study has shown that integrated computational approaches could be used for predicting
vaccine candidates against pathogens such as HCoV, with previously described, validated
procedures.In this way, in silico studies save both time and costs for researchers and can guide the
experimental work, with higher probabilities of finding the desired solutions and with fewer
trial and error repeats of assays.Phylogenetic tree, showing the evolutionary divergence among the different membrane
proteins of human coronaviruses.
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Authors: J A López; C Weilenman; R Audran; M A Roggero; A Bonelo; J M Tiercy; F Spertini; G Corradin Journal: Eur J Immunol Date: 2001-07 Impact factor: 5.532
Authors: Md Mahfuzur Rahman; Joynob Akter Puspo; Ahmed Ahsan Adib; Mohammad Enayet Hossain; Mohammad Mamun Alam; Sharmin Sultana; Ariful Islam; John D Klena; Joel M Montgomery; Syed M Satter; Tahmina Shirin; Mohammed Ziaur Rahman Journal: Int J Pept Res Ther Date: 2022-06-23 Impact factor: 2.191
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Authors: Amy B Papaneri; Reed F Johnson; Jiro Wada; Laura Bollinger; Peter B Jahrling; Jens H Kuhn Journal: Expert Rev Vaccines Date: 2015-04-11 Impact factor: 5.217
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