The causative agent of severe acute respiratory syndrome (SARS) reported by the Chinese Center for Disease Control (China CDC) has been identified as a novel Betacoronavirus (SARS-CoV-2). A computational approach was adopted to identify multiepitope vaccine candidates against SARS-CoV-2 based on S, N and M proteins being able to elicit both humoral and cellular immune responses. In this study, the sequence of the virus was obtained from NCBI database and analyzed with in silico tools such as NetMHCpan, IEDB, BepiPred, NetCTL, Tap transport/proteasomal cleavage, Pa3P, GalexyPepDock, I-TASSER, Ellipro and ClusPro. To identify the most immunodominant regions, after analysis of population coverage and epitope conservancy, we proposed three different constructs based on linear B-cell, CTL and HTL epitopes. The 3D structure of constructs was assessed to find discontinuous B-cell epitopes. Among CTL predicted epitopes, S257-265, S603-611 and S360-368, and among HTL predicted epitopes, N167-181, S313-330 and S1110-1126 had better MHC binding rank. We found one putative CTL epitope, S360-368 related to receptor-binding domain (RBD) region for S protein. The predicted epitopes were non-allergen and showed a high quality of proteasomal cleavage and Tap transport efficiency and 100% conservancy within four different clades of SARS-CoV-2. For CTL and HTL epitopes, the highest population coverage of the world's population was calculated for S27-37 with 86.27% and for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%, respectively. We identified overall 10 discontinuous B-cell epitopes for three multiepitope constructs. All three constructs showed strong interactions with TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families. These data demonstrated that the novel designed multiepitope constructs can contribute to develop SARS-CoV-2 peptide vaccine candidates. The in vivo studies are underway using several vaccination strategies.
The causative agent of severe acute respiratory syndrome (SARS) reported by the Chinese Center for Disease Coene">ntrol (China CDC) has been identified as a novel Betacoronavirus (SARS-CoV-2). A computational approach was adopted to identify multiepitope vaccine candidates against SARS-CoV-2 based on S, N and M proteins being able to elicit both humoral and cellular immune responses. In this study, the sequence of the virus was obtained fromNCBI database and analyzed with in silico tools such as NetMHCpan, IEDB, BepiPred, NetCTL, Tap transport/proteasomal cleavage, Pa3P, GalexyPepDock, I-TASSER, Ellipro and ClusPro. To identify the most immunodominant regions, after analysis of population coverage and epitope conservancy, we proposed three different constructs based on linear B-cell, CTL and HTL epitopes. The 3D structure of constructs was assessed to find discontinuous B-cell epitopes. Among CTL predicted epitopes, S257-265, S603-611 and S360-368, and among HTL predicted epitopes, N167-181, S313-330 and S1110-1126 had better MHC binding rank. We found one putative CTL epitope, S360-368 related to receptor-binding domain (RBD) region for S protein. The predicted epitopes were non-allergen and showed a high quality of proteasomal cleavage and Tap transport efficiency and 100% conservancy within four different clades of SARS-CoV-2. For CTL and HTL epitopes, the highest population coverage of the world's population was calculated for S27-37 with 86.27% and for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%, respectively. We identified overall 10 discontinuous B-cell epitopes for three multiepitope constructs. All three constructs showed strong interactions with TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families. These data demonstrated that the novel designed multiepitope constructs can contribute to develop SARS-CoV-2 peptide vaccine candidates. The in vivo studies are underway using several vaccination strategies.
The causative agent of severe acute respiratory syndrome (SARS) reported by the Chinese Center for Disease Coene">ntrol (China CDC) has been identified as a novel Betacoronavirus (SARS-CoV-2) [1]. The genomic sequence of SARS-CoV-2 was similar but its composition was diverse as compared to SARS-CoV’s and MERS-CoV’s genome [2]. Accumulated clinical and experimental knowledge on these previous coronaviruses has led to an easier prediction of host immune responses against this particular virus. Genomic RNA of SARS-CoV-2 encodes non-structural replicase polyprotein and structural proteins including spike (S), envelope (E), membrane (M) and nucleocapsid (N). The entry of SARS-CoV-2 into host cells is mediated by attachment of S glycoprotein on the virion surface to the angiotensin-converting enzyme 2 (ACE2) receptor [3] mainly expressed in type 2 alveolar cells of lungs [4]. Enhanced binding affinity between SARS-CoV-2 and ACE2 receptor was proposed to correlate with increased virus transmissibility [5]. The trimeric S protein will be cleaved into two subunits of S1 and S2 during viral infection [6]. S1 and S2 subunits are responsible for binding to the ACE2 receptor and the fusion of the viral and cellular membranes, respectively [3]. Being the main antigenic component, S protein has been selected as an important target for vaccine development.
Anti-viral drugs, broad-spectrum antibiotics such as Remdesivir, Chloroquine, Ribavirin, Favipiravir or Baricitinib are potential therapeutic strategies used to reduce the viral load [7] by blocking the SARS-CoV-2 replication [8, 9]. Recently, the plasma exchange using convalescent sera of COVID-19 showed promising results [10, 11]. Also, the monoclonal antibody (CR3022) binding with the spike receptor-binding domain of SARS-CoV-2 had the potential to be developed as a therapeutic candidate [12]. Efforts toward developing an effective vaccine have been ignited in many countries. Actually, several projects have been reported by companies and researchers to start SARS-CoV-2 vaccine development. There are different kinds of novel vaccines including DNA-based, viral vector-based, recombinant S protein-based, adenovirus-based, mRNA-based and peptide-based vaccines. The mRNA-1273 candidate, an encapsulated mRNA vaccine encoding S protein developed by Moderna (NCT04283461), the Ad5-nCov candidate, an adenovirus type 5 vector expressing S protein developed by CanSino Biologicals (NTC04313127), the INO-4800 candidate, a DNA plasmid encoding S protein developed by Inovio Pharmaceuticals (NCT04336410), the LV-SMENP-DC candidate, dendritic cells modified with lentiviral vector (NCT04276896), and the pathogen-specific aAPC candidate, an aAPC modified with a lentiviral vector (NCT04299724) both developed by Shenzhen Geno-immune Medical Institute are few vaccines in phase I of the clinical trial against SARS-CoV-2 [13].However, each type of vaccine has a number of advantages and disadvantages. Although platforms based on DNA or mRNA are flexible and effective for antigen manipulation, peptide-based vaccines are customizable multipurpose therapeutics which does not have the implication of stability or translation [14] and by the use of multiepitope approach, a single peptide-based vaccine can be designed to target different strains [15]. Despite safety and cost-effectiveness, peptide-based vaccines are difficult to design. The epitope-mapping is a crucial but time-consuming step in the design of a peptide-based vaccine. That is why no peptide-based vaccine for SARS-CoV-2 has reached phase I clinical trial to date. A successful peptide-based vaccine comprises immunodominant B-cell and T-cell being able to induce strong and long-lasting immunity against the desired pathogen [16]. Thus, the understanding of epitope interaction with major histocompatibility complex (MHC) is necessary. In the current study, a computational approach was adopted to identify multiepitope vaccine candidates against SARS-CoV-2 based on S, N and M proteins.
Materials and methods
Collection of targeted proteins sequences
The reference sequences of the targeted proteins including S, N and M proteins of SARS-CoV-2 were obtained from the NCBI database and used as an input for more bioinformatics analyses.
Linear B-cell epitope prediction
A successful vaccinemust elicit stroene">ng cellular and humoral immune responses. Thus, it is important to show that the constructed immunogens are able to induce protective immunity. It should be considered that optimal peptide-based vaccines must be presented in a desired secondary structure of peptides in order to induce a specific humoral response. In this subsection, we used BepiPred-2.0 prediction module (http://www.cbs.dtu.dk/services/BepiPred-2.0/) for linear B-cell prediction of the conserved regions in S, N and M proteins of SARS-CoV-2 to produce the B-cell mediated immunity. In this study, epitope threshold value was set as 0.5 (the sensitivity and specificity of this method are 0.58 and 0.57, respectively) [17].
T-cell epitope identification
The initial step on applying bioinformatics to design synthetic peptide vaccines is to determine whether epitopes are potentially immunoprotective. T-cell epitopes presented by MHC are linear form containing 12 to 20 amino acids. This fact facilitates modeling for the interaction of ligands and T-cells with accuracy [18]. Binding of the MHC molecule is the most selective step in the presentation of antigenic peptide to T-cell receptor (TCR).For MHC class I, we adapted Artificial Neural Networks (NetMHCpan4.1 server (http://www.cbs.dtu.dk/services/NetMHCpan/) to predict high-potential T-cell epitopes. This server is meant to predict MHC I binding with accuracy of 90–95% [19, 20]. Human alleles were used and the threshold for NetMHCpan was set at 0.5% for strong binders and 2% for weak binders.For MHC class II, we used NetMHCIIpan 4.0 server (http://www.cbs.dtu.dk/services/NetMHCIIpan/) [21] to predict potential interaction of helper T-cell epitope peptides and MHC class II. Human alleles were used and the threshold for strong and weak binders was set at 2% and 10%, respectively.
Prediction of MHC class I peptide presentation pathway
Best ranked peptides extracted fromNetMHCpan database were used in transporter associated with antigen presentation (TAP) transport efficacy and proteasomal cleavage analysis. In MHC class I presentation pathway, this section is as essential as binding affinity prediction. We employed NetCTL 1.2 server combined with Tap transport/proteasomal cleavage tools (http://www.cbs.dtu.dk/services/NetCTL) to assess the prediction of antigen processing through the MHC-I antigen presentation pathway. In this method, weight on C-terminal cleavage set on 0.15, and tap transport efficacy and epitope identification were set on 0.05 and 0.75, respectively.
Conservancy analysis
Up to now, more than 16667 full-sequences of SARS-CoV-2 have been registered globally in GISAID database classified into four clades of V, G, S and O. To calculate the degree of conservancy of each epitope, IEDB epitope conservancy tool (http://tools.immuneepitope.org/tools/conservancy/) was employed [22]. This tool computes the degree of conservancy of an epitope within a given protein sequence set at a given identity level. In this study, we determined epitope conservancy of each protein including S, N and M obtained from GISAID database.
Population coverage
Due to a phenomenoene">n known as denominated MHC restriction of T-cell responses, selecting multiple epitopes with different HLA binding specificities will afford more increases in population coverage. Prediction based on HLA binding at population level in defined geographical regions where the peptide-based vaccine might be employed is essential. Since MHC polymorphisms are dramatically at different frequencies in different ethnicities, without careful consideration, a vaccine with ethnically biased population coverage will result. In this study, we used IEDB population coverage tool [23] (http://tools.iedb.org/population/) to assess the coverage rate of population for each epitope.
Antibody-specific epitopes prediction
IgPred module [24] (https://webs.iiitd.edu.in/raghava/igpred/index.html) was developed for predicting different types of B-cell epitopes inducing different classes of antibodies. We used this server to identify epitope tendency for inducing IgG and IgA antibodies.
Prediction of cytokine inducer peptides
It is important to understand that all MHC class II binders will not induce the same type of cytokines. Thus, we used IL-10 Pred [25] (http://crdd.osdd.net/raghava/IL-10pred/) and IFNepitope [26] webserver (http://crdd.osdd.net/raghava/ifnepitope/index.php) to predict Il-10 and Interferon-gamma inducing peptides, respectively. We used Support Vector Machine (SVM)-based model as prediction model in both servers. Other features including SVM threshold left at the default value. Through using these servers, we improved insight into the future in vivo studies.
Allergenicity and cross-reactivity assessment
The prediction of potential allergenicity is an important step in safety assessment as proteins and polypeptides have significant roles in inducing allergenic reactions. The allergenicity of the selected epitopes was calculated by PA3P server (http://lpa.saogabriel.unipampa.edu.br:8080/pa3p/pa3p/pa3p.jsp) using AFDS-motif, Allergen online (6aa and 80-word match) algorithms. The specificity of these methods is 95.43% (6aa), 92.88% (80aa), and 88.1% (ADFS) [27].
Peptide-protein flexible docking
To estimate the formation of MHC-peptide complex, we used GalexyPepDock peptide-protein flexible docking server [28] (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=PEPDOCK). This study presents an example of GalexyPepDock performed by each epitope and available PDB file of HLA alleles, separately.
Vaccine construction
To construct effectual vaccine compoene">nents, we fused the antigenic epitopes with the help of specific peptide linkers. Three different coene">nstructs for each linear B lymphocyte (LBL), cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) were designed.
The physicochemical parameters
The physicochemical properties of the designed LBL, CTL and HTL epitopes including molecular weight, theoretical PI, positive and negative charge residue, solubility and stability were evaluated by ProtParam online server (http://us.expasy.org/tools/protparam.html) [29].
3D structure prediction
I-TASSER server [30] (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) was used for modeling the 3D structure of designed constructs. This server is in active development with the goal to provide the most accurate protein structure and function predictions using state-of-the-art algorithms. After analysis, the models with the highest confidence score (C-score) were selected for refinement analysis.
Refinement and validation of tertiary structure
GalaxyRefine 2 Server [31] (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE2) was used to refine predicted tertiary structures. GalaxyRefine2 performs iterative optimization with several geometric operators to increase the accuracy of the initial models. Final Refined models were analyzed by SAVE5.0 (https://servicesn.mbi.ucla.edu/SAVES/) server to validated tertiary structures. SAVE server gives Ramachandran plot of the whole structure, determines the overall quality of tertiary structure, and calculates buried protein atoms, stereochemical quality and atomic interaction of predicted 3D structure.
Discontinuous B-cell epitope prediction
Prediction of discontinuous B-cell epitope needs tertiary structure of a protein or polypeptide since the interaction between antigen epitopes and antibodies is very important. As regards, after refinement and validation analysis, the 3D structure of constructs were assessed by the Ellipro server [32] (https://tools.iedb.org/ellipro/help/) to find discontinuous B-cell epitopes. ElliPro web-based server uses modified Thornton’s method along with residue clustering algorithms. In this study, epitope prediction parameters (minimum score and maximum distance) were set to default values (0.5 and 6).
Docking between vaccine constructs and Toll-Like Receptors (TLRs)
TLRs are sensors recognizing molecular patterns of pathogens to initiate innate immune system. It was demonstrated that TLRs 2, 3 and 4 are more susceptible to Coronaviridae family including SARS-CoV and MERS-CoV [33-35]. Thus, PDB files of TLRs 2, 3 and 4 were obtained from Protein Data Bank (http://www.rcsb.org/) and then protein-protein docking between three vaccine constructs and TLRs were performed by ClusPro server [36] (https://cluspro.bu.edu/). ClusPro uses three steps algorithms containing 1) Rigid-body docking, 2) Cluster retained conformations, and 3) Refine by CHARMMminimization.
Results
The sequences of the structural SARS-CoV-2 proteins
The reference sequences of the structural proteins (S, N and M, NC_045512.2) were obtained fromNCBI. The sequence was downloaded in a FASTA format to carry out further analyses.
Prediction of linear B-cell epitopes
We obtained a total of 44 sequential linear B-cell epitopes with variable lengths from IEDB server within three main proteins of SARS-CoV-2 (i.e., S, N and M), and the ability of epitopes in inducing different classes of antibody in IgPred server were analyzed. In S protein, S1133-1172 (VNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGI), S440-501 (NLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTN), S59-81(FSNVTWFHAIHVSGTNGTKRFDN) and S304-322 (KSFTVEKGIYQTSNFRVQP), and in N protein, N232-269(SKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATKAYN), N164-216(GTTLPKGFYAEGSRGGSQASSRSSSRSRNSSRNSTPGSSRGTSPARMAGNGGD), N1-51(MSDNGPQNQRNAPRITFGGPSDSTGSNQNGERSGARSKQRRPQGLPNNTAS) and N361-390(KTFPPTEPKKDKKKKADETQALPQRQKKQQ) were chosen as they had the ability to induce antibody (Table 1). In case of M protein, we found three epitopes. However, we ruled out M epitope for potential B-cell epitope as they were unable to induce any class of the antibodies.
Table 1
The selected LBL* epitopes of SARS-CoV-2 based on binding affinity.
Identification of CD8+ cytotoxic T lymphocyte (CTL) epitopes is a crucial step in epitope-driven vaccine design as MHC class I restricted CTL plays a critical role in controlling viral infections. In this study, we employed NetMHCpan and NetMHCIIpan as mentioned procedure in below.
MHC class I prediction
The SARS-CoV-2 protein sequences were analyzed by NetMHCpan 4.1 server to identify the most immunodominant regions. In each protein, peptides with the highest binding affinity scores were determined as high-potential CTL epitope candidates. In each protein, the best epitopes with higher binding affinity were selected as the putative CTL epitope based on calculated average immunogenicity scores. Chosen MHC-I epitopes were listed in Table 2 with encountering MHC alleles, average rank scores, conservancy prediction and allergenicity assessment. Also, all of the chosen sequences of epitopes were non-allergen and 100% conserved within four clades.
Table 2
The selected CTL* epitopes of SARS-CoV-2 based on binding affinity.
Protein Name
Position
Epitope Sequence
No. of Alleles
NetMHCpan Average Rank Scores**
Conservancy
Allergenicity
S
27–37
YTNSFTRGVYY
12
0.67
S:100%***V: 100%G: 100%O: 100%
Non-allergen
686–696
VASQSIIAYTM
12
0.69
S:100%V: 100%G: 100%O: 100%
Non-allergen
191–199
FVFKNIDGY
10
0.66
S:100%V: 100%G: 100%O: 100%
Non-allergen
1051–1061
FPQSAPHGVVF
10
1.08
S:100%V: 100%G: 100%O: 100%
Non-allergen
257–265
WTAGAAAYY
9
0.44
S:100%V: 100%G: 100%O: 100%
Non-allergen
603–611
TSNQVAVLY
9
0.44
S:100%V: 100%G: 100%O: 100%
Non-allergen
868–876
MIAQYTSAL
9
0.61
S:100%V: 100%G: 100%O: 100%
Non-allergen
161–169
SANNCTFEY
8
0.96
S:100%V: 100%G: 100%O: 100%
Non-allergen
360–368
CVADYSVLY
8
0.48
S:100%V: 100%G: 100%O: 100%
Non-allergen
1094–1102
FVSNGTHWF
8
0.69
S:100%V: 100%G: 100%O: 100%
Non-allergen
N
103–113
LSPRWYFYYLG
10
1.04
S:100%V: 100%G: 100%O: 100%
Non-allergen
304–313
AQFAPSASAF
8
0.87
S:100%V: 100%G: 100%O: 100%
Non-allergen
47–55
NTASWFTAL
6
0.70
S:100%V: 100%G: 100%O: 100%
Non-allergen
264–273
TKAYNVTQAF
6
0.90
S:100%V: 100%G: 100%O: 100%
Non-allergen
52–60
FTALTQHGK
5
0.64
S:100%V: 100%G: 100%O: 100%
Non-allergen
M
36–46
FAYANRNRFLY
13
1.23
S:100%V: 100%G: 100%O: 100%
Non-allergen
168–180
TVATSRTLSYY
9
0.89
S:100%V: 100%G: 100%O: 100%
Non-allergen
195–204
YSRYRIGNYK
9
0.31
S:100%V: 100%G: 100%O: 100%
Non-allergen
90–99
MWLSYFIASF
7
1.05
S:100%V: 100%G: 100%O: 100%
Non-allergen
102–111
FARTRSMWSF
7
0.59
S:100%V: 100%G: 100%O: 100%
Non-allergen
* Cytotoxic T lymphocyte
** Lower rates show better binding affinity
*** There are four clades of SARS-CoV-2 according to GISAID database
* Cytotoxic T lymphocyte** Lower rates show better binding affinity*** There are four clades of SARS-CoV-2 according to GISAID database
MHC class II prediction
The SARS-CoV-2 protein sequences were analyzed by NetMHCIIpan 4.0 server to identify MHC-II epitope. Epitopes with the maximum number of binding HLA-DR alleles were selected as putative HTL epitope candidate. Chosen MHC-II epitopes were listed in Table 3 with encountering MHC alleles, average rank scores, conservancy and antibody-specific epitopes prediction, and allergenicity assessment. Also, all of the chosen sequences of epitopes were non-allergen and 100% conserved within four clades.
Table 3
The selected HTL epitopes of SARS-CoV-2 based on binding affinity.
Protein Name
Position
Epitope Sequence
No. of Alleles
NetMHCpan Average Rank Scores*
Conservancy
Allergenicity
IFN-gamma prediction
IL-10 prediction
S
196–231
NIDGYFKIYSKHTPINLVRDLPQGFS
25
4.29
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
303–323
LKSFTVEKGIYQTSNFRVQPT
25
3.96
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
313–330
YQTSNFRVQPTESIVRFP
25
3.17
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
1009–1030
TQQLIRAAEIRASANLAATKMS
25
3.89
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
32–53
FTRGVYYPDKVFRSSVLHSTQD
24
4.45
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
1057–1074
PHGVVFLHVTYVPAQEKN
22
5.7
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
689–704
SQSIIAYTMSLGAENS
21
5.2
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
801–817
NFSQILPDPSKPSKRSF
20
3.51
S:100%V: 100%G: 100%O: 100%
Non-allergen
Negative
Positive
1110–1126
YEPQIITTDNTFVSGNC
19
3.19
S:100%V: 100%G: 100%O: 100%
Non-allergen
Negative
Positive
114–130
TQSLLIVNNATNVVIKV
19
5.37
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
N
328–349
GTWLTYTGAIKLDDKDPNFKDQ
25
4.5
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
126–143
NKDGIIWVATEGALNTPK
24
3.85
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
342–361
KDPNFKDQVILLNKHIDAYK
24
4.39
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Positive
48–63
NTASWFTALTQHGKED
23
3.24
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
167–181
LPKGFYAEGSRGGSQ
23
2
S:100%V: 100%G: 100%O: 100%
Non-allergen
Negative
Negative
405–419
KQLQQSMSSADSTQA
22
3.58
S:100%V: 100%G: 100%O: 100%
Non-allergen
Negative
Negative
M
198–217
RYRIGNYKLNTDHSSSSDNI
22
4.76
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
173–191
SRTLSYYKLGASQRVAGDS
20
4.53
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
107–128
RSMWSFNPETNILLNVPLHGTI
19
6.02
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
163–181
DLPKEITVATSRTLSYYKL
17
3.57
S:100%V: 100%G: 100%O: 100%
Non-allergen
Positive
Negative
*lower rates show better binding affinity
*lower rates show better binding affinity
Tap transport/proteasomal cleavage
Tap transport and proteasomal cleavage are as important as binding affinity in antigen presentation pathway to CTLs. In this case, NetCTL1.2 server was used. All of the epitopes shown in Table 4 have upper cut off identification scores (> 0.75) which show a high quality of proteasomal cleavage and Tap transport efficiency. Among all epitopes, S257-265 and S603-611 have the highest epitope identification score of 3.14 and 3.07, respectively.
Table 4
Proteasomal cleavage and TAP transport efficiency scores of MHC-I predicted epitopes.
Protein Name
Position
Epitope Sequence
Proteasomal C -terminal cleavage Score*
TAP transport efficiency Score**
Epitope identification Score***
S
27–37
YTNSFTRGVYY
1.43
0.96
1.73
686–696
VASQSIIAYTM
1.49
0.93
1.73
191–199
FVFKNIDGY
1.75
0.94
2.03
1051–1061
FPQSAPHGVVF
0.98
0.93
1.18
257–265
WTAGAAAYY
2.85
0.94
3.14
603–611
TSNQVAVLY
2.87
0.95
3.07
868–876
MIAQYTSAL
1.04
0.93
1.33
161–169
SANNCTFEY
2.07
0.92
2.36
360–368
CVADYSVLY
2.27
0.97
2.56
1094–1102
FVSNGTHWF
1.46
0.86
1.72
N
103–113
LSPRWYFYYLG
2.05
0.96
2.34
304–313
AQFAPSASAF
1.06
0.66
1.31
47–55
NTASWFTAL
1.08
0.94
1.27
264–273
TKAYNVTQAF
1.01
0.94
1.30
52–60
FTALTQHGK
0.80
0.72
0.92
M
36–46
FAYANRNRFLY
1.40
0.95
1.69
168–180
TVATSRTLSYY
2.31
0.96
2.61
195–204
YSRYRIGNYK
1.36
0.84
1.64
90–99
MWLSYFIASF
0.66
0.97
0.96
102–111
FARTRSMWSF
1.10
0.95
1.38
*Higher rates show better quality of proteasomal cleavage
** Higher rates show better quality of tap transport efficiency
*** Higher rates show better quality of epitope identification
*Higher rates show better quality of proteasomal cleavage** Higher rates show better quality of tap transport efficiency*** Higher rates show better quality of epitope identificationAs mentioene">ned above, MHC polymorphisms are dramatically at different frequencies in various ethnicities. Thus, careful consideration should be given to the way of effective vaccine development. In this study, population coverage was estimated separately for each putative epitope in different geographical regions (Tables 5 and 6). For CTL epitopes, the highest population coverage of the world’s population was calculated for S27-37 with 86.27%. For helper T-cell epitopes, the highest population coverage of the world’s population was calculated for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%.
Table 5
Population coverage of putative SARS-CoV-2 CTL epitopes.
Area
S27-37
S686-696
S191-199
S1051-1061
S257-265
S603-611
S868-876
S161-169
S360-368
S1094-1102
N103-113
N304-313
N47-55
N264-273
N52-60
M36-46
M168-180
M195-204
M90-99
M102-111
Central Africa
58.3%
61.19%
47.7%
42.84%
45.43%
45.43%
47.79%
41.35%
41.35%
37.02%
39.4%
33.9%
31.58%
38.38%
27.68%
46.0%
43.11%
34.13%
25.22%
30.74%
East Africa
69.68%
67.18%
54.97%
51.17%
50.59%
50.59%
54.01%
44.19%
44.19%
48.33%
47.25%
27.24%
27.26%
29.76%
31.08%
53.0%
48.86%
41.54%
28.22%
28.53%
East Asia
68.17%
59.86%
55.61%
54.86%
55.61%
55.61%
60.36%
55.49%
53.53%
74.58%
78.7%
82.83%
31.06%
70.63%
32.55%
84.27%
54.94%
45.63%
71.67%
35.38%
Europe
93.95%
80.28%
77.35%
71.73%
73.88%
73.88%
79.54%
73.47%
73.45%
61.27%
76.7%
71.17%
52.65%
64.39%
64.85%
85.33%
70.88%
70.14%
59.55%
50.55%
North Africa
76.03%
73.45%
57.9%
50.94%
55.03%
55.03%
59.15%
52.23%
52.0%
52.87%
55.62%
44.7%
30.4%
34.85%
36.01%
57.8%
50.85%
46.2%
37.72%
29.94%
North America
84.8%
69.76%
64.08%
57.57%
61.39%
61.39%
70.66%
59.51%
59.28%
57.58%
69.4%
67.35%
38.65%
57.07%
44.89%
75.68%
57.57%
52.73%
51.7%
40.09%
Northeast Asia
73.97%
68.15%
66.77%
37.1%
66.51%
66.51%
38.22%
63.48%
63.43%
50.44%
73.37%
77.42%
13.17%
46.14%
52.93%
76.81%
65.94%
61.92%
41.39%
18.03%
Oceania
63.86%
58.32%
54.78%
34.54%
54.21%
54.21%
31.12%
52.96%
52.96%
66.01%
84.26%
83.22%
21.79%
59.46%
50.2%
86.64%
52.61%
51.88%
60.32%
17.61%
South Africa
64.94%
72.02%
57.59%
49.1%
55.81%
55.81%
44.82%
53.25%
53.25%
50.83%
58.64%
46.87%
28.43%
33.15%
41.9%
60.92%
50.64%
50.62%
37.25%
29.34%
South America
56.97%
45.93%
40.8%
25.13%
37.66%
37.66%
36.75%
36.75%
36.75%
39.03%
44.64%
52.03%
16.62%
37.72%
31.08%
57.06%
27.16%
34.24%
36.7%
14.41%
South Asia
77.05%
73.24%
71.64%
46.92%
70.83%
70.83%
38.84%
67.75%
67.75%
54.67%
67.42%
66.54%
29.6%
43.15%
59.67%
72.8%
55.62%
65.66%
37.59%
27.3%
Southeast Asia
66.67%
62.24%
58.2%
35.31%
55.92%
55.92%
28.83%
48.97%
48.97%
57.94%
77.29%
82.1%
17.1%
53.25%
43.57%
80.89%
52.67%
47.52%
51.06%
21.82%
Southwest Asia
77.27%
66.35%
59.6%
45.9%
56.9%
56.9%
54.88%
54.49%
54.49%
48.92%
58.54%
51.82%
29.33%
32.89%
47.47%
63.53%
51.56%
51.97%
38.63%
29.55%
West Africa
70.28%
74.43%
58.96%
53.85%
56.47%
56.47%
57.29%
51.92%
51.92%
48.96%
55.22%
47.07%
38.36%
43.67%
33.19%
61.05%
55.9%
41.03%
41.74%
42.38%
West Indies
80.64%
74.78%
63.01%
56.85%
59.44%
59.44%
64.77%
56.07%
56.07%
54.84%
64.26%
61.14%
41.35%
54.32%
47.92%
73.47%
55.9%
50.81%
49.62%
42.73%
World
86.27%
72.24%
68.17%
57.5%
65.34%
65.34%
67.31%
64.18%
64.0%
56.47%
70.23%
68.46%
39.15%
55.99%
54.27%
77.72%
62.15%
60.54%
50.99%
36.77%
Table 6
Population coverage of putative SARS-CoV-2 HTL epitopes.
Area
S196-231
S303-323
S313-330
S1009-1030
S32-53
S1057-1074
S689-704
S801-817
S1110-1126
S114-130
N328-349
N126-143
N342-361
N48-63
N167-181
N405-419
M198-217
M173-191
M107-128
M163-181
Central Africa
75.33%
75.33%
75.33%
75.33%
71.52%
59.02%
59.34%
60.66%
64.38%
66.12%
75.33%
67.04%
74.78%
66.93%
64.03%
56.8%
61.37%
70.37%
59.27%
65.04%
East Africa
81.34%
81.34%
81.34%
81.34%
76.57%
65.54%
58.27%
68.08%
62.42%
65.07%
81.34%
73.27%
80.55%
74.6%
69.31%
56.04%
65.89%
74.4%
62.42%
64.24%
East Asia
86.93%
86.93%
86.93%
86.93%
84.15%
84.31%
81.62%
62.84%
79.63%
73.29%
86.93%
86.13%
80.46%
79.92%
82.21%
77.93%
72.87%
72.62%
67.69%
66.34%
Europe
93.54%
93.54%
93.54%
93.54%
92.38%
81.75%
78.89%
72.95%
86.86%
87.15%
93.54%
90.14%
93.42%
77.57%
84.85%
76.43%
87.88%
91.26%
80.17%
81.71%
North Africa
88.26%
88.26%
88.26%
88.26%
87.65%
74.7%
72.59%
73.18%
80.37%
82.82%
88.26%
85.14%
87.95%
71.69%
78.01%
75.59%
83.78%
83.49%
68.5%
76.74%
North America
94.75%
94.75%
94.75%
94.75%
93.79%
84.42%
82.17%
77.86%
88.13%
88.84%
94.75%
92.18%
94.5%
73.57%
86.73%
79.13%
89.33%
93.19%
83.03%
83.13%
Northeast Asia
68.85%
68.85%
68.85%
68.85%
65.35%
63.16%
60.58%
47.01%
62.19%
54.36%
68.85%
66.94%
65.74%
78.32%
61.48%
54.28%
53.05%
59.87%
54.07%
56.44%
Oceania
74.82%
74.82%
74.82%
74.82%
69.27%
73.93%
62.41%
62.75%
61.98%
58.06%
74.82%
74.55%
68.85%
56.81%
68.59%
54.01%
64.28%
53.78%
52.62%
43.72%
South Africa
52.11%
52.11%
52.11%
52.11%
52.11%
8.42%
31.28%
50.86%
51.56%
50.86%
52.11%
32.76%
52.11%
66.55%
31.28%
32.76%
31.28%
52.11%
30.61%
52.11%
South America
76.19%
76.19%
76.19%
76.19%
75.35%
69.22%
68.04%
62.65%
58.82%
68.62%
76.19%
73.99%
67.94%
31.28%
70.7%
68.41%
68.85%
71.95%
65.09%
54.89%
South Asia
88.43%
88.43%
88.43%
88.43%
87.25%
77.38%
75.43%
66.62%
77.06%
82.25%
88.43%
84.78%
88.0%
68.74%
80.41%
71.04%
82.52%
85.41%
71.07%
79.51%
Southeast Asia
67.66%
67.66%
67.66%
67.66%
65.26%
62.42%
56.06%
50.07%
59.47%
50.67%
67.66%
67.25%
62.15%
73.82%
60.3%
53.94%
54.69%
56.91%
49.7%
51.81%
Southwest Asia
58.07%
58.07%
58.07%
58.07%
57.31%
48.2%
44.41%
45.91%
48.46%
50.87%
58.07%
54.83%
57.15%
56.04%
50.87%
45.49%
53.64%
52.81%
43.09%
43.57%
West Africa
84.27%
84.27%
84.27%
84.27%
83.28%
75.29%
72.9%
71.93%
64.44%
72.66%
84.27%
79.83%
83.27%
48.87%
79.11%
70.05%
73.8%
78.54%
60.66%
69.82%
West Indies
77.4%
77.4%
77.4%
77.4%
74.21%
69.63%
62.33%
52.95%
64.29%
64.15%
77.4%
73.64%
76.45%
77.74%
70.24%
59.75%
67.69%
72.67%
65.6%
61.47%
World
90.33%
90.33%
90.33%
90.33%
88.87%
79.8%
77.08%
70.7%
82.52%
82.55%
90.33%
87.34%
89.71%
74.87%
81.75%
74.33%
83.11%
87.1%
76.39%
76.94%
At first, available structure data of MHC-I and MHC-II were downloaded from RCSB PDB server (https://www.rcsb.org/). All potential epitopes and MHC PDB files were submitted to the server separately. Then, top models with the highest interaction similarity score (similarity of the amino acids of the target complex aligned to the contacting residues in the template structure to the template amino acids, obtained from GalexyPepDock server) were selected for each peptide and its MHC. For CTL epitopes, N103-113, S868-876, M36-46, S1094-1102, M102-111, S1051-1061, S360-368, S191-199 and S686-696 had the highest average of interaction similarity score, respectively (Table 7). For HTL epitopes, M107-128, N48-63, S689-704, S1057-1074, S196-231, N328-349, S32-53, N126-143, M163-181 and S114-130 had the highest average of interaction similarity score, respectively (Table 8). Overall, CTL epitopes showed better quality of docking in comparison with HTL epitope.
Table 7
Interaction similarity scores of the selected CTL epitopes using GalexyPepDock flexible docking server.
Protein
Epitope
HLA A0101
HLA A0201
HLA A0301
HLA A2402
HLA A1101
HLA B0702
HLA B0801
HLA B2705
HLA B3501
HLA B5101
Average*
S
YTNSFTRGVYY
170
192
184
172
181
186
199
175
219
202
188.0
SANNCTFEY
195
170
153
160
158
171
167
168
213
185
174.0
FVFKNIDGY
188
209
195
187
197
219
237
206
214
191
204.3
WTAGAAAYY
151
181
165
178
159
170
189
176
201
190
176.0
CVADYSVLY
203
231
223
190
216
201
192
207
232
212
210.7
TSNQVAVLY
157
163
152
170
151
170
158
171
216
188
169.6
VASQSIIAYTM
174
201
187
206
200
203
199
195
242
225
203.2
MIAQYTSAL
205
266
239
237
220
219
227
219
229
225
228.6
FPQSAPHGVVF
177
216
189
204
179
215
243
200
227
258
210.8
FVSNGTHWF
199
207
189
217
208
235
256
221
220
201
215.3
M
FAYANRNRFLY
210
232
202
268
213
213
236
211
253
226
226.4
MWLSYFIASF
171
231
204
219
185
197
214
187
191
195
199.4
FARTRSMWSF
195
201
196
260
198
211
249
204
200
215
212.9
TVATSRTLSYY
164
201
185
191
180
209
188
207
251
223
199.9
YSRYRIGNYK
154
176
171
215
170
180
215
178
219
206
188.4
N
NTASWFTAL
153
189
168
192
174
189
177
185
187
181
179.5
FTALTQHGK
167
186
166
170
176
206
232
191
188
176
185.8
LSPRWYFYYLG
217
244
237
204
237
262
247
254
300
278
248
TKAYNVTQAF
142
186
151
207
160
168
158
174
179
200
172.5
AQFAPSASAF
172
201
192
203
188
195
192
191
213
215
196.2
* Higher rates show better quality of modeling
Table 8
Interaction similarity scores of the selected HTL epitopes using GalexyPepDock flexible docking server.
Protein
Epitope
DRB1-0101
DRB1-0301
DRB1-0401
DRB1-1101
DRB1- 1501
DRB5-0101
Average*
S
NIDGYFKIYSKHTPINLVRDLPQGFS
139
146
132
132
132
132
135.5
LKSFTVEKGIYQTSNFRVQPT
124
126
124
126
124
126
125.0
YQTSNFRVQPTESIVRFP
118
105
118
118
108
118
114.1
TQQLIRAAEIRASANLAATKMS
133
123
113
123
113
113
119.6
FTRGVYYPDKVFRSSVLHSTQD
135
133
135
133
135
135
134.3
PHGVVFLHVTYVPAQEKN
170
128
128
128
132
128
135.6
SQSIIAYTMSLGAENS
141
129
141
141
141
141
139.0
NFSQILPDPSKPSKRSF
148
126
126
126
126
126
129.6
YEPQIITTDNTFVSGNC
123
116
120
116
123
120
119.6
TQSLLIVNNATNVVIKV
131
131
131
130
130
131
130.6
M
RYRIGNYKLNTDHSSSSDNI
117
113
117
117
117
117
116.3
SRTLSYYKLGASQRVAGDS
124
124
124
128
124
128
125.3
RSMWSFNPETNILLNVPLHGTI
169
169
169
139
169
169
164.0
DLPKEITVATSRTLSYYKL
137
124
125
137
137
137
132.8
N
GTWLTYTGAIKLDDKDPNFKDQ
137
131
137
137
137
131
135
NKDGIIWVATEGALNTPK
136
133
131
136
131
136
133.8
KDPNFKDQVILLNKHIDAYK
127
134
127
127
134
127
129.3
NTASWFTALTQHGKED
137
137
137
137
149
137
139.0
LPKGFYAEGSRGGSQ
107
107
107
105
105
107
106.3
KQLQQSMSSADSTQA
142
118
118
118
118
118
122.0
* Higher rates show better quality of modeling
* Higher rates show better quality of modeling* Higher rates show better quality of modeling
Construct design
According to above-mentioene">ned parameters including binding affinity between peptide and MHCs, epitope identification scores for T-cells, antibody-specific epitopes prediction for B-cells and T-cells, proteasomal cleavage and Tap transport scores, allergenicity, conservancy degree, population coverage and peptide-protein flexible docking scores, three different constructs were designed by top-ranked epitopes (Fig 1). For B-cell structure, S1133-1172, S440-501, S59-81, S304-322, N232-269, N164-216, N1-51 and N361-390 epitopes had the ability to induce antibodies. The B-cell epitopes were linked by KK linker [37]. For CTL structure, S27-37, S161-169, S191-199, S257-265, S360-368, S603-611, S686-696, S868-876, S1051-1061, S1094-1102, M36-46, M90-99, M102-111, M195-204, N47-55, N52-60, N103-113, N264-273 and N304-313 were selected. The CTL epitopes were linked by AAY linker [37], and finally, for Helper T-cell (HTL) structure, S32-53, S114-130, S196-231, S303-323, S313-330, S689-704, S801-817, S1009-1030, S1057-1074, S1110-1126, M107-128, M163-181, M173-191, M198-217, N48-63, N126-143, N167-181, N328-349, N342-361 and N405 were selected. The HTL epitopes were linked by GPGPG linker [37].
Fig 1
The suggested epitope constructs: a) the schematic diagram of the LBL epitope construct derived from the S and N proteins of the SARS-CoV-2 linked by KK linker; b) the schematic diagram of the CTL epitope construct derived from the S, M and N proteins of the SARS-CoV-2 linked by AAY linker; c) the schematic diagram of the HTL epitope construct derived from the S, M and N proteins of the SARS-CoV-2 linked by GPGPG linker.
Three constructs for each LBL, CTL and HTL epitope were analyzed by ProtParam server. Physicochemical properties of the constructed peptides were shown in Table 9. For LBL epitope, molecular weight (MW) was measured 36.5 kDa with theoretical isoelectric point (PI) of 10.24. For CTL and HTL epitopes, MWs were measured 28.6 and 49.5 kDa with PIs of 9.29 and 9.42, respectively. All constructs were soluble and stable.
Table 9
Physicochemical properties of the designed HTL and CTL and LBL epitopes.
Construct
Molecular weight
Theoretical PI
Positive charge residue
Negative charge residue
Solubility
Stability
LBL-epitope
36.5 kDa
10.24
63
26
Soluble
Stable
CTL-epitope
28.6 kDa
9.29
13
3
Soluble
Stable
HTL-epitope
49.5 kDa
9.42
42
31
Soluble
Stable
The designed structures were analyzed by I-TASSER server. This server generates some structural coene">nformations, then uses SPICKER program to cluster all structures based on the pair-wise structure similarity. Finally, the top five models corresponding to the five largest clusters were reported by the server. The assurance of each model was calculated by C-score. The C-score values show the accuracy of the predicted model which usually is in the range of -5 to 2. Also, the higher value of the C-score signifies the better quality of prediction. The C-scores of the models for LBL, CTL and HTL polypeptide constructs were -2.39, -4.42 and -0.63, respectively. Figs 2–4 show tertiary structures of the predicted LBL, CTL and HTL structures.
Fig 2
The tertiary structure of the predicted LBL construct: a) 3D prediction of LBL multiepitope construct, b) sequence prediction and conformation scores of each amino acid in LBL multiepitope construct.
Fig 4
The tertiary structure of the predicted HTL construct: a) 3D prediction of HTL multiepitope construct, b) sequence prediction and conformation scores of each amino acid in HTL multiepitope construct.
Refinement and validation of 3D structures
After tertiary structure prediction, the top model of each coene">nstruct was submitted separately to GalaxyRefine 2 server. GalaxyRefine server rebuilds side-chain, and performs side-chain repacking and structure relaxation by molecular dynamic simulation. After refinement process, refined models were submitted to SAVE5.05 server for validation. The data indicated that the quality of tertiary structure was improved after refinement process. Most of the residues were found in favored and allowed regions: 98.9% for LBL, 98.3% for CTL and 96.8% for HTL constructs. Figs 5–7 show refined characteristics including secondary structures, overall quality and Ramachandran plots.
Fig 5
Refined characteristics of LBL multiepitope construct: a) Refined 3D prediction of LBL multiepitope construct, b) overall quality of refinement, c) Ramachandran plot.
Fig 7
Refined characteristics of HTL multiepitope construct: a) Refined 3D prediction of HTL multiepitope construct, b) overall quality of refinement, c) Ramachandran plot.
Prediction of discontinuous antibody epitopes
Linear antibody epitopes could be predicted through sequence-based algorithms. In coene">ntrast, predictioene">n of discoene">ntinuous epitopes needs 3D structural information of the protein or polypeptide. Thus, the selected refined models were analyzed by Ellipro server to predict potential discontinuous B-cell epitopes. Ellipro servers identified 3 discontinuous B-cell epitopes for CTL, 4 for HTL and 3 for LBL. S1 Table indicates residues, number of residues and the 3D structure of putative B-cell epitopes in the designed constructs.
The peptide-protein docking between three vaccine constructs and TLRs 2, 3 and 4 were performed by ClusPro server. The lowest energy level was estimated for LBL-TLR2 complex-812.3, for LBL-TLR3 complex -894.8, for LBL-TLR4 complex -875.3, for CTL-TLR2 complex -1087.8, for CTL-TLR3 complex -1385.3, for CTL-TLR4 complex -1180.8, for HTL-TLR2 complex -1143.6, for HTL-TLR3 complex -1296.6 and for HTL-TLR4 complex -1119.6. Strong interactions between the designed constructs and TLRs 2, 3 and 4 supports the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families (Figs 8–10). All three constructs showed better interactions with TLR 3.
Fig 8
The peptide-protein docking between LBL construct and TLRs 2, 3 and 4: a) LBL-TLR2 complex with participated residues in interaction, b) LBL-TLR3 complex with participated residues in interaction, c) LBL-TLR4 complex with participated residues in interaction.
Fig 10
The peptide-protein docking between HTL construct and TLRs 2, 3 and 4: a) HTL-TLR2 complex with participated residues in interaction, b) HTL-TLR3 complex with participated residues in interaction, c) HTL-TLR4 complex with participated residues in interaction.
Discussion
The SARS-CoV-2 has become a major global public health issue and scientists are struggling to find the best way to treat the disease and develop a vaccine against the virus. Numerous immune-bioinformatics methods have been developed in vaccine researches which can potentially save time and resources. These tools could help us to identify antigenic domains to design a multi-epitope vaccine. Since now we know enough information about SARS-CoV-2’s genomics and proteomics, we can design peptide vaccines based on a neutralizing epitope. These immunoinformatics methods have made a significant impact on the immunology researches and we can see many examples of in silico design of epitope-based vaccines against many viruses including human immunodeficiency virus (HIV) [16], human papillomavirus (HPV) [38, 39], SARS-CoV [40], rhinovirus [41].SARS-CoV-2 is an RNA virus tending to mutate more frequently [42]. These mutations mostly occur at the surface of the protein like at S protein leaving the immune system in a blind spot. Being the main antigenic component, S protein of SARS-CoV-2 has been selected as an important target for vaccine development since it is a crucial factor modulating tropism and pathogenicity and has the ability to induce faster and longer-term immune response [43, 44]. Since the humoral response frommemory B-cells can easily be overcome by the emergence of antigens, it is important to design constructs based on cell-mediated immunity leading to lifelong immunity. Thus, our in-silico approaches were intended to design a universal SARS-CoV-2 vaccine for induction of B- and T-cell immunity with efficient reactions to the virus and long-term immune responses based on the S protein of the virus and also two other structural proteins including N and M.There are some efforts based oene">n designing epitope-based vaccines against SARS-CoV-2 since the outbreak. In one study, Abdelmageed et al. suggested certain peptides in E protein of SARS-CoV-2 as promising epitope vaccine candidates against T-cell [45]. In another study, Singh et al. proposed one construct including E, S, N and M proteins as an epitope vaccine candidate [46]. On the other hand, Feng et al. suggested some putative B- and T-cell epitopes based on S, M and E proteins of SARS-CoV-2 [47]. Also, Enayatkhani et al. designed a multiepitope vaccine candidate based on N, M and open reading frame (ORF) 3a of SARS-CoV-2 [48], and Teimouri et al. tried to predict B- and T-cell epitopes of SARS-CoV-2 in comparison with SARS-CoV [49]. These papers contain some very worthwhile suggestions for ease of multi-epitope vaccine development and all of them demonstrate that a multi-epitope peptide vaccine targeting multiple antigens should be considered as an ideal approach for prevention and treatment of SARS-CoV-2.
According to the aforementioene">ned finding, we tried to use computational and bioinformatics methods on the formulation of new SARS-CoV-2 vaccine against its structural proteins including S, N and M proteins in a more comprehensive way. In the beginning, the whole genome of SARS-CoV-2 was analyzed. Then, three major structural proteins including S, N and M were chosen for further analyses. We identified epitopes corresponding to B-cells and T-cells to design constructs being able to elicit both humoral and cellular immunity. We used BepiPred tool to predict putative B-cell epitopes and chose 8 putative epitopes of S and N protein being able to induce antibodies like IgG. In contrast, M protein of the virus could not induce any class of antibodies.
As CD8+ and CD4+ T-cells play a major role in antiviral immunity, we tried to evaluate the binding affinity to MHC class I and II molecules. Choosing S, N and M proteins of the virus as the antigenic site, we used NetMHCpan and NetMHCIIpan prediction tools to identify the most immunodominant regions. From all peptides predicted, we chose 20 putative epitopes for MHC class I and 20 putative epitopes for MHC class II. Since S protein is currently the most promising antigen formulation, we put the focus on the S protein epitopes and chose 10 epitopes of S protein and 10 epitopes of other proteins for each MHC class I and II. The IFN-γ and IL-10cytokines were also measured as candidates in MHC class II epitopes as they promote the development of T-helper cells being required for B-cell, macrophage and cytotoxic T-cell activation. Among the CTL predicted epitopes, S257-265, S603-611 and S360-368 and among HTL predicted epitopes, N167-181, S313-330 and S1110-1126 had better MHC binding rank.To predict antigen processing through the MHC class I antigen presentatioene">n pathway, we used NetCTL1.2 server. All of the predicted epitopes had upper cut off identification scores (> 0.75) showing a high quality of proteasomal cleavage and Tap transport efficiency. We also measured the epitopes for conservancy analysis. All predicted epitopes were 100% conserved within four different clades of SARS-CoV-2. In general, the selected epitopes had the potency to produce an immune response against S, V, G and O clades of SARS-CoV-2.
Population coverage is another important factor in vaccine design. We measured population coverage rate for CTL and HTL epitopes in 16 specified geographical regions. For CTL epitopes, and helper T-cell epitopes, the highest population coverage of the world’s population was calculated for S27-37 with 86.27%, and for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%, respectively. Overall, these results suggest a specific binding of CTL epitopes and HTL epitopes to the prevalent HLA molecules in the targeted populations. Another prominent obstacle in vaccine development is the probability of allergenicity since many vaccines stimulate the immune system into an allergenic reaction. In this study, we used PA3P to predict potential allergenicity and all of the epitopes were analyzed as non-allergen.For CTL epitopes, N103-113, S868-876, M36-46, S1094-1102, M102-111, S1051-1061, S360-368, S191-199 and S686-696 had the highest average of interaction similarity score, respectively and For HTL epitopes, M107-128, N48-63, S689-704, S1057-1074, S196-231, N328-349, S32-53, N126-143, M163-181 and S114-130 had the highest average of interaction similarity score, respectively. Overall, CTL epitopes showed better quality of docking in comparison with HTL epitopes. Finally, the vaccine construction was completed after joining the LBL, CTL and HTL epitopes with KK, AAY and GPGPG linkers, respectively.The molecular weights of the coene">nstructed LBL, CTL and HTL epitopes were obtained as 36.5, 28.6 and 49.5 kDa, respectively which were low molecular weights for a multiepitope vaccine. All constructs were soluble and stable indicating that the designed constructs had high solubility and stability for the initiation of an immunogenic reaction.
In the case of 3D modeling, we used I-TASSER server to predict the tertiary protein structure. The accuracy of the selected models was evaluated by C-score. The C-scores of the models for LBL, CTL and HTL polypeptide constructs were -2.39, -4.42 and -0.63, respectively. The higher value of the C-score is the better quality of prediction. Thus, HTL with the C-score of -0.63 showed higher accuracy of the predicted epitopes. Also, the quality of the predicted constructs was improved by refinement which leads to a higher quality of final models. Over the 96.8% of the residues were found in favored and allowed regions. At last, we used Ellipro server to predict potential discontinuous B-cell epitopes. Ellipro servers identified 3 discontinuous B-cell epitopes for CTL with 143 residues, 4 for HTL with 225 residues and 3 for LBL with 72 residues indicating the ability of the designed constructs for robust induction of humoral response. Also, peptide-protein docking between three vaccine constructs and TLRs 2, 3 and 4 were performed by ClusPro server, and all data showed strong interactions between the designed constructs and TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae family. All three constructs showed better interactions with TLR 3.Overall, we tried to consider threemajor structural proteins including S, N and M proteins of the virus and design three different constructs including LBL, CTL and HTL constructs to elicit more robust humoral and cellular immunity. Comparing our study with other studies in the field of multi-epitope vaccine design for SARS-CoV-2, all LBL epitopes obtained in Table 1 were reported in Bhattacharya et al. paper using the same server of Bepipred [50]. However, we chose the ones being able to induce different classes of antibodies including IgG and IgA. Among CTL epitopes obtained in Table 2, S686-696 (VASQSIIAYTM), S1051-1061 (FPQSAPHGVVF), N103-113 (LSPRWYFYYLG), N304-313 (AQFAPSASAF) and all of M epitope of CTL including M36-46 (FAYANRNRFLY), M168-180 (TVATSRTLSYY), M195-204 (YSRYRIGNYK), M90-99 (MWLSYFIASF) and M102-111 (FARTRSMWSF) have not been reported in any literature. Also, among HTL epitopes reported in Table 3, S303-323 (LKSFTVEKGIYQTSNFRVQPT), S1009-1030 (TQQLIRAAEIRASANLAATKMS), S801-817 (NFSQILPDPSKPSKRSF), N328-349(GTWLTYTGAIKLDDKDPNFKDQ), N126-143 (NKDGIIWVATEGALNTPK), N342-361 (KDPNFKDQVILLNKHIDAYK), N48-63 (NTASWFTALTQHGKED), N167-181 (LPKGFYAEGSRGGSQ), N405-419 (KQLQQSMSSADSTQA), M107-128 (RSMWSFNPETNILLNVPLHGTI) and M163-181 (DLPKEITVATSRTLSYYKL) have not been reported in any literature. The rest of the epitopes mentioned in Tables 2 and 3 were found in agreement with Teimouri et al. [49] for MHC class I and Feng et al. [47] for MHC class II, respectively. Also, we found one putative CTL epitope, S360-368 (CVADYSVLY) related to receptor-binding domain (RBD) region for S protein which was referred to the fragment of 347 to 520 amino acids [51]. We also identified overall 10 discontinuous B-cell epitopes for three multi-epitope constructs. Meanwhile, we investigated the interaction of three designed constructs with TLRs 2, 3 and 4 based on the previous studies on other Coronaviridae family such as SARS-CoV and MERS-CoV [33-35]. All three constructs showed strong interactions with TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families. Albeit, SARS-CoV-2 was identified for only 5 month, but researches have recently begun to design a multiepitope vaccine. Thus, the collected data and information are very limited and need to be accumulated to improve existing processes and the designed multi-epitope vaccine needs to be tested clinically to validate vaccine safety.
Conclusion
In conclusion, we determined three vaccine coene">nstructs against three major structural proteins of SARS-CoV-2 designed based on robust vaccine design criteria including non-allergenicity, conservancy, affinity measurement to multiple alleles of MHC, worldwide population coverage, 3D prediction, refinement and validation, discontinuous B-cell epitope prediction, docking and effectiveness of molecular interaction with their respective HLA alleles and TLRs. These constructs require validation by in vivo and clinical experiments. Generally, with the help of in silico studies, experimental researches can march rapidly with higher probabilities of finding the desired solutions and controlling the current outbreak.
Discontinuous B-Cell epitope on HTL, CTL and LBL polyepitope constructs.
Authors: Zaid Abdi Alkareem Alyasseri; Mohammed Azmi Al-Betar; Iyad Abu Doush; Mohammed A Awadallah; Ammar Kamal Abasi; Sharif Naser Makhadmeh; Osama Ahmad Alomari; Karrar Hameed Abdulkareem; Afzan Adam; Robertas Damasevicius; Mazin Abed Mohammed; Raed Abu Zitar Journal: Expert Syst Date: 2021-07-28 Impact factor: 2.812
Authors: Ahmad J Obaidullah; Mohammed M Alanazi; Nawaf A Alsaif; Hussam Albassam; Abdulrahman A Almehizia; Ali M Alqahtani; Shafi Mahmud; Saad Ahmed Sami; Talha Bin Emran Journal: RSC Adv Date: 2021-05-19 Impact factor: 4.036
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