Vaccine Design from the Ensemble of Surface Glycoprotein Epitopes of SARS-CoV-2: An Immunoinformatics Approach

The present study aimed to work out a peptide-based multi-epitope vaccine against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We predicted different B-cell and T-cell epitopes by using the Immune Epitopes Database (IEDB). Homology modeling of the construct was done using SWISS-MODEL and then docked with different toll-like-receptors (TLR4, TLR7, and TLR8) using PatchDock, HADDOCK, and FireDock, respectively. From the overlapped epitopes, we designed five vaccine constructs C1–C5. Based on antigenicity, allergenicity, solubility, different physiochemical properties, and molecular docking scores, we selected the vaccine construct 1 (C1) for further processing. Docking of C1 with TLR4, TLR7, and TLR8 showed striking interactions with global binding energy of −43.48, −65.88, and −60.24 Kcal/mol, respectively. The docked complex was further simulated, which revealed that both molecules remain stable with minimum RMSF. Activation of TLRs induces downstream pathways to produce pro-inflammatory cytokines against viruses and immune system simulation shows enhanced antibody production after the booster dose. In conclusion, C1 was the best vaccine candidate among all designed constructs to elicit an immune response SARS-CoV-2 and combat the coronavirus disease (COVID-19).


Introduction
The 2019 SARS-like coronavirus (SARS-CoV-2), a member of betacoronavirus, emerged in late December 2019, causing pneumonia [1]. Coronaviruses are enveloped, large, positive-sense RNA viruses belonging to the family of Coronaviridae, that can infect mammals, birds, and humans, causing deadly

Protein Sequence Retrieval
In the current strategy, the art of immunoinformatics approach was used for the development of the multi-epitope-based vaccine. The stepwise analysis followed for the study is depicted in Figure  2. The amino acid sequence (1273 amino acids) of the surface glycoprotein SARS-CoV-2 was retrieved from NCBI (Accession no: AYN64561.1) (https://www.ncbi.nlm.nih.gov/).

Epitope Prediction
For chimeric vaccine development, the surface topology of the glycoprotein was selected to identify immunogenic determinants for the vaccine construct. Identification of immunogenic T-Cells (MHC-I and II) was carried out by using the Immune Epitopes Database (IEDB) (https://www.iedb.org/) [13]. It employs a different prediction method for MHC epitope binding analysis. MHC class I and II molecules were predicted through the Stabilized Matrix Method (SMM) scoring and neutral-network based tool (net MHC-1.1) [14][15][16]. Epitopes were prioritized from the predicted ensemble, based on a threshold value of IC50 ≤ 200 nM. B-Cell immunogenic determinants were projected using BCPRED (http://ailab.cs.iastate.edu/bcpreds/), with the default parameters. BCpred predicts linear B-Cell epitopes [17], which are important for stimulating a humoral immune response, which activates B lymphocytes for antibody production [18]. Interferon-inducing epitopes were identified from the MHC-II binding epitopes by using the IFNepitope web server (http://crdd.osdd.net/raghava/ifnepitope/predict.php). IFNepitope predicts regions in the protein sequence or antigen, that cause induction of Interferon-gamma (IFN-gamma) (Dhanda et al., 2013). The overlapped epitopes were submitted to IFNepitope web server and IFN-gamma was predicted

Protein Sequence Retrieval
In the current strategy, the art of immunoinformatics approach was used for the development of the multi-epitope-based vaccine. The stepwise analysis followed for the study is depicted in Figure 2. The amino acid sequence (1273 amino acids) of the surface glycoprotein SARS-CoV-2 was retrieved from NCBI (Accession no: AYN64561.1) (https://www.ncbi.nlm.nih.gov/).

Epitope Prediction
For chimeric vaccine development, the surface topology of the glycoprotein was selected to identify immunogenic determinants for the vaccine construct. Identification of immunogenic T-Cells (MHC-I and II) was carried out by using the Immune Epitopes Database (IEDB) (https://www.iedb.org/) [13]. It employs a different prediction method for MHC epitope binding analysis. MHC class I and II molecules were predicted through the Stabilized Matrix Method (SMM) scoring and neutral-network based tool (net MHC-1.1) [14][15][16]. Epitopes were prioritized from the predicted ensemble, based on a threshold value of IC 50 ≤ 200 nM. B-Cell immunogenic determinants were projected using BCPRED (http://ailab.cs.iastate.edu/bcpreds/), with the default parameters. BCpred predicts linear B-Cell epitopes [17], which are important for stimulating a humoral immune response, which activates B lymphocytes for antibody production [18]. Interferon-inducing epitopes were identified from the MHC-II binding epitopes by using the IFNepitope web server (http://crdd.osdd.net/raghava/ifnepitope/ predict.php). IFNepitope predicts regions in the protein sequence or antigen, that cause induction of Interferon-gamma (IFN-gamma) (Dhanda et al., 2013). The overlapped epitopes were submitted to IFNepitope web server and IFN-gamma was predicted based on Support Vector Machine (SVM) and model was predicted by selecting IFN-gamma versus non-IFN-gamma [19].

Epitopes Selection and Vaccine Construction
The B-cell and T-cell epitopes are immunodominant and crucial to enhance neutralizingantibody response when exposed to toxins and contagions. Identification of B-cell epitopes is indispensable for epitope-based vaccine design and development [20]. MHC encodes cell surface proteins which are vital for the adaptive immune system, including MHC-II binding interferoninducing epitopes. B-Cell, MHC-I, MHC-II, and IFN epitopes were ranked based on cut off values and compared manually for overlapped peptides. Four overlapped and high score epitopes were selected for vaccine constructs. Universal T-helper epitopes, PADRE (Pan HLA-DR reactive epitope) and five different adjuvants were added. PADRE is projected as a simple carrier epitope, useful in making synthetic or recombinant vaccines and known to have enhanced response when coupled with adjuvant [21]. Adjuvant increases the immune response of a vaccine, leading to more antibody production and reduces the quantity of antigen input. Adjuvants used in this study were mined from literature and included heparin-binding hemagglutinin (HBHA) from Mycobacterium sp., Beta defensin, Ribosomal protein, and flagellin. HBHA has been implicated as a vaccine adjuvant, with demonstrated usage in antitumor immunotherapy as well [22]. Beta defensin is an antimicrobial peptide with a role in innate immune responses and can initiate cellular immune response [23]. Ribosomal protein has been identified as a non-traditional adjuvant [24], while flagellin activates

Epitopes Selection and Vaccine Construction
The B-cell and T-cell epitopes are immunodominant and crucial to enhance neutralizing-antibody response when exposed to toxins and contagions. Identification of B-cell epitopes is indispensable for epitope-based vaccine design and development [20]. MHC encodes cell surface proteins which are vital for the adaptive immune system, including MHC-II binding interferon-inducing epitopes. B-Cell, MHC-I, MHC-II, and IFN epitopes were ranked based on cut off values and compared manually for overlapped peptides. Four overlapped and high score epitopes were selected for vaccine constructs. Universal T-helper epitopes, PADRE (Pan HLA-DR reactive epitope) and five different adjuvants were added. PADRE is projected as a simple carrier epitope, useful in making synthetic or recombinant vaccines and known to have enhanced response when coupled with adjuvant [21]. Adjuvant increases the immune response of a vaccine, leading to more antibody production and reduces the quantity of antigen input. Adjuvants used in this study were mined from literature and included heparin-binding hemagglutinin (HBHA) from Mycobacterium sp., Beta defensin, Ribosomal protein, and flagellin. HBHA has been implicated as a vaccine adjuvant, with demonstrated usage in antitumor immunotherapy as well [22]. Beta defensin is an antimicrobial peptide with a role in innate immune responses and can initiate cellular immune response [23]. Ribosomal protein has been identified as a non-traditional adjuvant [24], while flagellin activates diverse cell types, which are part of innate and adaptive immunity, promoting cytokine production [25]. Effectiveness of all these adjuvants has been reported in the literature and they initiate cellular immune responses, leading to antigen-specific immune response stimulation. All of these are TLR agonists and it is reported that binding with TLRs shows enhanced results in activating immune responses because TLRs conduct signaling and activate the innate and adaptive immune response [26]. After addition of PADRE and the adjuvant, the vaccine constructs were admixed in different combinations with the help of GGGS, HEYGAEALERAG, and EAAAK linkers [27,28].

Physiochemical Properties
Expasy Protparam (https://web.expasy.org/protparam/) was used to predict physicochemical properties like amino acid composition, atomic composition, molecular weight, theoretical pI, extinction coefficient, estimated half-life, aliphatic index, grand average of hydrophobicity (GRAVY), and instability index of the vaccine constructs [32].

Homology Modeling and Model Validation
Homology models were built for the vaccine construct, using the SWISS-MODEL (https:// swissmodel.expasy.org/) online server, which is the most reliable server for protein modeling [33]. The 3D model structure was further validated using RAMPAGE Ramachandran plot (http://mordred. bioc.cam.ac.uk/~{}rapper/rampage.php).

Immune Simulation of Vaccine Construct
Immune simulation of vaccine construct C1 and positive control (construct having no adjuvant) using web server C-ImmSim (http://kraken.iac.rm.cnr.it/C-IMMSIM/) was done. It is an agent-based model implementation where information about the humoral as well as the cellular response of the mammalian immune system, invoked by antigen at the cellular level is obtained. The simulation time steps 1, 42, and 126 h (8 h correspond to one cell division cycle in real life according to Kaba et al. (2018)) [37] were chosen along with homozygous host haplotypes (HLA-A*0101, HLA-A*0201, HLA-B*0702, HLA-B*3901, HLA-DRB1*0101, and HLA-DRB1*0401), while other general simulation parameters were: random seed: 1234, simulated volume: 10, simulation steps 1000, Adjuvant = 100 [38]. The comparison was drawn between positive control and construct and results were interpreted.

Codon Optimization and In Silico Cloning
A codon optimization approach was used to enhance recombinant protein expression. Codon optimization is important because degeneracy of genetic code permits most of the amino acids to be encoded by multiple codons. Java codon adaptation index (JCAT) (http://www.jcat.de/) and EMBOSS Backtranseq (www.ebi.ac.uk/) were used in the codon system of E. coli to obtain the codon adaptation index (CAI) values and GC contents [39].
The optimized sequence of the final vaccine construct was inserted in a vector for expression, by using the Snapgene tool available at (https://www.snapgene.com/free-trial/). A compatible plasmid vector pET-28a (+) was used to integrate the optimized sequence and clone the constructed chimeric vaccine. pET-28a (+) is for expression of N-terminally 6 × His-tagged proteins and usually, N-terminal tags are advantageous over C-terminal tags, leading to enhanced purification, protein recovery, and stronger response.

Results
The T cell, B cell, and IFN-inducing epitopes of the antigenic surface glycoprotein were predicted with the help of Immune Epitope Database (IEDB) and IFNepitope servers. The predicted T cell epitopes were able to bind with MHC-1 and MHC-2 molecules to activate the adaptive immune response in the host. We searched the overlapped epitopes as shown in Table 1 (the epitopes bind with both MHC molecules, B cells, and IFN-gamma) to take advantage and produce humoral, cell-mediated immune responses and induce interferons which were our ultimate objective. Five different constructs were built by the integration of two antigenic peptides and several adjuvants and linkers. There were a lot of possible arrangements of segments but since the analysis of such a huge number of constructs was impossible, we designed these five constructs as shown in Table 2, as samples for our study. The sequence of these constructs differed from each other according to the adjuvant used and also the arrangement order of the constituent segments. The linkers "EAAAK" (blue) links the adjuvant (black) in the constructs. The GGGS and HEYGAEALERAG (grey) link the epitopes. Immunogenic epitopes (red) and the immune enhancer adjuvant, PADRE, as well as linker sequences were inserted as shown in Table 2.  All the five vaccine constructs were checked for allergenicity, antigenicity, and solubility by Algpred server, Vexijen v2.0 [40] (Designed by Medical University Sofia, Bulgari and The Jenner Institute, Oxford University, Compton, Berkshire, RG20 7NN, UK) and SOLpro, respectively as shown in Table 3. The least allergenic and most antigenic C1 vaccine construct was selected as a promiscuous vaccine to elicit host immune response. Physiochemical properties of all vaccine constructs were predicted by the ProtParam server as shown in Table 4. The molecular weight of the vaccine constructs was predicted between 23 kDa tõ 36 kDa. The GRAVY score [41] was found between the values −0.14 to −0.45, which shows a hydrophilic nature of the vaccine constructs. The high aliphatic index score (64.13 to 80.42) indicates their stability at several temperatures. All vaccines constructs showed good instability index (<40) except construct 3 (C3) and these stable constructs can initiate an immunogenic reaction [42]. The estimated half-life of construct-1 (C-1) is 1 h in mammalian reticulocytes (in vitro), 30 min in yeast (in vivo), and more than 10 h in Escherichia coli (in vivo). Antigenic propensity was calculated for 337 residues of vaccine construct. It was above one for majority residues and considerable variation was seen at some places, with decreased antigenicity near residue 50, 175, and 310. High span regions with enhanced antigenicity were 13 in total ( Figure 3A) and average antigenicity was slightly above 1, i.e., 1.0098.  These tertiary structures are considered as critical regarding their interactions with other proteins and molecules within the cell. Tertiary structures of the constructed vaccines were generated by using the SWISS-MODEL server. PatchDock, HADDOCK web server, and FireDock were used for molecular docking and refinement, respectively, and then validated by RAMPAGE. The final 3D modeled structure of the vaccine construct is shown in Figure 3B and the Ramachandran validation plot is displayed in Figure 3C. A vaccine able to bind to different HLA allelic proportion of the human population is most important for its proper function inside the host. The docking of all vaccine constructs was performed with TLR7, TLR8, and TLR 4/MD2 complexes (PDB ID 2Z65). After analyzing all vaccine constructs, we finalized the vaccine construct (C-1) based on different physiochemical properties and docking scores. The C-1 TLR7 complex has good global binding energy score (−65.88 Kcal/mol) obtained from PatchDock as shown in Table 5. Therefore, we considered construct 1, as a promising vaccine against the 2019 novel Coronavirus (2019-nCoV). Further molecular docking of the vaccine was performed These tertiary structures are considered as critical regarding their interactions with other proteins and molecules within the cell. Tertiary structures of the constructed vaccines were generated by using the SWISS-MODEL server. PatchDock, HADDOCK web server, and FireDock were used for molecular docking and refinement, respectively, and then validated by RAMPAGE. The final 3D modeled structure of the vaccine construct is shown in Figure 3B and the Ramachandran validation plot is displayed in Figure 3C.
A vaccine able to bind to different HLA allelic proportion of the human population is most important for its proper function inside the host. The docking of all vaccine constructs was performed with TLR7, TLR8, and TLR 4/MD2 complexes (PDB ID 2Z65). After analyzing all vaccine constructs, we finalized the vaccine construct (C-1) based on different physiochemical properties and docking scores. The C-1 TLR7 complex has good global binding energy score (−65.88 Kcal/mol) obtained from PatchDock as shown in Table 5. Therefore, we considered construct 1, as a promising vaccine against the 2019 novel Coronavirus (2019-nCoV). Further molecular docking of the vaccine was performed with the modeled TLR7 using the HADDOCK web server. The docking results revealed that the vaccine showed a strong binding affinity with the TLR receptor indicated in Table 6. Molecular interaction formed between vaccine and TLR can be seen in Figure 4, which shows 3 salt bridges, 10 hydrogen bonds, and 156 non bonded interactions between both molecules. Root mean square fluctuation (RMSF) of chain A was observed to be maximum at position 256. This residue was not involved in direct interaction with another chain. However, the nearby residue at position 252 was involved in non-bonded contact formation with Glu at position 769, which means that the interaction had an impact on RMSF. Similar observations were made for chain B where most fluctuations were observed at residue 122, 271, 354, 488, and near 818, but these residues were not directly involved in bond formation with chain A. Nearby residue interaction distorts geometry and causes fluctuation of main-chain atom coordinates from alpha carbon backbone. The immune simulation was carried out using C-ImmSim, which accounts for both B-cell and HLA class I/II epitope prediction as well as the interaction of the T-cell receptor with the peptide-HLA complexes. This prediction is made through an agent-based representation of immune cells and the interaction potential of amino acids [43]. Booster doses were given because initial immunization response is comparatively sluggish with low antibody concentration, while the booster immunization is pretty fast, with high-affinity antibody, mostly IgG production in bulk. This is evident for our vaccine construct simulation ( Figure 5B), where IgG increased after the booster dose. The positive control showed no change in antibody titer in the absence of the adjuvant. Total count of B lymphocytes and non-memory cells, as well as isotypes IgG1 and IgG2, were varied for C1 vs. control. CD4 T-helper lymphocytes count, especially memory cell count shown in green, is very high in C1 ( Figure 6B) compared to control. The concentration of cytokines and interleukins is also considerably increased after C1 administration ( Figure 6D) compared to the control.  The immune simulation was carried out using C-ImmSim, which accounts for both B-cell and HLA class I/II epitope prediction as well as the interaction of the T-cell receptor with the peptide-HLA complexes. This prediction is made through an agent-based representation of immune cells and the interaction potential of amino acids [43]. Booster doses were given because initial immunization response is comparatively sluggish with low antibody concentration, while the booster immunization is pretty fast, with high-affinity antibody, mostly IgG production in bulk. This is evident for our vaccine construct simulation ( Figure 5B), where IgG increased after the booster dose. The positive control showed no change in antibody titer in the absence of the adjuvant. Total count of B lymphocytes and non-memory cells, as well as isotypes IgG1 and IgG2, were varied for C1 vs. control.
CD4 T-helper lymphocytes count, especially memory cell count shown in green, is very high in C1 ( Figure 6B) compared to control. The concentration of cytokines and interleukins is also considerably increased after C1 administration ( Figure 6D) compared to the control.  The immune simulation was carried out using C-ImmSim, which accounts for both B-cell and HLA class I/II epitope prediction as well as the interaction of the T-cell receptor with the peptide-HLA complexes. This prediction is made through an agent-based representation of immune cells and the interaction potential of amino acids [43]. Booster doses were given because initial immunization response is comparatively sluggish with low antibody concentration, while the booster immunization is pretty fast, with high-affinity antibody, mostly IgG production in bulk. This is evident for our vaccine construct simulation ( Figure 5B), where IgG increased after the booster dose. The positive control showed no change in antibody titer in the absence of the adjuvant. Total count of B lymphocytes and non-memory cells, as well as isotypes IgG1 and IgG2, were varied for C1 vs. control.
CD4 T-helper lymphocytes count, especially memory cell count shown in green, is very high in C1 ( Figure 6B) compared to control. The concentration of cytokines and interleukins is also considerably increased after C1 administration ( Figure 6D) compared to the control. To analyze the expression and to clone the constructed vaccine inside a suitable vector, we translate back the protein amino acids sequence to a cDNA nucleotide sequence using the Java codon adaptation tool and EMBOSS Backtranseq. The final vaccine construct showed 54.0% GC content when analyzed by codon optimization tool. The GC content was in the normal range (30-70%). The codon optimization index (CAI) value was predicted 1.0, which indicates high expression in E. coli indicated in Figure 7.   To analyze the expression and to clone the constructed vaccine inside a suitable vector, we translate back the protein amino acids sequence to a cDNA nucleotide sequence using the Java codon adaptation tool and EMBOSS Backtranseq. The final vaccine construct showed 54.0% GC content when analyzed by codon optimization tool. The GC content was in the normal range (30-70%). The codon optimization index (CAI) value was predicted 1.0, which indicates high expression in E. coli indicated in Figure 7.  To analyze the expression and to clone the constructed vaccine inside a suitable vector, we translate back the protein amino acids sequence to a cDNA nucleotide sequence using the Java codon adaptation tool and EMBOSS Backtranseq. The final vaccine construct showed 54.0% GC content when analyzed by codon optimization tool. The GC content was in the normal range (30-70%). The codon optimization index (CAI) value was predicted 1.0, which indicates high expression in E. coli indicated in Figure 7.

Discussion
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new and challenging virus that causes severe infection in humans. The complete genome sequence of the pathogen was reported by [1] on 29 January 2020. In the present study, we took advantage of the available genomes of SARS-CoV-2 to predict an immunogenic, multi-epitope vaccine with different immune enhancer adjuvants and linker sequences. Different approaches, like an inactivated or weakened virus, replicating or non-replicating viral vector, DNA or RNA and protein-based are used for SARS-CoV-2 vaccine development, as indicated in Figure 8 [44].

Discussion
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new and challenging virus that causes severe infection in humans. The complete genome sequence of the pathogen was reported by [1] on 29 January 2020. In the present study, we took advantage of the available genomes of SARS-CoV-2 to predict an immunogenic, multi-epitope vaccine with different immune enhancer adjuvants and linker sequences. Different approaches, like an inactivated or weakened virus, replicating or non-replicating viral vector, DNA or RNA and protein-based are used for SARS-CoV-2 vaccine development, as indicated in Figure 8 [44].

Discussion
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new and challenging virus that causes severe infection in humans. The complete genome sequence of the pathogen was reported by [1] on 29 January 2020. In the present study, we took advantage of the available genomes of SARS-CoV-2 to predict an immunogenic, multi-epitope vaccine with different immune enhancer adjuvants and linker sequences. Different approaches, like an inactivated or weakened virus, replicating or non-replicating viral vector, DNA or RNA and protein-based are used for SARS-CoV-2 vaccine development, as indicated in Figure 8 [44].  Twenty-eight teams are working on vaccines with viral protein subunits, most of these groups are focusing on spike protein. We also selected spike protein to develop potent vaccine construct from the different immunogenic determinant of the spike glycoprotein of SARS-CoV-2. The immunogenic, multi-epitope, subunit vaccine was generated from MHC-I, MHC-II alleles, B-Cell and IFN-inducing epitopes of the surface glycoprotein (S) [16]. Only peptides having IC50 values < 200 were considered as effective peptides. All the selected epitopes were merged with different adjuvants, linkers, and Pan-DR sequence epitopes (PADRE). PADRE sequence is responsible for the reduction in polymorphism in HLA DR molecules in the population [45]. We have also used the G-rich linker GGGS which enhances the immunogenicity of the vaccine inside the host [46]. Five vaccine constructs (C1, C2, C3, C4, C5) were made. All the vaccine constructs were further analyzed for antigenicity, solubility, and allergenicity. We also predicted the physicochemical properties of vaccines and shortlisted the one vaccine construct, C1, which showed good properties among others with 1.0093 average antigenic propensity as shown in Figure 3. As the molecular weight of the candidate vaccine C1 is 35.9 kDa, this makes it possible to predict its solubility during expression, such a molecular weight being able to trigger an immune response. The theoretical pI [47] is predicted to be 6.0, indicating that the protein is acidic. In addition, the predicted instability index [48] indicates that the vaccine peptide will be stable upon expression, thus further firming its potential for use. The aliphatic index indicates good hydrophobicity [41,47]. All the above properties support our constructed vaccine as promiscuous against the SARS-CoV-2. The 3D model of the construct was built with the online server and validated by Ramachandran plot analysis. Furthermore, the docking analysis of the final vaccine C1 was done with different toll-like receptors (TLR4, TLR7, and TLR8). TLR4 has been involved in the recognition of viral structural and non-structural proteins leading to inflammatory cytokine production [49]. TLR4-activating viral proteins include the RSV fusion protein (F), the EBOV glycoprotein, the vesicular stomatitis virus glycoprotein (VSV G), and the dengue virus (DENV) [50]. TLR7 and 8 are key players in antiviral responses. TLR7-specific agonists activate plasmacytoid dendritic cells (pDCs) and B cells and mainly induce IFN-α and IFN-regulated cytokines. TLR8-specific agonists activate myeloid DCs, monocytes, and monocyte-derived DC, leading primarily to the production of proinflammatory cytokines and chemokines, such as TNF-α, IL-12, and MIP-1α [51]. The ability of TLR7 and TLR8 agonists to activate DCs and thus elicit Th1 and CD8+ T cell responses can be exploited to enhance the efficacy of vaccination [52,53]. Activation of TLR7/8 triggers different signaling pathways in human monocytes that halt viral pathogenesis by the induction of interferons (TFNs). In the innate immune system, plasmacytoid dendritic cells TLR7 and TLR9 trigger induction of proinflammatory cytokines and IFN-α/β. The docking results showed good global energy scores for TLR7 that we used during the analysis for the vaccine C1, which was the indication of eliciting an optimal immune response against the SARS-CoV-2. The docked complex when simulated using CABS-flex (2) dynamics revealed that both molecules remained stable with minimum fluctuations RMSF. The flexibility of both molecules can be seen in the trajectory in Figure 9. Twenty-eight teams are working on vaccines with viral protein subunits, most of these groups are focusing on spike protein. We also selected spike protein to develop potent vaccine construct from the different immunogenic determinant of the spike glycoprotein of SARS-CoV-2. The immunogenic, multi-epitope, subunit vaccine was generated from MHC-I, MHC-II alleles, B-Cell and IFN-inducing epitopes of the surface glycoprotein (S) [16]. Only peptides having IC50 values < 200 were considered as effective peptides. All the selected epitopes were merged with different adjuvants, linkers, and Pan-DR sequence epitopes (PADRE). PADRE sequence is responsible for the reduction in polymorphism in HLA DR molecules in the population [45]. We have also used the G-rich linker GGGS which enhances the immunogenicity of the vaccine inside the host [46]. Five vaccine constructs (C1, C2, C3, C4, C5) were made. All the vaccine constructs were further analyzed for antigenicity, solubility, and allergenicity. We also predicted the physicochemical properties of vaccines and shortlisted the one vaccine construct, C1, which showed good properties among others with 1.0093 average antigenic propensity as shown in Figure 3. As the molecular weight of the candidate vaccine C1 is 35.9 kDa, this makes it possible to predict its solubility during expression, such a molecular weight being able to trigger an immune response. The theoretical pI [47] is predicted to be 6.0, indicating that the protein is acidic. In addition, the predicted instability index [48] indicates that the vaccine peptide will be stable upon expression, thus further firming its potential for use. The aliphatic index indicates good hydrophobicity [41,47]. All the above properties support our constructed vaccine as promiscuous against the SARS-CoV-2. The 3D model of the construct was built with the online server and validated by Ramachandran plot analysis. Furthermore, the docking analysis of the final vaccine C1 was done with different toll-like receptors (TLR4, TLR7, and TLR8). TLR4 has been involved in the recognition of viral structural and non-structural proteins leading to inflammatory cytokine production [49]. TLR4-activating viral proteins include the RSV fusion protein (F), the EBOV glycoprotein, the vesicular stomatitis virus glycoprotein (VSV G), and the dengue virus (DENV) [50]. TLR7 and 8 are key players in antiviral responses. TLR7-specific agonists activate plasmacytoid dendritic cells (pDCs) and B cells and mainly induce IFN-α and IFN-regulated cytokines. TLR8specific agonists activate myeloid DCs, monocytes, and monocyte-derived DC, leading primarily to the production of proinflammatory cytokines and chemokines, such as TNF-α, IL-12, and MIP-1α [51]. The ability of TLR7 and TLR8 agonists to activate DCs and thus elicit Th1 and CD8+ T cell responses can be exploited to enhance the efficacy of vaccination [52,53]. Activation of TLR7/8 triggers different signaling pathways in human monocytes that halt viral pathogenesis by the induction of interferons (TFNs). In the innate immune system, plasmacytoid dendritic cells TLR7 and TLR9 trigger induction of proinflammatory cytokines and IFN-α/β. The docking results showed good global energy scores for TLR7 that we used during the analysis for the vaccine C1, which was the indication of eliciting an optimal immune response against the SARS-CoV-2. The docked complex when simulated using CABS-flex (2) dynamics revealed that both molecules remained stable with minimum fluctuations RMSF. The flexibility of both molecules can be seen in the trajectory in Figure  9.  Simulation by C-ImmSim through a virtual injection was done for HLA allele-specific (HLA-A*0101, HLA-A*0201, HLA-B*0702, HLA-B*3901, HLA-DRB1*0101, and HLA-DRB1*0401) outcome of the vaccine construct. This was to check the proficiency of construct to the adaptive immune system (reliant on T-cell receptor diversity, through V, J arrangements of the alpha and V, D, J arrangements of the beta unit in the thymus). The output of the simulator was a graphical illustration of the total count of lymphocytes, division amid isotypes, antibody, and cytokine concentration. Cells may bind or move and follow environmental harmonized behavior [43,54]. The decrease in antigen count was observed for C1 after 50 days and diversity finally reached zero around the 70th day of injection ( Figure 6D). The antibodies titers (IgM and IgG1 + IgG2) showed a high peak after booster doses of C1 injection. Greater IgM production is required for enhanced primary immune response, resulting in augmentation of B cell population and additional antibodies responsible for secondary and tertiary immune reactions [12]. Overall B cell population was found highest around the 50th day (in cells per mm 3 ) of injecting C1, before plateauing off. Increased CD4 T-helper cell population has a vital role in evoking protection and was evoked after injection ( Figure 6B). Nevertheless, this prediction is preliminary and the study of the construct with diverse HLA-alleles is suggested as antigen-specific immune response relies not only on age, dose, the time interval of booster dose, but mostly immunogenetics of population.
Vaccine construct C1 was cloned in silico with the help of Snapgene in the most suitable plasmid vector pET28a (+) by restriction enzymes SalI and BamHI to check its expression and purification in the bacterial cellular environment. Analysis of the virtual cloning, after codon optimization, validated the stance that translated chimeric vaccine construct appears proficient with enhanced gene expression and is capable of vaccine production at an economical cost.

Conclusions
In conclusion, we utilized the immunogenic B-cell, T-cell, and IFN-inducing epitopes to generate a peptide-based multi-epitope vaccine from the surface glycoprotein which elicits humoral and cell-mediated immunity, respectively, to eradicate viral particles. Predicted epitopes were merged using appropriate linkers and adjuvants to enhance the immunogenicity of the vaccine. Antigenicity, allergenicity, and solubility, as well as physiochemical properties and tertiary structure analysis, were confirmed. Docking and MD simulation analysis of TLR7 and vaccine were performed, allowing evaluation of the binding affinity and stability of the complex. The immune simulation showed an enhanced antibody titer production and CD4 T-cell count after C1 virtual administration. It also shows that adjuvant has an important role in healthy immune response elicitation. The final vaccine construct was back-translated and in silico cloned in a plasmid, which showed effective expression.