2.1. Study Protocol Employed in This Study

The current study employed computational databases and bioinformatics tools (depicted in Supplementary Table 1) to design an epitope-based peptide vaccine against the MPV. The workflow in Figure 1 represents the entire methodology for the final construction of the vaccine.

2.2. Retrieval of the Sequence and Phylogeny Analysis

The mPox viral protein sequence was retrieved as a FASTA file from the UniProt database (https://www.uniprot.org/) [21] by searching with the keyword “monkeypox cell surface binding protein.” Following that, we used our target sequence as the query sequence to analyze the BLASTp program [22] for evaluating phylogenetic information from the NCBI database (https://www.ncbi.nlm.nih.gov). Finally, in order to better understand the comparative evolution of the organisms, a phylogenetic tree was constructed and visualized using the Microreact database (https://microreact.org/) [23].

2.3. Three-Dimensional Structure Prediction, Energy Minimization, and Quality Assessment

A web-based program called trRosetta, which predicts protein structures fast and accurately, was used to construct the three-dimensional structure of the cell surface binding protein [24]. Direct energy minimizations using a constrained Rosetta are used to create the protein structure in this tool. A deep neural network is initially used to predict the interresidue geometries, including distance and orientations, using the input of a protein’s amino acid sequence. In the context of Rosetta, the predicted geometries are subsequently turned into restrictions to steer the structure prediction based on direct energy minimization [25]. To further increase the stability of the structure, the interresidual binding energy was minimized using the YASARA energy minimization server [26]. By enhancing physical realism, stereochemistry, and side-chain correctness in the 3D model, the YASARA energy minimization server can aid the homology modelling. ERRAT [27] and Verify3D [28] of the SAVES server and the Ramachandran plot [29] and QMEAN Z-scores [30, 31] from the Swiss model were used to validate the projected model’s quality. The final 3D model of the protein was captured with the PyMOL2 software [32].

2.4. B Cell Epitope Prediction

By extrapolating B cell epitopes from antigens, we can better comprehend the immunological principles underlying the identification of an antigen by an antibody. Additionally, it is crucial for designing vaccines and medications. Utilizing the ABCpred server, the antigens were subjected to linear B cell epitope prediction (http://www.imtech.res.in/raghava/abcpred/) [33]. Based on a recurrent neural network with a threshold value of 0.51, 16-mers was chosen as the preferred window length. The predicted epitopes’ antigenicity, allergenicity, and toxicity indexes were evaluated using the VaxiJen v2.0 [34] (threshold value of 0.5), AllerTOP 2.0 [35], and ToxinPred [36] servers, respectively. Moreover, the ability of B cell linear epitopes to induce IFN-γ was predicted by the IFNepitope server [37]. Finally, ElliPro (http://tools.iedb.org/ellipro/) [38], an online server, has been used to predict the discontinuous (conformational) B cell epitopes. In this tool, the minimum score for the prediction was set at 0.5, while the maximum distance was set at 6.

2.5. Prediction of Cytotoxic T Lymphocyte (CTL) Epitope and MHC I Binding Allele Analysis

Cytotoxic T cell epitope prediction is aimed at identifying the smallest peptides present in an antigen which has the capability to activate T cells or evoke immunogenicity [39]. These antigens have the ability to trigger CD4+ or CD8+ T cells. The CTL epitopes for (9-mers) 12 MHC class I supertypes (A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58, and B62) can be predicted by the NetCTL 1.2 server. The selected protein was submitted to the NetCTL v1.2 server (http://www.cbs.dtu.dk/services/NetCTL/) [40] in FASTA format in order to predict the CTL epitope (9-mer). The threshold value was fixed at 0.4 with a sensitivity and specificity of 0.89 and 0.94, respectively. We selected only the A1 supertype within the NetCTL parameters, even though the server can predict CTL epitopes for up to 12 MHC class I supertypes. Information about MHC ligand elution and MHC binding studies can be accessed in the Immune Epitope Database (IEDB), which offers a library of experimentally identified T cell epitopes [41]. The interacting alleles (MHC I-binder) with these epitopes were then revealed using the SMM method by the Immune Epitope Database (IEDB) (http://tools.iedb.org/) [42]. For further study, the MHC-I alleles with the higher affinity (IC50 < 500 nM) for the epitopes were considered.

2.6. Epitope Selection for Docking and Epitope Prioritization

Antigenicity is a crucial feature in the development of vaccines. The antigenicity of the peptides was assessed via the VaxiJen 2.0 server (http://www.ddg-pharmfac.net/vaxijen/) with a threshold of 0.4 to verify their capacity to induce an immunological response. The server AllerTOP 2.0 (http://www.ddg-pharmfac.net/AllerTOP/) was utilized to predict allergenicity. It was used to determine whether or not the epitopes were likely to result in allergic reactions in people. Additionally, we used the ToxinPred server (http://crdd.osdd.net/raghava/toxinpred/) to identify the toxicity of each epitope. Moreover, the obtained peptides were then placed into an online program called IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/scan.php) to evaluate the IFN-induction capacity using a hybrid method (motif and SVM) as well as an IFN-γ versus non-IFN-γ model.

2.7. Peptide Designing and Molecular Docking Analysis

APPTEST server [43] was utilized to design the three-dimensional structure of the peptide. With a neural network design and simulated annealing techniques, the APPTEST server can accurately predict the peptide’s tertiary structure. The binding interaction between the epitope and receptor molecule was scrutinized by a molecular docking experiment. To perform the docking analysis, the crystal structure of HLA-B ^∗15:01 (PDB ID–1xr8) was first downloaded from the Research Collaboratory for Structural Bioinformatics (RCSB) database [44]. The receptor molecule was subsequently prepared for the docking analysis by removing the associated ligands and water molecules. Subsequently, polar hydrogen molecules were added to the receptor molecule. The docking analysis between the human receptor HLA-B ^∗15:01 and our designed peptide (ligand) was performed by the AutoDock Vina tool [45]. The grid box size of the AutoDock Vina tool was set as 12.702, 31.843, and 18.307, respectively, for X, Y, and Z. Discovery Studio 2021 [46] software was utilized to investigate the binding interactions and residues in the interacting surface between the peptide and receptor. PDBsum [47] was subsequently used for the graphical illustration of the type of interacting bonds.

2.8. Molecular Dynamic Simulation

Molecular dynamics is a computer method for describing the molecular behaviors of the receptor and the epitope [48]. The web-based iMODS server (https://imods.iqfr.csic.es/) was utilized for the molecular dynamic simulation investigation to comprehend the residual effects of the vaccine with the receptor. All the parameters in this web tool were kept as default. This website uses NMA in internal coordinates to investigate the collective movements of protein sequences (torsional space) and provides different analyses, including the ability to calculate eigenvalue, B-factor, NMA mobility, and deformability [49].

3.1. Sequence Retrieval and Phylogeny Analysis

The search result with the keyword “monkeypox cell surface binding protein” in the UniProt database returned 14 protein sequences, four of which were from the Vaccinia virus. Among the ten MPV proteins, the protein with accession Q8V4Y0, which has 304 amino acid residues, was selected for this study as it was marked as reviewed by the UniProt database. By similarity searching, the UniProt database has declared that this sequence is connected with binding to chondroitin sulfate on the cell surface to facilitate the virion attachment to its target cell. As the function of this protein was already verified, it was selected for subsequent analysis. The BLASTp search results against a nonredundant database showed that the query protein shared higher homology with several viruses, including Cowpox, Rabbitpox, Ectromelia, Orthopoxvirus Abatino, Buffalopox, and Vaccinia virus. The analysis of the BLASTp results also showed that the monkeypox virus’s cell surface binding protein was homologous to a number of other proteins, including IMV membrane protein, carbonic anhydrase, and CPXV125 protein. A phylogenetic tree was constructed by the neighbor-joining method with the top 40 BLAST sequences (Supplementary Table 2) and is shown in Figure 2.

3.2. Homology Modeling, Energy Minimization and Quality Assessment

Recent advancements in structural genomics have made homology modeling a significant aspect of comparative modeling, which is commonly used to predict unknown structures via the use of various tools and databases [50]. When the amino acid sequence is available, homology modeling is an efficient method for predicting protein structure [51]. It has been previously reported in several studies that the 3D structure of a protein and its functional activity are closely linked [52, 53]. Hence, the 3D structure was predicted using the trRosetta server for homology modeling. trRosetta uses its own energy minimization strategy to deliver an accurate structure. Additionally, the YASARA energy minimization server also minimized the energy of the model protein from -126381.6 kJ/mol to -166643.8 kJ/mol to improve the consistency of the protein. A comparison analysis is given in Table 1. After that, the protein’s 3D structure was visualized using PyMOL, as shown in Figure 3(a).

However, the refined structure was used to conduct a quality assessment of the modelled protein using the Swiss model and SAVES server. The validity of the predicted model was determined using ERRAT, which assessed the statistics of nonbonded interactions between various atoms based on atomic interactions [54]. According to the ERRAT, the protein structure was found to be of good quality, with a quality factor of 95.556 (Figure 3(b)). The Verify3D server was used to assess an atomic model’s (3D) compatibility with its amino acid sequence, and the server ensured the structure with an overall 3D-1D score ≥ 0.2 for 82.24% of the residues (Figure 3(c)), suggesting that the structure was compatible and fairly good. The Swiss model’s Ramachandran plot indicated that 95.36% of the residues were in a preferred region (Figure 3(d)). The quality of our model, according to the QMEAN score, which was calculated to be 0.62 considered the predicted model as an excellent model (Figure 3(e)).

3.3. Prediction of B Cell Epitope

For triggering a humoral immune response, which activates B cells to produce antibodies, B cell epitopes are required. In our study, the ABCpred server predicted 30 peptide sequences of B cell epitopes (Supplementary Table 3). The antigenicity, allergenicity, and toxicity of the selected B cell epitopes were examined. Upon the analysis, we identified 11 peptides to be antigenic, allergenic, and nontoxic among 30 peptide sequences. The peptide QLSPINIETKKAISDT (beginning at position 183) was found to have the highest antigenicity score, and it was 1.206. These 11 B cell epitopes are listed in Table 2. The discontinuous (conformational) B cell epitopes have been predicted using the online server ElliPro. With values ranging from 0.623 to 0.873, five discontinuous B cell epitopes were predicted to have 162 residues and the conformational epitopes were 12 to 58 residues in size (Table 3). ElliPro demonstrated 5 conformational B cell epitopes in the query protein with their lengths, and scores are illustrated in Figure 4.

3.4. Prediction of CTL Epitope and Analysis of the MHC I Binding Alleles

The NetCTL 1.2 server predicted a total of 19 cytotoxic T lymphocyte (CTL) epitopes (9-mers) (Supplementary Table 4) according to the MHC A1 supertype but only 11 epitopes were found as antigenic, immunogenic, nonallergenic, and nontoxic among them (Table 4). On the other hand, IFN-γ inducing epitopes with anticipated positive and negative results from the IFNepitope server were selected. The MHC-I alleles with higher affinities (IC50 < 500 nM) for the epitopes are listed in Table 4. The 8 antigenic CD8+ T cell epitopes interact with at least one unique MHC-I HLA allele, as reported by the IEDB.

3.5. Epitope Selection for Docking and Epitope Prioritization

To be an effective vaccine candidate, an epitope or peptide sequence must fulfil a number of requirements. For example, the epitope must cause an immunogenic response in the host in order to meet the initial condition. Another critical phase of vaccine development is testing for toxicity. An effective vaccine candidate must not produce any toxic response in the host after vaccination. Allergenicity, however, is another significant obstacle to the development of vaccinations. The majority of immunizations produce immunoglobulin E and type 2 helper T (Th2) cells, which stimulate an unwanted allergic immune response [53, 55]. Finally, the design of potential vaccines must ensure that they cause the production of IFN-γ following immunization [56]. The majority of vaccines have recently been developed using B cell immunity; however, in this work, we chose to design the vaccine using a T cell epitope since it can result in long-lasting immunity [57]. We initially screened the epitope based on the number of MHC I bindings to find a suitable vaccine candidate. Upon the analysis with IEDB MHC I binding analysis prediction tool, it was found that epitope MSAPFDSVF interacts with the maximum number of alleles. But this epitope was abandoned as the VaxiJen score of this epitope identified the epitope as a nonantigen. Three epitopes, including TTSPVRENY, YVLSTIHIY, and ILFLMSQRY, were found to interact with 4 MHC I binding alleles, and all these epitopes fulfil all other requirements to be an effective candidate for the vaccine. But we selected the epitope “ILFLMSQRY” for further analysis as the VaxiJen-predicted antigenicity score for this epitope was found to be maximum. Four MHC I binding alleles that interacted with the ILFLMSQRY epitope were HLA-B ^∗15:01, HLA-A ^∗30:02, HLA-A ^∗03:01, and HLA-A ^∗32:01. This epitope satisfied each prerequisite that an epitope for vaccination must adhere to. The host’s immunogenic response to the epitope was assessed using the VaxiJen 2.0 antigenic analysis tool. The epitope has been identified as a potential antigen after examination with this tool (antigenicity score 1.0469). The epitope was recognized by the ToxinPred server as a nontoxic epitope. The AllerTOP program was used to calculate the sequence-based allergenicity prediction properly, and the predicted query epitope was discovered to be nonallergenic. IFN-γ response of our selected epitope was found to be positive (score—0.0499). All these findings have established the epitope as a promising vaccine candidate.

3.6. Molecular Docking Analysis

Ligand-receptor docking has been used to assess the suggested epitope vaccine’s affinity for the human leukocyte antigen HLA-B ^∗15:01. Analysis with the AutoDock Vina program has generated 9 binding models between the ligand (epitope) and the receptor (HLA-B ^∗15:01) molecule. The binding energy has been found between the ranges of -7.5 kcal/mol and -6.4 kcal/mol. After some rigorous analysis with PyMOL software, model 1 (illustrated in Figure 5) was identified as the best binding model. In this model, the epitope’s binding energy to the HLA-B ^∗15:01 receptor was estimated to be -7.5 kcal/mol. It was also discovered through the docking research that the anticipated epitope formed interactions with 22 amino acid residues, including Tyr67, Ser52, Tyr 63, Tyr9, Pro235, Arg8, Gln32, Phe241, Gln7, Glu232, Ser6, Tyr27, Ser57, Ala211, Met5, Pro210, Arg6, Phe3, Leu4, Ile1, Leu2, and Asp102. The high numbers of interacting bonds and residues and lower energy of the docked complex revealed a stable link between the ligand and the receptor molecule. Figure 6 displays the Discovery Studio 2021 software-derived three-dimensional structure of the peptide and the binding interactions between the peptide and HLA-B ^∗15:01 following docking analysis. Analysis with PDBsum has revealed 17 hydrogen bonds and 3 salt bridges between the docked complex, which also demonstrated a strong interaction between the receptor and ligand molecule (Figure 5). The interacting hydrogen bonds were TYR27-TYR63, GLN32-ASP53, ARG35-ASP53, ARG48-ASP53, GLN96-HIS31, GLN96-TRP60, ASP122-TRP60, GLU232-GLN8, GLU232-SER28, ARG234-GLN8, ARG234-MET99, ARG234-MET99, ALA236-ASN24, GLN242-ARG12, TRP244-MET99, TYR9-SER52, and TYR9-TYR67, and the chain distances were 3.13, 2.80, 2.61, 3.03, 2.73, 2.74, 2.73, 2.89, 2.54, 2.78, 2.67, 3.26, 2.84, 3.08, 3.12, 3.18, and 2.99, respectively. The graphical illustration of the docked complex and the types of interacting bonds is presented in Figure 5.

3.7. Analysis of the Molecular Dynamic Simulation Study

A simulation of molecular dynamics was executed to assess the system’s stability. The migration of the vaccine construct toward the TLR-4 was confirmed by the estimation of NMA mobility (Figure 7(a)). The decreased fluctuation of the protein residue in the docked complex was substantiated by the NMA calculation and PDB B-factor (Figure 7(d)). The elastic network model’s connection spring image (Figure 7(c)) revealed the existence of deformability and a few hinges within the residues, reflecting the increased stability of the docked complex (Figure 7(e)). The study of the eigenvalue (Figure 7(f)) and covariance matrix (Figure 7(b)) further supported that the value for deforming the molecular docked complex (1.392904e-04) is much greater, indicating that more energy is required to alter the stability of the vaccine-receptor docked complex.