In Silico Design and Analysis of an Epitope-Based Vaccine Targeting the VP24 Protein of Ebola Virus for Enhanced Immunogenic Response
CHAPTER 2
2. MATERIAL AND METHODS
2.1 Sequence Data Retrieval
The Protein core sequence data were obtained from the National Centre for Biotechnology Information (NCBI) in the FASTA format and further examined (Benson, Karsch-Mizrachi et al. 2010). This move towards allowed for the classification of key epitopes to facilitate could be used in the improvement of a subunit vaccination against Ebola virus infection. Development of sequence repossession and epitope prediction is crucial in designing vaccines that can effectively target and combat viral infections like Ebola virus. Accessing the protein progression from a dependable database like NCBI ensures the accuracy and reliability of the data used in the vaccine design process. Epitope predictions serve a significant role in finding the most immunogenic portions of protein, which are necessary for creating a powerful immunological response. Protein sequence was subsequently exposed to the different epitope projections
2.2 Antigenicity and Allergenicity Analysis
An antigen analysis was conducted on the VAXIJEN 2.0 server (http://www.ddg-pharmfac.net/vaxijen/ VaxiJen/VaxiJen.html) using the target organism Virus and a threshold value of 0.4. Allergen analysis was performed online using the Alleretope server (https://www.ddgpharmfac.net/AllerTOP/) (Benson, Karsch-Mizrachi et al. 2010, Alom, Shehab et al. 2021).
2.3 Prediction of Epitope MHC-1
To activate the immune system, an antigen must first be located on the surface of cells. Therefore, the MHC-I prediction by using the IEDB web server (http://ledb.or). We selected the most well-recognized MHC-I allele, specifically HLA-A01:01 and HLA-A02, and a peptide length of 9 amino acids. The analysis findings were achieved with a percentile rank value below 1.00 (Alom, Shehab et al. 2021, Fadaka, Sibuyi et al. 2021, Sharma, Kumari et al. 2021).
2.4 Prediction of Epitope MHC-II
The MHC-II epitope analysis was performed by using the IEDB server-approved 2.22 approach and utilizing the DRB1*01:01 allele coding, considering a sequence length of 15 amino acids. The analysis findings were achieved with a percentile rank value below 10.00 (Alom, Shehab et al. 2021, Fadaka, Sibuyi et al. 2021, Alizadeh, Amini-Khoei et al. 2022).
2.5 B-Cell Epitope prediction
The linear B-cell epitopes are predicted by the IEDB web server (http://ledb.org). The 4 B-cell epitopes are predicted by Kolaskar & Tongaonkar Antigenicity methods. The research indicated that B-cells exhibited specificity towards epitopes, which were peptides varying in length from 10 to 30 amino acids (Alizadeh, Amini-Khoei et al. 2022).
2.6 Antigenicity, Toxicity, and allergenicity Prediction of MHC-I, MHC-II, and B-cell epitopes
Antigenicity, allergenicity, and toxicity analysis of MHC-I, MHC-II, and B-cell epitopes from Vaxijen 2.0, Toxinpred and Allertope bioinformatics tools. One of the most important notes about protein and peptide vaccines is that these molecules should not have allergenicity and toxicity (Doytchinova and Flower 2007, Gupta, Kapoor et al. 2013, Dimitrov, Bangov et al. 2014).
2.7 Prediction of population coverage
By choosing HLA alleles of the associated epitope, the population coverage for each individual epitope was calculated using the IEDB population coverage tool.
2.8 Development of vaccine final construct
The linker and adjuvant design approaches were utilized to create the vaccination candidate. The following was used to formulate the vaccination candidate: adjuvant, GPGPG, AAY, MHC-I, MHC-II, KK, and B cell epitopes, each with the proper linkers (Alom, Shehab et al. 2021).
2.9 Physicochemical properties analysis
Finalization of vaccine construct The Expasy online service was utilized to conduct a physicochemical analysis, which involved determining the molecular weight, aliphatic index, amino acid composition, GRAVY, and instability index (https://web.expasy.org/protparam/).
Protein-sol web server used for solubility of vaccine construct (https://protein-sol.manchester.ac.uk/) (Alom, Shehab et al. 2021).
2.10 2D and 3D Structure Prediction of Final Vaccine
PSIPRED webserver (http://bioinf.cs.ucl.ac.uk/psipred/) is used to analyze the 2d structure of the final vaccine construct (Alom, Shehab et al. 2021).TrRosetta (https://yanglab.qd.sdu.edu.cn/trRosetta) is used to predict the 3D structure of the vaccine, and this visualization by chimera software.
2.11 Molecular Docking analysis
ClusPro 2.0 software tools (https://cluspro.org/login.php?redir=/models.php?job=1067279) are used for the molecular docking of vaccine construct and Receptor (Kozakov, Hall et al. 2017). ClusPro webserver was selected on the basis of a CAPRI (Critical Assessment of Predicted Interactions) assessment conference that’s related to higher than other docking tools like HADDOCK, SWARMDOCK, and GRAMM-X (Kozakov, Hall et al. 2017). The 3D visualization of the vaccine was studied using the Discovery Studio software.
2.12 Molecular dynamics simulations:
Molecular simulations are performed by the Schrödinger LLC Desmond software for 100 and 50 nanoseconds (Ferreira, Dos Santos et al. 2015). Before doing the MD simulation, the docking of the vaccine design and TLR4 was considered an initial and crucial step that provided a static prediction of the molecule's interaction location at the active site of the protein (Hildebrand, Rose et al. 2019). MD simulations often utilize Newton's classical equation of motion to simulate the movement of atoms over time and forecast the state of ligand binding in a physiological context (Rasheed, Iqbal et al. 2021). The ligand-receptor complex experienced pre-processing using Maestro's Protein Preparation Tool. This involved reduction, and filling in any missing residues, if appropriate. Additionally, the system was formed using the System Builder tool. The TIP3P solvent model, which depends on a linear box, was used. The system was maintained at an internal temperature of 300 K, a pressure of 1 atm, and utilized the OPLS_2005 force field (Shivakumar, Williams et al. 2010).
2.13 Codon optimization and in silico cloning
The Java Codon Adaptation Tool (JCat) (http://www.jcat.de/) was utilized for reverse translation, codon optimization, and calculating the codon adaptation index (CAI) value and GC content of the vaccine construct in E. coli (strain K12) (Grote, Hiller et al. 2005). Three options were chosen here: avoid rho-independent transcription terminators, avoid prokaryotic ribosome binding sites, and avoid cleavage sites of restriction enzyme. The XhoI and BglII restriction sites were tagged at the 5′ and 3′ ends of the vaccine's DNA sequence, respectively. Finally, using SnapGene software (https://www.snapgene.com/free-trial/), the obtained sequence was inserted into the pET28a (+) expression vector between the XhoI and BglII restriction sites.
CHAPTER 3
3. RESULTS
3.1 Sequence Data Retrieval
The genomic sequence of Hepatitis C virus subtype 1b is obtained from NCBI (31647) and has a core protein that is non-allergenic and antigenic, with an antigen value of 0.4372 ANTIGEN according to VaxiJen. Additional B-cell and T-cell epitopes will be predicted based on this protein.
3.2 Prediction of (MHC-I & MHC-II) T-Cell Epitopes
The T-cell is a vital element of cell-mediated immunity. T-Cell Helper (TH) recognizes virus peptides bound to MHC-II proteins, while Sell-T Cytotoxic (Tc) identifies viruses associated with MHC-I proteins (Bhatnagar, Bhasin et al., 2021). From the MHC-I study utilizing the allele codes HLA-A01:01 and HLA-A02:01, a few specific epitopes were identified. These epitopes have a length of 9 amino acids and a percentile rank below 1.00. The MHC-II epitope research identified several selected epitopes with a 15-amino acid length and a percentile rank below 10.00, as displayed in Table 1. These epitopes are associated with the Allele DRB1*01:01 coding. Every epitope is chosen based on its antigenicity, non-allergenicity, and non-toxicity to humans; these epitopes are suitable for the development of vaccines.
Table 1: T Cell Epitopes Analysis Results (MHC I & MHC II)
MHC I
| peptides | Allele | Antigen | Allergen | Toxin | Percentile Rank |
|---|---|---|---|---|---|
| RVLEDGVNY | HLA-A*01:01 | non | Yes | non | 0.51 |
| CLTIPASAY | HLA-A*01:01 | yes | Yes | non | 0.86 |
| VIDTLTCGF | HLA-A*01:01 | non | Non | non | 0.9 |
| DLMGYVPLV | HLA-A*02:01 | non | Non | non | 0.17 |
| FLLALLSCL | HLA-A*02:01 | non | Non | non | 0.27 |
| VLEDGVNYA | HLA-A*02:01 | non | Yes | non | 0.29 |
| NLGKVIDTL | HLA-A*02:01 | non | Non | non | 0.39 |
MHC II
| peptides | Allele | Antigen | Allergen | Toxin | Percentile Rank |
|---|---|---|---|---|---|
| AQPGYPWPL | HLA-DRB1*02:01 | non | Non | non | 0.43 |
| ALAHGVRVL | HLA-DRB1*02:01 | non | Non | non | 0.46 |
| QIVGGVYLL | HLA-DRB1*02:01 | non | Yes | non | 0.51 |
| RVLEDGVNY | HLA-DRB1*02:01 | non | Yes | non | 0.51 |
| CLTIPASAY | HLA-DRB1*02:01 | non | Yes | non | 0.86 |
| GLGWAGWLL | HLA-DRB1*02:01 | Yes | Non | non | 0.9 |
| VIDTLTCGF | HLA-DRB1*02:01 | non | Non | non | 0.9 |
| GQIVGGVYL | HLA-DRB1*02:01 | non | Yes | non | 0.98 |
| TIPASAYEV | HLA-DRB1*02:01 | non | Yes | non | 0.99 |
3.3 Prediction of B cell epitopes
The main components of humoral immunity are B-cells. B-cell-produced antibodies are essential for stopping the spread of viral diseases (Kurosaki, Kometani et al. 2015). Antigen-specific antibodies are produced by linear B-cell epitopes (Alexander, Fikes et al. 1998). B-cell epitopes are anticipated using the IEDB website. 4 epitopes VGGVYLLPR, GKVIDTLTCGFA, MGYVPLVGAPLGGVARALAHGVRVLEDG, and LPGCSFSIFLLALLSCLTIPASAY are predicted from the IEDB tool by the Kolaskar & Tongaonkar Antigenicity methods. The results of several epitopes are shown in Table 2. Figure 1 shows that B-cell epitopes have a 1.02 score. The yellow colour shows positive B-cell epitope predictions, and the green colour shows negative B-cell epitopes.
| Peptides | Antigen | Allergen | Toxin | Percentile Rank |
|---|---|---|---|---|
| VGGVYLLPR | Yes | Non | non | 9 |
| GKVIDTLTCGFA | non | Non | non | 12 |
| MGYVPLVGAPLGGVARALAHGVRVLEDG | non | Non | non | 28 |
| LPGCSFSIFLLALLSCLTIPASAY | non | Non | non | 24 |
Table 2: B cell epitopes Analysis Results
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Figure 1: B-cell epitopes predicted graph of core protein |
Examine the epitopes of T-cells (MHC-I, MHC-II) and B-cells for the vaccine construct. Upon examination of the antigen, allergen, and toxin, specific epitopes are derived from the T-cell (MHC-I, MHC-II) and the B-cell for the purpose of constructing a vaccine. These epitopes are presented in Table 3.
Table 3: Selected Epitopes
| Epitope prediction | Peptides | Antigen | Allergen | Toxin | Percentile Rank |
|---|---|---|---|---|---|
| MHC1 | GLGWAGWLL | Yes | Non | Non | 0.9 |
| MHC 2 | MGYVPLVGAPLGGVA | Yes | Non | Non | 0.06 |
| MHC2 | PLGGVARALAHGVRV | Yes | Non | Non | 7 |
| B-cell | VGGVYLLPR | Yes | Non | Non | 9 |
3.5 Construction of multiple-epitope Vaccine
The four conserved epitopes selected for vaccine development are derived from antigens that are highly immunogenic, non-allergenic, and non-toxic. Human β-defensin HBD3 was employed as an adjuvant to augment the immune system response to the vaccine. The linkers EAAAK (Glu-Ala-Ala-Ala-Lys), AAY (Ala-Ala-Tyr), GPGPG (Gly-Pro-Gly-Pro-Gly), and KK (bi-lysine) are employed to join with the vaccination sequence (Alom, Shehab et al. 2021, Alizadeh, Amini-Khoei et al. 2022). As seen in Figure 2. Furthermore, these linkers are interconnected following the order of the vaccine as presented in figure 2.
 |
Figure 2: Structure of vaccine construct
The ProtParam server is utilized for the physiological evaluation of vaccinations, whereas protein sol web servers are employed to assess the solubility of vaccines. The ProtParam service provides the following information about the vaccine constructs: the total length of amino acids is 135, the molecular weight is 14207.08, the molecular formula is C648H1046N188O155S8, and the total number of atoms is 2045. The vaccine construct has a theoretical PI (protrusion index) of 10.31, which provides insight into the characteristics of the vaccine. The half-life of E. coli in vivo is 20 hours. The aliphatic index value of 38.03 demonstrates the thermally stable quality of the substance over different temperatures. The combined count of negatively charged residues (Asp + Glu) was 3, while the combined count of positively charged residues (Arg + Lys) was 20. The average hydropathicity score, often known as GRAVY, is 0.237. A negative GRAVY number indicates a tendency to be attracted to water, while a positive value indicates a tendency to repel water. Based on its average hydropathicity score, the protein is predominantly hydrophobic and has a higher affinity for adjacent water molecules (Ahammad and Lira 2020). The solubility of the vaccine is determined by the protein solubility web, and it has a score of 0.615.
3.7 2D and 3D structure prediction of final vaccine
The PRISPRED online server is used to predict the secondary structure of the vaccine. The predicted structure consists of helices, coils, and strands, as depicted in Figure 3A. The strands are depicted in yellow, the helix sections are illustrated in pink, and the regions involved in membrane contact and transmembrane helix are displayed in gray. The hydrogen bonds in the helix structure act as the molecular framework, which is why transmembrane proteins are often characterized by the helix structure. The hydrogen bonding of the protein does not complicate the peptide connections in the coil. However, the coil structure facilitates elasticity and conformational variations(Ruslin, Putri et al. 2019). The TrRosetta algorithm is used to anticipate the 3D structure of the final vaccine construct. The TrRosetta PDB format file was visualized using the Chimera software, and the results were obtained. As depicted in (Figure 3B).
 |
Figure 3: a) 2D structure of vaccine and b) 3D structure of Vaccine
3.8 Molecular docking analysis
The molecular docking of the TLR4 receptor (PDB ID: 2z62) with the vaccination construct was investigated using ClusPro 2.0. The vaccine design serves as the ligand in the process of molecular docking with the TLR4 receptor. Out of the 30 models that were created, only the one with the lowest energy score and the correct engagement of the vaccine by the receptor was selected. The selected model, identified as model number 1 and seen in Figure 4, fulfills all the necessary criteria and is considered the most optimal docking solution based on the obtained data. The dock complex with the most negative energy score, -1027 kcal/mol, was found to have an inverse correlation with the binding affinity. We identified a docked complex in our model that had a high binding affinity due to its considerably lower energy score. The Discovery Studio software reveals the docking data, indicating a significant interaction between the TLR4 receptor and the vaccination. The red hue represents the vaccine, whereas the alternative color signifies the receptor inside the docking complex.
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Figure 4: Molecular docking analysis of vaccine with TLR4 Receptor
For an understanding of the results of a 50ns simulation of the molecular docking complex between receptors and a vaccine construct. The simulation investigation is conducted to investigate many crucial inquiries regarding the structural stability of the ultimate complexes. The vaccine model had an average root-mean-square deviation (RMSD) of 3.0 Ã…. In contrast, the vaccine-TLR4 complex had an RMSD of 1.4 â„« with low convergence, indicating its steady behavior during the 50ns simulation. The typical variation in the order of 1-3 Ã… is considered acceptable in the simulation. Image 5 A displays the evolution of the protein's RMSD. During the initial 20 nanoseconds, an adaptation period occurred, characterized by a steady surge in the complex root-mean-square deviation (RMSD). The complex's sudden exposure to a dynamic environment, which allowed it to attain the correct conformation, can be attributed to its high RMSD pattern. Throughout the simulation period, the system maintained stability after it achieved a stable conformation. The variations seen in the docking complex RMSF graph may be attributed to the repositioning of the residues to attain a favorable and stable conformation of the docking complex, as depicted in Figure 5B.
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Figure 5: MD simulation A) shows the RMSD value, and B) shows the RMSF value
The protein sequence of the resulting vaccine construct was transcribed in reverse to create an ideal coding sequence using the Java Codon adaptation tool (https://www.jcat.de/) in order to clone and produce multiepitope-based subunit vaccine. Escherichia coli was selected as the expression host.
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Figure 6: In silico cloning of vaccin
EBOV is the main health issue in the world because it is caused by hepatocellular carcinoma (HCC) and chronic liver disease(Manns, Buti et al. 2017). Vaccination is the optimal approach for managing the transmission of illnesses and upholding public health. Nevertheless, attenuated and live vaccines are being created that are both more effective and expensive, with a shorter development timeline (Li, Joshi et al. 2014). Multiple studies on EBOV elucidate that the management of acute EBOV infection is associated with a robust T-cell response, characterized by the effective synthesis of cytokines, such as IFN-y (Shin, Sung et al. 2016). A recent introduction of research focuses on the creation of an improved EBOV vaccine. This research is motivated by the successful synthesis of robust CD4 and CD8 T-cell responses, which resemble the immune responses observed during acute EBOV infection (Shin, Sung et al. 2016). The primary goals of the present multiepitope T-cell vaccine are to avoid the chronicity of EBOV and inhibit reinfection by stimulating a wide range of immune responses that target distinct genetic differences in both humans and viruses (HLA-specific). Nevertheless, the primary objective of the study was to create a multi-epitope vaccination that provides a high level of protection against EBOV infection. Due to their ability to induce both humoral and cellular immunity, multiepitope vaccinations are considered more efficacious than monovalent vaccines (Amanna and Slifka 2011). There is a significant demand for vaccines to effectively address the public health concerns associated with EBOV. Currently, there are numerous immunoinformatic-based techniques accessible for efficient and advanced multiple epitope design(MarÃa, Arturo et al. 2017). Our study focuses on the core protein, a structural protein involved in chronic infection. This protein was utilized in the creation of a vaccine that targets several epitopes. The goal of this vaccine design is to enhance the reactivity of B-cells and T-cells, which play a crucial role in halting the reproduction of the Hepatitis C virus (EBOV). This vaccine exhibits robust protection against EBOV and possesses the qualities of being non-allergenic and non-toxic.
At present, the genetic code of the central protein of EBOV genotype 1b is obtained from NCBI's taxonomy ID (31647 NCBI). Once we assess its antigenicity using the Vaxijen.2 webpage, we find that it has a score of 0.4372, indicating that it is likely to be an antigen. Additionally, we evaluate its allergenicity using the Allotrope webserver. Epitopes for T-cells (MHC-1 and MHC-2) and B-cells can be predicted using the IEDB web server. The epitopes are chosen based on their percentile rank and then examined for their antigenicity, allergenicity, and toxicity, as demonstrated in Table 1 and Table 2. Table 3 displays the epitopes that have been chosen, which are both antigens and non-allergens and are also non-toxic. The final vaccine is formulated with these specific epitopes. They are connected using adjuvants and linkers. The addition of the human β-defensin HBD3 adjuvant improves the effectiveness of the vaccine in stimulating a robust immune response. The linkers used in the vaccine sequence, namely EAAAK (Glu-Ala-Ala-Ala-Lys), AAY (Ala-Ala-Tyr), GPGPG (Gly-Pro-Gly-Pro-Gly), and KK (bi-lysine), are connected to the final vaccine sequence depicted in Figure 3 and Table 4. The final vaccine has a total length of 135 amino acids, a molecular weight of 14207.08, a molecular formula of C648H1046N188O155S8, and a total of 2045 atoms. Theoretical PI (protrusion index) is 10.31, indicating the vaccine's characteristics. The half-life of E. coli in living organisms is 20 hours. The aliphatic index was measured to be 38.03. The combined count of negatively charged residues, including Aspartic acid (Asp) and Glutamic acid (Glu), was 3. Conversely, the overall count of positively charged residues, including Arginine (Arg) and Lysine (Lys), was 20. The average hydropathicity score, often known as GRAVY, is 0.237. The TrRosetta web server is used to predict the 3D structure of the final vaccine, which is then displayed using chimera software (Figure 3A). The PRISSPRED web server is used to predict the 2D structure of the vaccine (Figure 3B).
Figure 4 displays the molecular docking of the vaccine design, which acts as a ligand with the TLR4 receptor. After conducting a 50 ns molecular dynamics (MD) simulation on docking complexes with low binding energy, it is shown that the complex exhibits a strong binding affinity. The root mean square deviation (RMSD) of the structures shows an upward trend in atom movement over time, peaking at 20 ns. After that, the RMSD value stabilizes at approximately 1.4 Ã…, as represented by the corresponding table. The root mean square fluctuation (RMSF) value indicates the consistent and predictable behavior of the dynamics, as depicted in Figure 5(A & B). Despite observing larger fluctuation rates in the graph, the parallel patterns of the RMSF and RMSD and minimal changes at various time intervals indicate the strong structural stability of the vaccine constructions.
5. CONCLUSION
The treatment of EBOV is limited due to its recurrence, anti-viral resistance, and various side effects. Currently, it is possible to develop a highly effective and cost-effective vaccine using computational methods. We have designed a multiepitope vaccine with a potential sequence as candidates. This vaccine, based on recombinant technology, has demonstrated improved antigenicity, immunogenicity, non-allergenicity, stability, and non-toxicity. Furthermore, the vaccine has been successfully docked with the TLR4 receptor, indicating its efficiency through Insilco. These findings suggest that the vaccine candidates may be used for EBOV treatment. However, further in vitro and in vivo research is necessary to validate these results.