1. Introduction:
Treponema species are anaerobic or microaerophilic spirochetes connected to their hosts and are members of the family Spirochaetaceae. Treponema subsp. Pallidum, endemicum, and pertenue are the causative agents of venereal syphilis, endemic syphilis, and yaws, respectively. On the contrary, Treponema carateum, the causative agent of pinta, is the human pathogen most resistant to in vitro generation [1]. Until today, only three strains of T. pallidum causing syphilis have been entirely sequenced: Nichols [2], SS14 [3], and Chicago [4], with the ability to spread from person to person only [5]. Following acquired immunodeficiency syndrome (AIDS), syphilis is the second most fatal sexually transmitted disease. Syphilis can cause systemic diseases in adults by brutally damaging multiple organs [6]. In 1999, the World Health Organization (WHO) claimed that 12 million new cases of syphilis were detected, with more than 90% of these cases occurring in developing countries. In most of these documented cases, congenital syphilis is the leading cause of miscarriage and infant mortality [7]. Although most T. pallidum strains are not drug-resistant, syphilis has recently been prevalent in humans [8].
Syphilis and AIDS share the same transmission routes [9]. Syphilis disease, if left untreated, can last for many years and is divided into phases. Primary Syphilis, secondary Syphilis, and early latent Syphilis are all types of early-stage disease, while late syphilis includes late latent Syphilis and tertiary Syphilis (neurosyphilis, cardio syphilis, and gumma) [10,11].
Syphilis has resurrected in certain developed nations, despite the availability of advanced diagnostic tests and antibiotic therapy. In the 1990s, large-scale syphilis outbreaks in Russia and China predominantly affected heterosexuals. In contrast, fewer outbreaks occurred in homosexuals (primarily men and men) in the United States, Canada, and England [12,13]. However, recent increases in the incidence of syphilis among expected mothers in the United States suggest that heterosexually transmitted syphilis is also becoming a major concern in the United States [13]. Around 2 million new syphilis infections are recorded worldwide [8].
The fact that syphilis in an early stage (i.e., primary and secondary stages) promotes HIV transmission by 2 to 5 times, therefore, it is an active entity, facilitating the spread of HIV, and it is a crucial threat related to raising syphilis statistics [1,14]. Various reports in 1964 [15] and 1976 have reported the failure of erythromycin therapy for syphilis in pregnant women [16]. Erythromycin may not penetrate the placental barrier effectively [17].
Moreover, T. pallidum remains extremely susceptible to penicillin, despite the fact that it has been used to treat syphilis disease for more than seven decades. The use of azithromycin as an oral antibiotic has grown in popularity as treatment complications have increased. However, resistance to macrolides has been documented in many countries [5,18]. Various recent reviews reported that the prevalence of syphilis has increased in developed and civilized nations in the past decade, highlighting the need for an effective diagnosis of syphilis and the development of effective vaccines against syphilis.
Previous research by Zhao et al., 2011 used membrane proteins as vaccine candidates. Their findings revealed that T. pallidum outer membrane proteins play an essential role in T. pallidum virulence and are the primary target of protective immunity of the host [8,19].
Approximately 20 T. pallidum OMP antigens have been discovered so far. Gpd antigen [20,21], Tp92 antigen [22], and Tpr family antigens [23,24] have been thoroughly investigated in terms of cellular localizations, structures, functions, and gene conservation. These OMPs are believed to cause animals to synthesize opsonic antibodies, which turn these proteins into opsonin targets, followed by phagocytosis and the destruction of T. pallidum. Due to its high homologies between strains and relatively good immunogenicity and protective potential, antigen Tp92 [18,22,25] is the best choice for T. pallidum vaccination trials among the membrane proteins outlined above. The Tp92 gene of the Nichol strain is highly conserved and contains 95.5–100% sequence identity with other Treponema species. As a result, the Tp92 antigen may be a suitable option for vaccine research. Such vaccinations could provide a high degree of protection against the Nichols strain and other strains of T. pallidum [8].
Although high-throughput methods and synthetic chemistry have drastically accelerated the drug development process in recent years, it will still take almost 10–15 years to bring a new drug to market, which requires significant investment [26]. Recently, using an in silico technique to work with bacterial pathogens, many targets have been found that are drug resistant or for which no relevant vaccine is available [27]. In the post-genomic age, reverse vaccinology is a common and popular method for quickly identifying new vaccine targets [28,29]. This study aims to use reverse vaccinology and subtractive genomics, in which we are primarily interested in identifying potential vaccines and novel therapeutic targets for syphilis disease.
2. Materials and Methods
2.1. Protein Sequence Retrieval
Figure 1 displays the sequential analysis employed in this investigation. The 1027-protein proteome of T. pallidum (strain Nichols) (UniProt ID: UP000000811) was obtained from UniProtKB “
Paralogous protein with 80% sequence homology was filtered using the cluster database with high tolerance (CD-HIT) suite “
2.2. Prioritization of Essential Genes
The non-paralogous proteins were then compared to the DEG database
2.3. Subcellular Localization
PSORTb server “
2.4. Druggability of Cytoplasmic Membrane Proteins
Likewise, the critical non-homologous cytoplasmic membrane proteins were investigated using BLASTp with an E-value of 105 against the DrugBank database “
2.5. Resistance Protein Analysis
The current study identified resistance proteins using the database ARG-ANNOT (Antibiotic Resistance Gene ANNOTation) database [41]. This database contains nucleotide or protein sequences resistant to multiple classes of antibiotics, including beta-lactamases, fluoroquinolones, aminoglycosides, fosfomycin, and sulfamide [42]. To identify antibiotic resistance-associated proteins, a local BLAST algorithm of cytoplasmic membrane proteins was run against antibiotic resistance sequences in ARG-ANNOT with an E-value cut-off of 10–5 in Bio-edit software.
2.6. Virulent Proteins Evaluation
Using BLASTp against the Virulence Factor Database (VFDB), pathophysiology and virulence variables related to disease development were preserved [43]. With an identity significantly greater than 25% and a bit score greater than 100%, these proteins were classified as pathogenic [44], making them good candidates for the development of vaccines. We chose a 25% identity cut-off since the proteins had the most significant numbers. Therefore, we needed to minimize it. This cut-off has been utilized in earlier research [45].
2.7. Prediction of Antigenic Proteins
Antigenicity testing was performed on the finalized virulent proteins; antigenicity was estimated utilizing VaxiJen 2.0 “
2.8. Protein–Protein Interaction Network Analysis
Using the STRING protein interaction database “
2.9. MHC-I Binding Epitopes (CTL) Prediction Epitopes
Cytotoxic T lymphocyte epitopes (CTL) were identified with the NetCTL1.2 server “
2.10. Evaluation of Predicted CTL Epitopes for Antigenicity, Allergenicity, and Immunogenicity
Using the VaxiJen v2.0 server, the antigenicity of isolated CTL epitopes was evaluated [46]. To determine the development of an appropriate immune response within the human body, the immunogenicity of these epitopes was evaluated using the IEDB Immunogenicity tool “
2.11. MHC-II Binding Epitopes (HTL) Prediction Epitopes
For class II MHC, helper T cell lymphocyte epitopes were predicted using the Immune Epitope Database (IEDB) “
2.12. Evaluation of Predicted HTL Epitopes for Toxicity, Antigenicity, and Allergenicity
VaxiJen 2.0 [46] was used to verify the antigenicity of the final HTL epitopes, and the AllerTOP v.2.0 server [50] was selected for allergenicity prediction. Antigenic epitopes were run through the ToxinPred server “
2.13. Identification of Cytokine-Inducing HTL Epitopes
HTLs release interferon-gamma (IFN-γ) cytokines that play a critical role in adaptive and innate immune responses. HTL epitopes can potentially suppress the proinflammatory response, reducing tissue damage. Therefore, the IFN-γ inducing HTL epitopes were predicted using the IFN epitope server “
2.14. Linear B Cell Epitope Prediction and Evaluation
B cells are required to activate the humoral immune response and plasma cell production in response to a specific antigen. The ABCpred epitope server “
2.15. Discontinues the Prediction of the B Cell Epitope
The multi-epitope 3D structure was refined and validated and then uploaded to the ElliPro server “
2.16. Assembling of Vaccine Construction Final Multi-Epitope
The final vaccine construct contains 4 CTL, 4 HTL, 11 LBL epitopes, and the adjuvant. Toll-like receptor 2 (TLR2) agonist ESAT6 (Accession: AEP68523.1) was taken as an adjuvant [57]. ESAT6 stimulates the secretion of IL-6 and TGF-β by dendritic cells in a TLR2-dependent way; it also induces Th17 immune responses, which are essential for optimal vaccine efficacy [58]. ESAT6 generated by an E. coli expression system increased IFN- gene expression [59]. The selected epitopes were fused with the help of specific peptide linkers. Each CTL epitope was a linker through the AAY linker [60], HTL epitopes fused with the GPGPG linker [57], and each LBL epitope was joined using KK linkers [61]. The EAAK linker was used to attach the adjuvant with CTL epitopes to the N-terminal of the vaccine construct [62].
2.17. Evaluation of the Physicochemical Properties, Antigenicity, and Allergenicity of the Vaccine Construct
After the vaccine construct was designed, the number of physicochemical parameters was determined using Expasy’s ProtParam “
2.18. Prediction of the Secondary and Tertiary Structure of the Vaccine Design
Using the PSIPREDV3.3 web server “
The three-dimensional (3D) model of the designed vaccine construct was obtained through Robetta Server “
2.19. Refinement and Validation of 3D Structure
Unfortunately, it is possible that the predicted 3D structures of proteins using computational methods do not precisely match their natural structures. The 3D structure was refined to improve its resolution from the first low-resolution prediction. The predicted model was refined utilizing GalaxyRe-fine “
Finally, the ProSA-web server “
2.20. Molecular Docking of Constructed Vaccine with TLR2 and TLR-4
To evaluate the interaction between a protein and its receptor, scientists have developed a computational method called molecular docking [71]. The structures of the TLR 2 and TLR 4 receptors (PDB ID: 6ING and PDB ID: 2Z63, respectively) were downloaded from the Protein Data Bank (
2.21. Molecular Dynamics Simulation
We performed a molecular dynamics simulation to analyze the stability of the vaccine–receptor complex [73,74]. To study the molecular behavior and assess the stability of the protein–ligand complex, a molecular dynamics simulation was applied, as it provides an overview of the physical basis of the complex analyzed [75]. The iMODS server “
2.22. Immune Simulation
Using C-IMMSIM v10.1, the immunological responsiveness to the constructed vaccine was simulated. Using the C-ImmSim server accessible at “
2.23. Codon Optimization of Vax Sequence and In Situ Cloning
When attempting to express a foreign gene in a host organism, it is often necessary to optimize the codons used. The Java Codon Adaptation Tool “
3. Results
3.1. Proteome Collection
Various immunoinformatic and subtractive proteomic approaches were used to design multiple epitope vaccines to protect against infection caused by T. pallidum [31]. UniProtKB was used to extract the whole reference proteome of the T. pallidum strain Nichols with 1027 proteins in FASTA format (UniProt ID: UP000000811).
3.2. Removal of Homologous Proteins
Specificity filter against the human proteome (taxonomic ID: 9606) using NCBI BLASTP “
3.3. Prediction of Paralogous Proteins
The CD-Hit server “
3.4. Essential Proteins Prediction
The non-paralogous proteins were run against DEG to determine essential proteins for T. pallidum survival using BLASTp at the 10−5 cut-off value. The results of BLASTp characterized 476 proteins as essential for T. pallidum survival. Non-essential proteins were excluded.
3.5. Subcellular Localization of the Essential Proteins
Using the PSORTb server for the localization of remaining essential proteins in the cell, the server localized the proteins based on their location as; 300 Cytoplasmic, 105 cytoplasmic membranes, six outer membranes, one extracellular, eight periplasmic, and 56 unknown proteins and another software “Cello” server localized the protein as; 321 cytoplasmic, 92 inner membranes, 30 outer membranes, 26 periplasmic, seven extracellular [38]. Figure 2 shows that surface proteins such as outer, extracellular, and cytoplasmic membranes are associated with pathogenicity, helping adhere to pathogens, invasion, proliferation of host tissue, and ultimately successful survival. Targeting these proteins is more suitable for vaccine design [44].
3.6. Druggability of Cytoplasmic Membrane Proteins
Only 15 of 95 cytoplasmic membrane proteins demonstrated interaction druggability potential with FDA-approved medications, according to the DrugBank database. All fifteen of these proteins potentially act as therapeutic targets in developing antibiotics against this infection. In addition, the remaining 80 that had no similarity to any recognized therapeutic targets in the DrugBank database were declared novel therapeutic potential targets. Consequently, only these proteins underwent additional investigation.
3.7. Resistance Protein Analysis
The resistance protein involved in the resistance process might be used as a therapeutic target. Using Bioeditor software, the list of cytoplasmic membrane proteins was then BLAST against the ARG-ANNOT database. The ARG-ANNOT database identified approximately 12 cytoplasmic membrane proteins were identified by the ARG-ANNOT database; these proteins are responsible for inducing antibiotic resistance in T. palladium.
3.8. Virulent Protein Analysis
Virulent protein prediction is crucial, and these proteins allow bacterial pathogens to bypass host immune responses. BLASTp screening of cytoplasmic membrane proteins against VFDB (Virulence Factor Database) identified nine proteins; Membrane lipoprotein TpN32 (UniProt ID: O07950), Uncharacterized periplasmic metal-binding protein (UniProt ID: O83077), DNA translocase FtsK (UniProt ID: O83964), Lipoprotein-releasing system ATP-binding protein (UniProt ID: O83590), Protein Soj homolog (UniProt ID: O83296), Site-determining protein (UniProt ID: F7IVD2), amino acid ABC transporter, ATP-binding protein (UniProt ID: F7IVD2), ABC transporter, ATP-binding protein (UniProt ID: O83930) and Sugar ABC superfamily ATP-binding cassette transporter, ABC protein (UniProt ID: O83782) proteins as virulent with >25% identity and 100% bit-score, and selected for future investigation [42].
3.9. Vaccine Protein Prioritization
The virulent proteins were subjected to the Vaxijen server to predict antigenic potential. Out of nine virulent proteins, six proteins were shortlisted; Membrane lipoprotein TpN32 (UniProt ID: O07950), DNA translocase FtsK (UniProt ID: O83964), Protein Soj homolog (UniProt ID: O83296), F7IVD2 site-determining protein (UniProt ID: F7IVD2), ABC transporter, ATP-binding protein (UniProt ID: O83930) and the sugar ABC superfamily ATP-binding cassette transporter, ABC protein (UniProt ID: O83782) having high antigenic score as; 0.5303, 0.4699, 0.4275, 0.4611, 0.5249 and 0.5621 were predicted by Vaxijen server at threshold 0.4. The molecular weight of the protein is one of the most important parameters in vaccine design. A protein with the least molecular weight can be efficiently purified during the subsequent validation process. An online protein molecular weight server was used to predict the molecular weight of proteins. The molecular weight of the membrane lipoprotein TpN32 protein was 29.09 kDa, the DNA translocase FtsK protein was 86.62 KDa, the Soj homolog protein was 27.35 KDa, the site-determining protein was 33.72 ABC transporter, the ATP-binding protein was 25.18 KDa and the Sugar ABC superfamily ATP-binding cassette transporter, the ABC protein was 42.65 KDa, respectively, and thus strongly consider future vaccine development.
3.10. Protein–Protein Interaction Network Analysis
The antigenic proteins were subjected to STRING database for a protein–protein interaction study. STRING database revealed that the membrane lipoprotein TpN32 shows interaction with ten proteins, such proteins are metN (Methionine abc superfamily), potD (Spermidine/putrescine abc superfamily), troA (Periplasmic zinc-binding protein), metI, oppA, TPANIC_0545, TPANIC_0308, TPANIC_0309, TPANIC_0822 and TPANIC_0142 (uncharacterized) (Figure 3A). Figure 3B reveals that the FtsK DNA translocase interacts with ftsQ, ftsZ, ftsA (cell division protein), mreC (Cell shape determining protein), polA (DNA-directed DNA polymerase), parB (chromosome partitioning protein), topA (DNA topoisomerase), recA (recombination protein 7), TPANIC_0623 and TPANIC_0279 (uncharacterized). Protein Soj homolog (SOJ_TREPA) shows a connection with parB (chromosome partitioning protein), polA (DNA-directed DNA polymerase I), dnaA (DNA-directed DNA replication initiator protein), ispDF (2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase), ftsZ (essential cell division protein), topA (DNA topoisomerase topa), dnaN (confers DNA tethering and processivity to DNA polymerases and other proteins), dnaB (replicative DNA helicase) and uncharacterized TPANIC_0273 and TPANIC_0939 proteins (Figure 3C). Figure 3D shows that the site-determining protein F7IVD2_TREPA interacts with flhF, fliY, fliM flhA, flhB, fliR, fliQ, flip, fliL2, and cheA. Of these, flhA, flhB, fliR, fliQ and flip are (virulence-related) secretory pathway proteins that belong to the type iiisp family iii; similarly, fliY and fliM are flagellar motor switch proteins, fliL2 is a flagellar basal body-associated protein that controls the rotational direction of flagella during chemotaxis. flhF is a flagellar-associated gtp-binding protein, and cheA (Sensor histidine kinase) is involved in transmitting sensory signals. Figure 3E exhibits that ABC transporter, ATP-binding protein interacted with TPANIC_0966, TPANIC_0967, TPANIC_0968 (Hypothetical protein), TPANIC_0969 (Putative outer membrane protein), ftsY (Sec family Type I general secretory pathway protein), TPANIC_0963, TPANIC_0962, macA, lolE1, and lolE2 (Uncharacterized proteins). Figure 3F shows that the Sugar ABC superfamily ATP-binding cassette transporter, ABC protein was found with interaction with potB potC potD (Spermidine/putrescine abc superfamily ATP-binding), gpsA (glycerol-3-phosphate dehydrogenase), ugpA, ugpE, msmE, ugpB, TPANIC_0505 and TPANIC_0803 (uncharacterized proteins).
3.11. Selection and Evaluation of T-Cell Epitopes
For each virulent protein, the NetCTL 1.2 predicted 40 CTL epitopes (9-mer). The antigenicity, allergenicity, as well as immunogenicity scores of the epitopes were also calculated. Only four of the 40 epitopes were chosen for vaccine development because they met the criteria of being non-allergenic, highly antigenic, and immunogenic (Table 2). According to a reference set of seven human HLAs, the IEDB server also predicted HTL epitopes (15-mer). Only four epitopes were predicted to have the ability to act as antigenic and nonallergenic. They were also nontoxic and could induce IFN-γ were selected for additional investigations (Table 3).
3.12. Selection and Evaluation of B-Cell Epitopes
The ABCpred epitope server was used to predict linear B cells (each of 20 lengths) with a precision of 75%. The ABCpred server identified a total of 22 linear B-cell epitopes; Only 11 B-cell epitopes were chosen for vaccine constructs based on their evaluated properties as non-allergenic, antigenic, and high conservancy properties (Table 4).
3.13. Epitope-Based Subunit Vaccine Construct
The selected epitopes were fused with a specific linker to design a vaccine construct of multiple epitopes. It was joined by 4 CTL epitopes, while GPGPG joined 4 IFN inducer HTL epitopes, and a KK linker was used to fuse 11 LBL epitopes, respectively. To improve immunization and epitope effectiveness, the N-terminal of the vaccine construct was linked to the TLR-2 agonist ESAT6 using an EAAAK linker. The final constructed vaccine is 460 amino acid residues long (Figure 4).
3.14. Antigenicity and Allergenicity Physicochemical Properties of the Vaccine Construct
The model was constructed, and then its allergenicity and antigenicity were determined. According to our findings, the created model is highly antigenic (scoring 0.7878 at a 0.0.4 threshold on the Vaxijen server) and non-allergenic (as predicted by AllerTOP v2.0 [52] and AllergenFP v.1.0 [62]). The ProtParam software was then used to analyze the physiochemical properties of the constructed vaccine. The theoretical pI and GRAVY (Grand average of hydropathicity) of the generated vaccine were found to be 9.50 and −0.420 (negative sign indicates hydrophilic nature), respectively, and its molecular weight was determined to be 48,782.81 kD. With an instability index of 34.31, the constructed system is stable within the host environment. The vaccine construct’s aliphatic index was 77.09, which guarantees its thermostability. The half-life of the vaccine is approximately 10 min in yeast cells and greater than 10 h in E. coli (in vivo).
3.15. Analysis of Secondary Structure
The PSIPRED server was utilized to investigate the secondary vaccine structure. According to this server, 208 (45.22%) amino acids in the entire vaccine formed extended Beta strands,169 (36.74%) amino acids were found in alpha helixes, and 83 (18.04%) amino acids formed coils, as shown in Figure 5 [65].
3.16. Tertiary Structure Prediction, Refinement, and Validation of Design Vaccine
The 3D structures of the vaccine constructions were managed by the Robetta server. For any query peptide provided, the system generates five predicted configurations. A thorough evaluation led to the selection of model 2 for further study (Figure 6). The GalaxyWEB server’s GalaxyRefine module was then used to further improve the 3D structure. It was assumed that the ER-RAT, ProSA-web, and PROCHECK servers would be used to investigate and correct any structural flaws. The improved model received a Z score of −9.1 from ProSA-web, which is above the mean Z score for similar natural proteins (Figure 7A). Energy as a function of amino acids in the protein structure was another way in which Prosa-web proved the correctness of the local model (Figure 7C). Ramachandran analysis was performed using the PROCHECK service, which verified 90.7% of residues in the red region (most favorable), 6.3% of residues in the yellow region (additional allowances), and 1.6% of residues in the pale yellow area (generous allowances). A 1.4% of its residues are located in forbidden locations (highlighted in white) Figure 7B. According to the ERRAT server, the 3D structure of the vaccine has an overall quality of 86.7% (Figure 7D).
3.17. Molecular Docking of the Constructed Vaccine with Human TLR-2 and TLR-4
Using the HADDOCK server, the molecular docking of vaccine constructs was performed with human TLR-2 and TLR-4. In the case of TLR-2(6ING) docking, HADDOCK clustered 130 structures in 18 clusters, representing 65% of the water-refined models generated by HADDOCK as shown in Figure 8. The top-ranked cluster with the lowest Z-score is the most significant for docking analysis. The lowest HADDOCK score is −52.2 +/−6.4, and Z-score −2.1 is the most reliable among all clusters, and it suggests that the vaccine structure and TLR-2 interact appropriately. The creation of an excellent quality docked complex is indicated by a lower RMSD value of the docked complex. Table 5 illustrates the electrostatic, solvation, restraints violation, and van der Waals energies, in addition to Z-Score values computed by the HADDOCK. Figure 8 reveals the Pi and hydrogen interaction between vaccination and TLR-2. Analysis of the vaccine–TLR complex revealed that LYS422 binds to the benzene ring of HIS22 by Pi bond with a 5.0 Å distance. The hydrogen bonds were found between SER27 to GLU194 at 3.27 Å, SER39 to ARG156 at 2.99 Å, SER42 to GLU194 at 2.63 Å, ASN61 to LYS196 at 3.11 Å/2.78 Å, ARG321 to GLY6/GLN1 at 2.96 Å/3.26 Å, TYR323 to ASN3 at 3.26 Å, LYS347 to SER11/GLN15 at 3.03 Å/2.64 Å, LEU399 to ARG70 at 3.04 Å/2.87 Å, LYS422 to GLN66 at 2.85 Å, SER424 to ASN63 at 2.72 Å, ARG447 to GLN59 at 2.84 Å, LYS488 to GLN52 at 2.67 Å and LYS561 to ALA37 at 2.69 Å, respectively. The interaction was revealed through the PDBsum online server and PyMol software.
As required, a representative model from this top cluster was refined. The HADDOCK refinement server grouped the 20 generated structures into a single cluster, indicating 100% of the HADDOCK created by the water-refined model. The HADDOCK score is −250.0 +/− 4.6 with a 0.0 Z-score; however, the buried surface area (BSA) score for this refined cluster is 4493.9 +/− 92.1 (Figure 9).
HADDOCK grouped 135 structures into 14 clusters for TLR4 (2z63) docking, representing 67% of the water-refined models developed by the HADDOCK cluster Figure 10. The cluster with the lowest HADDOCK score, 17.7 +/− 17.5, is the most reliable. The BSA score and Z-score for this docking are 2678.3 +/− 408.6 and −1.0, respectively. A decrease in the RMSD value of the docked complex indicates the development of a high-quality complex. Table 6 displays the electrostatic, solvation, restraints violation, van der Waals energies, and Z score values calculated by the HAD-DOCK software.
Figure 11 displays the molecular interaction (hydrogen bonds) between vaccination and TLR-4; GLU27 bind to TYR102 at 2.83 Å distance, GLU31 to ASN17 at 3.19 Å, ASP84 to ASN3/ALA5 and GLY6 at 3.02 Å/3.14 Å and 2.73 Å, ARG87 to GLN1 at 2.60 Å/2.91 Å, ARG87 to THR87 at 3.03 Å, LYS230 to GLU83 at 2.69 Å, ARG234 to THR82 at 2.66 Å, LYS582 to ASP55 at 2.60 Å, LYS588 to GLY40 at 3.11 Å, ARG591 to ASP26 at 2.70 Å, respectively.
The refined representative model of this top cluster was implemented as required. All of the HADDOCK models that were refined in water were included in a single cluster of 20 structures created by the server. This refined cluster has a HADDOCK score of −135.8 +/− 3.5, a buried surface area score of 2543.7 +/− 92.8, and a Z-score of 0.0.
3.18. MD Simulation
The iMODS server performed a molecular dynamics simulation of the docking complexes. This server uses normal mode analysis. Figure 11 and Figure 12 illustrate the simulation results for both vaccine–TLR2 and vaccine–TLR4. Figure 12 and Figure 13B demonstrate the deformability plot of both complexes, respectively, where the peaks indicated the non-rigid regions of the complexes. Eigenvalues values of vaccine–TLR2 and TLR4 docking complexes were 1.990346e−6 and 1.707782e−6, respectively, shown in Figure 12C and Figure 13C. (Figure 12 and Figure 13D) show the variance matrix graph of residues, which are inversely related to eigenvalue, where red indicates individual variance and green is a cumulative variance. (Figure 12E and Figure 13E) reveal that the covariance matrix signifies coupling between pairs of residues, red represents experience correlated, white represents uncorrelated, and blue color shows anti-correlated motions. (Figure 12F and Figure 13F) show an elastic network of the complexes, where dots indicate one spring and a gray area indicates stiffer springs. The overall analysis of iMODS suggests that vaccine constructs with TLR2 and TLR4 complexes are stable.
3.19. Discontinuous B-Cell Epitope Prediction
Protein folding may break up residues and produce discontinuous or conformational B-cell epitopes. The validated and refined multi-epitope 3D structure was uploaded to the ElliPro server in order to forecast the existence of these epitopes. As demonstrated in Table 7 and Figure 14, this server predicted the presence of seven conformational epitope regions. During vaccine development, the interaction between continuous and discontinuous B-cell epitopes indicated that they are adaptable and therefore can interact with antibodies.
3.20. In Silico Immune Simulation
The immunological response generated by the C-ImmSim immune simulator against a pathogen was identical to the actual immune response (Figure 15). (Figure 15A) shows that antibody levels (IgM, IgG1, IgG2) levels were more significant in secondary and tertiary reactions, corresponding to fading of antigen concentrations. Long-lasting B cell isotypes were also reported, which demonstrated memory B cell development and swapping ability (Figure 15B). Similarly, memory development was confirmed in the T-helper and cytotoxic T-cell populations, and it was crucial to complement the immune response (Figure 15C). There was a noticeable increase in macrophage activity and interaction and a significant expansion of dendritic cells (Figure 15D). (Figure 15E) shows that there were also elevated levels of interferon-gamma (IFN-γ) and interleukin-2 (IL-2). When cytokine levels increased, the Simpson index D showed high risks, which led to difficulties during the immune response.
3.21. Codon Adaptation and In Silico Cloning of the Vaccine Construct
The following vaccination constructions were proposed for the JCat E. coli K12 server to facilitate codon adaptation. A codon sequence of 730 nucleotides is ideal. The GC content of the optimal codon sequence was 53.47%. In contrast, the expression levels of the vaccine design in E. coli K12 should be between 30 and 70%, and a codon adaption index (CAI) of 0.961 indicates this. The ends of the vaccine gene were modified to include two restriction sites, EcoRI and BamHI. In the end, Snap gene software was used to insert the vaccination gene into the restriction site of the pET28a (+) plasmid (Figure 16). Overall, the clone measured 6099 bp in length.
4. Discussion
Sexually transmitted infections (STIs) are caused by various pathogens that are mostly transmitted through sexual intercourse.
Vaccination helps stimulate the immune response as well as defend against pathogen-borne contagious diseases. The prediction and use of surface antigenic epitopes are critical for the development of an efficient vaccine to protect against infectious diseases. Recently, using an in silico technique to work with bacterial pathogens, many targets have been found that are drug resistant or for which no vaccination is available [25]. In the post-genomic age, reverse vaccinology is a common and popular method for quickly identifying new vaccine targets [26,27]. In this study, we mainly used reverse vaccinology and subtractive genomics. We are primarily interested in identifying potential vaccines and therapeutic targets for syphilis disease.
It is worth mentioning that our followed approach has been reported as a successful method for proteome filtration and vaccine candidates selection in several targeted bacteria including Klebsiella Pneumoniae [81], Staphylococcus aureus [82], Mycobacterium tuberculosis [83], Shigella flexneri [84], Pseudomonas aeruginosa [85] and Moraxella catarrhalis [86]. Furthermore, the vaccine generated showed protective functions when it was validated by wet laboratory techniques. For example, a vaccine designed against Echinococcus granulosus through an immunoinformatic approach has activated mice humoral immunity and cellular immunity and has good antigenicity and immunogenicity [87]. Furthermore, the evaluation of a multitope vaccine against uropathogenic Escherichia coli showed that IgG and IgA antibody levels improved in serum and mucosal samples from vaccinated mice [88]. It is important to mention that the current study has applied an in silico approach for the design and evaluation of the potential vaccine; given the limitations of our method, we were very stringent and only chose top candidate epitopes confirmed by multiple tools. While immunoinformatics integrated with the reverse vaccinology approach was used to propose the potential vaccine of the current study and it was predicted immunogenic, future wet lab experiments are essential to comprehensively validate our findings.
The entire proteome of T. pallidum (strain Nichols) was obtained from UniProtKB (UniProt ID: UP000000811) [31]. To prevent an autoimmune response, human homologs were identified and subsequently removed. Furthermore, paralogous, non-essential, non-membrane, and non-virulent proteins were eliminated [33,34]. Antigenic virulence proteins are attractive candidates for the formation of computational vaccines. The ability of a pathogen to infect its host depends on the presence of virulent proteins [42].
Previous research by Zhao et al. 2011, used membrane proteins as vaccine candidates. These researchers found that the outer membrane proteins of T. pallidum are, indeed, the main targets of host protective immunity and play a crucial role in the pathogenicity of T. pallidum [14,15]. Following the screening, six proteins were identified as promising candidates for vaccine development. It includes the membrane lipoprotein TpN32 (UniProt ID: O07950), DNA translocase FtsK (UniProt ID: O83964), protein Soj homolog (UniProt ID: O83296), site-determining protein (UniProt ID: F7IVD2), ABC transporter, ATP-binding protein (UniProt ID: O83930) and sugar ABC superfamily ATP-binding cassette transporter, ABC protein (UniProt ID: O83782). These proteins were submitted to the STING database to determine their interaction with other proteins [48].
Furthermore, these proteins were subjected to immunoinformatic tools for vaccine design. To choose suitable vaccine candidates, various databases and web servers were used to predict Helper T lymphocytes (HTL), cytotoxic T lymphocytes (CTL), and B cell epitopes. The final epitope sequences for both T- and B-cell cell epitopes were determined. Immunogenicity, toxicity, allergenicity, and antigenicity were all important variables in selecting optimal epitopes.
The vaccine was produced by integrating the CTL, HTL, and B-cell epitopes with the corresponding AAY, GPGPG, and KK linkers. Vaccines need linkers to improve their folding, stability, as well as expression [61]. Multiepitope-based vaccines require adjuvant coupling to boost their immunogenicity, durability, influence stability, immune responses, antigen development, and protection from pathogens. The adjuvant (TLR-2 agonist ESAT6; Accession: AEP68523.1) was linked to the starting location using the EAAAK linker. ESAT6 promotes the TLR2-dependent production of IL-6 and TGF- by dendritic cells. Additionally, it stimulates Th17 immune responses, which are required for optimum vaccination effectiveness. Therefore, the structure of the developed vaccine was subjected to physiochemical assessment. The manufactured vaccine was calculated to have a molecular weight of 48,782.81 kD. The theoretic pI of the construct of 9.50 shows that it is strongly alkaline and provides a steady physiological pH. Furthermore, the aliphatic index and GRAVY score reflect the thermostability and hydrophilicity of the substance. The vaccine has a mean half-life of >10 h in E. coli and 10 min in yeast cells (in vivo). Additionally, the vaccination has been described as non-allergenic and highly antigenic. The 2ry and 3ry structures of the protein provide data on its functions, dynamics, and interactions with other proteins or ligands. PSIPRED V3.3 and the Robetta server predict the secondary and tertiary structure of a vaccine. The secondary structure of the developed vaccine consists of 45.22% beta strands, 36.74% Alpha helix, and 18.04% random coil. Numerous validation tools, including ERRAT, ProSA-web, and PROCHECK, were utilized to identify defects in the tertiary structure of the final product vaccine.
The significant interaction between vaccination and innate immune receptors shown by the docking score suggests that the vaccine can activate TLRs. The docking complexes were then subjected to the online server iMODS for molecular dynamics simulation. The MD simulation study of the docked complexes with TLR receptors showed good stability, deformability, and low eigenvalue.
Codon optimization was performed with the Jcat (Java Codon Adaptation Tool) software to enable optimum vaccine expression in the E. coli system. We also employed an in silico immune simulator to model vaccine immunological responses in the current study, revealing a good immune response pattern. We administered three doses based on B-cell isotypes and T-cell-mediated immunological reactions, with a significant number of memory B cells with a half-life of several months, whereas repeated vaccination doses improved immune responses. Our simulated immune response indicates a more active immunological response than the first primary dose. IgG and IgM antibody production gradually increases at subsequent and tertiary doses. Numerous vaccinations resulted in prolonged production of IFN- and IL-2, demonstrating that the vaccine effectively induced a response of the humoral immune system to improve immunoglobulin secretion.
Vaccines are formulated using traditional methods, and these vaccines function better in the immune systems of model species. Unfortunately, they are indeed completely ineffective whenever administered to people because of the complexities of the immune system. As a result, using reliable subtractive proteomics and immunoinformatic technologies, this scientific study developed a safe, specific and highly efficient vaccine that could provide long-term protection against Syphilis infections. These vaccines required more clinical trials to verify their vaccine safety and efficacy in vivo.
5. Conclusions
T. pallidum is among the most common causes of syphilis. This study used immunoinformatics, reverse vaccinology, and subtractive genomics to provide insight into the critical targets of T. pallidum for creating a potentially successful vaccine. B- and T-cell epitopes were identified from the pathogenic proteins of T. pallidum to develop effective, safe, non-allergenic, extensively antigenic, and specific multiple epitope vaccines. In addition to adjuvant sequences, suitable linkers were applied to improve the stability, effectiveness, and immunological responsiveness of vaccine constructions. The suggested vaccine must exhibit the structural, physicochemical, and immunological qualities required to trigger humoral and cell-mediated immunogenicity. The contact and binding potentials between the receptors (TLR-2 and TLR-4) and the vaccine protein were reported to be stable and high.
Furthermore, simulations of the immune system demonstrated efficient immunogenicity in vivo through reverse translation and codon optimization. To ensure practical expression and durability, the final vaccine was cloned in E. coli pET28a + plasmid. The designed vaccine designed requires additional laboratory testing to validate its safety and effectiveness.
Conceptualization, S.K., M.R., A.Z. and A.U.; methodology, S.K., M.A.E., A.U.R. and M.R.; software, S.K., A.U.R., S.K. and A.Z.; validation, S.K., M.R. and A.Z.; formal analysis, S.H. and A.U.R.; investigation, S.H., G.M.A. and A.E.A.; resources, M.R., R.A.E. and M.S.A.Z.; data curation, M.A.E.; writing—original draft preparation, M.A.E. and A.U.; writing—review and editing, S.K. and M.A.E. visualization, S.H., G.M.A. and A.E.A.; supervision, M.A.E., N.A.T.N. and M.M.A.-D.; project administration, R.A.E. and M.S.A.Z.; funding acquisition, N.A.T.N. and M.M.A.-D. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 1. The flowchart represents the overall methodology for developing a multi-epitope subunit vaccine construct.
Enlarge this image.Figure 2. Subcellular localization of essential proteins predicted by PSORTB CELLO server. PSORTB predicted 63% cytoplasmic, 22% cytoplasmic membrane, 2% periplasmic, 1% outer membrane, 0% extra membrane and 12% unknown proteins, while Cello showed 57% cytoplasmic, 38% inner membrane, 2% periplasmic, 1% extracellular and 2% outer membrane protein.
Enlarge this image.Figure 3. The antigenic proteins and protein interaction (PPI) through the STRING database. (A) Protein 1: 007950|TPN32_TREPA Membrane lipoprotein TpN32; (B) Protein 2: O83964|FTSK_TREPA DNA translocase FtsK; (C); Protein 3: O83296|SOJ_TREPA Protein Soj homolog (D); Protein 4: F7IVD2_TREPA site-determining protein; (E) Protein 5: O83930_TREPA ABC transporter, ATP-binding protein; (F) Protein 6: O83782_TREPA Sugar ABC superfamily ATP-binding cassette transporter, ABC protein.
Enlarge this image.Figure 4. EAAAK, AAY, GPGPGP and KK linkers were used to create the final vaccine candidate configuration, consisting of an adjuvant accompanied by cytotoxic T lymphocyte (CTL), Helper T lymphocyte (HTL), and B cell epitopes.
Enlarge this image.Figure 5. Secondary structural features of the constructed vaccine. Herein, α-helix (36.74%), β-strands (45.22%), and random coils (18.04%) are represented with pink, yellow, and blue colors, respectively.
Enlarge this image.Figure 6. Predicted 3D structure of T. pallidum vaccine constructs, visualized by Pymol software.
Enlarge this image.Figure 7. Validation of the three-dimensional structure of the vaccine constructed. (A) ProSA web evaluation of the vaccine structure (Z-scores −9.1); (B) investigation of the protein structure using the Ramachandran plot following molecular optimization; (C) ProSA graphical plot (local model quality); (D) ERRAT server predicted the overall quality of the 3D structure.
Enlarge this image.Figure 7. Validation of the three-dimensional structure of the vaccine constructed. (A) ProSA web evaluation of the vaccine structure (Z-scores −9.1); (B) investigation of the protein structure using the Ramachandran plot following molecular optimization; (C) ProSA graphical plot (local model quality); (D) ERRAT server predicted the overall quality of the 3D structure.
Enlarge this image.Figure 8. The docked complex between the designed vaccine constructs and the TLR2 receptor. Vaccine, the surface is represented in cyan, while the TLR-2 receptor is represented in green.
Enlarge this image.Figure 9. The figure shows the binding interaction between active residues of a docked complex of human TLR-2 and vaccine construct. Chain A represents active residues of TLR-2, while chain B represents active residues of the vaccine construct.
Enlarge this image.Figure 10. Toll-like receptor 4 (TLR4) complex docked with vaccine constructs. The Cyan color represents the vaccination surface, and the green color represents the TLR-4 receptors.
Enlarge this image.Figure 11. The figure shows the binding interaction between the active residues of the docked complex of human TLR-4 and the vaccine construct. Chain A represents active residues of TLR-4, while chain B represents active residues of the vaccine construct.
![View Image - Figure 12. Outcomes of MD simulations of TLR-2 and the designed vaccine construct. (A) NMA mobility; (B) deformability plot; (C) eigenvalue plot; (D) variance plot (individual variances are red, while cumulative variances are green); (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions]; (F) elastic network (darker grey regions indicate stiffer regions) of the complex.](https://media.proquest.com/media/hms/THUM/8/74VaQ?_a=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&_s=vEjA8XShLvwuK4OMn9R8uqeHn80%3D "View Image - Figure 12. Outcomes of MD simulations of TLR-2 and the designed vaccine construct. (A) NMA mobility; (B) deformability plot; (C) eigenvalue plot; (D) variance plot (individual variances are red, while cumulative variances are green); (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions]; (F) elastic network (darker grey regions indicate stiffer regions) of the complex.")
Enlarge this image.Figure 12. Outcomes of MD simulations of TLR-2 and the designed vaccine construct. (A) NMA mobility; (B) deformability plot; (C) eigenvalue plot; (D) variance plot (individual variances are red, while cumulative variances are green); (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions]; (F) elastic network (darker grey regions indicate stiffer regions) of the complex.
![View Image - Figure 13. Outcomes of MD simulations of TLR-4 and the designed Vaccine construct. (A) NMA mobility, (B) deformability plot, (C) eigenvalue plot, (D) variability plot (individual variances are brown, while cumulative variances are green), (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions], (F) elastic network (darker grey regions indicate stiffer regions) of the complex.](https://media.proquest.com/media/hms/THUM/10/74VaQ?_a=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&_s=weNghgVc99APchVmfhdCkUUep%2Fc%3D "View Image - Figure 13. Outcomes of MD simulations of TLR-4 and the designed Vaccine construct. (A) NMA mobility, (B) deformability plot, (C) eigenvalue plot, (D) variability plot (individual variances are brown, while cumulative variances are green), (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions], (F) elastic network (darker grey regions indicate stiffer regions) of the complex.")
Enlarge this image.Figure 13. Outcomes of MD simulations of TLR-4 and the designed Vaccine construct. (A) NMA mobility, (B) deformability plot, (C) eigenvalue plot, (D) variability plot (individual variances are brown, while cumulative variances are green), (E) covariance map [correlated (red), uncorrelated (white), or anti-correlated (blue) motions], (F) elastic network (darker grey regions indicate stiffer regions) of the complex.
Enlarge this image.Figure 14. ElliPro predicted discontinuous B-cell epitopes. (1–6): 3D visualization of conformational or discontinuous epitopes of T. pallidum most antigenic protein. Yellow surfaces indicate epitopes, whereas grey sticks represent most of the protein.
Enlarge this image.Figure 15. Simulation of the immunological response of the multiepitope-based vaccination construct in silico, (A) represents antibody production. In contrast, the black vertical lines show antigen, (B) B lymphocytes Population after three injections, (C) helper T cell activation throughout the injections, (D) Throughout the injections, the active cytotoxic T cell population in each state increased, (E) Concentration of cytokines and interleukins with Simpson index D.
Enlarge this image.Figure 15. Simulation of the immunological response of the multiepitope-based vaccination construct in silico, (A) represents antibody production. In contrast, the black vertical lines show antigen, (B) B lymphocytes Population after three injections, (C) helper T cell activation throughout the injections, (D) Throughout the injections, the active cytotoxic T cell population in each state increased, (E) Concentration of cytokines and interleukins with Simpson index D.
Enlarge this image.Figure 16. In silico restriction, cloning was used to introduce the final vaccine construct into the pET28a (+) expression vector, in which the red area represents the vaccine insert as well as the black circle represents the vector.
The CD-HIT suite identified paralogous proteins using an 80% threshold.
| Cluster | Size | Protein ID | % Similarity |
|---|---|---|---|
| >Cluster 0 |
|||
| 215aa |
P56822 |
98.14% |
|
| >Cluster 1 |
|||
| 756aa |
O83337 |
85.45% |
|
Predicted linear cytotoxic T-lymphocyte epitopes, and its major histocompatibility complex class 1 (MHC-I) binding affinity with antigemicity score.
| Protein Name | Protein ID | Peptide Sequence | MHC Binding |
Rescale Binding |
C-Terminal Cleavage |
Transport |
Prediction |
MHC-I |
VaxiJen Score | AllerTOP v.2.0 | Immunogenicity |
|---|---|---|---|---|---|---|---|---|---|---|---|
| FTSK_TREPA DNA translocase FtsK | O83964 | LALLGAELY | 0.178 | 0.7558 | 0.7357 | 3.047 | 1.0185 | Yes | 0.5305 | Non-allergen | 0.13309 |
| SOJ_TREPA Protein | O83296 | TSAINLGAY | 0.6054 | 2.5705 | 0.4577 | 2.971 | 2.7877 | Yes | 0.4485 | Non-allergen | 0.18134 |
| TREPA Site-determining protein | F7IVD2 | IATNMAIAY | 0.2248 | 0.9546 | 0.539 | 3.105 | 1.1907 | Yes | 0.6396 | Non-allergen | 0.0071 |
| TREPA ABC transporter, ATP-binding protein | O83930 | TVGFVFQQY | 0.1452 | 0.6164 | 0.9747 | 3.011 | 0.9131 | Yes | 0.4966 | Non-allergen | 0.11376 |
Predicted helper T-lymphocyte, interferon-gamma (FN-γ) inducing epitopes.
| Name | Uniport ID | Start | End | Alleles | Peptide Sequence | Method | Toxicity | Antigenicity | Allergenicity | IFN-γ |
|---|---|---|---|---|---|---|---|---|---|---|
| FTSK_TREPA DNA translocase FtsK | O83964 | 26 | 40 | HLA-DRB5*01:01 | TLSTFLPLFTLHRAS | Consensus (smm/nn/sturniolo) | Non-toxic | 0.589 | Non-allergenic | Positive |
| SOJ_TREPA Protein | O83296 | 141 | 155 | HLA-DRB4*01:01 | VFIPLQCEYFALEGL | Consensus (comb.lib./smm/nn) | Non-toxic | 0.7306 | Non-allergenic | Positive |
| TREPA Site-determining protein | F7IVD2 | 34 | 48 | HLA-DRB1*03:01 | KLLLIDPKIVELKLY | Consensus (smm/nn/sturniolo) | Non-toxic | 1.3598 | Non-allergenic | Positive |
| TREPA Sugar ABC superfamily ATP-binding cassette transporter | O83782 | 39 | 53 | HLA-DRB4*01:01 | FGLRIRKIPQQEIIR | Consensus (comb.lib./smm/nn) | Non-toxic | 0.6532 | Non-allergenic | Positive |
Predicted linear B-cell epitopes.
| Peptide | Protein | Score | Antigenicity | Conservancy % |
|---|---|---|---|---|
| PHMQQFNQEHNGDLVSVGNV | TPN32_TREPA membrane lipoprotein TpN32 | 0.983 | 0.408 | 100.00% |
| GGRVRTYLKERYKGGEVAPA | TPN32_TREPA Membrane lipoprotein TpN32 | 0.901 | 0.7478 | 100.00% |
| IPAQDDEQGPPRPIPASAAP | FTSK_TREPA DNA translocase FtsK | 1 | 0.6798 | 100.00% |
| PSDVHAPASPGSLPSVIPAQ | FTSK_TREPA DNA translocase FtsK | 0.998 | 0.4694 | 100.00% |
| TGIKKGPVVTMFELLPPPGI | FTSK_TREPA DNA translocase FtsK | 0.996 | 0.7765 | 100.00% |
| PEASAPPEGQFSTEVPLQGG | FTSK_TREPA DNA translocase FtsK | 0.99 | 0.6035 | 100.00% |
| RDLMQEKNARERVERHQHRT | TREPA site-determining protein | 0.967 | 0.8618 | 100.00% |
| LKDGKIVGDHVRGHGGADGG | TREPA ABC transporter, ATP-binding protein | 0.981 | 1.5311 | 100.00% |
| ILGPSGSGKSTCMHMIGCLD | TREPA ABC transporter, ATP-binding protein | 0.948 | 0.9457 | 100.00% |
| LQGGTSQVATVHAPPEISTG | TREPA Sugar ABC superfamily ATP-binding cassette transporter | 0.966 | 0.9404 | 100.00% |
| RPEAITPRTEETLARECANV | TREPA Sugar ABC superfamily ATP-binding cassette transporter | 0.946 | 0.7421 | 100.00% |
Protein–protein docking results between TLR-2 and vaccine construct.
| Cluster 1 | |
|---|---|
| HADDOCK score |
−52.2 +/− 6.4 |
Protein–protein docking results between TLR-4 and vaccine construct.
| Cluster 10 | |
|---|---|
| HADDOCK score |
17.7 +/− 17.5 |
Discontinuous B-cell epitopes predicted by the ElliPro.
| No. Residues | Number of Residues Score | Score |
|---|---|---|
| 1 | A:K286, A:S288, A:D289, A:V290, A:H291, A:A292, A:P293, A:A294, A:S295, A:P296, A:G297, A:S298, A:L299, A:P300, A:S301, A:V302, A:I303, A:P304, A:A305, A:Q306, A:K307 | 0.801 |
| 2 | A:Q1, A:W2, A:N3, A:F4, A:A5, A:G6, A:I7, A:E8, A:A9, A:A10, A:S11, A:S12, A:A13, A:I14, A:Q15, A:G16, A:T19, A:N63, A:Q66, A:N67, A:L68, A:A69, A:R70, A:T71, A:I72, A:S73, A:E74, A:A75, A:G76, A:Q77, A:A78, A:M79, A:Q80, A:S81, A:T82, A:E83, A:G84, A:N85, A:V86, A:T87, A:G88, A:E89, A:A90, A:A91, A:A92, A:K93, A:L94, A:A95, A:L96, A:L97, A:G98, A:A99, A:E100, A:L101 | 0.798 |
| 3 | A:P337, A:E338, A:G339, A:Q340, A:F341, A:V365, A:E366, A:H368, A:Q369, A:H370, A:R371, A:T372, A:K373, A:K374, A:L375, A:K376, A:D377, A:G378, A:K379, A:I380, A:V381, A:G382, A:D383, A:H384, A:V385, A:R386, A:H388, A:G390, A:A391, A:D392, A:G393, A:G394, A:K395, A:K396, A:I397, A:L398, A:G399, A:P400, A:S401, A:G402, A:S403, A:G404, A:K405, A:S406, A:T407, A:C408, A:M409, A:H410, A:M411, A:I412, A:G413, A:C414, A:L415, A:D416, A:K417, A:K418, A:L419, A:Q420, A:G421, A:G422, A:T423, A:S424, A:Q425, A:V426, A:A427, A:T428, A:V429, A:H430, A:A431, A:P432, A:P433, A:E434, A:I435, A:S436, A:T437, A:G438, A:K439, A:R441, A:P442, A:E443, A:A444, A:I445, A:T446, A:P447, A:R448, A:T449, A:E450, A:E451, A:T452, A:L453, A:A454, A:R455, A:E456, A:C457, A:A458, A:N459, A:V460 | 0.754 |
| 4 | A:Y129, A:T130, A:V131, A:G132, A:F133, A:V134, A:F135, A:Q136, A:Q137, A:Y138, A:G139, A:P140, A:G141, A:P142, A:G143, A:T144, A:L145, A:S146, A:T147, A:F148, A:L151, A:L154, A:H155, A:A157, A:S158, A:G159, A:P160, A:G161, A:G163, A:Q169 | 0.64 |
| 5 | A:T33, A:K34, A:A36, A:A37, A:A38, A:W39, A:G40, A:G41, A:S42, A:G43, A:S44, A:E45, A:Q48, A:Q52 | 0.612 |
| 6 | A:S342, A:T343, A:E344, A:V345, A:P346, A:L347, A:Q348, A:K351, A:E358, A:R362 | 0.57 |
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; Hassan, Said 5 ; Ashfaq Ur Rehman 6
; Eid, Refaat A 7
; Zaki, Mohamed Samir A 8
; Albadrani, Ghadeer M 9
; Altyar, Ahmed E 10
; Nehal Ahmed Talaat Nouh 11 ; Abdel-Daim, Mohamed M 12
; Ullah, Amin 13 1 School of Life Sciences, Northeast Normal University, Changchun 130024, China
2 Center for Biotechnology and Microbiology, University of Swat, Kanju Campus, Swat 19120, Pakistan
3 Department of Biotechnology, Quaid-i-Azam University, Islamabad 45320, Pakistan
4 Cell Biology, Histology & Genetics Division, Biology Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
5 Institute of Biotechnology and Microbiology, Bacha Khan University Charsadda, Peshawar 24540, Pakistan
6 Departments of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697-3900, USA
7 Department of Pathology, College of Medicine, King Khalid University, Abha 62529, Saudi Arabia
8 Anatomy Department, College of Medicine, King Khalid University, Abha 62529, Saudi Arabia; Department of Histology and Cell Biology, College of Medicine, Zagazig University, Zagazig 31527, Egypt
9 Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
10 Department of Pharmacy Practice, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia
11 Department of Microbiology, Medicine Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia; Inpatient Pharmacy, Mansoura University Hospitals, Mansoura 35516, Egypt
12 Department of Pharmaceutical Sciences, Pharmacy Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia; Pharmacology Department, Faculty of Veterinary Medicine, Suez Canal University, Ismailia 41522, Egypt
13 Department of Health and Biological Sciences, Abasyn University Peshawar, Peshawar 25000, Pakistan