Lumpy skin disease (LSD) is an economically important vector-borne transboundary disease of ruminants caused by lumpy skin disease virus (LSDV) (Kayesh et al., 2020). LSDV is a double-stranded DNA virus under the family Poxviridae and the genus Capripoxvirus (Sprygin et al., 2019; Tulman et al., 2001). LSDV contains a large genome of about 151 kb that codes for 156 putative genes and with 2 identical inverted terminal repeat regions of about 2.4 kb at both ends of the central coding region (Tulman et al., 2001). The genus Capripoxvirus contains three very closely related animal viruses such as sheeppox virus (SPPV), goatpox virus (GTPV) and LSDV, and serologically, these viruses cannot be separated (Babiuk et al., 2008; Badhy et al., 2021). LSD is mechanically transmitted by blood-feeding mosquitos, biting flies and ticks (Chihota et al., 2001; Sanz-Bernardo et al., 2021; Sohier et al., 2019; Tuppurainen et al., 2013). LSDV has a narrow host range, infecting cattle (Bos indicus and Bos taurus) and buffaloes (Bubalus bubalis) (Kar et al., 2022). The B. taurus cattle is more susceptible to LSDV compared to the B. indicus cattle (Gupta et al., 2020). LSDV causes significant economic losses in cattle and buffalo industry globally, which causes enlisting of LSD as a World Organization for Animal Health (WOAH/OIE)-notifiable disease. Previously, LSD was endemic only in African countries; however, recently, it has spread to many Asian cattle-rearing countries (Khan et al., 2021).
Until now, there is no antiviral treatment for LSDV infection (Uddin et al., 2022). Although strict quarantine measures and vector control are essential for preventing its spreading, however, vaccination remains the mainstay of preventing the spread of the infection in endemic areas as well as in newly affected regions (Tuppurainen et al., 2021). Vaccination is crucial for maintaining the good health of animals and the economic sustainability of cattle and buffalo farming against LSD threats (Tuppurainen et al., 2021). For controlling LSDV infection, homologous vaccines consisting of live attenuated LSDV can be used (Sprygin et al., 2020). Due to cross-protection within the Capripoxvirus genus, heterologous vaccines consisting of live attenuated SPPV or GTPV can also be used for controlling LSDV infection (Tuppurainen et al., 2021). However, the vaccine efficacy is not consistent rather varies with quality, efficacy, safety, side effects and even price (Tuppurainen et al., 2021). Moreover, there are the issues of improper characterization of circulating wild strains as well as viral vaccine strains in terms of host specificity, vaccination failures and safety concerns with capripox vaccinations (Sumana et al., 2020). Moreover, the administration of heterologous vaccinations in Turkey was not able to stop LSD outbreaks, indicating that the success of the vaccine is not adequate, necessitating the development of new vaccine candidate for control and prevention of LSD.
Immunoinformatics and computational vaccinology are useful in providing insight into the host-pathogen interactions thus playing an important role in vaccine development. Immunoinformatics and computational vaccinology become an invaluable tool for more rapid and precise vaccine design (Ishack & Lipner, 2021). The design of a multi-epitope vaccine targeting viral structural and non-structural proteins through immunoinformatics tools appears promising (Behmard et al., 2022; Fadaka et al., 2021; Huang et al., 2022). However, there is very limited information on immunoinformatics approaches to develop LSD vaccine. Therefore, in this study by applying immunoinformatics approaches, we have designed possible vaccine candidates targeting LSDV. However, the designed vaccine candidates are required to be further experimentally investigated for the efficacy as potential vaccines against LSDV infection.
MATERIALS AND METHODS Retrieval of protein sequences and structural analysisThe gene sequence of intracellular enveloped virus (IEV) and extracellular enveloped virions (EEV) membrane glycoprotein of LSDV was obtained from National Centre for Biotechnology Information Database (
Prediction of major histocompatibility complex (MHC) class I molecules (MHC I) binding of conserved epitopes was accomplished by immune epitope database (IEDB) MHC I Binding Predictions Tool (
The IEDB MHC II server (
The potential linear B-cell epitopes were selected by BepiPred (
A vaccine candidate must have the antigenicity, which can be measured by freely accessible online tool VaxiJen v2.0 (
Vaccine construction was done by obtaining adjuvant sequence from NCBI, and all potential epitopes were exploited to develop the multi-epitope vaccine construct. To enhance the immunogenicity of the vaccine candidate, a toll-like receptor 4 (TLR4)-inducing 50S ribosomal protein L7/L12 (UniProt ID: P9WHE3) was used as an adjuvant, which was linked to the N-terminus of the vaccine candidate. Four different linkers such as EAAAK, AAY, GPGPG and KK were used. 6× His tag was added at the C-terminal to carry out the expression and binding of histidine protein.
Assemblage of multi-epitopic vaccine candidate sequenceTo obtain the complete sequence of the candidate vaccine adjuvant sequence, linker sequences of EAAAK, AAY, GPGPG and KK linkers, and the sequences of MHC I, MHC II and B-cell epitopes were manually merged (Majid & Andleeb, 2019; Sami et al., 2021). The sequence of merged final vaccine construct is shown in Tables 1–3.
TABLE 1 Prediction of major histocompatibility complex (MHC) I epitopes.
| MHC I | |||
| Epitopes | Antigenicity | Allergenicity | Toxicity |
| GLVKKKNNI | 1.8445 | Non allergen | Non toxic |
| LTYGRQFWY | 0.7367 | Non allergen | Non toxic |
| AIFMLVSTI | 0.4443 | Non allergen | Non toxic |
| YVSYIICVK | 1.0688 | Non allergen | Non toxic |
| VSYIICVKR | 0.9729 | Non allergen | Non toxic |
| LQLSLYGGV | 0.6519 | Non allergen | Non toxic |
| GQFKNVSCN | 0.6314 | Non allergen | Non toxic |
Physicochemical properties of the vaccine construct like theoretical isoelectric point (pI), composition of amino acid, molecular weight (MW), in vitro and in vivo half-life, instability and aliphatic index and grand average of hydropathicity (GRAVY) were predicted by using Expasy ProtParam (
The antigenicity of the multi-epitope vaccine construct was analysed by Vaxigen 2.0 (Doytchinova & Flower, 2007), and the allergenicity of multi-epitope vaccine construct was predicted using AllerTOP (Dimitrov et al., 2014; Magnan et al., 2010). Furthermore, toxicity analysis of the construct was done using ToxinPred (Gupta et al., 2015).
Secondary structure predictionThe secondary structure of the multi-epitope vaccine construct was conducted using the GOR IV online tool (
The tertiary or three-dimensional (3D) modelling of the multi-epitope vaccine was done using transform restrained Rosetta (trRosetta) server (
To further improve the 3D model of the multi-epitope vaccine construct, the GalaxyRefine web server (
Tertiary structure validation is a difficult step of the model construction approach because it finds possible errors in the predicted 3D models (Khatoon et al., 2017). ProSA-web server (
More than 90% of B-cell epitopes are known to be discontinuous in nature. ElliPro, a web-tool (
Disulfide bonds are covalent interactions offering considerable stability and strengthen the geometric conformation of proteins. The disulfide engineering of the vaccine construct was conducted with the online DbD2 server (
Cluspro2.0 online docking server (
The iMODS (
To express the chimeric protein in an expression system, the sequence of the vaccine construct was reverse translated by the backtranseq program of EMBOSS 6.0.1 (
To determine the immune response of the vaccine construct, an immune simulation was performed by a web-based simulation server, C-ImmSim (
The retrieved sequence was antigenic (0.5962; which indicates probable antigen), non-allergen and non-toxic. The expected half-life was 30 h in mammalian reticulocytes (in vitro) >20 h in yeast (in vivo) and over 10 h in E. coli (in vivo) as estimated by ExPASy-ProtParam. The GRAVY score was −0.226, whereas the protein instability index (II) was 35.84, indicating a stable protein. Moreover, the vaccine protein has 171 amino acids, and its MW was 19.51165 kDa. The theoretical pI was predicted to be 9.66. The chemical formula of the vaccine construct was C875H1383N237O248S10, composed of 2753 atoms and 89.42 aliphatic index. For TM helix prediction, the server showed a number of predicted TMHs: 1. possible N-term signal sequence inside 1–20, TMhelix 21–38 and outside 39–171 (Figure 1a). GOR IV online server revealed that the alpha helix (Hh): 19 is 11.11%, extended strand (Ee): 66 is 38.60%, random coil (Cc): 86 is 50.29% (Figure 1b) and secondary structure predicted by PSIPRED online server (Figure 1c). This implies that the protein is suitable for vaccine design.
Enlarge this image.FIGURE 1. Results of retrieved sequence (a) TMHMM server, (b) results from Garnier–Osguthorpe–Robson (GOR IV) server and (c) 2D structure predicted by position-specific iterated prediction (PSIPRED) online server.
MHC I epitopes were predicted using the IEDB MHC I web-server fixed at the threshold value for epitope documentation. Among all the predicted MHC I epitopes, only seven epitopes were selected for vaccine construct based on their high scores binding affinity towards MHC I, antigenicity, non-allergenicity and non-toxicity, as shown in Table 1. MHC II binding peptides were predicted using the NN-align 2.3 (NetMHCII 2.3), IEDB MHC II server. A total of six MHC II epitopes were selected for the final vaccine construct on the basis of binding affinity, antigenicity, non-allergenicity and non-toxicity (Table 2).
TABLE 2 Prediction of major histocompatibility complex (MHC) II epitopes.
| MHC II | |||
| Epitopes | Antigenicity | Allergenicity | Toxicity |
| HLKVISLTY | 1.6335 | Non allergen | Non toxic |
| VSCNYVSYI | 1.1895 | Non allergen | Non toxic |
| KRASAPTAI | 0.5516 | Non allergen | Non toxic |
| ISLTYGRQF | 1.2993 | Non allergen | Non toxic |
| LKVISLTYG | 1.4699 | Non allergen | Non toxic |
| LQLSLYGGV | 0.6519 | Non allergen | Non toxic |
The predicted B-cell epitopes by BepiPred server with a cut-off binding score >0.7, high antigenic, non-allergenic and non-toxic resulted in two B-cell epitopes for final selection for vaccine construct as listed in Table 3.
TABLE 3 Prediction of linear B-cell epitopes.
| B cell epitopes | |||
| Epitopes | Antigenicity | Allergenicity | Toxicity |
| KYNAKIPNVSI | 0.9236 | Non allergen | Non toxic |
| GLVKKKNNIWVDVNSNSTVDMNKNTELSNIKKSSKGDI | 0.7491 | Non allergen | Non toxic |
The finally selected six MHC I and seven MHC II-binding epitopes and two B-cell epitopes were used to design the multi-epitope vaccine construct. An adjuvant 50S ribosomal protein L7/L12 (UniProt ID: P9WHE3) also added to the N-terminal of the vaccine construct. EAAAK linker's link adjuvant to the epitopes, AAY linkers to link MHC I epitopes, GPGPG linker link to MHC II epitopes, KK linkers were used to link B-cell, and at the C-terminal, 6× His tag was added. The constructed vaccine sequence was again tested for antigenicity, non-allergenicity, non-toxicity, solubility and fulfilling all the criteria. The schematic presentation of the final multi-epitope vaccine construct is shown in Figure 2a. TMHMM v2.0 server was used to check the TM helices of the structural proteins. The number of predicted TMHs was 1, possible N-term signal sequence inside 178–359, TMhelix 155–177 and outside 1–154 (Figure 2b). Generally, proteins displaying >1 TM helices are not considered suitable vaccine targets, as these proteins are difficult to purify and ineffective in cloning and expression. Therefore, we excluded those proteins in vaccine design.
Enlarge this image.FIGURE 2. (a) Vaccine construct; purple: adjuvant, yellow: EAAAK, black: major histocompatibility complex (MHC) I, sky blue: AAY, red: GPGPG, blue: MHC II, grey: KK, pink: B cell epitope, green: 6× His tag, (b) TMHMM server output.
The chemical formula of the multi-epitope vaccine construct is C1721H2738N448O495S8, containing 5410 atoms and 359 amino acids with an MW of 37.88186 kDa. The estimated pI of the vaccine construct was 9.29, which points out the alkaline nature of the vaccine candidate. The computed instability index was 20.92, aliphatic index of the construct was 93.54, and the GRAVY was 0.008, suggesting that the candidate vaccine is stable, polar in nature, and the candidate vaccine may be hydrophilic by nature and capable of interacting with the watery environment, as indicated by the GRAVY score of 0.008. The total number of negatively charged residues (Asp+ Glu) predicted to be 30, whereas the total number of positively charged residues (Arg+ Lys) was 43. The estimated half-lives in mammalian reticulocytes (in vitro), yeast cells (in vivo) and E. coli (in vivo) were 30, >20 and >10 h, respectively.
Antigenicity, allergenicity and toxicity prediction of vaccine constructThe antigenicity of the vaccine construct was analysed by VaxiJen v2.0 web tool, and the candidate antigen was found as a good antigen. In the VaxiJen v2.0 tool, the default threshold of 0.4 was chosen as the antigenicity criterion. The antigenicity score for multi-epitope vaccine was 0.5878 as predicted by the VaxiJen v2.0 server. The vaccine construct was found non-allergenic by AllerTOP v2.0. Protein Sol solubility revealed that the pI of the construct was 9.720, and the solubility was 0.592 (Figure 3). The solubility prediction using the SOLpro server when the protein structure is over-expressed in E. coli illustrated that the vaccine construct is soluble with a probability of 0.912393. The results indicate that the constructed vaccine was highly antigenic. The vaccine sequence on ToxinPred server revealed the vaccine construct as non-toxic.
Enlarge this image.FIGURE 3. Solubility analysis of vaccine constructs using ProtSol with a score of 0.592 upon expression.
The candidate vaccine was estimated to have 42.90% α-helix, 20.33% β-strand and 36.77% random coil. Figure 4a shows the graphical descriptions of the GOR IV server and Figure 4b shows the performance of the secondary structure obtained from the PSIPRED server.
Enlarge this image.FIGURE 4. (a) Graphical output from Garnier–Osguthorpe–Robson (GOR IV) server, (b) secondary structure prediction of vaccine constructs using the position-specific iterated prediction (PSIPRED) server having (42.90%) alpha helices, (20.33%) beta-strands and (36.77%) random coils.
The trRosetta server modelled five tertiary structures of the designed multi-epitopic vaccine protein. The selected model had an estimated TM score of 0.454, and Z-score was −3.05 by the ProSA-web-server (before refinement). The TM score indicates the structural similarity between two structures. A TM score of >0.5 indicates a model of correct topology, whereas a TM score of <0.17 suggests random similarity. 3D structure modelled by trRosetta was subjected to the PROCHECK server, where the Ramachandran plot was generated and the output revealed 93.7% residues that were present in the favoured region. The Ramachandran plot allows us to visualize energetically allowed and disallowed dihedral angles psi (ψ) and phi (φ) (Figure 5).
Enlarge this image.FIGURE 5. (a) Three-dimensional (3D) structure of vaccine construct, (b) predicted pre-residue local distance difference test (LDDT) for this model transmembrane (TM) 0.454, (C) Ramachandra plot before refinement.
The loop refinement and energy minimization were carried out for obtaining the high quality of the predicted structure. The GalaxyRefine web-server was used for the refinement of the initial ‘crude’ vaccine model that generated five model structures. Model 1 structure was the most significant one among all developed structures, based on several factors such as GDT-HA (0.9777), RMSD (0.338) and MolProbity (1.384). The other parameters such as the clash value were 6.0, the low rotamers-value was 0.0, and Rama favoured value was 97.8. This model 1 structure was also selected for additional study. trRosetta modelled 3D structure was subjected to the PROCHECK server, where the Ramachandran plot was generated and the output revealed 96% residues that were present in the favoured region (Figure 6a). The Ramachandran plot allows us to visualize energetically allowed and disallowed dihedral angles psi (ψ) and phi (φ). The Z-score for the input vaccine (after refinement) was found to be −3.27 by the ProSA-web-server (Figure 6b). The overall results from RAMPAGE and ProSA-web indicate the 3D modelled protein as of outstanding quality.
Enlarge this image.FIGURE 6. (a) Ramachandra plot before refinement, (b) Z-score by the ProSA-web-server.
One hundred eighty-five residues were present in four discontinuous B-cell epitopes, with values from 0.506 to 0.802. The size of the conformation epitopes ranged between 11 and 61 residues (Table 4). For the selection of the discontinuous peptides by Ellipro, the score value of the predicted peptides 0.66 or more was selected (Figure 7a–e and Table 5). Multiple discontinuous epitope residues were selected from vaccine sequence of varied epitope residues length such as 1–48 (48 epitope residues), between 65 and 106 (42 epitope residues), between 111 and 128 (18 epitope residues), between 270 and 281 (12 epitope residues) and between 301 and 359 (59 epitope residues). The score of each of the discontinuous epitopes has been shown in Figure 8.
TABLE 4 Predicted discontinuous epitope(s).
| No. | Residues | No. of residues | Score |
| 1 | A:M1, A:A2, A:K3, A:L4, A:S5, A:T6, A:D7, A:E8, A:L9, A:L10, A:D11, A:A12, A:F13, A:K14, A:E15, A:M16, A:T17, A:L18, A:L19, A:E20, A:L21, A:S22, A:D23, A:F24, A:V25, A:K26, A:K27, A:F28, A:E29, A:E30, A:T31, A:F32, A:V34, A:T35, A:A36, A:A37, A:A38, A:P39, A:V40, A:A41, A:V42, A:A43, A:A44, A:A45, A:G46, A:A47, A:A48, A:P49 | 48 | 0.802 |
| 2 | A:V65, A:I66, A:L67, A:E68, A:A69, A:A70, A:G71, A:D72, A:K73, A:K74, A:I75, A:G76, A:V77, A:I78, A:K79, A:V80, A:V81, A:R82, A:E83, A:I84, A:V85, A:S86, A:G87, A:L88, A:G89, A:L90, A:K91, A:E92, A:A93, A:K94, A:D95, A:L96, A:V97, A:D98, A:G99, A:A100, A:P101, A:K102, A:P103, A:L104, A:L105, A:V108, A:A109, A:E111, A:A112, A:A113, A:D114, A:E115, A:A116, A:K117, A:A118, A:K119, A:L120, A:E121, A:A122, A:A123, A:G124, A:A125, A:T126, A:V127, A:T128 | 61 | 0.762 |
| 3 | A:P248, A:G249, A:I308, A:P309, A:N310, A:V311, A:S312, A:I313, A:K314, A:K315, A:G316, A:L317, A:V318, A:K319, A:K320, A:K321, A:N322, A:N323, A:I324, A:W325, A:V326, A:D327, A:V328, A:N329, A:S330, A:N331, A:S332, A:T333, A:V334, A:D335, A:M336, A:N337, A:K338, A:N339, A:T340, A:E341, A:L342, A:S343, A:N344, A:I345, A:K346, A:K347, A:S348, A:S349, A:K350, A:G351, A:D352, A:I353, A:H354, A:H355, A:H356, A:H357 | 52 | 0.677 |
| 4 | A:G269, A:R270, A:Q271, A:F272, A:G273, A:P274, A:G275, A:P276, A:G277, A:L278, A:K279, A:V280, A:I281 | 13 | 0.644 |
| 5 | A:N216, A:G217, A:P218, A:G219, A:P220, A:H222, A:L223, A:K303, A:N305, A:A306, A:K307 | 11 | 0.506 |
Enlarge this image.FIGURE 7. Three-dimensional representation of conformational or discontinuous B cell epitopes of the designed multi-epitope based vaccine. (a–e) A yellow surface represents the conformational or discontinuous B cell epitopes, and the bulk of the polyprotein is represented in grey sticks.
TABLE 5 Discontinuous peptides and predicted liner epitopes using Ellipro.
| No. | Chain | Start | End | Peptide | No. of residues | Score |
| 1 | A | 1 | 48 | MAKLSTDELLDAFKEMTLLELSDFVKKFEETFEVTAAAPVAVAAAGAA | 48 | 0.813 |
| 2 | A | 65 | 106 | VILEAAGDKKIGVIKVVREIVSGLGLKEAKDLVDGAPKPLLE | 42 | 0.81 |
| 3 | A | 301 | 359 | KKKYNAKIPNVSIKKGLVKKKNNIWVDVNSNSTVDMNKNTELSNIKKSSKGDIHHHHHH | 59 | 0.671 |
| 4 | A | 111 | 128 | EAADEAKAKLEAAGATVT | 18 | 0.662 |
| 5 | A | 270 | 281 | RQFGPGPGLKVI | 12 | 0.66 |
Enlarge this image.FIGURE 8. The individual score of discontinuous B cell epitopes was predicted in the multi-epitope subunit vaccine.
Disulfide engineering was performed to stabilize the vaccine construct by particular geometric confirmations (Hasan et al., 2019; Pandey et al., 2018). A total number of 27 pairs of amino acid residues were predicted to form a disulfide bond by the DbD2 server. Of which, only six residues, including LEU 4-GLU 8, PHE 63-ALA 113, GLY 76-ALA 123, GLU 131-ALA 134, PRO 248-ASN 329 and ASP 335-LYS 338, were replaced by cysteine residues, enabling disulfide bond formation following the residue assessment by chi3 and B-factor energy parameters (Figure 9). Residue screening was performed on the basis of −87 to +97 chi3 values and <2.5 energy values.
Enlarge this image.FIGURE 9. (a) Initial model without disulphide bonds. (b) Mutant model; the yellow stick represents the disulphide bond formation.
The interaction between the immune cell and the vaccine construct is required for an effective and stable immune response. The Cluspro program yielded 10 different clusters, which had higher interaction energies. Based on the energy score, the first cluster was selected that had better energy than other clusters (Figure 10).
Enlarge this image.FIGURE 10. (a) Three-dimensional (3D) structure of Toll-like receptor 4 (TLR4), (b) docked product of TLR4 and vaccine construct by Cluspro.
The results of molecular dynamics simulation and normal mode analysis (NMA) of the vaccine construct and TLR4 docked complex are illustrated in Figure 11a. The simulation study was conducted to determine the movement of molecules and atoms in the vaccine construct. The deformability graph of the complex shows the peaks in the graphs, which represent the regions of the protein with deformability (Figure 11b). The eigenvalue of the complex is 1.059586e − 07 as shown in Figure 11c. The variance graph displays the cumulative variance as indicated by green colour and individual variance by purple colour (Figure 11d). The B-factor graph gives a clear visualization of the relation of the docked complex between the NMA and the PDB sector (Figure 11e). The covariance map of the complex indicates the correlated motion between a pair of residues, which is indicated by red colour, uncorrelated motion indicated by white colour and anti-correlated motion by blue colour (Figure 11f). The complex's elastic map shows the relation between the atoms and darker grey regions, indicating stiffer regions (Figure 11g).
Enlarge this image.FIGURE 11. The of molecular dynamics simulation results: (a) normal mode analysis (NMA) mobility, (b) deformability, (c) eigenvalues, (d) variance, (e) B-factor, (f) co-variance map (correlated (red), uncorrelated (white) or anti-correlated (blue) motions) and (g) elastic network (darker grey regions indicate stiffer regions).
JCat was used for maximal protein expression in E. coli (strain K12) and D. melanogaster. The optimized codon sequence contained 1077 nucleotides. Initial CAI value of pasted sequence was 0.3383, and GC content was 66.48%. After adaptation, improved CAI value for E. coli (strain k12) was 0.4857, GC content was 66.48%, and GC content of E. coli was 50.73%. For D. melanogaster, CAI of pasted sequence was 0.8879, and GC content was 66.4809%. After adaptation improved CAI value was 0.9308, GC content 66.945% and GC content of D. melanogaster was 50.73%. Based on accuracy, the expression system of TLR4 and higher CAI value D. melanogaster was chosen as host. To perform in silico cloning, the multi-epitope vaccine construct was examined for restriction enzyme sites, including the PpuMI and TatI restriction sites, and was used for in silico cloning in the pET29a (+) vector. A construct of 5199 bp was achieved after the introduction of the vaccine sequence into the pET29a (+) vector using SnapGene software (Figure 12).
Enlarge this image.FIGURE 12. Expression vector pET29a (+). In silico restriction cloning of the multi-epitope vaccine sequence into the pET29a (+) expression vector using SnapGene software free-trial (https://www.snapgene.com/free-trial/).
The immune simulation of the vaccine construct was performed with C ImmSim server, which studies the successive and effective immune responses of the state of the cell and the memory of immune cells by a mechanism that increases their half-life. ImmSim server immune simulation outcomes confirmed consistency with real immune reactions, as illustrated by high IgM levels in primary response. Moreover, there was an increase in the B-cell population, characterized by an increase in the immunoglobulin (IgG1 + IgG2, IgM and IgG + IgM) expression, resulting in a decrease in antigen concentration (Figure 13a,b). There was a clear increase in the population of helper and cytotoxic T-cells with increased memory (Figure 14a,b). An increased IFN-γ production and active B-cell population were identified after immunization (Figure 14c,d).
Enlarge this image.FIGURE 13. In silico immune simulation of vaccine construct: (a) immunoglobulin production in response to antigen injections (black vertical lines); specific subclasses are shown as coloured peaks, and (b) the evolution of B-cell populations after the three injections.
Vaccines are important tools for the host defence against particular pathogen. The conventional vaccine development process is extremely meticulous, expensive and time-consuming as the identification of immunodominant protein of a pathogen is tedious process. With the advent of immunoinformatics tools, the design and development of a vaccine have become fast and successful (Shilpa & Shivakumar, 2022). Immunoinformatics tools help in detecting the immunogenic proteins with diverse immune-dominant epitopes, which can stimulate both humoral and cell-mediated immune responses against the pathogen (Mugunthan & Mani Chandra, 2021). Therefore, a multi-epitope-based peptide vaccine can be easily designed with immunogenic proteins of a pathogen. Recently, a large number of peptide vaccines are under development, majority of which are for human infectious diseases and tumours (Kar et al., 2020; Kolesanova et al., 2013, 2019; Rapin et al., 2010). However, a limited number of studies are reported in the field of in silico vaccine development for poultry and animals. Multi-epitope vaccine development for animal diseases such as foot and mouth disease (Liu et al., 2017) and animal trypanosomiasis (Michel-Todó et al., 2020) have been reported, which offered effective immunity as evaluated with currently available vaccines (Mugunthan & Harish, 2021). Several studies utilized the immunoinformatics tools to design multi-epitope vaccines against infectious poultry diseases (Hasan et al., 2019; Ingale & Goto, 2014; Osman et al., 2016; Unni et al., 2019).
By the use of immunoinformatics tools, evaluation of complete antigenic epitopes and molecular modelling for the probable binding with host proteins are possible (Jabbar et al., 2018; Tosta et al., 2021). The in silico validations like molecular docking and in silico cloning and immune simulation were included in this study to design and develop multi-epitope vaccine construct against LSDV infection (Ashfaq et al., 2021; Behbahani et al., 2021; Hossain et al., 2021; Sanches et al., 2021). The purpose of multi-epitope vaccine is to enhance the humoral and cell-mediated immune response by identifying the specific MHC I, MHC II and B-cell epitopes from the antigenic proteins. The selected proteins were found antigenic, non-allergic and non-toxic during initial analyses of the vaccine construct. A good vaccine candidate should initiate proper immune response without an allergic reaction, and expectedly, our vaccine construct was antigenic but non-allergic in nature. Vaccine response can be enhanced by incorporating adjuvant in the vaccine construct (Shey et al., 2019); therefore, 50S ribosomal protein L7/L12 (UniProt ID: P9WHE3) was used as an adjuvant at the N-terminal followed by a sequence of various MHC I, MHC II and B-cell epitopes present in the peptide vaccine (Henderson & Jensen, 2006). In this study, the MW of the vaccine construct and the theoretical pI signifies the basic nature of the vaccine construct. The aliphatic index and low GRAVY score indicate the thermostable and hydrophilic nature of the vaccine candidate (Abdi et al., 2022; Susithra Priyadarshni et al., 2022; Validi et al., 2018). Moreover, the GRAVY value 0.008 of the vaccine suggesting that the vaccine may interact with water (Abdi et al., 2022), and this vaccine is ideal for its usage in endemic areas (Bachmann & Jennings, 2010; Omoniyi et al., 2022). The secondary structure analysis of the vaccine construct indicated that the alpha-helices dominating the structure followed by random coil and beta sheets, which are suggestive of the existence of natively unfolded protein regions that can be identified by antibodies, produced in response to infection (Bibi et al., 2021). The tertiary structure of the construct was selected based on the highest TM score, and our construct had a lower RMSD score of 0.338, indicating better stability (Yang et al., 2021). The structure was further refined, and a better structure was obtained after refinement. According to Ramachandran plot, 96% residues were present in the favoured region, which is higher than the acceptable range (Omoniyi et al., 2022; Yang et al., 2021). The Z-score determined by ProSA indicates overall model quality, in which the negative values signify that no components of the structural model are flawed (Shantier et al., 2022). The estimated Z-score for the designed vaccine candidate was −3.27, indicating that it is satisfactory. The predicted discontinuous and continuous B-cell epitopes of the vaccine construct showed possible interaction with antibodies and were flexible. Furthermore, disulfide engineering was performed to stabilize the vaccine construct. The interaction prototype of the vaccine construct with TLR4 was analysed by molecular docking, which indicated a better interaction and the docked complex was energetically viable. Expression systems play a crucial role in the serological immunoreactivity screening process that is also critical for vaccine validation. Codon optimization was carried out in D. melanogaster to ensure that the vaccination protein was fully expressed (Akmammedov et al., 2017; Gori et al., 2013). Codon optimization was done for the D. melanogaster strain; the estimated GC content and CAI indicated enhanced transcriptional and translational efficiency (Omoniyi et al., 2022). A D. melanogaster-based expression system selected because it enables recombinant protein production on a large scale at a low cost (Omoniyi et al., 2022). The expression of codon was adequate, and the administration of the multi-epitope vaccine induced a strong immune response. The immune response to the chimeric vaccine was comparable with actual immune responses with higher tertiary and secondary responses. Current commercial vaccines are widely employed in commercial farms; nevertheless, these vaccines cannot aid in control during the abrupt onset of LSD infection; rigorous biosecurity must be observed to manage and eradicate the infection. The current study addresses the newly formulated multi-epitope vaccine candidate using robust immunoinformatics tools, and the results show that the designed vaccine construct has every aspect of being developed as an effective vaccine candidate, such as immune specificity, small size and the absence of adverse effects.
Enlarge this image.FIGURE 14. In silico immune simulation of vaccine construct. (a) The evolution of T-helper and (b) cytotoxic cell populations per state after the injections. The resting state represents cells not presented with the antigen, whereas the anergic state characterizes tolerance of the T-cells to the antigen due to repeated exposures. (c) The main plot shows cytokine levels after the injections. The insert plot shows IL-2 level with the Simpson index, D shown by the dotted line. D is a measure of diversity. Increase in D over time indicates emergence of different epitope-specific dominant clones of T-cells. The smaller the D value, the lower the diversity. (d) Active B cell population state after vaccination.
This study uses immunoinformatic tools to design and develop a multi-epitope vaccine against LSDV. The candidate vaccine was found highly immunogenic, non-allergenic, non-toxic and antigenic with a high affinity for the TLR4 immune receptor. Furthermore, the simulated immune response showed cellular and humoral immune responses, as well as efficient memory cell development. Although this study should provide a platform for new vaccine development, however, these in silico findings should be further validated by in vitro and in vivo studies before further advancement.
AUTHOR CONTRIBUTIONSConcept and investigation: Md. Salauddin. Data arrangement and analysis: Md. Salauddin. Initial draft: Md. Salauddin, Mohammad Enamul Hoque Kayesh and Md. Suruj Ahammed. Critical review and writing the final version of the manuscript: Md. Salauddin, Mohammad Enamul Hoque Kayesh, Sukumar Saha and Md. Golzar Hossain. All authors approved the final version of the manuscript.
CONFLICT OF INTEREST STATEMENTThe authors declare no conflicts of interest.
FUNDING INFORMATIONThe authors would like to express their gratitude to City Bank for providing a research grant (Project No.: 2023/13/Other) through Bangladesh Agricultural University.
DATA AVAILABILITY STATEMENTAll the data are available in the manuscript.
ETHICS STATEMENTThis is a bioinformatics based study. Ethical approval is not required.
PEER REVIEWThe peer review history for this article is available at
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Details
; Mohammad Enamul Hoque Kayesh 2
; Ahammed, Md Suruj 3
; Saha, Sukumar 4 ; Hossain, Md Golzar 4
1 Department of Microbiology and Public Health, Khulna Agricultural University, Khulna, Bangladesh
2 Department of Microbiology and Public Health, Patuakhali Science and Technology University, Barishal, Bangladesh
3 Department of Chemistry, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
4 Department of Microbiology and Hygiene, Bangladesh Agricultural University, Mymensingh, Bangladesh