Contriving a multi-epitope vaccine against African swine fever utilizing immunoinformatics

doi:10.21203/rs.3.rs-1978238/v1

African swine fever (ASF), a highly fatal haemorrhagic viral disease of domestic pigs has been ravaging swine industries in affected countries. Effective management of this malady is hamstrung by lack of protective vaccines. A cost-effective avenue to develop potent ASF vaccines is by harnessing immunoinformatic tools to construct a multi-epitope broad spectrum subunit vaccine. Therefore, CD8 + T-cell, CD4 + T-cell, B-cell and IFN-γ epitopes of the ASF virus major coat protein p72, CD2 homologue (CD2v) and C-type lectin-like proteins which are good vaccine candidates were computationally appended to develop ASF multi-epitope subunit vaccine. Molecular docking and molecular dynamic simulation were employed to assess the interaction between the vaccine construct and immune receptors Toll-like-9 (TLR-9) and the Swine Leukocyte Antigen-1 (SLA-1) and stable interactions were observed between the vaccine construct and immune receptors. in silico cloning and codon optimization were used to bolster the efficient expression of the vaccine in an E. coli expression system. The efficacy of the vaccine to provoke effective immune responses was assessed using in silico immune simulation. All these computational approaches revealed that the designed vaccine is structurally stable and capable of inducing both humoral and cell-mediated immune responses against ASF.

African swine fever (ASF) is a highly contagious, fatal haemorrhagic disease of domestic pigs and wild boars (Sus scrofa) with detrimental effect on the development of the swine industry in affected countries. The disease was first described in Kenya in 1921 (1) and it is now enzootic in sub-Saharan Africa, the Caucasus, the Russian Federation, and Eastern Europe and recently China (2, 3, 4, 5, 6). Currently, there is no vaccine or effective therapeutics for proper management this malady and as such, culling infected pigs is the only method of disease control. African swine fever pandemic has resulted in about $2 billion loss to the swine industry worldwide (6). The causative ASFV belongs to the family Asfarviridae, the genus Asfarvirus and it is the only known DNA-containing arbovirus (7). African swine fever virus is a nucleocytoplasmic large double-stranded DNA virus with a diameter of about 200 nm. The virion is made up of an enclosed nucleoid and core shell, icosahedral inner capsid, an icosahedral membrane, the outer capsid and outer envelope (8, 9). A plethora of proteins such as p72, p49, and p17 are involved in the formation of the outer capsid but p72 encoded by B646L gene is the major capsid protein of ASFV. It is a vital target for ASF vaccine development and has been used to characterize the virus into genotypes (8, 10). The outer envelope harbors the virus attachment protein p12, the EP402R and EP153R gene products CD2v and C-type lectin, respectively. CD2v is a homologue of CD2, a cell adhesion molecule expressed by T-lymphocytes (T-cells) and natural killer (NK) cells (11; 12). CD2v and C-type lectin proteins have been demonstrated to be responsible for the hemadsorption propensity of the ASFV and this has been employed in haemadsorption inhibition (HAI) serological typing of ASFVs into eight serogroups, although more serogroups might still be existing undetected (13, 14). These serotype-specific proteins CD2v and/or C-type lectin have also been revealed to be crucial for protection against homologous ASF infection (14) indicating that ASF protective immunity may be serotype specific. Since there is no effective vaccine against ASF currently, and taking into cognizance the immunogenicity of p72, CD2v and C-type lection proteins; a pragmatic approach is the development of a multi-epitope vaccine against the virus. Multi-epitope vaccines are advantageous over classical vaccines because of the following features: (I) they introduce some components of innate immune response like IFN-γ epitopes and also adjuvant capacity that can enhance the immunogenicity and long-lasting immune responses; (II) they consist of cytotoxic T- lymphocytes (CTL), T-helper (Th) and B-lymphocyte (B-cell) epitopes. As such, the problem of the presence of various ASFV serotypes/genotypes in vaccine development could be resolved, (III) they reduce unwanted components that can provoke hypersensitivity and immunopathologic responses and very importantly (IV) they possess multiple major histocompatibility (MHC)-restricted epitopes that can be recognized by T-cell receptors (TCRs) of multiple clones from various T-cell subsets. The presentations of viral peptides in the context of the MHC class I and class II are essential for anti-viral T-cell responses (15). Unlike B-cell epitopes responsible for eliciting neutralizing antibodies, T-cell epitopes can be located anywhere in a viral protein since cells can process and present both intracellular and extracellular viral peptides (16). MHC class I–restricted T-cell epitopes are usually from intracellular antigens that are cleaved by the proteasome and transported into the endoplasmic reticulum by transporter associated with antigen processing proteins (TAP), where they can bind to MHC class I molecules that get transported to the cell surface, where they can be recognized by CD8 + T-cells. However, MHC class II–restricted T-cell epitopes are primarily derived from extracellular proteins taken up by professional antigen presenting cells that are cleaved in lysosomal vesicles, where they can bind to MHC class II molecules, be transported to the cell surface, and thereafter recognized by CD4 + T-cells (17, 18, 19). Vaccine induced CD8 + T-cell-mediated immune response might be long-lived and cross-serotype and thus deserves further attention (20). The swine MHC designated swine leukocyte antigen (SLA), consisting of SLA class I loci (SLA-1, SLA-2, SLA-3) and SLA class II loci (DRB1, DQB1, DQA) (21, 22) has been shown to be involved in swine immune response to various viral infections such as Foot-and-mouth disease virus and influenza-A viruses (15, 23). This is possible because of the presence of six pockets (A to F) in the peptide-binding groove (PBG) of SLA-1 that viral peptides could interact with (24). Since the mainstay of development of an efficacious multi-epitope vaccine is the selection of appropriate candidate antigens and their immunodominant epitopes; these sites can be easily selected using immunoinformatics. Therefore, immunoinformatic approach is a good alternative for streamlining a cheap and easy experimental approach since experimental approach in selection of B- and T-cell epitopes is time consuming and expensive. Immunoinformatics employs computational methods of artificial intelligence such machine learning, artificial neural network and deep learning to predict B- and T-cell epitopes. Herein, B- and T-cells of ASFV CD2v, C-type lectin and p72 epitopes were predicted and validated using immunoinformatic methods. These epitopes were employed to construct and validate the ASFV multi-epitope vaccine. These findings may provide a basis for development other multi-epitope vaccines.

Analysis of the selected three structural proteins. The best consensus sequences of CD2v (AJB28366), C-type like protein (AAC28412) and p72 (QID21249) (Supplementary material 1) after multiple sequence alignments and Blast-p analysis were used for screening epitopes using VaxiJen v2.0. VaxiJen v2.0 antigenicity scores showed all the three proteins as antigenic. p72 is the most antigenic with the score 0.5063 followed by C-type lectin protein with 0.4583 and CD2v with 0.4171.

T-cell epitopes predictions. Selection of very immunogenic CTL and HTL is very important for the vaccine to produce long lasting immunity. In particular, CTL response is more important for effective viral clearance. On the other hand, HTL epitopes play a crucial role in generating both humoral and cellular immune responses. The extracellular domains of CD2v, C-type lectin and p72 were submitted to IEDB server to predict (10mer) CTLs and (15mer) HTLs epitopes, applying the IEDB recommended 2020.04 (NetMHCpan EL 4.0) and IEDB recommended 2.22 (Consensus) prediction methods, respectively. Predicted CTL (Supplementary Tables 1–3) and HTL (Supplementary Tables 4–12) epitopes with strong binding affinities (based on low percentile rank) were screened further using allele criteria promiscuity, antigenicity and non-toxicity.to select the best epitopes. Using these criteria, 10 SLA-I (CD2v-1, C-type lectin-4, and p72-5) and 3 MHC-II (CD2v-1, C-type lectin-1, and p72-1) (Tables 1 and 2).

Table 1

CTL epitopes predicted using NetMHCpan 1.2 revealing promiscuity. VaxiJen v2.0 was used for predicting antigenicity scores keeping a threshold of 0.4 and ToxiPred was used to predict toxicity.
Protein		SLA I
CD2v	Peptides (positions)	Alleles	Antigenicity	Toxicity
	TNKSFLNYYW (151–160)	SLA-20402, SLA-10501, SLA-2*0401	0.5346	Non-toxic
C-type lectin	KYTGLIDKNY (6–15)	SLA-20102, SLA-10501	1.1132	Non-toxic
	FSNNIDEKNY (81–90)	SLA-2*1002	0.4413	Non-toxic
	KKYNYESGYW (116–125)	SLA-10501, SLA-30701, SLA-30301, SLA-30303, SLA-3*0401	1.0042	Non-toxic
	KKVNYTGLLF (145–154)	SLA-10401, SLA-30601, SLA-30301, SLA-30303, SLA-30304, SLA-10801, SLA-3*0701	1.2916	Non-toxic
p72	YGKPDPEPTL (40–49)	SLA-11101, SLA-30101, SLA-20701, SLA-20501, SLA-30501, SLA-30502, SLA-30503, SLA-60101, SLA-60102, SLA-60103, SLA-60104, SLA-60105	1.4480	Non-toxic
	KPYVPVGFEY (65–74)	SLA-10701, SLA-10702, SLA-20102, SLA-20101, SLA-10501, SLA-20302, SLA-20401, SLA-20402.	1.3466	Non-toxic
	SVSIPFGERF (347–356)	SLA-11201, SLA-10201, SLA-10202, SLA-20302, SLA-10601, SLA-20501, SLA-20101, SLA-10401, SLA-10501, SLA-21001.	0.8502	Non-toxic
	SRRNIRFKPW (387–396)	SLA-30701, SLA-30301, SLA-30303, SLA-30304, SLA-30401, SLA-30601, SLA-2*0402.	1.9015	Non-toxic
	SISDISPVTY (521–530)	SLA-21002, SLA-10401, SLA-20302, SLA-11301, SLA-10801, SLA-10601, SLA-21001, SLA-20401, SLA-10201, SLA-10202.	1.5700	Non-toxic

Table 2

HTL epitopes predicted using consensus revealing promiscuity. VaxiJen v2.0 was used for predicting antigenicity scores keeping a threshold of 0.4 and ToxiPred was used to predict toxicity.
MHC II
Protein	Peptides (position)	Alleles	Antigenicity	Toxicity
CD2v	LVYSRNRINYTINLL (96–110)	HLA-DRB302:02, HLA-DRB301:01, HLA-DRB115:01, HLA-DRB107:01, HLA-DRB103:01, HLA-DRB501:01, HLA-DRB4*01:01	0.8727	Non-toxic
		HLA-DPA101:03/DPB108:01, HLA-DPA101:03/DPB105:01, HLA-DPA101:03/DPB101:01, HLA-DPA101:03/DPB102:02, HLA-DPA101:03/DPB106:01, HLA-DPA101:03/DPB103:01, HLA-DPA101:03/DPB104:02.
		HLA-DQA101:01/DQB102:05, HLA-DQA101:01/DQB102:01, HLA-DQA101:01/DQB102:02, HLA-DQA101:01/DQB102:04.
C-type like lectin	TKKYNYESGYWVNYS (115–129)	HLA-DRB301:01, HLA-DRB115:01, HLA-DRB302:02, HLA-DRB107:01, HLA-DRB501:01, HLA-DRB103:01, HLA-DRB4*01:01	1.0513	Non-toxic
		HLA-DPA101:03/DPB104:01, HLA-DPA102:01/DPB1105:01, HLA-DPA103:02/DPB104:01, HLA-DPA104:01/DPB1126:01,
		HLA-DQA105:01/DQB105:06, HLA-DQA103:03/DQB106:28, HLA-DQA101:02/DQB103:14,
p72	HHAEISFQDRDTALP (502–516)	HLA-DRB401:01, HLA-DRB301:01, HLA-DRB103:01, HLA-DRB115:01, HLA-DRB302:02, HLA-DRB107:01, HLA-DRB5*01:01	2.0206	Non-toxic
		HLA-DPA101:06/DPB176:01, HLA-DPA102:04/DPB141:01, HLA-DPA103:01/DPB1109:01, HLA-DPA101:03/DPB103:01.
		HLA-DQA101:01/DQB102:01, HLA-DQA101:04/DQB104:03, HLA-DQA101:02/DQB105:12.

B-cell epitopes predictions. Linear B cell epitopes were selected using ABCpred server whereas discontinuous B cell epitopes were predicted from the proteins using IEDB Discotope 2.0. The predicted linear B cell epitopes were ranked according to their scores obtained by trained recurrent neural network. Higher score of the peptide indicates the higher probability to be an epitope. Out of the 14mers generated (Supplementary Tables 13–16) from input sequences, 6 B-cells (CD2v-1, C-type lectin-4, and p72-2 as linear epitopes; p72-2 as discontinuous epitopes) were short listed based on their scores and VaxiJen antigenicity (Tables 3 and 4).

Table 3

B-cell receptors linear epitopes prediction with ABCpred server. Antigenicity and toxicity were predicted using VaxiJen and ToxiPred, respectively.
Protein	Peptides (position)	Score	Antigenicity	Toxicity
CD2v	SLITCEKTNGTNIR (125–138)	0.68	0.6030	Non-toxic
C-type lectin	NDTNLLNLTKKYNY (107–120)	0.87	1.1367	Non-toxic
p72	CSHTNPKFLSQHFP (268–281)	0.68	0.6627	Non-toxic
p72	DITPITDATYLDIR (294–307)	0.58	1.7313	Non-toxic

**IFN-γ epitopes prediction.** IFNepitope server was employed to predict out the IFN-γ epitopes. Predicted epitopes with strong binding affinities (based on low percentile rank) were screened further using allele criteria promiscuity, antigenicity and non-toxicity to select the best epitopes (Supplementary Tables 17–19). Using these criteria, 3 IFN-γ (CD2v-1, C-type lectin-1, and p72-1) were selected (Table 5).

Table 4

B-cell receptors discontinuous epitopes with Discotope 2.0. Antigenicity and toxicity were predicted using VaxiJen and ToxiPred, respectively.
Protein	Peptides (position)	Antigenicity	Toxicity
p72	FPGLFVRQSRFIAGR (371–385)	0.4942	Non-toxic
	SRRNIRFLPWFIPGV (387–401)	1.5164	Non-toxic

Table 5

IFN-γ epitopes predicted against 3D structure of the ASFV proteins CD2v, C-type lectin and p72. Antigenicity and toxicity of the epitopes were predicted using VaxiJen and ToxiPred servers, respectively.
Protein	Peptides (position)	Score	Antigenicity	Toxicity
CD2v	TVTLNSNINSETEGI (25–40)	12	0.2457	Non-Toxic
C-type lectin	ESGYWVNYSLANNQS (120–135)	1	0.7730	Non-Toxic
p72	SNIKNVNKSYGKPDP (30–45)	6	1.0862	Non-Toxic

Multi‑epitope subunit vaccine construction, structural modelling and validation. The vaccine construct was developed by joining epitopes with the highest prediction scores. A total of 22 epitopes comprising 10 CTL epitopes, 3 HTL, 6 B-cell epitopes and 3 IFN-γ epitopes were selected based on their prediction score and were joined together using their corresponding linkers. HTL epitopes, B‐cell epitopes, IFN-γ and CTL epitopes were joined together using GPGPG, KK and AAY linkers, respectively. To boost the vaccine construct immunogenicity, a 45 amino acid‐long sequence of Sus scrofa β‐defensin was added as an adjuvant at the N‐terminal of the construct with the aid of EAAAK linker. EAAAK linkers aid in limiting the interaction of the adjuvant with other protein regions with efficient separation and bolstering stability (25). At the C terminal end TAT protein which is cell penetrating was appended to the construct to help it in intracellular delivery (Fig. 2A). The final vaccine construct was 437 amino acid residues long and was visualized using PyMol (Fig. 2B).

Vaccine construct’s physicochemical parameters. ExPASy ProtParam tool revealed various physiochemical properties of the vaccine construct. The construct is composed of 437 amino acid residues with a molecular weight of 48991.45 g/mol, theoretical pI of 9.84, revealing that the vaccine construct is basic. The instability index (II) score of 34.91 classifies the vaccine construct as being a stable protein in nature since values above 40 are regarded as being unstable in nature. The GRAVY index of -0.708, validates the hydrophilic nature of the vaccine construct revealing that it can form interactions with surrounding water molecules. The aliphatic index 59.70 shows that the construct is thermostable in nature. The estimated half-life was 1 h (mammalian reticulocytes, in vitro), 30 min (yeast, in vivo) and greater 10 hours (E. coli, in vivo) (Supplementary material 2). The antigenicity of the vaccine construct was predicted using VaxiJen server and was found to be a good antigen with a score of 0.6782 with the default set threshold value of 0.4 for viruses. Furthermore, AlgPred server was employed to determine the allergenicity of the vaccine construct. using a hybrid approach (SVMc + IgE epitope + ARPs BLAST + MAST). The vaccine construct was observed to be non-allergenic. The 3D structure of the vaccine construct was modelled from its linear structure using trRosetta; then, the modelled structure was refined using ModRefiner. Thereafter, the 3D structure was validated using ERRAT score at the ERRAT server (https://servicesn.mbi.ucla.edu/ERRAT/) which evaluates the calculation of unbounded interactions in the vaccine construct structure (Fig. 2C). Ramachandran plot analysis was also generated through ERRAT server (Fig. 2D). This was proceeded by ProSA-web analysis to validate the structure further based on Z-score predicted (Fig. 2E).

Molecular docking of vaccine construct (ligand) with immune receptors (TLR-9) and SLA-1. To optimize the interaction affinity between the vaccine construct and TLR‐9 (PDB:3WPB)/SLA-1 (PDB: 3QQ4), molecular docking was carried out using Haddock 2.4 server. For vaccine construct-TLR-9 interaction, the server clustered 138 structures in 12 cluster(s), which represents 69% of the water-refined models generated. The top cluster is the most reliable according to HADDOCK. Its Z-score of -1.6, indicates how many standard deviations from the average this cluster is located in terms of score. The HADDOCK score was − 30.2 +/- 8.2. The best cluster was selected from the docked clusters depending on lowest HADDOCK score. Thereafter, HADDOCK Refinement Interface was employed to refine the chosen cluster wherein HADDOCK clustered 10 structures in 1 cluster(s), which represents 100% of the water-refined models HADDOCK generated. The top cluster had a Z-score of 0.0 and HADDOCK score of -171.8 +/- 4.1. Docking of the vaccine construct-SLA-I receptors was also carried out using HADDOCK 2.4. HADDOCK clustered 154 structures in 6 cluster(s), which represents 77% of the water-refined models HADDOCK generated. The top cluster had a Z-score of -1.7 and HADDOCK score of -84.0 +/- 5.9. When HADDOCK Refinement was carried out, HADDOCK clustered 10 structures in 1 cluster(s), which represents 100% of the water-refined models HADDOCK generated. The top cluster had a Z-score of 0.0 and HADDOCK score of -106.5 +/- 3.0. The best structure after refinement from each docked complex were chosen and their binding affinity was determined using PRODIGY web server (Table 6). The low binding energy signifies the formation of a strong complex. Docking interaction between vaccine construct and TLR-9 revealed 14 H-bonds and 5 salt bridges that are involved in the interaction. In a like manner, docking analysis between vaccine construct and SLA-1 uncovered 14 hydrogen bonds and 4 salt bridges formed during the interaction (Fig. 3A). Also, in the docked vaccine construct-SLA-1 complex, the vaccine construct interactions are mainly with the PBG pockets A, E and F. In pocket A, the vaccine construct K427 interacted with the SLA-1 E63 through H-bond and salt bridge; L163 of the vaccine construct interacted with the L163 through non-bonded interactions. In the E pocket, E416 of the vaccine construct interacted with the R156 through H-bond and salt bridge; also, R413 of the vaccine construct interacted with E152 and R114 through salt bridge and non-bonded interactions, respectively. In the F pocket, the vaccine construct interacted P405 interacted with T80 through H-bond and non-bonded interaction, while P406 interacted with K146 through H-bonding. Also in the F pocket, T398 of the vaccine construct bond to T73 through H-bond, R413 of the vaccine construct bond to T74 through H-bond and non-bonding interactions, with T147 through non-bonded interactions and R114 through non-bonded interaction (Fig. 3B).

Table 6

The binding affinity (ΔG) and dissociation constant (K_d) predicted values of the docked complexes vaccine construct-TLR-9 and vaccine construct-SLA-1 at 25.0℃. The prediction was carried out at PRODIGY server.
Vaccine construct-Receptor complex	Gibbs free energy (kcal mol^− 1)	Kd (M)
Vaccine construct-TLR-9	-17.0	3.5E-13
Vaccine construct-SLA-1	-8.9	3.1E-07

Molecular dynamics simulation. To estimate the stability of the vaccine construct- immune receptor complexes, molecular dynamic simulation of the refined docked complexes (vaccine constrcuct-TLR-9 and vaccine construct-SLA-1) were carried out using GROMACS. The compactness of the two simulation systems around their protein axes was revealed by the radius gyration plots. Vaccine construct-TLR-9 and vaccine construct-SLA-1 complexes had the mean gyration values of 4.0 nm and 4.5 nm, respectively (Fig. 4A-B). This result indicates a relatively stable folded structure of the two complexes. For the density of the docked complexes, vaccine construct-TLR-9, the simulation system had 1007.78 kg/m³ with a total drift of 0.086 kg/m³ while that of the vaccine construct-SLA-1 was 1000.69 kg/m³ with a total drift of 0.073 kg/m³(Fig. 4C-D). The temperature and pressure of the simulation system during the production run was around 300 K and 1.3 bar, respectively for the two complexes showing a stable system and successful molecular dynamics simulation run. (Figs. 4E–H). The RMSD of vaccine construct-TLR-9 complex had some fluctuations during the simulation (Fig. 4I). Vaccine construct-SLA-1 had fluctuations 0–4 ns, thereafter, it became stable and the RMSD value remained around 1.1 nm, indicating that the conformation of this complex was stable (Fig. 4J). RMSF plots for the two complexes had high peaks indicating high degrees of flexibility in the vaccine construct (Fig. 4K-L).

Codon optimization of the vaccine construct and in silico cloning. Codon adaptation carried out using JCat was used to optimize the codon usage of the vaccine construct in E. coli (strain K12) and to obtain the gene sequence of the vaccine construct. Codon adaptation index was 0.92, GC content was 53.01% and the optimized codon sequences were 1311 nucleotides long. To obtain good protein expression, the GC content is expected to be in the range of 30%-70%; Therefore, the GC content of 53.01% is indicative of vaccine construct good expression in E. coli. To ensure translation efficiency, RNAfold web server (26) was employed to predict the secondary structure of the optimized codon sequences. The predicted secondary structure of the optimized sequence was found to be stable and it lacked hairpin loop or pseudoknot at its starting site (Fig. 5A). Certain codon features like restriction enzyme cleavage sites (BamHI and EcoRI) and prokaryotic ribosome binding sites in the E. coli plasmid pET-28(+) using the SnapGene software (https://www.snapgene.com/) were also taken into cognizance to ensure successful expression of the gene sequence. The whole clone was 6665 bp in size (Fig. 5B). For immune simulation, antibody response was observed with high levels of IgM and IgG antibodies which is common in virus vaccinations (Fig. 5C). Also, high levels of cytokine, T-helper cells and cytotoxic T-cells were observed (Fig. 5D-F). These observations point to an evidence a potent immune response that would be induced with the vaccine construct.

African swine fever is a devastating disease of domestic and wild pigs with serious socioeconomic consequences in affected countries. It has been over 100 years since it was first reported by Montgomery in 1921, yet the disease is still ravaging swine population due lack effective vaccines and ineffective control measures. Development of an effective vaccine against ASF is hampered by various genotypes and serotypes of the causative virus. Therefore, development of an effective and cheap ASF vaccine using convention approach is not a short-term project. There have several efforts at developing ASF vaccine using convectional approaches. For instance, a candidate genetically modified live attenuated vaccine was developed by deleting the I177L gene from the genome of a highly virulent ASFV strain (27). Other researchers (28) have also developed a vaccine candidate by deleting the E184L gene from the highly virulent ASFV Georgia 2010 (ASFV-G) isolate that resulted in a reduction in virus virulence during the infection in pigs. However, development of effective and cost-efficient ASF vaccine development can be streamlined using immunoinformatics approaches. This approach involves the screening the viral proteome to select immunogenic epitopes that induces highly targeted immune responses. These immunogenic peptides can be joined together to produce a multiepitope vaccine. The multiepitope vaccine designed in this study is more advantageous than live attenuated vaccine and other recombinant vaccines because it is a combination of the main epitopes of the virus with the absence of peptides capable of inducing allergic reaction or other immunopathologies. The peptides in the vaccine construct have capabilities of inducing innate, humoral and cell-mediated immune responses (Fig. 5). There are various reports on the use of immunoinformatic to develop vaccines against various diseases especially COVID-19 (29, 30, 31) but no report yet on ASF. As such, this study was embarked upon to design a multiepitope vaccine against ASFV using immunoinformatics tools. Vaccine candidate peptides were selected form HTL, B cell, IFN‑γ and CTL epitopes from conserved amino acid sequences of ASFV immunogenic CD2v, C-type lectin and p72 based on their antigenicity, toxicity, and allergenicity. The reliability of the immunogenic peptide selection procedure was authenticated by the observation of two motifs CSHTNPKFLSQHF and DITPITDATY present in p72 immunogenic peptides 268-CSHTNPKFLSQHFP-281 and 294-DITPITDATYLDIR-307 selected from this study and p72 monoclonal antibody–mapped epitopes 265-QRTCSHTNPKFLSQHF-280 and 290-AGKQDITPITDATY-303 by Heimerman and others (32). The vaccine construct was developed by joining selected HTL epitopes, B-cell epitopes, IFN-γ and CTL epitopes together using GPGPG, KK and AAY linkers, respectively. Joining the epitopes with linkers mimics the structure of naturally-occurring multi-domain proteins. This will decrease the chance of junctional antigens being created and improve vaccine construct antigen processing and presentation (33). To improve the immunogenicity of the vaccine construct, an adjuvant Sus scrofa β-defensin were fused to the N-terminal with the aid of an EAAAK linker. EAAAK was employed at this spot because of its capability of providing rigidity and decreasing the likelihood of interference of other protein’s regions in the interaction between adjuvant and its receptor. At the C terminal end, TAT protein which is cell penetrating was appended to the vaccine construct to help it in intracellular delivery. This protein has been harnessed for delivery of protein-based vaccines such as such as Hepatitis B virus (34) and Leishmania major (35), and also invokes enhanced cellular and humoral immune responses (36). As such, these linkers are very important in maintaining the structures of the epitopes so that after injection of the vaccine construct into pigs they can still function properly (37). The final vaccine construct was made up of 437 amino acids with the instability index score of 34.91 showing the vaccine construct as stable since a protein with instability index of less than 40 is considered stable. The allergenicity, antigenicity, and stability analyses of the designed vaccine construct showed that it is non-allergic, antigenic and stable. The 3D structure of the generated vaccine was predicted by using rtRosetta. To attain a good satisfactory expression of the vaccine construct in E. coli, codon optimization was employed to enhance transcriptional and translational efficiency. This was attained by developing CAI, total GC content of DNA sequence, codon frequency distribution and mRNA stability. Solubility of the vaccine in the E. coli is one of the vital necessities of a plethora biochemical and functional investigations. The vaccine construct exhibited a justifiable percentage of solubility in an overexpressed state. The polar nature and solubility of the vaccine construct is reflected by its low GRAVY index of -0.708, indicating its effective interaction with water (38). The high aliphatic index 59.70 of the vaccine construct shows that it may be thermostable in nature (39). The estimated half-life of the vaccine construct 1 h (mammalian reticulocytes, in vitro), 30 min (yeast, in vivo) and greater 10 h (E. coli, in vivo) signifies the time taken by the vaccine construct to attach 50% of its concentration after its production in the cell. Ramachandran plot analysis validated the 3D structure of the vaccine construct with showing 91.8% of residues located in most-favored regions, 7.0% in additional allowed regions, 0.3% in generously allowed regions and only 0.8% in disallowed regions. This was further validated by the overall quality factor plot (ERRAT) of the final vaccine construct after refinement processes of 76.1124%. In ERRAT plot, reveals regions of the 3D model that can be rejected at the 95% confidence level are depicted in gray lines and regions that can be rejected at the 99% level are shown in black lines. The Z-score − 7.9 revealed that the vaccine construct falls in the plot which consists of the Z-scores of the already determined structures solved by NMR and X-ray crystallographic experiments i.e is in the range of native protein conformation (40).

To eliminate ASFV in the system both innate and adaptive immune systems have to be conscripted, the vaccine construct in silico immune response simulation predicted the production of high levels of IFN-γ and a long-lasting cellular response. Furthermore, C-ImmSim server predicted the production of IgM + IgG, IgM, IgG1 + IgG2, and IgG1 after immunization with the vaccine construct. These revealed the likelihood of the vaccine construct of being effective against ASF. However, studies have shown that some ASFV proteins; for instance, the I329 L protein, inhibits dsRNA-stimulated activation of NF-κB and IRF3, which are two vital players in the innate immune response (41). The double-stranded DNA genome of ASFV is expected to be recognized by TLR-9. TLR-9s are type 1 transmembrane endosomal horseshoe-shaped proteins expressed on monocytes, macrophages, myeloid dendritic cells and plasmacytoid DC (42, 43). It interacts with PAMPs and signal via MyD88-dependent or TRIF-dependent pathways resulting in production of IFNs or pro-inflammatory cytokines. There have been reports of immune evasion mechanism of ASFV mediated by various genes present in the multigene families MGF360 and 530/505 in virulent ASFV that inhibit type I IFN induction in ASFV-infected macrophages (44, 45, 46). However, interferon type II IFN-γ production has been reported to be activated by homologous or attenuated isolates of ASFV, but not by stimulation with heterologous or virulent isolates in pigs (47, 48). For the adaptive immune response, CTL plays a pivotal role in immune protection against intracellular pathogens especially viruses (49) including ASF. DNA Vaccination studies have revealed a correlation between the protection due to lethal challenge and the presence of a large number of vaccine-induced antigen-specific CD8 + T-cells in vaccinated animals (50, 51). Molecular docking revealed detailed interaction between vaccine construct–TLR-9 and Vaccine construct-SLA-1 complexes. Docking interaction between construct–TLR-9 revealed 14 H-bonds and 5 salt bridges that are involved in the vaccine construct–TLR-9 interaction and 14 hydrogen bonds and 4 salt bridges formed during the vaccine construct-SLA-1 interaction. Also, in the docked vaccine construct-SLA-1 complex, the vaccine construct interactions are mainly with the PBG pockets A, E and F. The major part of the interactions is with pocket F of PBG. This might be because the F pocket shows strong hydrophobic properties. Molecular docking analyses of the vaccine construct-TLR-9 and vaccine construct-SLA-1 complexes point to a very strong interactions in the complexes.

Molecular dynamic simulation carried out for 10 ns for each complex showed stable interactions of vaccine construct with the immune receptors, which indicates that the immune response can be easily induced. The RSMD plots of each complex revealed that they were stable. RMSF result of each complex showed that, the vaccine construct had the lowest fluctuations in the regions with the most interactions with the complexes especially with SLA-1 which induces cell mediated immune response. RMSD and RMSF results from this study is in agreement with other studies where stability and flexibility of proteins were determined (52, 53). Codon optimization was embarked upon to enhance the expression of the vaccine construct in E. coli strain K12. The adapted vaccine construct sequence GC content of 53.01 is reliable since GC content between 30 and 70% is appropriate for good expression. Furthermore, the vaccine construct was analyzed if it could simulate the host immune system after interacting with the pig immune receptors in the body. The vaccine construct is expected to induce both cellular and humoral immune responses. Due to repeated administration of the vaccine construct, there was a general increase in immune response. There was increase in the level of cytokines IFN-γ, IL-2, IL-10 and IL-12. IFN-γ and IL-2 plays roles in inhibition of viral replication, T-cells and natural killer cells production, respectively. These roles result in viral clearance. There was also increase IgM + IgG and IgG1 + IgG2 levels which is very important in virus neutralization at the extracellular spaces. There was also increase in TH cell levels which will support both innate and adaptative immune response as a result of the vaccination. There substantial increase in Tc level is expected based the vaccine construct design. This will ensure effective viral clearance from the host system. Furthermore, to obtain a good expression of the vaccine construct protein in the E. coli system, codon optimization, and mRNA secondary structure analyses were carried out. Thereafter, BamHI and EcoRI restriction sites were introduced to the N terminal and C-terminal of the optimized sequence, respectively. Then, the sequence with restriction sites was inserted into the pET-28a(+) vector. The expressed vaccine construct showed good solubility based on its polar nature and solubility as reflected by its low GRAVY index from ProtParam analysis. Since it has been difficulty to develop an effective vaccine against ASF using convectional approaches, immunoinformatics is very promising in developing a cheap and effective vaccine against the disease. There has been report on deploying immunoinformatics to develop multi-epitope vaccines against other viral pathogens such as SARS-CoV2, Yellow fever virus Dengue fever virus etc. (29, 53, 54) This approach is promising could be a harbinger for developing safe and highly effective vaccine for all diseases.

African swine fever has been ravaging swine industries in affected countries. Since there is no effective vaccine against the disease. We decided to employ immunoinformatics to develop a multiepitope vaccine against the disease. The vaccine was developed and tested through computational pipelines and it was found to be promising. Although, experimental studies need to carried out to validate this claim.

Protein sequences. The amino acid sequences of CD2v, C-type lectin and p72 of all serotypes/genotypes of ASFV were retrieved from the GenBank. The retrieved sequences for each protein were inter-serotype aligned using Geneious software. The consensus sequences of each protein were subjected to Blast-p (http://blast.ncbi.nlm.nih.gov/Blast.cgi) analysis. Best matches for each protein sequences CD2v (AJB28366), C-type like protein (AAC28412) and p72 (QID21249) were picked for screening epitopes. Antigenicity (at a threshold of 0.4) of selected proteins were evaluated via VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), respectively (55).

T-cell and B-cell epitopes prediction. Immune Epitope Database and Analysis Resource (IEDB) which is a web-based epitope analysis resource that includes tools for T cell epitope prediction, B cell epitopes prediction was deployed. This resource employs methods based on artificial neural network (ANN), stabilized matrix method (SMM), and combinatorial peptide libraries (CombLib) to predicts peptides the way they are processed naturally and presented by MHC I. IEDB MHC-I NetMHCpan EL 4.1 was used to screen the extracellular domains of CD2v (amino acid positions 17–204), C-type lectin (amino acid positions 52–161) and p72 as epitopes for SLAs that would be presented to CD8 + T-cells (CTLs). Whereas for SLA-II, HLA-II alleles were employed to select peptides that would be presented to CD4 + T-cells since SLA-II is not present at IEDB. It has been shown that HLA-II can be used to approximate for other mammals (56). As such, IEDB recommended method 2.22 was employed to screen for the aforementioned proteins for potential peptides as epitopes for MHC-II (HLA-II) that would be presented to CD4 + T-cells (HTLs). The ABCpred (http://crdd.osdd.net/raghava/abcpred/) server was used for B-cell epitope prediction. Conformational (discontinuous) B-cell epitopes were predicted from the proteins using IEDB Discotope 2.0 (http://tools.iedb.org/discotope/) with default parameters (57, 58, 59). The discontinuous epitopes were selected against the resolved structure of ASFV p72 (PDB:6KU9). Epitopes were selected based on the following criteria: promiscuity, antigenicity and non-toxicity. Antigenicity of the selected epitopes were checked by Vaxijen v2.0 (at a threshold of 0.4) (54). ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) was used for nontoxic/toxic properties prediction of epitopes (60).

IFN‑γ epitope prediction. It provokes both innate and adaptive immune responses by activating macrophages and NK cells (61). Hence, IFN-γ inducing epitopes are important for designing a potential multi-epitope vaccine. IFNepitope server (https://crdd.osdd.net/raghava/ifnep itope /) was used to predict out the IFN-γ epitopes in the target proteins.

Multi‑epitope subunit vaccine construction, structural modelling, refinement and validation. The vaccine was constructed by linking the Suis scrofa β defensin, the selected CTL, HTL, B-cells, IFN-γ inducing epitopes, and TAT protein together via linkers such as EAAAK, GPGPG, KK and AAY. β defensing are endogenous alarmins/antimicrobial, responsible for promoting both local innate and adaptive systemic immune responses in hosts against pathogens (62). This antimicrobial peptide was appended to the vaccine construct at its N-terminal to bolster immune response that will be provoked by the vaccine construct. At the C-terminal of the vaccine construct, a 12-mer cell penetrating peptide with the residues GRKKRRQRRRPQ derived from the Human immunodeficiency virus-1 (HIV-1) transactivator of transcription (TAT) protein (63) was grafted. This peptide with its cell penetrating propensity will assist the vaccine construct in intracellular delivery and sub-cellular localization. β defensin amino acid residues at the N-terminal of the vaccine construct were liked with CTL epitopes through EAAAK linkers, the CTL epitopes were linked through AAY linkers. CTL epitopes were joined with HTL epitopes by the GPGPG while B cells, IFN-γ and TAT protein via KK linkers. These linkers are essential in protein folding, flexibility and separation of functional domains of the vaccine construct. Protein-protein Blast search was employed using default parameters to ensure that the vaccine construct is not homologous to pig proteome. Proteins with less than 37% identity with 100% coverage are regarded as non-homologous (64). Physiochemical parameters of the vaccine construct were determined using ProtParam tool (http://web.expasy.org/protparam/) (65). ProtParam predicts various physiochemical parameters such as number of amino acids, molecular weight, theoretical isoelectric point (pI), amino acid composition, atomic composition, formula, extinction coefficients, estimated half-life, instability index, aliphatic index, and GRand AVerage of hydropathY (GRAVY).

The Vaxijen v2.0 server (http://www.ddgpharmfac.net/vaxijen/VaxiJen/VaxiJen.html) was used determine the antigenicity of the vaccine construct. Vaxijen predicts antigenicity through an alignment free approach that is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. A very importance feature of the vaccine is that it should be non-allergenic. The allergenic proteins are capable of inducing hypersensitivity in the host, as such the vaccine should be non-allergenic. AlgPred server (http://www.imtech.res.in/raghava/algpred/) was used to determine the allergenicity of the vaccine construct. A hybrid approach (SVMc + IgE epitope + ARPs BLAST + MAST) in AlgPred was used to predict the allergenicity of the vaccine construct based on its high accuracy and sensitivity (66). The tertiary or three-dimensional (3D) structure of the vaccine construct from its linear structure was generated using transform-restrained Rosetta (trRosetta), which is an algorithm that predicts protein 3D structure based on predicted inter-residue orientations through deep learning-based method (67). The predicted 3D structure was refined using ModRefiner which is an algorithm used for atomic-level, high-resolution protein structure refinement, that can start from either C-alpha trace, main-chain model or full-atomic model (68). Thereafter, the 3D structure was validated using ERRAT score at the ERRAT server (https://servicesn.mbi.ucla.edu/ERRAT/) which evaluates the calculation of unbounded interactions in the vaccine construct structure. The overall quality of the generated model of vaccine construct was also validated by PDBSum Generate (https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html) for Ramachandran plot analysis. The Ramachandran plot reveals favored regions for backbone dihedral angles Phi (ϕ) and Psi (ψ) against amino acid residues in protein structure (69). This was followed by ProSA-web analysis to validate the structure based on Z-score predicted (40, 70).

Molecular docking of vaccine construct with swine immune receptors. Germline-encoded pattern recognition receptors (PRRs) such as toll-like receptors (TLRs) activate antigen presenting cells (APCs) to work in concert with adaptive immunity for pathogen elimination and induction of long-term immunity (71). These receptors recognize conserved viral molecular pathogen-associated molecular patterns (PAMPs) and subsequently provoke the induction of immune response in the host if an antigen or vaccine interacts properly with the target immune cells. Therefore, molecular docking analysis was carried out to show binding affinity between the vaccine construct and immune receptors TLR-9 (TLR-9, PDB:3wpb) and SLA-1 (SLA-1*0401, PBD:3qq4). The docking of the vaccine construct with TLR-9 and SLA-I receptors were carried out using High Ambiguity Driven protein-protein DOCKing (HADDOCK) 2.4 (https://www.bonvinlab.org/software/haddock2.4/) (72). The best cluster was selected from the docked clusters depending on lowest HADDOCK score. Thereafter, HADDOCK Refinement Interface was employed to refine the chosen cluster. The best structure after refinement from each docked complex was chosen and their binding affinity was determined using PROtein binDIng enerGY prediction (PRODIGY) web server (73). PRODIGY employs an easy but powerful predictive model established on intermolecular contacts and properties obtained from non-interface surface to the binding affinity of protein-protein complexes via their 3D structure (74). Finally, the interacting residues between the vaccine construct and the immune receptors were mapped using PDBsum (https://www.ebi.ac.uk/thorn ton-srv/datab ases/pdbsu m/Generate.html) (75).

Molecular dynamics simulation. Molecular dynamics simulation was carried out with the help of inbuilt commands of GROMACS (GROningen MAchine for Chemical Simulations) to evaluate the structural properties and interactions in vaccine construct-TLR-9 and vaccine construct-SLA-1 complexes. Assisted Model Building and Energy Refinement 03 (AMBER03) force field was employed to create the topology file essential for energy minimization and equilibration. spc/e water was employed as the solvent to simulate the complex with periodic boundary conditions. The net charge of the complex was determined, and charged ions were added to neutralize the system. Energy minimization was carried out before simulation to ensure adequate geometry of the system and lack of steric clashes using the steepest descent algorithm approach. The trajectories generated from the simulation (10 ns) were analyzed for the stability of the energy minimized complex in terms of the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of backbone atoms and side chains, respectively.

Codon optimization and in silico cloning. To generate an appropriate plasmid construct harboring the vaccine construct sequence, codon optimization was embarked upon. Codon optimization is an approach for the translation effectiveness of foreign genes in the host if the use of codon is different in the two organisms. The Java Codon Adaptation Tool (JCat) (https://www.jcat.de/) was used for codon optimization to generate DNA sequence of the vaccine construct that can be used for an efficient expression in E. coli K-12 strain (76). Codon adaptation index (CAI) (77) and the guanine and cytosine (GC) contents were evaluated. To ensure translation efficiency, RNAfold web server (26) was employed to predict the secondary structure of the optimized codon sequences. Sticky ends of the restriction sites of BamHI and EcoRI restriction enzymes were grafted to allow restriction and cloning, at the start (N terminal) and end (C terminal) of the modified vaccine sequence, respectively. The modified nucleotide sequence of vaccine was cloned into the E. coli pET-28a (+) vector by SnapGene tool (https://www.snapgene.com/) to ensure its in vitro expression.

Immune Response Simulations. To predict and characterize immune response profile of the vaccine construct in the host, computational immune simulations were employed by the C-ImmSim server (http://kraken.iac.rm.cnr.it/C-IMMSIM/) (78). The C-ImmSim uses the Celada-Seiden model for describing both humoral and cell mediated profiles of a mammalian immune system against designed vaccine. C-IMMSIM is an agent-based model that employs position-specific scoring matrices (PSSM) for peptide prediction derived from machine learning techniques for predicting immune interactions. The summary of the pipeline for the vaccine development is described (Fig. 1).

Data availability

Data generated and analysed in this study are available from the corresponding author on request.

Animal experiment

No experiment on animal and/or the use of animal tissue samples was involved in this study.

Acknowledgement

The authors thank Dr. ‘Deji Alaka for the graphic design.

Author contributions

O. A. designed experiments, carried out epitope selection prediction, molecular docking, molecular dynamics simulation and immune simulation experiments; and helped in writing. C. O. and O. O. helped in writing and editing the manuscript.

Competing interests

There are no competing interests.

Additional information

Supplementary information is available for this paper

Correspondence and requests for materials should be addressed to O. A.

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No competing interests reported.

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Contriving a multi-epitope vaccine against African swine fever utilizing immunoinformatics

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