Abstract
Onchocerciasis is a parasitic disease with high socio-economic burden particularly in sub-Saharan Africa. The elimination plan for this disease has faced numerous challenges. A multi-epitope prophylactic/therapeutic vaccine targeting the infective L3 and microfilaria stages of the parasite’s life cycle would be invaluable to achieve the current elimination goal. There are several observations that make the possibility of developing a vaccine against this disease likely. For example, despite being exposed to high transmission rates of infection, 1 to 5% of people have no clinical manifestations of the disease and are thus considered as putatively immune individuals. An immuno-informatics approach was applied to design a filarial multi-epitope subunit vaccine peptide consisting of linear B-cell and T-cell epitopes of proteins reported to be potential novel vaccine candidates. Conservation of the selected proteins and predicted epitopes in other parasitic nematode species suggests that the generated chimera could be helpful for cross-protection. The 3D structure was predicted, refined, and validated using bioinformatics tools. Protein-protein docking of the chimeric vaccine peptide with the TLR4 protein predicted efficient binding. Immune simulation predicted significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2. Overall, the constructed recombinant putative peptide demonstrated antigenicity superior to current vaccine candidates.
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Introduction
Human onchocerciasis (river blindness), caused by a parasitic nematode Onchocerca volvulus, is transmitted by the repeated bites of infected Simulium black flies. It remains one of the most debilitating yet neglected tropical diseases (NTDs)1. Recent estimates indicate that approximately 15.5 million people worldwide currently live with onchocerciasis, including 12.2 million people with onchocercal skin disease (OSD) and 1.025 million with vision loss2. An additional 172 million at-risk people are reported to be in need of preventive chemotherapy3. Although O. volvulus infection has generally been regarded as a chronic but non-fatal condition, recent analyses have indicated the parasite can result in human death, predominantly in those already blinded by the parasite4,5. More than 99% of infected people live in Africa6, and the vast majority of those severely affected by OSD and visual loss live in sub-Saharan Africa or Yemen. Some severely affected people also live in Venezuela and Brazil2. The public health concern and socio-economic burden of onchocerciasis has led to the creation of various control programmes7, and large-scale control efforts have been ongoing for over 40 years8. Onchocerciasis control programmes have been successful in the Americas; the number of endemic foci decreased from 13 in six countries to two in two countries during the 4-year period from 2013–2017. The success of onchocerciasis control programmes in Africa however, has been limited to a few isolated communities9,10,11. The lower degree of success of onchocerciasis control programmes in Africa is due to the multiple challenges faced in this part of the world, including risks associated with mass drug administration (MDA) in the relatively large areas of co-endemicity with loiasis12, ivermectin (IVM) resistance reported in some endemic foci13, and the logistic/financial burden involved in implementing IVM mass drug administration programs14. The disease distribution worldwide, as well as the status of preventive chemotherapy in endemic countries was recently published by the World Health Organization (WHO)15. In addition, disease modelling studies have suggested that depending on compliance, therapeutic coverage, and levels of parasite transmission, it may not be possible to achieve onchocerciasis elimination even after 50 years of annual IVM treatments14. It has therefore been suggested that implementation of MDA programs using IVM alone will not be sufficient to achieve onchocerciasis elimination16 especially in Africa where the disease burden is highest17. There is therefore a need for new tools to combat this disease18,19,20. The development of a prophylactic/therapeutic vaccine would be ideal to complement present control strategies.
Several observations support the feasibility of a vaccine approach to combat onchocerciasis. Firstly, studies of human populations have provided evidence that naturally acquired immunity against O. volvulus infection can occur in humans. As an example, in regions where onchocerciasis is endemic, 1 to 5% of the population who have been exposed to high rates of infection transmission do not show any clinical manifestation of the disease and are thus regarded as putatively immune individuals (PI)21. Similar observations have been reported in cattle that are infected by a related parasite species, Onchocerca ochengi22. Specific antibody responses against the infective L3 larval stage develop over years of exposure in endemic regions23; this likely explains the effective anti-L3 immunity acquired with age24, consistent with the concept of concomitant immunity23. In addition, immunisation of cattle and mice with irradiated L3 larvae protects against Onchocerca infection22,25. Finally, immuno-epidemiological evidence has led to the conclusion that the concurrent and predominant transmission of O. ochengi by Simulium damnosum in sub-Saharan Africa could lead to the protection of humans against onchocerciasis caused by O. volvulus (zooprophylaxis)26. The Onchocerciasis Vaccine for Africa (TOVA) Initiative was launched in 2015 as a response to the London Declaration on Neglected Tropical Diseases16. It was of course not the first call for vaccine development initiatives. The selection of vaccine candidates reported in previous studies included either antigens immunogenic in putatively immune individuals or molecules supposedly important for parasite development2. The most recent vaccine development initiatives led to the selection of two candidate vaccine antigens, Ov-RAL-2 and Ov-103, for further clinical development2. It has however been reported in the majority of cases that single recombinant antigens used for immunoprophylaxis induced insufficient immunity and thus elicited limited protective effects27,28. Thus far, vaccine design has mainly targeted the microfilariae (mf) and L3 larval stages of the parasite’s life cycle. The L3 larval stage, together with the moulting larva (mL3) stage, is where O. volvulus infection is established. Thus, an antigen promoting anti-L3 larval immunity would block infection29 and result in a preventative vaccine. On the other hand, a vaccine targeted at the mf stage, the parasite stage which ensures transmission to the insect vector and which is associated with most of the pathology, would be both therapeutic and help to prevent transmission but would not eliminate the infection30. Most previous studies have focused on the use of either irradiated parasites31,32,33 or single recombinant antigens34,35; however, the immune responses generated have been inadequate to warrant their use in the development of an effective protective tool32. Alternatively, we propose the development of a multi-epitope vaccine candidate which could lead to the generation of a more potent protective immune response27. Multi-epitope vaccine candidates have already been designed for several diseases, including leishmaniasis, cholera, dengue36,37,38, and cancers39,40, and their efficacies have been further reported in Helicobacter, Schistosoma and cancer27,41,42,43.
This work is an attempt to respond to the need for novel tools to help achieve the onchocerciasis elimination goal. According to studies in human and animal models, immunity to O. volvulus mf and L3 is associated with B-cell and T-cell responses in addition to antibody-dependent cellular cytotoxicity (ADCC) mechanisms21,44,45,46. IL-5 and IL-4 are deemed necessary for protection against irradiated L3 larva as elimination of either of these cytokines (using either monoclonal antibodies or knockout mice) significantly reduced the protective effects of vaccination against larval O. volvulus in mice models32. Immunity to microfilariae in a mouse model has however been demonstrated to be independent of IL-447. Furthermore, mf negativity is associated with IL-5 and IL-13 production indicating the role of CD4+ cells in resistance to infection48. Studies in mice also suggest that infection with microfilariae first stimulates CD8+ T-cell reactivity, which has some detrimental effect on the initial establishment of parasites, and subsequently stimulates a CD4+ T-cell response that overwhelms and destroys the resident parasite population45. TLR4 involvement has also been reported in immune protective responses31. The focus of this work was thus the in-silico generation of a potential multi-B/T-epitope subunit vaccine candidate for onchocerciasis based on immuno-informatics analyses of six O. volvulus antigens selected from previous immunomics studies. Four of these antigens (OVOC10819, OVOC5395, OVOC11598, and OVOC12235) are expressed mainly during the development of the early stages of the infective-stage larvae, L3, in the human host. The other two proteins selected (OVOC8619 and OVOC7083) are expressed predominantly by the microfilariae. These antigens are recognized by sera from the putatively immune individuals who have never developed a patent infection with microfilaridermia49.
To address the cross-protection potential of the designed vaccine candidate, the constituent proteins and predicted epitopes were evaluated for amino acid conservation with homologous proteins in other related parasites selected based on their importance in livestock or humans. These include Onchocerca ochengi and Onchocerca flexuosa responsible for onchocerciasis in livestock leading to huge financial losses50, Loa loa which is a human filarial nematode that poses a significant challenge to onchocerciasis control programs51, and lastly Brugia malayi and Wuchereria bancrofti which are responsible for lymphatic filariasis, another debilitating filaria disease52.
Results
Collection of protein sequences and preliminary analyses
The amino acid sequences for six proteins were retrieved from the WormBase database and used to design a potential multi-epitope vaccine against onchocerciasis. Four L3 larval antigens, OVOC10819, OVOC5395, OVOC11598, and OVOC12235, as well as two microfilarial antigens, OVOC8619 and OVOC7083, were selected based on their reactivities in immunomic assays2,49. The functional sequences were obtained for OVOC10819, OVOC11598, and OVOC12235 after cleavage of the signal peptide as predicted by SignalP 4.1. No signal peptides were predicted for the other proteins. Localisation analyses predicted these proteins to be localized either in the nucleus (OVOC8619), cell membrane (OVOC10819), extracellular compartment (OVOC12235), or the cytoplasm (OVOC5395, OVOC7083, and OVOC11598). The functional sequences for the proteins were then subjected to linear B-cell and T-cell epitope prediction. The Mycobacterium tuberculosis 50S ribosomal protein L7/L12 (RL7_MYCTU), P9WHE3 was retrieved from the UniProt database and used as an adjuvant for the immune interaction based on its ability to act as an agonist for TLR437. Immune adjuvants are a key requirement in vaccine formulation and play a critical role in enhancing the efficacy of vaccines53.
Linear B-cell epitope prediction
Linear B-cell epitopes of varying residue lengths were predicted using different servers, but recurrent epitopes simultaneously predicted with BepiPred 2.0 and one other epitope prediction server (BCpreds, ABCpreds or SVMTrip) were selected for the final vaccine peptide. Some of the predicted linear B-cell epitopes were also predicted to be T-cell epitopes and used in generating the chimera. Overlapping B-cell and T-cell epitopes have the same serial numbers (Table 1B).
Cytotoxic T Lymphocytes (CTL)
A total of 53 CTL (9-mer) ligands were predicted for the six selected proteins using NetCTL 1.2 server set at the default threshold score for epitope identification. From these, 14 epitopes selected either on the basis of their high scores or on their overlap with predicted linear B-cell epitopes were used in generating the chimera (Table 1).
Helper T Lymphocytes (HTL) epitopes prediction
High-binding MHC-II epitopes for human alleles HLA-DR, HLA-DQ and HLA-DP, predicted with the NetMHCII 2.2 web server based on their IC50 scores, were defined as HTL epitopes. A total of eight high-binding HTL epitopes were selected for the final vaccine peptide. Some of the predicted B-cell epitopes overlapped with HTL epitopes (Table 1).
Protein and epitope conservation in related nematodes
A BLAST search of the selected proteins against the UniProt database revealed a high degree of conservation for all the proteins in related nematodes (ranging from 27.7–100%). Two of the proteins were found to be absent in at most one of the selected nematodes (Onchocerca ochengi, Onchocerca flexuosa, Loa loa, Brugia malayi, Wuchereria bancrofti). The greatest percentage conservation was found within the Onchocerca species (Table 1A). Multiple sequence alignment of the predicted B-cell and T-cell epitopes also revealed a large degree of conservation across the selected homologous proteins. Epitope percentage identities ranged from 0% to 100%. The epitopes and their percentage identities (in brackets) with homologous proteins in the five other selected nematode species are presented in Table 1B.
Construction of multi-epitope subunit vaccine
The total number of predicted epitopes used in designing the chimera were 14 linear B-cell epitopes, 14 CTL epitopes, and eight HTL epitopes. Some of the predicted overlapping epitopes were merged to form contiguous sequences (Table 1B). The predicted peptide sequences containing linear B-cell epitopes and T-cell epitopes were fused using GPGPG and AAY linkers. The TLR4 (PDB ID: 4G8A) agonist, 50S ribosomal L7/L12 (Locus RL7_MYCTU) with accession no. P9WHE3, was chosen as an adjuvant and added to the amino terminus of the vaccine peptide using an EAAAK linker in order to potentiate antigen-specific immune responses. In addition, a 6xHis tag was added at the C-terminal to aid in protein purification and identification. The final vaccine peptide generated consisted of 599 amino acid residues derived from 22 merged peptide sequences (Fig. 1).
Schematic presentation of the final multi-epitope vaccine peptide. The 599-amino acid long peptide sequence containing an adjuvant (green) at the amino terminal end linked with the multi-epitope sequence through an EAAAK linker (cyan). B epitopes and HTL epitopes are linked using GPGPG linkers (yellow) while the CTL epitopes are linked with the help of AAY linkers (purple). A 6x-His tag is added at the Carboxy terminus for purification and identification purposes.
IFN-γ inducing epitope prediction
A total of 127 potential IFN-γ inducing epitopes (15-mer) were predicted for the adjuvant; however, none of these scored above the epitope prediction threshold. On the other hand, a total of 461 potential epitopes (having both negative and positive prediction scores) were predicted for the main vaccine sequence by the IFNepitope server which predicts epitopes using the MERCI software. From the main vaccine sequence a total of 50 IFN-γ inducing epitopes with positive scores were predicted. This prediction was consistent with the simulated level of IFN-γ produced after immunization with the peptide using the C-ImmSim server (http://150.146.2.1/C-IMMSIM/index.php) for immune simulation (Fig. 2).
C-ImmSim simulation of the cytokine levels induced by three injections given 4 weeks apart. The main plot shows cytokine levels after the injections. The insert plot shows IL-2 level with the Simpson index, D indicated 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.
Prediction of the antigenicity and allergenicity of the vaccine candidate
The antigenicity of the final sequence (including the adjuvant sequence) was predicted by the VaxiJen 2.0 server to be 0.7428 with a bacteria model and 0.5287 with a parasitic model at a threshold of 0.5 and 0.931486 with ANTIGENpro. The main vaccine sequence (without adjuvant) gave scores of 0.8360 and 0.5920 with bacteria and parasite models respectively at a threshold of 0.5 on VaxiJen 2.0 and 0.937603 on ANTIGENpro. The results indicate that the generated sequences (with and without adjuvant) are both antigenic in nature. The vaccine sequence with and without the adjuvant were both predicted to be non-allergenic on both the AllerTOP v.2 and AllergenFP servers.
Physiochemical properties and solubility prediction
The molecular weight (MW) of the final protein was predicted to be 62.3 kDa with a theoretical isoelectric point value (pI) of 5.13. The protein is predicted to be slightly acidic in nature based on the pI. The half-life was assessed to be 30 hours in mammalian reticulocytes in vitro, and >20 hours in yeast and >10 hours in E. coli in vivo. The protein was predicted to be soluble upon expression with a solubility score of 0.755. An instability index (II) of 36.90 was predicted, classifying the protein as stable (II of >40 indicates instability). The estimated aliphatic index was predicted to be 66.91, indicating thermostability54. The predicted Grand average of hydropathicity (GRAVY) was −0.407. The negative value indicates the protein is hydrophilic in nature and can interact with water molecules36.
Secondary structure prediction
The final chimeric peptide was predicted to contain 22% alpha helix, 10% beta strand, and 67% coil (Fig. 3A). In addition, with regards to solvent accessibility of amino acid residues, 15% were predicted to be exposed, 61% medium exposed, and 22% were predicted to be buried. A total of 292 residues (48%) were predicted to be located in disordered domains by the RaptorX Property server (Fig. 3B). The PSIPRED pictorial prediction of secondary structure and the disorder of the final vaccine sequence is shown below.
Graphical representation of secondary structure features of the final subunit vaccine sequence. (A) The protein is predicted to comprise alpha-helices (22.0%), beta strands (10.0%) and coils (67.0%), and (B) 48% of positions are predicted as disordered.
Tertiary structure modelling
The I-TASSER server predicted five tertiary structure models of the designed chimeric protein based on 10 threading templates, of which 1rquA, 5lqwQ, 1dd3A, 2nbiA, and 2ftc were best. All the 10 chosen templates showed good alignment as per their Z-score values (ranging from 1.18 to 5.46). The five predicted models had C-score values ranging from −3.15 to −0.35. The C-score range is typically between −5 and 2, with higher values indicating higher confidence. The model with the highest C-score from the homology modelling was selected for further refinement (Fig. 4A). This model had an estimated TM-score of 0.67 ± 0.13 with an estimated RMSD of 8.5 ± 4.5 Å. The TM-score has been proposed as a scale for measuring the structural similarity between two structures55. The TM-score was proposed to overcome the problem of RMSD, which is sensitive to local error. A TM-score >0.5 indicates a model of correct topology, and a TM-score <0.17 means random similarity. These cut-off values are independent of protein length.
Protein modelling, refinement and validation. (A) The final 3D model of the multi-epitope vaccine obtained after homology modelling on I-TASSER. (B) Refinement: superimposition of the refined 3D structure (coloured) on the ‘crude model’ (gray) by GalaxyRefine. Validation of the refined model with (C) Ramachandran plot analysis showing 94.5%, 4.5% and 1.0% of protein residues in favoured, allowed, and disallowed (outlier) regions respectively and (D) ProSA-web, giving a Z-score of −3.45.
Tertiary structure refinement
Refinement of the initial “crude” vaccine model on the ModRefiner server followed by further refinement on the GalaxyRefine server yielded five models. Based on model quality scores for all refined models, model 2 was found to be best based on various parameters including GDT-HA (0.9215), RMSD (0.495), and MolProbity (2.091). The clash score was 14.4, poor rotamers score was 0.2, and Ramachandran plot score was 94.5%. This model was chosen as the final vaccine model for further analysis (Fig. 4B).
Tertiary structure validation
The Ramachandran plot analysis of the modelled protein revealed that 94.5% of residues in the protein are in favoured regions. This is consistent with the 94.5% score predicted by the GalaxyRefine analysis. Additionally, 4.5% of the residues were predicted to be in allowed regions and only 1.0% in disallowed region (Fig. 4C). The quality and potential errors in the crude 3D model were verified by ProSA-web and ERRAT. The chosen model after refinement had an overall quality factor of 87.9% with ERRAT (not shown) while ProSA-web gave a Z-score of −3.45 (Fig. 4D) for the input vaccine protein model. The protein fell outside the score range commonly found for native proteins of comparable size.
Conformational B-cell epitopes prediction
A total of 324 residues were predicted to be located in sixteen discontinuous B-cell epitopes with scores ranging from 0.561 to 0.875. The conformation epitopes ranged in size from five to 40 residues.
Molecular docking of subunit vaccine with immune receptor (TLR4)
The CASTp server was used to determine the protein binding and hydrophobic interaction sites on the protein surface. A binding pocket was identified between residue one and 298 that could act as a potential binding site for TLR4. The molecular surface area of the pocket was 325.1 Å2 with a molecular surface volume of 581.3 Å3, the mouth molecular area was about 197.8 Å2, and the molecular circumference sum was 80 Å. The immune response of TLR-4 against vaccine construct was estimated by analysing the overall conformational stability of vaccine protein-TLR4 docked complex with respect to that of adjuvant-TLR4 docked complex after performing data-driven docking of the complexes. The following active interface amino acid residues: T35, A36, A38, P39, V40, A41, V42, A43, A44, A45, G46, A47, A48, P49 from the adjuvant; T35, A36, A37, A38, V40, A41, A43, A44, A47, A48, G51 from the adjuvant part of the chimeric protein and I48, D50, F54, Y72, S73, F75, S76, S100 from the B chain of TLR4 were predicted and used to ‘drive’ the docking. The top-ranking poses for each docked complex with the lowest total intermolecular energies (chimeric protein-TLR4 complex (−355.25 Kcal/mol), adjuvant-TLR4 complex (−90.48 Kcal/mol)) were selected from HADDOCK clusters with the least average pairwise backbone RMSD (chimeric protein-TLR4 complex (1.1 Å), adjuvant-TLR4 complex (0.8 Å)) at the interface. The relative binding free energies (ΔG) of protein-TLR4 complex (−45.31 Kcal/mol) and adjuvant-TLR4 complex (−17.5 Kcal/mol) suggests that the linking of the chimeric protein to the adjuvant elicits conformational changes that favours stimulation of the TLR4 receptor. Consistently, the number of contacts made at the interface (IC) per property (ICs charged-charged:14, ICs polar-polar: 4, ICs apolar-apolar: 23) for the vaccine protein-TLR4 complex were more than those (ICs charged-charged:3, ICs polar-polar: 1, ICs apolar-apolar: 21) made at the interface of the adjuvant-TLR4 complex. Based on input data used to drive docking, further examination of interface residues common to both complexes were performed as a validation of protocols used for their selection. A41 and A43 of the adjuvant sequences (free adjuvant and adjuvant section of chimeric protein) were involved in interactions at both interfaces, with three distinct residues (D50, Y72 and F75) on the TLR4 B chain. These data not only validate selected docked complexes but may also explain the positive conformational shift on the adjuvant structure (A41 and A43 both interact with D50 of TLR4, in the chimeric protein-TLR4 complex while in the adjuvant-TLR4 complex, A41 and A43 respectively interact with F75 and Y72) resulting from binding of the chimeric protein (Fig. 5).
Molecular docking of subunit vaccine with immune receptor (TLR4). (A) Docked complexes for adjuvant-TLR4 complex with adjuvant colored light pink, chain B of TLR4 colored light blue and the interface colored hot pink and blue, and (B) chimeric protein-TLR4 complex with protein colored light pink, B chain of TLR4 colored light blue and the interface colored in blue and hot pink. (C) Interface active residues for adjuvant-TLR4 complex with adjuvant active residues colored hotpink and TLR4 active residues colored in blue, and (D) chimeric protein-adjuvant complex with protein active residues colored hotpink and TLR4 active residues colored blue.
Codon optimization of the final vaccine construct
In order to optimize codon usage of the vaccine construct in E. coli (strain K12) for maximal protein expression, the Java Codon Adaptation Tool (JCat) was used. The length of the optimized codon sequence was 1,797 nucleotides. The Codon Adaptation Index (CAI) of the optimized nucleotide sequence was 0.98, and the average GC content of the adapted sequence was 52.3% showing the possibility of good expression of the vaccine candidate in the E. coli host. The optimal percentage range of GC content is between 30% and 70%. Finally, the sequence of the recombinant plasmid was designed by inserting the adapted codon sequences into pET30a (+) vector using SnapGene software (Fig. 6).
In silico restriction cloning of the final vaccine sequence into the pET30a (+) expression vector where the red part represents the gene coding for the vaccine and the black circle represents the vector backbone. The His-tag is located at the Carboxy-terminal end.
Immune simulation
C-ImmSim server immune simulation yielded results consistent with actual immune responses as evidenced by a general marked increase in the generation of secondary responses. The primary response was characterized by high levels of IgM. The secondary and tertiary responses saw marked increases in B-cell populations and in levels of IgG1 + IgG2, IgM, and IgG + IgM antibodies with a corresponding decrease in antigen concentration (Fig. 7A,B). This profile indicates development of immune memory and consequently increased clearance of the antigen upon subsequent exposures (Fig. 7C). A similarly high response was seen in the TH (helper) and TC (cytotoxic) cell populations with corresponding memory development (Fig. 7C,D). Repeated exposure with 12 injections (given four-week apart) triggered increasing levels of IgG1 and decreasing levels of IgM while IFN- γ concentration and TH cell population were maintained at high levels throughout the duration of exposure. The chimera without the RL7_MYCTU adjuvant elicited a slightly stronger immune response than the chimera with the adjuvant (data not shown), further supporting the intrinsic antigenicity properties mentioned earlier.
C-ImmSim presentation of an in silico immune simulation with the chimeric peptide. (A) Immunoglobulin production in response to antigen injections (black vertical lines); specific subclasses are indicated as coloured peaks. (B) The evolution of B-cell populations after the three injections. (C) The evolution of T-helper, and (D) T-cytotoxic cell populations per state after the injections. The resting state represents cells not presented with the antigen while the anergic state represents tolerance of the T-cells to the antigen due to repeated exposures.
Discussion
It is estimated that more than two billion people, almost 30% of the world’s population, suffer from at least one NTD, including for example leishmaniasis, lymphatic filariasis, onchocerciasis, schistosomiasis, and soil-transmitted helminthiasis (STHs)56. Together, these aforementioned clinical conditions are considered the most common infections in low- and middle-income countries, where they produce a level of global disability and human suffering equivalent to that caused by human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and malaria57. Though chemotherapeutic measures do exist for many of these diseases, they are not sustainable countermeasures on their own due to rates of reinfection, risk of drug resistance, and inconsistent maintenance of drug treatment programs. Preventative and/or therapeutic NTD vaccines are needed as long-term solutions18,58. Lustigman et al. (2017) highlighted the need for a vaccine “now” [2] in order to achieve onchocerciasis elimination59. Many vaccines against onchocerciasis, including subunit and whole-organism vaccines, have been tested in animal models2,28,60,61,62; however, none have been translated for use in humans. The focus has recently shifted towards the development of subunit vaccines as they are associated with better safety profiles and are logistically more feasible63. Epitope-based vaccines represent a novel approach for generating a specific immune response and avoiding responses against other unfavourable epitopes (like epitopes that may drive immunopathogenic or immune modulating responses) in the complete antigen64. Potential advantages of epitope-based vaccines also include increased safety, the opportunity to rationally engineer the epitopes for increased potency and breadth, and the ability to focus immune responses on conserved epitopes43.
This work therefore focussed on the in silico design and development of a potential multi-epitope vaccine peptide for onchocerciasis using six proteins expressed in one of two parasite stages (the microfilarial and infective larval stages). These stages are preferentially targeted in vaccine development approaches, as any vaccine generated may have both prophylactic and therapeutic potentials. The potential for cross-protection also exists as the selected proteins and predicted epitopes used in generating the chimeric peptide exhibited considerable conservation across the related nematodes analysed. Cross-protection following vaccination with either purified antigens or whole attenuated parasites has been reported in the literature26,62,65. The proteins that we selected had exhibited potential to be vaccine candidates in immunomic studies49.
It has been reported that immunity to onchocerciasis is dependent on both B- and T-cells33,45. The role for antibody-dependent cell-mediated cytotoxicity46,66,67 and the TLR4 pathway in immunity to onchocerciasis has also been documented31. The potential role of TLR in mediating interactions between helminth parasites and the host immune system has been described68. Many filarial parasites (including O. volvulus) indeed harbour a Wolbachia endosymbiont which can interact with the innate immune system through TLR2 and TLR469. It has been reported that signalling through TLR in filarial nematodes is associated with the major surface protein of Wolbachia (wsp)70. In the same study, Brattig et al. also showed that wsp induced an inflammatory response evidenced by proinflammatory cytokine expression in murine macrophages and DCs stimulated through a TLR2- andTLR4-dependent mechanism. Mice deficient in either of these TLRs failed to elicit the same response. Studies have reported reduced expression of TLR1, 2, 4, and 9 on B-cells, both at the protein and mRNA levels, in subjects infected with lymphatic filariasis compared to uninfected controls. It was thus concluded that down-regulation of TLR1, 2, 4, and 9 appears to be an important mechanism of immune evasion in filarial infections69. It has been demonstrated in a murine model of T. cruzi infection that TLR4 signalling is required for optimal production of IFN-gamma, TNF-alpha, and nitric oxide (NO) in the spleen of infected animals. TLR4(−/−) mice display higher levels of parasitaemia71. We first predicted B-cell and T-cell epitopes from the selected proteins and fused them using appropriate linkers in order to generate a multi-epitope peptide. Vaccine design is improved through the use of specialized spacer sequences27. Previously reported AAY and GPGPG linkers36,37 were incorporated between the predicted epitopes to produce sequences with minimized junctional immunogenicity, therefore allowing the rational design construction of a potent multi-epitope vaccine27. The EAAAK linker, previously reported for bifunctional proteins72, was also added between the adjuvant sequence and the fused epitopes in order to reach a high level of expression and improved bioactivity of the fusion protein. Immuno-informatics analyses of the generated vaccine candidate indicated that it contains a large number of high-affinity MHC Class I, MHC Class II, and linear B-cell epitopes. Additionally, interferon-γ (IFN-γ) epitopes were predicted in the final vaccine peptide. It has been reported that IFN-γ secretion may help to control O. volvulus infection and results in amicrofilaridermic individuals73. The lack of allergenic properties of the designed protein chimera further strengthens its potential as a vaccine candidate. To further assess the potential of the vaccine peptide developed through our work, its antigenicity was compared with that of Ov-RAL-2 and Ov-103. The latter are reported to be lead vaccine candidates for onchocerciasis74. The multi-epitope peptide we generated showed higher scores than Ov-RAL-2 and Ov-103 both on Vaxijen v2.0 and ANTIGENpro. Strikingly, the protein chimera we generated gave an antigenic score almost twice that of Ov-103 on ANTIGENpro (0.93 for the chimeric peptide and 0.59 for Ov-103). Vaccines carrying multiple epitopes are often poorly immunogenic and require coupling to adjuvants27; however, the designed protein showed similar antigenicity scores with and without the addition of an adjuvant sequence. The absence of the TLR4 agonist sequence even led to slightly higher predicted antigenicity. These results suggest that it may be worthwhile to express the chimeric peptide without the adjuvant used here and assess it in various tests with other adjuvants. Different adjuvants used in immunoprophylactic studies in onchocerciasis have been reported to influence the nature of the elicited immune response74,75. Finally, the stability profile of the chimeric peptide was better than Ov-RAL-2 and Ov-103. Ov-RAL-2 was predicted to be unstable.
The molecular weight of our vaccine candidate is 62.3 kDa and it was predicted to be soluble upon expression: this is consistent with its simulated immunogenicity. The solubility of the overexpressed recombinant protein in the E. coli host is one of the essential requirements of many biochemical and functional investigations37. The theoretical pI is predicted to be 5.13 indicating that the protein is acidic in nature. Also, the predicted instability index indicates that the protein will be stable upon expression, thus further strengthening its potential for use. The aliphatic index showed that the protein contains aliphatic side chains, indicating potential hydrophobicity. All these parameters indicate the recombinant protein is thermally stable and therefore suited for use in endemic areas, most of which are found in sub-Saharan Africa. The knowledge of secondary and tertiary structures of the target protein is essential in vaccine design27. Secondary structure analyses indicated that the protein consisted predominantly of coils (67%), with 48% of residues disordered. Natively unfolded protein regions and alpha-helical coiled coils peptides have been reported to be important forms of “structural antigens”. These two structural forms, when tested in synthetic peptides, have the ability to fold into their native structure and thus be recognized by antibodies naturally induced in response to infection76. The 3D structure of the vaccine candidate improved markedly after the refinement and showed desirable properties based on Ramachandran plot predictions. The Ramachandran plot shows that most of the residues are found in the favoured and allowed regions (99.0%) with very few residues in the outlier region; this indicates that the quality of the overall model is satisfactory.
Previous studies have reported the involvement of TLR4 in immune protection against O. volvulus and other pathogens31,69,77. To assess potential immune interaction between TLR4 and the chimeric vaccine peptide, a data-driven protein-protein docking analysis was performed since a TLR4 agonist was used as an adjuvant in the designed chimera. Though, molecular dynamic simulation is more appropriate to predict protein-protein binding efficiency, this option was not chosen because the interface between the TRL4 and the adjuvant was already reported36. Our findings support the idea that this interface is not perturbed upon fusion of the adjuvant to our polypeptide. Moreover, the binding energies obtained from the adjuvant alone and the adjuvant-chimera interactions with TLR4 indicated that our designed chimera could indeed have a higher stimulation effect. In vitro, purified TLR4 and the recombinant protein could be investigated for such direct interaction. In vivo, this chimeric protein is therefore expected to interact with the TLR4 on professional antigen presenting cells, eliciting a potentially protective immune response.
Immune simulation showed results consistent with typical immune responses. Following repeated exposure to the antigen, there was a general increase in the generated immune responses. In onchocerciasis, IgG1, IgG3 and IgE responses to parasite antigens are implicated in disease protection24,46,78. The development of memory B-cells and T- cells was evident, with memory in B-cells lasting several months. Helper T cells were particularly stimulated. Another interesting observation was that levels of IFN-γ and IL-2 rose after the first injection and remained at peak levels following repeated exposures to the antigen. This indicates high levels of TH cells and consequently efficient Ig production, supporting a humoral response. The Simpson index, D for investigation of clonal specificity suggests a possible diverse immune response. This is plausible considering the generated chimeric peptide is composed of several B and T epitopes. It is not yet clear how CD8+ T cells may be involved in host protective immunity to Onchocerca microfilariae45. This may however be a response to stimulation by Th1 and Th2 cells79 as cytokines secreted from both cells types (including IL-2 and IL-4) have been reported to be involved in the development of primary and long-lived memory CD8+ responses80,81. In addition, interferon-γ (IFNγ)-dominant TH1-type responses, also reported in putative immune individuals82,83, are associated with increased numbers of TH1 cells, cytotoxic CD8+ T cells, neutrophils, and macrophages84.
One of the first steps in validating a candidate vaccine is to screen for immunoreactivity through serological analysis85. This requires the expression of the recombinant protein in a suitable host. Escherichia coli expression systems are the preferred choice for the production of recombinant proteins86,87. In order to achieve high-level expression of our recombinant vaccine protein in E. coli (strain K12), codon optimization was carried out. Both the codon adaptability index (0.98) and the GC content (52.3%) were favourable for high-level expression of the protein in bacteria. The next step projected at the moment, is to express this peptide in a bacterial system and perform the various immunological assays needed to validate the results obtained here through immuno-informatics analyses.
Conclusion
The elimination of onchocerciasis will not be achieved without novel control methods16. These involve diagnostic and chemotherapeutic tools as well as a vaccine if possible2. In this study, immuno-informatics tools were employed to design a potential vaccine peptide coding for multiple B-cell and T-cell (HTL and CTL) epitopes. Given that the proteins containing these epitopes are expressed in the microfilariae and infective stages of the parasite, the vaccine peptide could potentially provide both prophylactic and therapeutic benefits. This chimeric vaccine peptide could potentially be used as complementary tool to achieve onchocerciasis elimination. As a result of the cross-protection expected from the engineered vaccine candidate, other NTD control programs focusing on related filarial diseases such as lymphatic filariasis and loiasis, as well as control programs for onchocerciasis in livestock, could benefit from this initiative.
Methodology
Protein selection for vaccine preparation
The complete amino acid sequences of the six Onchocerca volvulus proteins (OVOC10819, OVOC5395, OVOC11598, OVOC12235, OVOC8619, and OVOC7083) were retrieved from WormBase (http://www.wormbase.org/) in the FASTA format. These proteins, which are highly expressed in the microfilariae or L3 larval stages of the parasite, were selected based on previous reports of reactivity with serum from putative immune individuals2. The proteins were then subjected to signal peptide analyses using SignalP 4.1 server (http://www.cbs.dtu.dk/services/SignalP/) to discriminate classical secretory and non-secretory proteins. SignalP integrates a prediction of cleavage sites and signal/non-signal peptide prediction based on a combination of several artificial neural networks88; it was reported to outperform other predictors in benchmark tests89. Localisation predictions were conducted using DeepLoc (http://www.cbs.dtu.dk/services/DeepLoc/), a template-free algorithm that uses deep neural networks to predict protein subcellular localization using only sequence information, achieving good accuracy90.
Linear B-cell epitope prediction
B-cell epitopes are antigenic determinants recognized by the immune system and represent the specific piece of the antigen to which B lymphocytes bind. These play a vital role in vaccine design. Linear B-cell epitopes were predicted predominantly using the BepiPred-2.0 web server (http://www.cbs.dtu.dk/services/BepiPred/). BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This method was superior to other available tools for sequence-based epitope prediction with regards to both epitope data derived from solved 3D structures and a large collection of linear epitopes downloaded from the IEDB database91. In addition, the proteins were subjected to linear epitope prediction using three other selected servers. Initially, epitopes for 14-mers were predicted using ABCpred, with a default threshold of 0.51 based on recurrent neural network (http://www.imtech.res.in/raghava/abcpred/). SVMTriP, a Support Vector Machine method to predict linear antigenic epitopes which combines the Tri-peptide similarity and Propensity scores (SVMTriP), was also applied to epitope prediction. Application of SVMTriP to non-redundant linear B-cell epitopes extracted from IEDB achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value for SVMTrip is reported to be 0.702. The combination of similarity and propensity of tri-peptide sub-sequences was reported to improve the prediction performance for linear B-cell epitopes (http://sysbio.unl.edu/SVMTriP/)92. Finally, the BCPreds (http://ailab.ist.psu.edu/bcpred/) server, a novel method for predicting linear B-cell epitopes using kernel methods, was applied. The predictive performance of BCPreds (AUC = 0.758) and its output performance is based on SVM as well as the implementation of AAP (Amino acid pair antigenicity; AUC = 0.7)93. The use of multiple tools in epitope prediction has been reported to improve the rate of true positives94.
Cytotoxic T lymphocytes (CTL) epitope prediction
CytotoxicT lymphocyte (CTL) epitopes for proteins selected were predicted using the freely-accessible NetCTL 1.2 server (http://www.cbs.dtu.dk/services/NetCTL/). The prediction method integrates prediction of MHC class I binding peptides, proteasomal C-terminal cleavage, and TAP (Transporter Associated with Antigen Processing) transport efficiency. Though the server allows for predictions of CTL epitopes restricted to 12 MHC class I supertypes, only the A1 supertype was used for this work. MHC class I binding and proteasomal cleavage are performed using artificial neural networks. TAP transport efficiency is predicted using a weight matrix95. The threshold value for epitope identification was set at 0.75 (default score) for the prediction of CTL epitopes.
Helper T-cell (HTL) epitope prediction
The NetMHCII 2.2 Server (http://www.cbs.dtu.dk/services/NetMHCII/) was used to predict HTL epitopes of 15-mer length for human alleles. The NetMHCII 2.2 server predicts binding of peptides to HLA-DR, HLA-DQ, and HLA-DP alleles using artificial neuron networks96. The prediction of MHC II epitopes was based on receptor affinity, which can be inferred from the IC50 values and percentile ranks assigned to each predicted epitope. High-affinity peptides should have IC50 values <50 nM. An IC50 value <500 nM indicates intermediate affinity, while values <5000 nM indicate low affinity37. The percentile rank may therefore be inversely related to the affinity of the epitope and directly related to the IC50.
Protein and epitope conservation in related nematodes
To assess the possibility of cross-protection conferred against other related nematode infections by the chosen proteins and predicted epitopes, a BLAST search was conducted on the UniProt database and the percentage identities for homologous relatives of the selected protein were obtained. The percentage conservation of epitopes was calculated for the different predicted epitopes following multiple sequence alignment of homologous proteins in related nematodes. The nematodes selected for comparison included Onchocerca ochengi, Onchocerca flexuosa, Loa loa, Brugia malayi, and Wuchereria bancrofti. These were selected based on their importance in humans and livestock.
Construction of multi-epitope vaccine candidate sequence
Selected high-scoring CTLs, high-affinity HTLs epitopes, as well as B-cell epitopes simultaneously predicted by BepiPred-2.0 and another tool, were used to generate the vaccine candidate sequence. It was noted that some sequences could act as both B-cell and T-cell epitopes. The different epitopes were linked together using AAY and GPGPG linkers. Furthermore, a 50 S ribosomal protein L7/L12 (Locus RL7_MYCTU) was chosen as an adjuvant (Accession no. P9WHE3) to improve the immunogenicity of the vaccine and was added at the N-terminal through an EAAAK linker. The sequence of the adjuvant was retrieved from the UniProt database (http://www.uniprot.org/).
IFN-γ inducing epitope prediction
Interferon gamma (IFN-γ), a cytokine that plays a critical role in adaptive and innate immune responses, stimulates macrophages and natural killer cells and provides a heightened response to MHC antigens. 15-mer IFN-γ epitopes for the designed peptide were predicted using the IFNepitope server (http://crdd.osdd.net/raghava/ifnepitope/scan.php). IFN- γ epitope predictions were carried out separately for the adjuvant and the main vaccine peptide due to restrictions in the number of residues that can be used for prediction by the server. The server constructs overlapped sequences from which the IFN-γ epitopes are predicted. The prediction was performed by a motif and support vector machine (SVM) hybrid approach. The server is based on a dataset which consists of IFN-γ inducing and non-inducing MHC class-II binder, which can activate T-helper cells97.
Antigenicity and allergenicity prediction of the designed vaccine peptide
ANTIGENpro and VaxiJen v2.0 were used to predict the antigenicity of the protein chimera. ANTIGENpro (http://scratch.proteomics.ics.uci.edu/), which uses protein antigenicity microarray data to predict protein antigenicity, was used to generate an antigenicity index. The accuracy of the server using the combined dataset was estimated to be 76% based on cross-validation experiments98. The freely accessible VaxiJen 2.0 server (http://www.ddgpharmfac.net/vaxijen/VaxiJen/VaxiJen.html), which is based on auto- and cross-covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties was used to further assess the antigenicity of the multi-epitope vaccine peptide. The prediction of antigenicity using VaxiJen v2.0 is alignment-free, like ANTIGENpro, and is based on various physiochemical properties of the protein99.
AllerTOP v2.0 and AllergenFP were used to predict multi-epitope vaccine allergenicity. AllerTOP v2.0 (http://www.ddg-pharmfac.net/AllerTOP) utilizes amino acid E-descriptors, auto- and cross-covariance transformation, and the k nearest neighbours (kNN) machine learning methods for classification of allergens. The accuracy of this method at 5-fold cross-validation has been reported as 85.3%100. AllergenFP (http://ddg-pharmfac.net/AllergenFP/) on the other hand is an alignment-free, descriptor-based fingerprint approach to identify allergens and non-allergens. The approach is based on a four-step algorithm. Initially, the protein sequences are described in terms of their properties, including hydrophobicity, size, relative abundance, and helix and β-strand forming propensities. Subsequently, the generated strings which vary in length are converted into vectors of equal length by auto- and cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of the Tanimoto coefficient. The approach was applied to known allergens and non-allergens and correctly identified 88% of them with a Matthews correlation coefficient of 0.759101.
Physiochemical properties and solubility prediction
Various physiochemical parameters were assessed using the online web server ProtParam (http://web.expasy.org/protparam/)102. These parameters included amino acid composition, theoretical pI, instability index, in vitro and in vivo half-life, aliphatic index, molecular weight, and grand average of hydropathicity (GRAVY) The solubility of the multi-epitope vaccine peptide was evaluated using the PROSO II server (http://mbiljj45.bio.med.uni-muenchen.de:8888/prosoII/prosoII.seam). PROSO II works based on a classifier exploiting subtle differences between soluble proteins from TargetDB and the PDB and notoriously insoluble proteins from TargetDB. The accuracy was 71% at a default threshold of 0.6. When evaluated by 10-fold cross-validation it achieved 71% accuracy (area under ROC curve = 0.785)103.
Secondary structure prediction
PSIPRED and RaptorX Property were used for secondary structure predictions. PSIPRED, a web-based freely-accessible online server (http://bioinf.cs.ucl.ac.uk/psipred/), was used to predict the protein secondary structure for the input of primary amino acid sequences in a precise manner. Position-specific iterated BLAST (Psi-Blast) was performed to identify and select sequences showing significant homology to the vaccine protein. PSIPRED 3.2 accomplished a normal Q3 score of 81.6% when an exceptionally stringent cross-approval strategy was utilized to assess its performance. The selected sequences were utilized to construct a position-specific scoring matrix37. The RaptorX Property web server (http://raptorx.uchicago.edu/StructurePropertyPred/predict/), which is a template-free approach, was further employed to predict the secondary structure properties of the protein chimera. The server uses an emerging machine learning model called DeepCNF (Deep Convolutional Neural Fields) to simultaneously predict secondary structure (SS), solvent accessibility (ACC), and disorder regions (DISO)104. This server achieved ~84% Q3 accuracy for 3-state SS, ~72% Q8 accuracy for 8-state SS, ~66% Q3 accuracy for 3-state solvent accessibility, and ~0.89 Area Under the ROC Curve (AUC) for disorder prediction105.
Tertiary structure prediction
Homology modelling of the final multi-epitope vaccine peptide was carried out using the I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). The I-TASSER (Iterative Threading ASSEmbly Refinement) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations106. I-TASSER has been ranked the best server for protein structure prediction in the last five community-wide CASP experiments107.
Refinement of the tertiary structure
The 3D model obtained for the multi-epitope vaccine peptide was refined in a two-step process, initially using ModRefiner (https://zhanglab.ccmb.med.umich.edu/ModRefiner/) and then the GalaxyRefine server (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE). The construction and refinement of protein structures from Cα traces is carried out by the ModRefiner server based on a two-step, atomic-level energy minimization. This results in improvements in both global and local structures, with more accurate side chain positions, better hydrogen-bonding networks, and fewer atomic overlaps108. The GalaxyRefine server on the other hand is based on a refinement method that has been successfully tested in community-wide CASP10 experiments. This method initially rebuilds side chains and performs side chain repacking and subsequently uses molecular dynamics simulation to achieve overall structure relaxation. The GalaxyRefine server method performed the best in improving local structure quality according to the CASP10 assessment. This method, when used to refine the models generated by state-of-the-art protein structure prediction servers can improve the quality of both global and local structures109.
Tertiary structure validation
Model validation is a critical step in the model building process as it detects potential errors in predicted 3D models37. ProSA-web (https://prosa.services.came.sbg.ac.at/prosa.php) was used initially to achieve this. ProSA-web provides an easy-to-use interface to the program ProSA and is commonly used in protein tertiary structure validation. ProSA calculates an overall quality score for a specific input structure, and this is displayed in the context of all known protein structures. Also, any problematic parts of a structure are shown and highlighted in a 3D molecule viewer. If the calculated score falls outside the range characteristic of native proteins the structure likely contains errors110. The ERRAT server (http://services.mbi.ucla.edu/ERRAT/) was also used to analyse non-bonded atom-atom interactions compared to reliable high-resolution crystallography structures. A Ramachandran plot was obtained through the RAMPAGE server (http://mordred.bioc.cam.ac.uk/~rapper/rampage.php). The Ramachandran plot is a way to visualize energetically allowed and disallowed dihedral angles psi (ψ) and phi (φ) of an amino acid and is calculated based on van der Waal radius of the side chain. The results from RAMPAGE include the percentage of residues in allowed and disallowed regions which define the quality of the modelled structure. The server uses the PROCHECK principle to validate a protein structure by using a Ramachandran plot and separates plots for Glycine and Proline residues111.
Discontinuous B-cell epitope prediction
It has been estimated that >90% of B-cell epitopes are discontinuous, i.e. they consist of segments distantly separated in the pathogen protein sequence that are brought into proximity by the folding of the protein112,113. ElliPro (http://tools.iedb.org/ellipro/) was used to predict discontinuous (conformational) B-cell epitopes for the validated 3D structure. ElliPro implements three algorithms to approximate the protein shape as an ellipsoid, calculate the residue protrusion index (PI), and cluster neighbouring residues based on their PI values. ElliPro provides a score for each output epitope described as a PI value averaged over each epitope residue. An ellipsoid with a PI value of 0.9 consists of 90% included protein residues while the remaining 10% of residues lie outside of ellipsoids. For each epitope residue, the PI value is calculated based on the centre of mass of residue residing outside the largest possible ellipsoid. In comparison with other structure-based methods used for epitope prediction, ElliPro performed the best and gave an AUC value of 0.732 considering the most significant prediction for each protein114.
Molecular docking of designed chimeric protein with TLR4
The generation of an appropriate immune response is dependent on the interaction between an antigenic molecule and a specific immune receptor. The binding pockets or cavities in the TLR-4 receptor was predicted by using the CASTp server (http://sts.bioe.uic.edu/castp/). In CASTp, voids are defined as buried, unfilled empty spaces inside proteins following removal of all heteroatoms inaccessible to water molecules (modelled as a spherical probe of 1.4 Å) from outside. Pockets on the other hand are defined as concave caverns with constrictions at the opening on the surface regions of proteins. CASTp is excellent at providing identification and measurements of surface accessible binding pockets along with the information of inner inaccessible cavities for protein molecules115. Molecular docking of the multi-epitope vaccine peptide with the TLR4 (PDB ID: 4G8A) receptor was performed using the HADDOCK 2.2 web server in order to evaluate the interaction between ligand and receptor and consequently the development of an immune response. Protective immunity against the larval stages of Onchocerca volvulus is reported to be dependent on TLR431. Consistently, mice with a natural mutation of TLR4 (C3H/HeJ) show a higher degree of susceptibility to Litomodoides sigmodontis infection77. TLR4 has also been reported to play a major role in ocular pathogenesis116.
In order to predict such interaction, data-driven docking of designed chimeric protein-TLR4 and adjuvant-TLR4 complexes were performed. The adjuvant which is a 50S ribosomal protein with the accession number P9WHE3 has the ability to stimulate TLR4, thus serving as a TLR4 agonist37. While information-driven docking can predict interacting subunits and the overall shape of complexes, knowledge about which specific residues involved in the interaction is needed to drive the docking calculations. In this regard, CPORT (https://milou.science.uu.nl/services/CPORT/)117 was used to predict active interface residues of chimeric protein (including adjuvant), adjuvant and TLR4. The HADDOCK webserver (version2.2) (http://haddock.science.uu.nl/services/HADDOCK2.2) was then employed to perform docking simulations of the chimeric protein-TLR4 and adjuvant-TLR4 complexes due to the server’s ability to incorporate experimental or bioinformatics data to ‘drive’ the docking and to perform short MD simulations of docked complexes118,119. HADDOCK (High Ambiguity Driven DOCKing) consists of a collection of python scripts that makes use of Crystallography and NMR Systems for structure calculation. After proper orientation and docking calculations of the complexes are performed, HADDOCK passes resulting structures into, and invokes a final MD simulation stage consisting of a gentle refinement in an 8 Å shell of TIP3P water molecules. The MD steps (5000) are performed at 300 K with position restraints only on noninterface heavy atoms and a final cooling stage of 1000 MD steps run at 300, 200, and 100 K120. Inter- and intramolecular energies are evaluated using full electrostatic and van der Waals energy terms with an 8.5 Å distance cut-off using OPLS nonbonded parameters and the final structures are clustered using the pairwise backbone RMSD at the interface. Lastly, the binding affinities of the top ranking docking pose of chimeric protein-TLR4 complex and adjuvant-TLR4 complex were respectively, predicted using the PRODIGY (PROtein binDIng enerGY prediction) webserver (https://nestor.science.uu.nl/prodigy/)121.
In silico cloning optimization of designed vaccine candidate
Reverse translation and codon optimization were performed using Java Codon Adaptation Tool (JCat) server (http://www.prodoric.de/JCat) in order to express the multi-epitope vaccine construct in a selected expression vector122. Codon optimization was performed in order to express the final vaccine construct in the E. coli (strain K12) host, as codon usage of E. coli differs from that of the native host O. volvulus, where the sequence of the final vaccine construct is derived. Three additional options were selected to avoid the rho-independent transcription termination, prokaryote ribosome binding site, and restriction enzymes cleavage sites. The JCat output includes the codon adaptation index (CAI) and percentage GC content, which can be used to assess protein expression levels. CAI provides information on codon usage biases; the ideal CAI score is 1.0 but >0.8 is considered a good score123. The GC content of a sequence should range between 30–70%. GC content values outside this range suggest unfavourable effects on translational and transcriptional efficiencies36. To clone the optimized gene sequence of the final vaccine construct in E. coli pET-30a (+) vector, Nde I and Xho I restriction sites were introduced to the N and C-terminals of the sequence, respectively. Finally, the optimized sequence (with restriction sites) was inserted into the pET-30a (+) vector using SnapGene tool to ensure vaccine expression.
Immune Simulation
To further characterize the immunogenicity and immune response profile of the chimeric peptide, in silico immune simulations were conducted using the C-ImmSim server (http://150.146.2.1/C-IMMSIM/index.php). C-ImmSim is an agent-based model that uses a position-specific scoring matrix (PSSM) for immune epitope prediction and machine learning techniques for prediction of immune interactions. It “simultaneously simulates three compartments that represent three separate anatomical regions found in mammals: (i) the bone marrow, where hematopoietic stem cells are simulated and produce new lymphoid and myeloid cells; (ii) the thymus, where naive T cells are selected to avoid auto immunity; and (iii) a tertiary lymphatic organ, such as a lymph node”124. In accordance with the TOVA approach for the target product profile of a prophylactic onchocerciasis vaccine, three injections were given at intervals of four weeks125. All simulation parameters were set at default with time steps set at 1, 84, and 168 (each time step is 8 hours and time step 1 is injection at time = 0). Therefore, three injections were given four weeks apart. Furthermore, 12 injections of the designed peptide were given four weeks apart to simulate repeated exposure to the antigen seen in a typical endemic area so as to probe for clonal selection. The Simpson index, D (a measure of diversity) was interpreted from the plot.
Data Availability
All data generated or analysed during the study are included in the submitted manuscript. The sequences of the protein analysed can be retrieved from WormBase (wormbase.org) and UniProt database (uniport.org) using their accession numbers.
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Acknowledgements
The authors would like to acknowledge the Académie de Recherche et d’Enseignement supérieur – Commission de la Coopération au Développement (ARES-CCD), Belgium for the ARES-PRD2013CMR grant to JS and ARES-SOFT fellowship to RAS, the Bureau des Relations Internationales et de la Coopération de Université Libre de Bruxelles (ULB-BRIC), Belgium for the fellowship award to RAS.
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Protocol designed by R.A.S., N.D.N., K.K.E., L.V., S.M.G. and J.S. Methodology performed by R.A.S., K.K.E, N.D.N., N.F.N., S.C.M., F.N.N. and B.F.A. Original manuscript written by J.S., R.A.S., S.G.M. and L.V. Manuscript reviewed by all authors.
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Shey, R.A., Ghogomu, S.M., Esoh, K.K. et al. In-silico design of a multi-epitope vaccine candidate against onchocerciasis and related filarial diseases. Sci Rep 9, 4409 (2019). https://doi.org/10.1038/s41598-019-40833-x
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DOI: https://doi.org/10.1038/s41598-019-40833-x
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