A multi-method and structure-based in silico vaccine designing against Echinococcus granulosus through investigating enolase protein

Introduction

Transmission dynamics of Echinococcus granulosus mainly depends on the dog-sheep cycle in several endemic areas. As such, adult worm’s eggs is transferred from definitive hosts feces (dog) to the body of intermediate hosts (sheep and human) during grazing. The eggs can penetrate into the small intestine’s mucosa and lead to the formation of hydatid cysts – comprising abundant protoscoleces (PSCs) and cyst fluid (CF) – in the internal organs of intermediate hosts (e.g., liver and lungs). The dog will be infected upon feeding the cystic viscera of the intermediate hosts and then PSCs are released and attached to the gut mucosa of the dog which grow and differentiate to the adult worm.^1,2 The last report of the World Health Organization (WHO) in terms of different aspects of echinococcosis has highlighted the main facts about the potential and actual risks of E. granulosus.³

Vaccination acts through eliciting the host immune system – the natural barrier for fighting against infectious agents.^4,5 Along with the development of newer technologies, the informatics-based vaccines as next-generation of the vaccines were born from the multidisciplinary sciences.⁶ The current challenges for vaccine development against such a multi-stage parasite is originated from major funding constraints, and socio-cultural and political problems.⁷ Therefore, focusing on the multivalent and multistage vaccines seems to be a rational and time- and cost-effective approach.⁸ In the past decade, the immunobiological data about the function of MHC molecules, host-pathogen interactions, and immunological signaling pathways have provided new avenues for the design and development of rational vaccines.⁹ The vaccines that are designed by immunoinformatic analysis seem to be a promising vaccinology platform for the prevention of the parasite life cycle. Many candidate antigens have been used for the production of epitope-based recombinant vaccines against E. granulosus.⁹ Nevertheless, in designing such vaccines, the preliminary in silico analyses using immunoinformatic tools have yet to be considered thouroughly. Some of the in silico-based investigations were only concentrated on the prediction of B-cell epitopes (BCEs) exploiting different vaccine candidate antigens regardless of T-cell epitopes.^10-12 However, it should be noted that there is not any web-server for the T-cell epitope prediction based on the canine MHC alleles. Therefore, for designing a host-specific epitope vaccine, one needs to switch from the sequence-based methods to the structure-based techniques.^13,14 For this reason, the docking tools can be recruited for the simulation of binding affinity and molecular interactions between T-cell epitope and MHC alleles.

As reported previously, the B lymphocyte cells have a central function in protective immune responses against the extracellular parasitic pathogen.¹⁴ Despite such fact, B-cell provoking mechanisms are mainly dependent on the effectors of T-helper cells,¹⁵ which implies that the B-cell based epitope vaccines, to be more efficient and immunogenic, need to be linked to the antigen-specific T helper cells.¹⁶

In this study, we capitalized on the EgEnolase, a multifunctional glycolytic and excretory-secretory (ES) antigen, for designing an epitope-based vaccine construct. The EgEnolase has a significant expression and localization in the PSCs and adult worm stages of the parasite.¹⁷ This enzyme is involved in the key processes such as motility, adhesion, invasion, growth, and differentiation of the parasite.^18-20 In this line, the 3D structure of the enolase was modeled and the T-helper epitopes were predicted using the molecular docking methods and based on the most frequent canine leukocyte antigen (DLA) alleles. To the best of our knowledge, this is one of the first studies for in silico epitope mapping against this pathogen, which might provide a creative platform for the construction of the host-specific and immunogenic recombinant, DNA, and edible-based vaccines against such parasitic pathogens.²¹

Materials and Methods

Results

Prediction of immunological non-accessible residues

Membrane-spanning and signal peptide regions

The probability plot of TMHMM shows that there is no significant alpha-helix transmembrane (TM) topology in the EgEnolase protein sequence. In TMPred web-server only scores more than 500 were predicted as significant TM helix. In this case, two fragments 325–345 (Score: 529) and 358–377 (Score: 856) were predicted to be the strong inside-to-outside and outside-to-inside orientations of membrane spanning, respectively. Among five algorithms of the TOPCONS web-server, only one 20-amino-acid-segment (733–753) was predicted using the SPOCTOPUS method. According to the outputs of TOPCONS, SignalP, and Signal-3L online engines, there is no signal sequence at the beginning of the EgEnolase protein sequence (Fig. S1, Supplementary file 1).

Post-translational modifications

The NetOglyc web-server uses the neural network algorithms to predict the mucin-type GalNAc O-glycosylation sites in the mammalian proteins.³¹ In this server, only residues with a prediction score of more than 0.5 are predicted as local O-glycosylated. The server output showed there was no O-glycosylation site in the EgEnolase protein sequence. In NetNGlyc server based on the threshold of 0.5, only one potential N-linked glycosylation site (position 17; sequence: NPTV; score: 0.8082) was observed (Fig. S2A, Supplementary file 1). The output of NetPhos v2.0 server indicated 20 phosphorylated serine residues (S), 6 threonine (T), and 4 tyrosine (Y) phosphorylation sites in the EgEnolase protein sequence (Fig. S2B, Supplementary file 1).

Identification of the non-accessible and hydrophobic residues

The non-accessible and hydrophobic areas of EgEnolase protein sequence are shown as plots in Figs S3A and B (Supplementary file 1). The non-accessible regions of the EgEnolase were considered during the prediction of conformational BCEs.

Homology modeling, energy minimization, and validation

The homologous PDB templates that were used for homology modeling are shown in Table S2 (Supplementary file 1). The 3D structures of the modeled EgEnolase, DLA-DRB1*01101, TLR-2 and TLR-4 proteins before and after energy minimization and their energy are shown in Fig. 1.

Fig. 1

Three-dimensional structure of the initial and energy minimized modeled proteins. (A)E. granulosus Enolase protein. (B) DLA-DRB1*01101. (C)C. lupus Toll-like receptor 2. (D)C. lupus Toll-like receptor 4. The Chimera software (freely available at http://www.rbvi.ucsf.edu/chimera) was used for the energy minimization and ribbon style representation of the 3D models.

The models were re-arranged and structurally optimized after the energy minimization (Table 1). Afterward, the homology modeling procedure was validated, providing two modeling validation scores (GA341, and the normalized DOPE scores). The energetic value of the GA341 and DOPE scores for the 3D models are represented in Table 1. The alpha carbon (Cα) atoms of the modeled structures were analyzed by ProSA server Z-score plot. Herein, the Z-score values for each model showed the overall quality of the models is favorable (Table 1). The local quality of the models was checked by the energy plot of the ProSA-web. Their entropy plots are provided in Fig. S4 (Supplementary file 1). The ERRAT and Verify3D scores for the modeled structures are exhibited in Table 1.

Table 1.Summary of energy minimization and structure validation for the 3D structures
Model	Energy minimization		MODELLER			Web-based tools		Ramachandran*
	EM 0	EM 1	GA341	DOPE ¶	ProSA z-score	Verify3D	ERRAT	Favored	Allowed
EgEnolase	-66467.1	-81191.8	1.0	– 33668.3	– 10.5	90.1	98.6	97.0	2.6
01101	-12337.2	-25241.1	1.0	– 29341.6	– 5.9	86.8	97.1	96.3	3.2
TLR-2	-24084. 7	-45941.9	1.0	– 26093.7	– 5.9	89.9	80.9	87.4	10.6
TLR-4	-27676.5	-54266.7	1.0	– 30419.1	– 7.3	97.2	84.0	93.9	5.3
EM 0: Energy for the initial model (Kj/mol). EM1: Energy minimized model. * Totality of the residues within the favored regions of psi/phi Ramachandran plot is reported. ¶kcal/mole. ERRAT: A good 3D model should have a value > 50%. Verify3D: This criterion assigns a score to each residue between -1 to +1, and in good 3D models more than 80% of the amino acids should score >0.2.

B-cell epitope prediction and variability metrics for the predicted epitopes

We used both the physicochemical and the machine learning-based BE prediction tools to increase the accuracy of in silico BCE mapping. In this multimethod analysis, the high-rank epitopes of each predictor tool were schematically revealed under the EgEnolase protein sequence. Through this multimethod analysis a vivid overview was provided to discover the high immunogenic regions of EgEnolase protein, which facilitated and improved the accuracy of selecting the best BCEs (Fig. 2).

Fig. 2

The result of high score predicted BEs based on the multimethod BCE prediction methods. The prediction method consists of 19 different BCE prediction algorithms. The position of high score epitopes is characterized by different colors. The conformational BCEs are marked by the use of the colored squares above each one-letter amino acids. As shown in the “signs guide”, the conformational BEs are differentiated from each other using different colored squares.

Total 12 BCEs with different lengths were predicted based on the multimethod BCE mapping analysis. Of these, 5 epitopes were finalized (Table 2) due to their high surface accessibility score and allergen analysis (Fig. S5, Supplementary file 1).

Table 2. The predicted BCEs, their positions in the sequence, amino acid length, and variability score of each epitope
No.	Predicted B-cell Epitopes	Position	Length	Mean entropy value
1	MSILKIHARQIFDSRGNPTVEVDLTT	1 – 26	26	0.236
2	PSGASTGVHEAVELRDADKNAYMGK*	36 – 60	25	0.149
3	IKEKFVVTDQQRIDEFMIKLDGSPNKGKLG*	78 – 107	30	0.393
4	KAGAAEKGVPL	120 – 130	11	0.126
5	LAGNKDVVLP	137 – 146	10	0.208
6	MGTEVYHHLKSVIKGKYGLDACNV*	184 – 207	24	0.173
7	KTAIDKAGYTGKVK*	228 – 241	14	0.245
8	SEFYQDGNYNLDFKNPKAAASSIVSGSKLSDI*	249 – 280	32	0.335
9	PFDQDDWAAWTEFNAKAGI	296 – 314	19	0.222
10	DLTVTNPERVQQAIDRKAC	320 – 338	19	0.109
11	VMVSHRSGETEDSTIAD	368 – 384	17	0.484
12	GQIKTGAPCRSERLAKYNQLLRIEEELGPKAVYAGEHFR	388 – 430	42	0.458
* Indicates the selected B-cell epitopes.

Due to the comprehensive multimethod analysis, we guess our vaccine can effectively elicit humoral immunity. As shown in Table 2, the mean entropy (Hx) values of the computationally predicted BEs are less than 1.0 (threshold). This implies the epitopes are conserved among different E. granulosus enolase isolates and can be used for the immunization against different strains of E. granulosus. The Shannon entropy (Hx) plot is represented in Fig. 3.

Fig. 3

Entropy plot of deduced amino acid sequences of different EgEnolase protein sequences. The Shannon entropy plot was attained using the BioEdit program. Entropy (Hx) values varied from 0 (highly conserved) to 1.0 (hypervariable).

Helper T-lymphocyte (HTL) epitope prediction

The EgEnolase 3D structure was subjected to Chimera software to be cleaved manually into the eighty-four 13-mer peptides. In order to identify the highest immunogenic epitope(s), all 13-mer peptides were docked with a DRB1*01101 molecule, separately. The binding energy corresponding to each receptor-ligand complex is shown in Table S3. Of these, twelve 13-mer peptides with the lowest binding energy were selected for constructing the minigene vaccine scaffold (Table 3).

Table 3.Final selected helper T-cell epitopes
No.	Sequence	Binding energy (kJ/mol)
		Center	Lowest energy
1	KLAMQEFMILPTG	-906.4	-1016.2
2	GALIIHARQIFDS	-825.7	-999.1
3	AMQEFMILPTGAK	-882.7	-983.0
4	EFMILPTGAKSFS	-801.7	-946.9
5	LIIHARQIFDSR	-793.3	-945.2
6	MSRAAGWGVMVSH	-922.9	-922.9
7	AGWGVMVSHRSGE	-819.7	-916.8
8	LRIEEELGPKAVY	-710.7	-909.9
9	KAVYAGEHFRNPL	-743.9	-903.3
10	YPIVSIEDPFDQD	-876.3	-896.0
11	VLPVPSFNVLNGG	-844.0	-896.6
12	GYTGKVKIGMDVA	-859.7	-859.7

The arrangement of the minigene vaccine construct

The nucleotide and amino acid sequence of the minigene vaccine are shown in Fig. S6 (Supplementary file 1). The final vaccine construct of 601 amino acid residues was designed by means of 5 BEs and 12 helper T-cell epitopes, 2 intramolecular adjuvants, and 5 types of different peptide linkers. To design an adjuvant vaccine with the maximum immune response, TLR-2 and TLR-4 agonists (Lsa21 and RSO9, respectively) were linked to the N-terminal of the minigene construct as intramolecular adjuvants. Since the designed minigene vaccine construct consists of immunogenic BCEs and HTEs along with two immunogenic intramolecular adjuvant and suitable peptide spacers, it may have a high ability to induce the innate and adaptive anti-echinococcus immune responses within the host body.^13,72,73

The 3D structure prediction, refinement, energy minimization, and validation

The 3-dimensional structure of the vaccine construct represents good information about the arrangement of epitopes and important residues. The vaccine 3D structure modeling was used by the RaprorX server and the best template for this work was the crystal structure of Drosophila enolase (PDB ID: 5WRO). All residues were modeled as three domains with 39 positions (6%) disorder. The secondary structure of the 3D model displayed 21% helix, 23% β-sheet, and 54% coiled. The P-value of the homology modeling was 1.12e-06, which is a parameter of relative quality of the model. In this case, a small P-value refers to a good modeling quality.⁷⁴

The GalaxyRefine web-server performed some repeated structure perturbations and also structural relaxation by short molecular dynamics simulation to refine the vaccine 3D structure. In this procedure, the number of residues that were located in the favored region of the Ramachandran plot was increased from 87.3% to 91.3%. The refined structure was also energy minimized by the UCSF-Chimera and the system’s energy decreased to -15990.18 kJ/mol.

To validate the general and local quality of the vaccine construct, the Ramachandran plot was depicted after refinement and energy minimization of the model. As represented in Fig. 4, the vaccine model showed that only 4.49% of the residues is placed in the outlier regions. The general quality of the 3D structure of the vaccine was evaluated by ERRAT, Verify3D, and ProSA web-servers (Fig. 4). The overall quality scores of the model obtained from the ERRAT and Verify3D servers were 77.37% and 71.71%, respectively. ProSA server resulted in the Z-score of -4.6 for the uploaded vaccine 3D model, which was laid close to the native protein structures.

Fig. 4

Structural validation of the modeled vaccine. (A) The ProSA Z-score of the vaccine 3D model. (B) Ramachandran map analysis of the modeled vaccine construct, showing 95.51% of residues in favored and allowed regions. (C) The ERRAT plot of the vaccine 3D model. (D) The Verify3D plot of the vaccine 3D model. The overall modeling quality values showed the quality of the modeled vaccine is relatively satisfactory.

Evaluation of the vaccine efficiency

Prediction of antigenicity, allergenicity, and autoimmunity

The result of ANTIGENpro online server showed that the designed vaccine had an antigenicity probability of 0.847. This value represents the antigenic nature of the designed vaccine.

An allergic vaccine can cause the allergen-specific cross-reactive immune responses and symptoms in the host body such as skin rash and swelling of the mucosal membranes. Allergenicity of the designed vaccine was predicted by two web-servers (i.e., AlgePred and AllerTOP). The NCBI protein-protein BLAST against Canis lupus familiaris proteome was used for prediction of possible molecular mimicary between the vaccine sequence and endogenous proteins in dogs' body. The results showed the epitopic vaccine will not be associated with any allergic or autoimmune responses in vivo and is safe to be administrated to dogs (Fig. S5, Supplementary file 1).

Physicochemical properties of the vaccine

The ProtParam server was used for the assessment of nine important physicochemical parameters of the designed vaccine. The molecular weight (kDa) and theoretical pI of vaccine construct were predicted to be ~ 63 and 9.75, respectively. The total number of positively (Arg and Lys) and negatively (Asp and Glu) charged residues were 71 and 44, respectively. The estimated half-life of the vaccine construct in mammalian reticulocytes was 20 hours, in vitro; while 30 min and 10 hours in yeast and Escherichia coli, in vivo. The extinction coefficient at 280 nm wavelength was computed in water to be 36330 M-¹ cm^-1, assuming that all Cys residues are reduced. The instability index (II) of the vaccine construct was computed at 27.78, which indicates the stable nature of the minigene construct. ­The estimated values of the aliphatic index and the Grand average of hydropathicity (GRAVY) were 65.57 and −0.241, respectively.

The vaccine capability to interact with TLR-2 and TLR-4

The vaccine construct was engineered in such a way to be able for effective interaction with TLR-2 and TLR-4 receptors. In this step, we analyzed this protein-protein interaction using the ClusPro v2.0 server.

Vaccine–TLR-2 complex

Docking between the vaccine construct and TLR-2 leads to the generation of total 9 models. Of these, the model number 000.01 showed the lowest energy scores (-1193.2 kJ/mol), the highest binding affinity, which was properly located in interaction with the receptor, thus was selected as the best-docked complex (Fig. 5). The intramolecular interactions including hydrophobic and hydrogen bonds are represented in Fig. 5. The DIMPLOT analysis exhibited that nineteen residues of the vaccine construct were involved in hydrogen bond (H-bond) with TLR-2 and the length of H-bonds was ranged from 2.55 to 3.04 Å (Fig. 5).

Fig. 5

The docked complex of the minigene vaccine and the TLR-2 receptor. (A) Various components of the vaccine construct, including adjuvant (e.g., Lsa21, and APPHALS), linkers (e.g., EAAAK, FFRK, GPGPG, GPSL, and GGGGSGGGGS), and B- and T-cell epitopes are shown in contact with TLR-2 (B) The hydrophobic and hydrogen bonds between the vaccine construct and TLR-2.

Vaccine–TLR-4 complex

Among total 9 generated complexes, the model number 000.00 showed the lowest binding energy values (-1018.3 kJ/mol) between the vaccine construct and TLR-4. This model was properly located in interaction with the receptor, thus was selected as the best-docked complex (Fig. 6A, B). The intramolecular interactions including hydrophobic and hydrogen bonds are represented in Fig. 6C. The DIMPLOT analysis exhibited that nineteen residues of the vaccine construct were involved in hydrogen bond (H-bond) with TLR-2 and the length of H-bonds was ranged from 2.55 to 3.04 Å (Fig. 6).

Fig. 6

The interaction between the minigene vaccine and TLR-4 receptor as three- and two-dimensional representations. (A) The docked complex of vaccine construct and its receptor TLR-4. (B) Various components of the vaccine construct, including intramolecular adjuvants (e.g., Leptospira surface adhesion (Lsa21), and APPHALS), linkers (e.g., EAAAK, FFRK, GPGPG, GPSL, and GGGGSGGGGS), and B- and T-cell epitopes are shown as a docked complex with TLR-4. (C) The hydrophobic and hydrogen bonds between the vaccine construct and TLR-4.

Codon optimization and analysis of mRNA stability

To achieve the maximal protein expression in the E. coli expression system, the protein sequence of the vaccine construct was backtranslated to the DNA sequence and then codon usage of the sequence was adapted based on E. coli K12 codon frequency table and using the visual gene developer v1.7 software. In this sense, the value of codon adaptation index (CAI), the GC-content, effective number of codons (Nc), and tRNA adaptation index (tAI) before and after codon optimization are represented in Table 4.

Table 4.The parameters of codon usage bias pre- and post-codon adaptation of the vaccine construct
Parameter	Before	After
CAI	1.0 (0.558)	0.9
Nc	13.8	15.0
tAI	0.4	0.4
Overall G/C content (%)	57.5	57.3
G/C content at 1st place (%)	57.4	57.4
G/C content at 2nd place (%)	47.8	47.8
G/C content at 3rd place (%)	66.9	65.7
CAI: Codon adaptation index; Nc: Effective number of codons; tAI: tRNA adaptation index.

The overall Gibbs free energy (ΔG) of mRNA secondary structure pre- and post-optimization were -832.90 and -815.80 kcal/mol, respectively. The ΔG shows the favorability of the mRNA folding process.

Discussion

Vaccination of animals might be one of the important treatment modalities for efficient, rapid and affordable controlling of the zoonotic infectious diseases among the human populations. The current study is focused on the rational vaccinology (e.g., epitope-based vaccines) in comparison with the traditional vaccinology (e.g., killed, attenuated or live vaccines) because the rationally designed vaccines contain specific immunogenic machinery of the organisms responsible for the disease rather than the whole-cell vaccines.⁷⁵ Over the past decades, the genome sequencing projects lead to accessibility to the genomic and proteomic data of E. granulosus and the other pathogenic microorganisms in databases. This information from the pathogens aid vaccinologists in rational antigen discovery using the computational methods.⁷⁶

The excretory/secretory (ES) products of adult E. granulosus have a critical role in physical interaction with the host proteins (likely plasminogen) during the host-pathogen interactions. Among various ES products, enolase is the most abundant one, which is known as a multifunctional tegumental enzyme. The EgEnolase in various parasites shows antigenic properties and in E. granulosus is also involved in the invasion and evasion mechanisms through the immunomodulation of the host immune system.^77,78 An epitope-based vaccine administrated to animal or human host body should ideally (i) be immunogenic sufficiently, (ii) has a high ability to stimulate significant humoral and cellular immune response, and finally (iii) activate the memory cells against the pathogenic epitopes. In our study, seven E. granulosus enolase protein sequences were checked in terms of antigenicity and the best ones were selected for further immunoinformatics analysis (Table S1, Supplementary file 1). Surface accessible clusters of residues in the folded antigenic proteins predominantly consist of hydrophilic amino acids, which can be mainly defined as BCE. The surface regions of antigens contribute to the direct interaction with the humoral immune cells especially immunoglobulins (e.g., IgG). Therefore, identification of the solvent surface accessible residues is the necessary step for more accurate recognition of the conformational BCEs.⁷⁹ Therefore, prior to the BCE mapping, the primary sequence was scanned for the presence of non-accessible residues such as a hydrophobic, transmembrane and signal peptide. B lymphocyte cells are the main arm of the humoral immune system. In vaccine designing against extracellular pathogens, the main immunological function is the detection of the epitope by the B-cell receptor.⁸⁰ In the current study, BCEs were identified based on the multi-method process that consists of different physicochemical, structural, and machine learning based methods (Fig. 2). CD4⁺ T-lymphocytes are the main player for coordination of both humoral and cellular immune responses.⁸¹ Therefore, the helper T-cell receptor specific epitopes are the main components for vaccine designing against infectious diseases^13,61 as well as cancers.⁷³ The estimated physicochemical parameters of the final vaccine construct showed the vaccine protein contains aliphatic side chains and is hydrophobic, thermos-stable, and basic in nature.⁸² The 3D structure of the EgEnolase, TLR-2, TLR-4, and DLA-DRB1*01101 was modeled with the minimum structural error (Fig. 1). The structural validation scores and tools including GA341, DOPE, ERRAT, Verify3D, and ProSA showed that the overall quality of the modeled vaccine construct was satisfactory. The GA341 score is ranged from 0.0 (worst model) to 1.0 (native-like model) and the model with more negative DOPE score counts as a stable model. The zero and negative ProSA Z-score values are related to the stabilize models.³⁷ Ramachandran plot indicated that a high percentage of residues (~96%) were clustered tightly into the favored and allowed regions of the map (Fig. 64 and Table1). The codon optimization parameters especially GC-content and CAI value of the improved sequence showed the nucleotide sequence can be effectively expressed in the E. coli system. Some disapproved hairpin loops in initial nucleotides of the mRNA were optimized to improve the mRNA stability especially in the ribosome binding site.⁹ The formation of mRNA secondary structure is a key step in translation initiation, elongation, and generally mRNA biogenesis.⁸³ In this sense, the mRNA sequence should be optimized in terms of the codon usage bias of expression system (E. coli K12). In our research, this step was accomplished by analysis of guanine-cytosine (GC) content, codon adaptation index (CAI), an effective number of codons (Nc), and tRNA adaptation index (tAI) of the nucleotide sequence. The hydrophilicity and thermos-stability nature of the overexpressed vaccine construct in E. coli K12 are emergence factors for the investigation of the protein function. Leptospirosis surface adhesion 21 (Lsa21) is the main virulence factor in Leptospira pathogenesis and it is identified that has strong TLR-2 and TLR-4 activity leading to the control of Leptospira infection. Thus, Lsa21 suggested has a potent immunostimulatory capacity (or adjuvanticity) to induce strong innate responses.⁵⁸ Our designed vaccine showed an acceptable extent of the solubility in an overexpressed state. Moreover, we observed a high binding affinity between the selected HTEs and DRB1*01101 allele (Table 3). The overall vaccine construct displayed has a high binding affinity to the TLR-2 and TLR-4 receptors (Figs. 5 and 6). In this study, we aimed to integrate the comprehensive immunoinformatics tools to the known immunological data of dog-echinococcus interactions to design a non-allergen, safe, and immunogenic minigene vaccine. The immunological potency of the predicted epitopes for animal trial against E. granulosus is under the investigation by histopathological and immunological bioassays.

Conclusion

E. granulosus is an asymptomatic infection that can lead to the high-frequent cyst development to the various internal organs, such as liver, lung, kidney, and central nervous system. At any one time, more than one million people are affected with echinococcosis. Lack of an effective vaccine and high fiscal burden of the controlling programs especially in the developing countries resulted in the erosion condition among the people living in the endemic areas. On the other side, vaccination in animals is a cost-effective strategy to develop animal health and have an important impact on public health by controlling the zoonotic diseases. Therefore, it is of great necessity to develop the novel strategies to combat against the zoonotic pathogens such as E. granulosus. The previous studies revealed that the secretory proteins of E. granulosus (e.g., enolase) not only respond to the pathogenesis but also are the vital immunogenic proteins in the host-pathogen interactions. Therefore, this study was designed to establish a step ahead in the vaccinology. We used EgEnolase protein sequence to design an epitope-based vaccine construct. The minigene vaccine has T-helper and B-cell determinants that were selected based on the multiple methods. The minigene vaccine showed stable physicochemical properties as well as a proper binding affinity for the TLR-2 and TLR-4 receptors. Generally, this study was performed based on the step-by-step immunoinformatics analysis to design a rational vaccine that may fight prophylactically against the E. granulosus infection. In our on-going project, we are going to revalidate through in vitro and in vivo bioassays to prove the vaccine safety and immunogenicity.

Funding sources

This work is part of the military soldier service project, financially supported by the AJA University of Medical Sciences (grant No.: 997889).

Ethical Statement

There is none to be declared.

Acknowledgments

The authors thank Professor Yadollah Omidi for providing some facilities in terms of the computational methods and his helpful comments in improving the manuscript quality.

Competing interests

No potential conflict of interests was reported by the authors.

Author contributions

MMP contributed to the bioinformatics analysis and wrote the manuscript. MY, and MA supervised the study. GM and AN did result interpretation and discussion.

Supplementary Materials

Supplementary file 1 contains Figs. S1-S6 and Tables S1-S3.

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