Introduction

Toxocariasis is one of the most distributed zoonotic infectious diseases worldwide with a wide range of clinical manifestations (Baneth et al. 2016; Aghaei et al. 2018; Mohammadzadeh et al. 2018). The disease is mostly caused by the larvae of T. canis and T. cati, which are intestinal nematodes found in canids and felids as definitive hosts (Despommier 2005; Fakhri et al. 2018; Aghamolaie et al. 2018). Children are at higher risk for infection with Toxocara spp., due to more direct contact with the soil contaminated with eggs (Baneth et al. 2016; Siyadatpanah et al. 2013). Based on the migration of larvae in different organs (such as the liver, brain, and eyes), clinical manifestations vary in different patients (Despommier 2005; Smith et al. 2009).

Human patients are diagnosed based on serological tests alongside of clinical symptoms and signs, and laboratory findings such as eosinophilia, leukocytosis, and hyperglobulinemia (Rubinsky-Elefant et al. 2010). Direct detection of larvae in tissue by microscopy from biopsies is rarely successful (Rubinsky-Elefant et al. 2010). Serological diagnosis of toxocariasis is usually performed by commercial immunoglobulin G-enzyme-linked immunosorbent assay (IgG-ELISA) kits using T. canis excretory–secretory (TES) antigens of second-stage larvae, although this method could sometimes be laborious and time-consuming with limited production capacity (Overgaauw 1997). Moreover, in the other developing countries, due to the high rate of soil-borne worms, serum samples from patients with ascariasis and strongyloidiasis cross-react with the native TES antigen in immunoassay (Noordin et al. 2005; Fong et al. 2003). Therefore, the development of highly specific and sensitive methods for timely and accurate detection of anti-Toxocara antibodies is critical to improve the diagnosis of human toxocariasis (Rubinsky-Elefant et al. 2010). For this purpose, the employment of recombinant antigens was widely investigated in recent years.

Advantages of recombinant antigens include unlimited production, highly sensitive and specific results, and minimized possibility of cross-reaction with antigens of the other parasites (Norhaida et al. 2008; Yamasaki et al. 2000). Previous studies indicated several recombinant antigens suitable for the serodiagnosis of toxocariasis, namely TES-26, TES-30, and TES-120 (Norhaida et al. 2008; Fong and Lau 2004; Mohamad et al. 2009). The surface of parasite molecules contains many overlapping antibody-binding sites called epitopes (Frank 2002). All epitopes have fuzzy regions that can be exactly recognized by the antibodies paratope (i e, antigen binding site). Antigenic cross-reactivity is a common occurrence, since at any time antibodies can recognize a large number of epitopes (Frank 2002). This place can help to reduce the specificity of antibodies in binding to the antigen. The antigenicity is the capacity of an epitope to react with an antibody in terms of the recognition, immunogenicity, and the ability to induce the immune system to produce antibodies in a competent vertebrate host (Frank 2002; Dai et al. 2013; Sela-Culang et al. 2013). If it is not done correctly, it could cause problems in the design of vaccines and diagnostic kits. Therefore, the prediction of epitopes can be beneficial in the development of vaccines and diagnostic tests (Karpenko et al. 2014; Dipti et al. 2006).

Recently, bioinformatics software is employed to predict antigenic epitopes by researchers in biotechnology and immunology research centers (Karpenko et al. 2014; Kulkarni et al. 2012). The employment of the software could be helpful for a good prediction and the exact identification of epitopes in terms of high availability and antigenicity (Pruess and Apweiler 2003). It also designs and constructs the recombinant multi-epitope antigen; using such epitopes may provide a new tool and an alternative to reach cost-effective and more accurate diagnostic kits (Dipti et al. 2006). Moreover, experimental studies revealed that the use of peptide-based antigens can meet the need for serological test standardization and increase the sensitivity and specificity of ELISA (Dai et al. 2013; Lv et al. 2016). Multi-epitope approach as a potential antigen-capture is assessed in different studies for a range of pathogens, but such studies are not conducted to determine antigenic epitopes of T. canis (Hajissa et al. 2015). According to the above statements, the present study aimed at predicting and designing a novel synthetic protein consisting of multiple immunodominant B-cell epitopes of several T. canis antigens, TES-26, TES-30, TES-120, and analyzing its immunogenicity and preliminarily evaluation to improve the accuracy of serodiagnostic kits for human toxocariasis.

Methods

A flowchart for the creation of new synthetic construct is presented in Fig. 1. The different main steps performed in the methodology are shown.

Fig. 1
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Flowchart summarizing the steps of a multi-epitope Toxocara canis design for diagnosis of human infections

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Protein Sequence

Nucleotide sequences were retrieved from the National Centre for Biotechnology Information (NCBI) Nucleotide Database. TES-120 (GenBank: U39815), TES-30 (GenBank: AB009305) and TES-26 (GenBank: U29761), sequence of protein was recovered from UniProt (Universal Protein resource) that is an easily accessible database which comprises data of proteins. The protein sequence was recovered through their accession number and it was in FASTA format.

Membrane Protein Topology and Signal Peptide Prediction

The transmembrane structure of proteins were predicted by TMHMM Server v. 2.0 (https://www.cbs.dtu.dk/services/TMHMM/). The sequence of proteins were input, and three regions, including outside, transmembrne and inside regions, were studied (Krogh et al. 2001).

The signal peptide prediction of proteins were predicted by SignalP 4.1 Server (https://www.cbs.dtu.dk/services/SignalP/) (Geourjon and Deleage 1995).

Prediction of Linear and Conformational B Cell Epitopes

For prediction of linear B-cell epitopes, the IEDB, ABCpred, Kirloskar, Bcepred, LBTope, SVMTriP, Bepipred and Emini surface accessibility web servers were employed. These tools evaluates the epitopes, on the basis of Chou and Fasman beta-turn, Emini surface accessibility prediction, Karplus & Schulz Flexibility Prediction, Kolaskar & Tongaonkar Antigenicity, Parker Hydrophilicity Prediction (Kolaskar and Tongaonkar 1990; Singh et al. 2013; Yang 2004; Saha et al. 2005). Also, for prediction of conformational B-cell epitopes, the CBTOPE server was used. This server can predict conformational B cell epitope by antigen primary sequence in the lack of any homology with the known structures (Zhang et al. 2011).

Designing and Modeling Multi-epitope Antigenic (Construct) and Improving Immunogenicity

Primary, secondary structures of the proteins were analyzed by using several various online software. Finally the peptides which included most high score B-cell epitopes, possessing higher antigenicity and corresponding to most prediction results were chosen. To achieve the best immunization in construct, first, incorporated two repeats of TES-120 epitope in the beginning and the end of constructs and put linkers between epitopes. Finally, more distances between epitopes were created by adding two or more amino acids in the constructs. These tactics can help to achieve the better immunization in construct.

Reverse Translation and Codon Optimization & Prediction of Open Reading Frame (ORF)

The B cell protein construct was backtranslated into nucleotide sequences using backtranseq program of mEMBOSS 6.0.1 (https://www.ebi.ac.uk/Tools/st/emboss_ backtranseq/). The degeneracy of the genetic code cause backtranslation potentially obscure since most amino acids are encoded by multiple codons. Backtranseq was restricted to codon uses within the Bos tau. Codon optimization is a method for higher gene expression of vectors to arrive optimum expression of a foreign gene (Sandhu et al. 2008). Large numbers of C–G sequences in the messenger RNA (mRNA) can prevent protein translation from increased constitution of secondary structures, So, increase mRNA stability that considerably improves immune responses (Kalwy et al. 2006). Constructs were optimized by the codon adaptation tool server (http: //www. jcat.de/Start.jsp). At the –NH2 and –COOH terminus of construct, an initiation codon ATG and a termination codon TAA were added (Ramakrishna et al. 2004) For confidence of ORF, we used the gorf tool available at the NCBI server (https://www.ncbi.nlm.nih.gov/gorf/). This server distinguishes all ORF by the standard or alternative genetic codes.

Primary, Secondary & Tertiary Structure Prediction of Construct

Protein sequence statistics for constructs including length, molecular weight, isoelectric point (IEP), total number of positive and negative residues, instability index, grand average hydropathicity (GRAVY), aliphatic index, and amino acids distribution were calculate by using the ExPASy ProtParam server (https://expasy.org/cgi-bin/protpraram) (Gasteiger et al. 2003). The secondary structure of Construct was assessed by the Self‑Optimized Prediction method With Alignment (SOPMA) Server (https://npsa-prabi.ibcp.fr/cgi-bin/secpred_sopma.pl). This method predicts 69.5% of amino acids for a three-state description of the secondary structure (a-helix. (3-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins (Geourjon and Deleage 1995).

The tertiary structure of construct was predicted by the online prediction server I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). I-TASSER (Iterative Threading ASSEmbly Refinement) is one of the top tools for automatic protein structure prediction. This server is in active development with the aim to provide the most accurate structural and function predictions using state-of-the-art algorithms (Yang et al. 2015). For confirmation of the predicted structures, the ProSA-web at https://prosa.services.came.sbg.ac.at/prosa.php was applied to recognize the potential errors in modeled structure before and after minimization process and Ramachandran plot was studied through PROCHECK analyses in the PSVS server v. 1.5 (http://psvs.nesg.org/) too (Bhattacharya et al. 2007; Wiederstein and Sippl 2007). Lastly, the 3D model result generated in a PDB format was analysed and visualized using PyMOL version 1.3 available at https://pymol.org/2/ (DeLano 2002).

Results

Analysis of Transmembrane Topology & Signal Peptide Properties Proteins

The results showed that outside regions of TES-120, TES-30 and TES-26 were located at positions 1–176, 1–225 and 1–262 respectively, thus all the proteins in this study are in the outer part of the membrane. The results are displayed in Supplementary Fig. 1. The signal peptide prediction results for proteins are displayed in Supplementary Fig. 2, that due to their interference role, these sequences should be remove from the epitope.

Designing and Modeling B Cell Construct

A combination of the results predicted for every three proteins by the different parameters and bioinformatics software for predicting antigenic epitopes TES-120, TES-30, and TES-26 are presented in Table 1. Five epitopes were found to have a consensus and used for in silico concatenation, two consensus epitopes were selected for TES-30 and TES-26 and for TES-120 an epitope was selected which was repeated at the beginning and the end of the multi-epitope sequence (Table 2).The individual epitope lengths varied)from 35 to 65 amino acids( and each epitope with a flexible linker sequence (Gly-Ser-Gly-Ser-Gly) was connected to another epitope (Fig. 2).

Fig. 2
figure 2

Schematic illustration of Connect epitopes to each other with flexible linker sequence (Gly-Ser-Gly-Ser-Gly)

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Table 1 Predicted epitopes and selected final consensus epitope (Antigen regions were selected)
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Table 2 Number of predicted epitopes for TES-120, TES-30 and TES-26 and selected final consensus epitope
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Reverse Translation and Codon Optimization

Protein sequences of make were reverse translated into nucleotide sequences using the backtranseq program. The numbers of C–G sequences were optimized. That, it may cause increase mRNA stability and significantly improves immune response (Besse and Ephrussi 2008). Also, the position of the relevant restriction enzymes) BamH I, Hind III) was designed at the beginning and end of the B cell construct.

Final Construct & ORF Checking

A schematic view of the final pattern and location of consensus epitopes in the B cell construct is shown in Supplementary Fig. 3. So, ORF examination show no errors, thus exhibit optimal expression of the construct.

Evaluation of the Primary, Secondary & Tertiary Structure of Designed Construct

A summary of the obtained results of the primary of B cell construct is show in Table 3. The average length of the constructs is 342 bp. IEP is the pH point that the surface of protein is covered with charge, but the net charge of protein is zero. IEP is important for the evaluation of solubility and the mobility in an electric field. The amount IEP was calculate to be 5.78 for B cell construct. The computed value for B cell construct is lower than 7 demonstrating that acidic nature of the protein. The aliphatic index for B cell construct is 55.64. So, indicates that this construct is stable for a wide range of temperature. However the instability index (44.39) offers the estimation of the stability of protein in vitro, and the results classified the constructs with moderate stability. Grand Average Hydropathicity (GRAVY) was negative (− 0.180) for B construct. The GRAVY values indications the degree of protein hydrophilicity and increasing positive score indicates greater hydrophobicity. This one is clear that construct has high hydrophilicity and tendencies to interact with surrounding water molecules.

Table 3 Parameters computed for B cell construct predicted by Expasy ProtParam tool
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Secondary structure B cell construct was predicted using SOPMA Server, and the results is given in Fig. 3. Our results shown that α-helixes, β-turns, random coils and extended strands account for 8.19, 6.14, 60.53, and 25.15% of the secondary structures, respectively. The high proportion of random coils and extended strands in the structure of B cell construct suggest that the protein may form antigenic epitopes.

Fig. 3
figure 3

Secondary structure of B Cell Construct predicted by using SOPMA software

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For epitopes prediction, it is essential to determine three-dimensional structure of the proteins. Therefore, using I-TASSER servers, tertiary structures of the proteins were predicted. For each target, I-TASSER simulations generate a large ensemble of structural conformations, called decoys. To select the final models, I-TASSER uses the SPICKER program to cluster all the decoys based on the pair-wise structure similarity and reports up to five models which corresponds to the five largest structure clusters. The confidence of each model is quantitatively measured by C-score that is calculated based on the significance of threading template alignments and the convergence parameters of the structure assembly simulations. C-score is typically in the range of (−5, 2), where a C-score of a higher value signifies a model with a higher confidence (Roy et al. 2010).These results are displayed in Fig. 4. The modelled structure was evaluated by RAMPAGE and was used to generate a Ramachandran plot. The results of the Ramachandran plot showed 56.2% of residues were found in the favored regions of the plot and the number of residues in the allowed region was 41.3%. Conversely, 2.5% of the residues were found in the outlier region (Fig. 5). Also, ProSA-web zscore plot for 3D structure for B Cell Construct predicted before and after minimization showed that the z-score of the initial model was − 2.92 and the z-score of the model after minimization processes was − 2.94 (Fig. 6a, b). These results indicate that the 3D modeled structure is reliable.

Fig. 4
figure 4

Tertiary structure B cell construct predicted by I-TASSER server. a Model 1: C-score =  − 3.03   b model 2: C-score =  − 3.86    c model 3: C-score =  − 3.73   d model 4: C-score =  − 4.09     e model 5: C-score =  −  3.95

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Fig. 5
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Ramachandran plot models for B cell construct predicted

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Fig. 6
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ProSA-web z-score plot for 3D structure of B cell construct predicted before and after minimization. a The z-score of the initial model is − 2.92 and b the z-score of the model after minimization processes is − 2.94

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Discussion

Toxocariasis is a serious zoonotic parasitic disease which affects vertebrates with high impact on public health worldwide (Despommier 2005). The development of a highly specific, sensitive and reliable assay to detect the presence of anti-Toxocara antibodies is an key aim toward improving the diagnosis (Roldan and Espinoza 2009). Commercial diagnostic kits are known to have issues with specificity when used in countries endemic with soil-transmitted helminthiasis, which is because of the non-specific nature of components of native TES antigens that cross-react with other helminth antigens. Therefore, native TES antigen is suitable only for differential diagnosis, and test interpretation is difficult when the result is positive. To control this limitation, recombinant antigen can standardize diagnostic methods and increase the sensitivity and specificity of these tests. One description for the high specificity of the recombinant antigen is that it is a molecule with a molecular mass with high immunogenicity, while TES consists of multiple components with a wide range of molecular masses (Maizels et al. 1984). Also, in contrast to glycosylated TES the recombinant antigen produced in bacteria is not glycosylated. This can also lead to a decrease in cross-reactivity with antibodies that recognize the sugar moieties of the TES produced in T. canis larvae (Maizels et al. 1987). Therefore, the study of proteins can be useful for diagnosis and therapeutic purposes. Also, the use of tools for in silico analysis are needed to predict structural and functional features of proteins (Pruess and Apweiler 2003). Recently, with the development of bioinformatics studies, epitope prediction has drastically developed (Frank 2002; Karpenko et al. 2014). Performing predictions with a multi-parameter and method analysis greatly enhances the accuracy of the epitope prediction (Saha et al. 2005). So, the aim of the present study was prediction and design of B cell multi-epitope antigenic T. canis and evaluation of its immunogenicity. For this purpose, a number of online prediction software applications, including IEDB, Bcepred and ABCpred, were used. Making predictions using a multi-parameter and multi-method analysis improves the accuracy of epitope prediction significantly. For example the flexibility parameter prediction show the ability to bend of protein. By a greater flexibility, protein has a high capacity to bend, thus helping the formation of a secondary structure. The hydrophilicity parameter prediction describes the location of hydrophilic residues in the amino acid sequence of the protein. The hydrophilic residues are located on the out of the protein and are suitable for ligand binding. The dominant epitopes are more likely to be in regions with a high hydrophilicity.

According to the results of this study, the distribution of amino acids & the number of hydrophilic residues in selected epitopes are more than hydrophobic amino acids. Therefore the most of them are located outside the membrane. Also, the transmembrane structure of proteins was predicted using the online CBS prediction software TMHMM Server. The results of transmembrane topology can be useful in the selection of proper epitope indications. These analysis of transmembrane structure, show that multi-epitope is more outside regions. Thus, it demonstrations that the proteins have suitable solubility and have a decent condition of exposure for immune system in body.

So, the secondary structure of protein is predicted by SOPMA which evaluates the percentage of alpha (α) helices, extended strand, random coils and beta (β) turn. The secondary structure of protein was closely related with antigenic features. So, it show that the selected multi-epitope in this study has suitable resistance. The tertiary structure is a three-dimensional globular structure composed of additional coiling and folding of secondary structure elements, such as α-helixes, β-turns, random coils and extended strands therefore, it can account the degree of similarity in sequence protein can be modeled using this tool. Alignment of sequence can be ameliorated manually, and homology methodology is utilized to build the structure of protein.. The modelled structure was evaluated by RAMPAGE and was used to generate a Ramachandran plot. This tool was used in determining the energetically stable conformations of the psi (ψ) and phi (Φ) torsion or dihedral bond angles for each amino acid in the structure. These results indicate that the modeled structure is reliable. Moreover, ProSA-web plot assessment after minimization showed to resulted in modeling of a high quality 3D model. Therefore we conclude that for TES-120 protein, the residues in the region between 97–167 & for TES-30 in the residues 52–102, 172–207 and for TES-26 regions among 33–83, 130–180 have the most immunogenic potential. These regions have decent potential for designing epitopes. These epitopes were fused to each other with proper linkers. Proper linkers have an important role in functional and structural features of a B cell construct and applying various linkers may result in generation of a new protein with different characteristics. So, furthermore to immunological function of linkers, they prevent the formation of new epitopes (Dorosti et al. 2019; Saadi et al. 2017).

Conclusion

This study aimed to obtain the bioinformatic characteristics the B cell epitopes for three proteins TES-120, TES-30, TES-26 and analyze its immunogenicity. The results of the secondary structure prediction demonstrated that there are potential epitopes in these proteins. So, it can provide a theoretical basis for investigating its antigenicity and provides a theoretical foundation for epitope diagnosis development for human toxocariasis.