1 Introduction

Toxoplasma gondii (T. gondii) as an obligate intracellular parasite is classified under the phylum Apicomplexa. Cats serve as the main hosts for this parasite, while a variety of warm-blooded vertebrates such as marine mammals, humans, livestock, and birds act as intermediate hosts [1,2,3]. Surveys on the prevalence of T. gondii suggest that it is widely distributed and has a high occurrence rate in various regions worldwide [4, 5]. Human infection can happen through ingesting oocysts excreted by cats, consuming contaminated meat containing tissue cysts, or through congenital transmission [6,7,8]. In immunocompetent individuals, toxoplasmosis is typically asymptomatic; however, in immunocompromised patients, it can lead to severe and life-threatening complications [9]. In pregnant women, acute infections can lead to severe consequences in the fetus, including hydrocephalus, miscarriage, vision impairment, and cognitive disabilities [10]. T. gondii infections can trigger encephalitis by reactivating dormant cysts in individuals with weakened immune systems [11], making it a significant opportunistic pathogen in HIV patients [12]. Among livestock, toxoplasmosis can cause abortions, stillbirths, and deaths in newborn animals, resulting in considerable economic losses. Infected livestock serve as a primary infection source for humans [13]. Presently, there is no perfect medication available for preventing or treating T. gondii infections. Nonetheless, existing insights into protective immunity against T. gondii infections propose that vaccination could serve as a feasible approach for disease management [14]. Bioinformatics stands out as a contemporary research domain employed to tackle biological challenges more efficiently [15]. This field is widely applied for gene and protein expression analysis, protein structure prediction, assessment of immunogenicity, and overall characteristics. Through the examination of physical, chemical, and immunogenic attributes of proteins, researchers can deepen their comprehension of these molecules and pinpoint suitable epitopes for vaccine development [16]. Using bioinformatics strategies for identifying protein epitopes is invaluable for diagnostic applications and vaccine production [17]. Improving computational approaches increases the reliability of in silico methods in biological studies [18]. Depending on this, the methods are often preferred for predicting antigenic proteins even if they are not expressed in vitro [18]. In silico methods also offer several advantages such as time/effort and cost-effectiveness. For this reason, in silico methods are essential as a pre-analysis strategy prior to starting wet lab investigations [18]. Although the number of proteins can be easily predicted using in silico methods for antigen discovery, a very important step in vaccine design studies, as a further recommendation, three conditions should be considered; Choosing the right analysis tools, using multiple parameters to get the right results, and further validating them through wet lab studies [18]. It is important to recognize that the continuous efforts of scientists in developing candidate vaccines for T. gondii have yielded significant advancements with key antigens, like surface antigens (SAG), microneme antigens (MIC), dense granule antigens (GRA), and ROP antigens [19]. Among these, ROPs are particularly noteworthy as promising vaccine candidates because of their distinctive performance [20]. Proteins encoded by the ROP family genes are promising candidates for DNA vaccines, with several members such as ROP5, ROP18, ROP17, and ROP19 being identified as promising vaccine candidates against T. gondii [21,22,23,24]. Similar to other ROP proteins, ROP34 is localized to the rhoptries. However, its structure and epitopes remain not well-defined. Hence, the current study aimed to precisely assess the bioinformatics characteristics of the ROP34 protein from T. gondii. This involved examining various aspects such as allergenicity, antigenicity, physico-chemical properties, signal peptide, post-translational modification (PTM) sites, transmembrane domains, structural analysis, and subcellular localization. Additionally, comprehensive immunoinformatics approaches were employed to predict the epitopes of helper T-lymphocyte (HTL) and cytotoxic T-lymphocyte (CTL) associated with these proteins.

2 Material and methods

2.1 Amino acid sequence

Initially, the complete ROP34 amino acid sequence was achieved from the National Center for Biotechnology Information (NCBI), a publicly accessible sequence database. Links to all bioinformatics web servers used in this study are provided in Supplementary Table 1.

2.2 Predicting physico-chemical properties

To determine the physico-chemical properties of ROP34, the Expasy ProtParam tool was used. This analysis provided information on amino acid composition, protein molecular weight (MW), aliphatic index (AI), grand average of hydropathicity (GRAVY), isoelectric point (pI), instability index (II), extinction coefficients, in vivo and in vitro half-life, as well as the residue numbers with negative and positive charges [25].

2.3 Allergenicity, antigenicity, and solubility assessment

The ANTIGENpro [26] and VaxiJen 2.0 [27] servers assessed the ROP34 protein antigenicity. ANTIGENpro predicts antigenicity based solely on microarray data, with no relying on pathogen and alignment data. In contrast, VaxiJen uses a novel alignment-free approach, transforming sequences through auto cross covariance (ACC) into vectors of principal amino acid properties. The accuracy of VaxiJen predictions ranges between 70 and 89% depending on the target organism (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen_help.html). Additionally, allergenicity was predicted using the AlgPred server, which employs a hybrid approach (IgE epitope + SVMc, ARPs Blast + MAST) with 85% accuracy and a threshold of − 0.4 [28]. The SOLpro server predicted the solubility of the protein after overexpression [29].

2.4 Signal peptide prediction and post-translational modification (PTM) sites of ROP34

The SignalP server identified the presence of signal peptides in the sequence. The protein had a Sec signal peptide (Sec/SPI), a Lipoprotein signal peptide (Sec/SPII), a Tat signal peptide (Tat/SPI), or no signal peptide at all (Other) [30]. Phosphorylation sites for tyrosine, threonine, and serine, were identified using neural network ensembles in the NetPhos 3.1 server [31]. Additionally, GPS-PAIL 2.0 [32] was used to detect possible acylation sites in the ROP34 sequence. Furthermore, the NetNGlyc1.0 [33] and NetOGlyc 4.0 servers respectively anticipated N-linked glycosylation and O-linked glycosylation regions [34]. The NetNGlyc1.0 server determined N-linked glycosylation sites by selecting "Predict on all Asn residues," and default features were applied for the O-linked glycosylation areas.

2.5 Identification of subcellular localization and transmembrane domains

The PSORT II server predicted the ROP34 subcellular localization. Additionally, the protein transmembrane domains were assessed by the TMHMM 2.0 server [35].

2.6 Prediction of tertiary and secondary structures

The Garnier-Osguthorpe-Robson (GOR) server predicted the secondary structure [36]. To further elucidate the ROP34 secondary structure, the PSIPRED server was utilized for position-specific iterated anticipation, by the outputs of PSI-BLAST [37]. Disulfide bonds within the protein were predicted by the DIpro server [38]. The ROP34 sequence 3D models were generated through homology modeling using the SWISS-MODEL server [35,36,37,38,39].

2.7 Tertiary structure confirmation and refinement

Of the developed templates and built models using SWISS-MODEL, one characterized by suitable sequence identity and coverage was considered for refinement using the GalaxyRefine online server, working following the CASP10-tested method [40, 41]. It can rearrange side chains, repack them, and relax the entire structure through molecular dynamics simulation. The SWISS-MODEL server generated Ramachandran plots for validating the tertiary structure [42]. According to Narula et al., the “Ramachandran plot is utilized to predict the likelihood of a particular amino acid forming secondary structures based on the dihedral angles ɸ and Ψ (allowed and disallowed) of amino acids, which are calculated based on the van der Waals radius of side chain atoms” [43].

2.8 Predicting conformational and linear B-cell epitopes

Several servers predicted linear B-cell epitopes. Initially, the ABCpred server identified the linear B-cell epitopes [44]. Additionally, these epitopes were predicted using the Bcepred web-based algorithm. Continuous B-cell epitopes were predicted by the Bcepred tool, with 58.7% accuracy considering physico-chemical characteristics, like antigenic propensity, hydrophilicity, exposed surface, accessibility, polarity, flexibility, and turns [45, 46]. Furthermore, the B-cell epitopes were predicted by BCPREDS 1.0 with the default specificity of 75% and 20-mer epitopes [47] and SVMTriP [48]. The ProtScale tool generated a graphical representation of linear epitopes, using hydrophobicity, mean flexibility, alpha-helix, beta-turn, and the percentage of accessible residues [25]. The ElliPro server anticipated discontinuous B-cell epitopes. Each epitope receives a protrusion index (PI), which is the average of its residues. ElliPro uses ellipsoids for these calculations; for example, a PI value of 0.8 indicates that 80% of residues are within the ellipsoid, while the rest are outside. Residues with higher PI values are linked to greater solvent accessibility. Additionally, the R distance, which is the interval between a residue’s mass center measured in Angstroms, affects the clustering of epitopes. An increase in the R value suggests a higher number of conformational B-cell epitopes [49]. These immune epitope database (IEDB) further identified epitopes. Finally, the VaxiJen 2.0, AllergenFP 1.0 [50], and PepCalc servers evaluated antigenicity, allergenicity, and water solubility of the epitopes, respectively.

2.9 Predicting major histocompatibility complex (MHC)-specific epitopes

The Immune Epitope Database (IEDB) recommended method was utilized to predict the half-maximal inhibitory concentration (IC50) scores of peptides binding to MHC class I and II molecules. Specifically, predictions were made for MHC-I specific epitopes (10-mers) for mouse alleles H2-Db, H2-Dd, H2-Kb, H2-Kd, H2-Kk, and H2-Ld. Additionally, predictions were conducted for MHC-II epitopes (15-mers) for mouse alleles H2-IAb, H2-IAd, and H2-IEd. Furthermore, forecasts were conducted with peptides consisting of ten amino acids employing the IEDB recommended technique. Subsequently, the identified epitopes underwent immunogenicity prediction utilizing the IEDB MHC Class I immunogenicity prediction tool [51]. The prediction process adhered to the IEDB recommended methodology, with the expected peptide length set at 15 amino acids and arranged by percentile rank. Following this, the antigenicity and the potential for IFN-γ and IL-4 induction for each epitope were forecasted using the VaxiJen v2.0, IFNepitope, and IL4-pred online platforms, respectively.

2.10 Prediction of cytotoxic T lymphocyte (CTL) epitopes

Finally, CTL epitopes were analyzed and predicted by the CTLpred tool was following a combined method with 75.8% accuracy. The cutoff points for the support vector machine (SVM) and artificial neural network (ANN) were respectively set at 0.51 and 0.36 [52]. Subsequently, the IEDB MHC Class I immunogenicity prediction tool anticipated the immunogenicity of the epitopes [51].

2.11 Immune simulation

The C-ImmSim server predicted the virtual immunological simulation process that was provoked by ROP34 [53]. As previously shown, the minimum time between each injection is a four-week interval and is usually recommended for a standard clinical protocol [54]. Thus, the server was set for three inoculation doses of ROP34 at four-week intervals with time points of 1, 84, and 168 (each time point is equivalent to eight h). Other settings for this computer-aided simulation included a simulation volume of 50, 1050 simulation steps, and a random seed of 12,345.

3 Results

3.1 General features of T. gondii ROP34 gene

The NCBI database retrieved the amino acid sequence of the T. gondii ROP34 gene in FASTA format. It comprises 553 amino acid residues with a theoretical pI of 6.29 and a MW of 61,601.49 Da. The sequence contains 79 positively charged residues (Lys + Arg) and 84 negatively charged residues (Glu + Asp). It has 8638 atoms and an extinction coefficient of 29,005 M−1 cm−1 in water at 280 nm. The ROP34 protein estimated half-life is over 20 h in yeast (in vivo), 30 h in mammalian reticulocytes (in vitro), and over 10 h in Escherichia coli (E. coli) (in vivo). The II score is 49.66, classifying the protein as unstable. Additionally, the AI and GRAVY were respectively 73.98 and − 0.554.

3.2 Allergenicity, antigenic properties, and solubility evaluation

The ROP34 antigenicity was 0.526563 by ANTIGENpro and 0.6025 by VaxiJen, with a threshold of 0.5. The AlgPred hybrid method evaluated the allergenicity of ROP34, which indicated that ROP34 is a non-allergen. Furthermore, the SOLpro server predicted that the protein, when overexpressed in E. coli, would be soluble with a likelihood of 0.598.

3.3 Prediction of PTM sites of ROP34

According to the NetPhos 3.1 analysis, the ROP34 protein sequence contains 40 phosphorylation sites, including 27 serine, 9 threonine, and 4 tyrosine residues (Fig. 1). Additionally, the protein has 9 acetylation sites as determined using a medium threshold setting (Table 1). The SignalP Server predicted a Sec signal peptide (Sec/SPI) in the ROP34 protein with a possibility of 0.9878 (Fig. 2). Furthermore, the ROP34 protein had 9 N-linked glycosylation sites (Fig. 3) and 19 O-linked glycosylation areas.

Fig. 1
figure 1

NetPhos server output for ROP34 phosphorylation sites. A The number of predicted sites, based on S (serine), T (threonine) and Y (tyrosine); B Prediction diagram of ROP34 phosphorylation sites (http://www.cbs.dtu.dk/services/NetPhos-2.0/output.php)

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Table 1 Acetylation sites predicted using GPS-PAIL 2.0 server with Medium threshold
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Fig. 2
figure 2

Signal peptide prediction of the ROP34 protein of T. gondii, using SignalP-5.0 online tool. SP (Sec/SPI): type of signal peptide predicted; CS: the cleavage site; Other: the probability that the sequence does not have any kind of signal peptide

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Fig. 3
figure 3

The output from NetNGlyc server for N-glycosylation sites of ROP34

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3.4 Prediction of subcellular localization and transmembrane domains of ROP34

The subcellular localization assessment using PSORT II provided the following distribution: endoplasmic reticulum (44.4%), mitochondrial (22.2%), plasma membrane (11.1%), nuclear (11.1%), and golgi apparatus (11.1%). Additionally, the TMHMM server analysis indicated that the ROP34 sequence does not contain any transmembrane domains (Fig. 4).

Fig. 4
figure 4

Bioinformatics analysis of transmembrane domain of ROP34 sequence. A Number of predicted TMHs: The number of predicted transmembrane helices; Exp number of AAs in TMHs: The expected number of amino acids in transmembrane helices. If this number is larger than 18 it is very likely to be a transmembrane protein (OR have a signal peptide); Exp number, first 60 AAs: The expected number of amino acids in transmembrane helices in the first 60 amino acids of the protein. If this number more than a few, you should be warned that a predicted transmembrane helix in the N-term could be a signal peptide; Total prob. of N-in: The total probability that the N-term is on the cytoplasmic side of the membrane; POSSIBLE N-term signal sequence: a warning that is produced when “Exp number, first 60 AAs” is larger than 10 (https://services.healthtech.dtu.dk/services/TMHMM-2.0/); B Graphical illustration of transmembrane domain analysis of ROP34

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3.5 Tertiary and secondary structure assessment

The GOR IV and PSIPRED servers predicted the secondary structure elements of the ROP34 protein sequence, including random coil, alpha helix, and extended strand (Figs. 5 and 6). According to the GOR IV analysis, the ROP34 protein consists of 292 (52.80%) random coil, 61 (11.03%) extended strand, and 200 (36.17%) alpha helix elements. Additionally, the analysis identified 23 cysteine residues in the ROP34 protein, which are expected to form disulfide bonds at specific positions, including residues 41 and 267. The ROP34 3D model was produced by the SWISS-MODEL tool. From the proposed models, the one with 98.37% sequence identity and high coverage was selected. Figure 7 shows the complete details of the SWISS-MODEL output.

Fig. 5
figure 5

Analysis of secondary structure of ROP34 using GOR IV. A Predicted secondary structure. B Graphical frame of secondary structure prediction of ROP34 protein. h, helix; e, extended strand; c, coil

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Fig. 6
figure 6

Graphical result from secondary structure prediction of vaccine using PSIPRED

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Fig. 7
figure 7

SWISS-MODEL server output of ROP38. A Computed 3D model; B Model-template alignment; C Global quality estimate; D Sequence identity and coverage; E Comparison with non-redundant set of PDB structures; and F Local quality estimate

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3.6 Validation and refinement of the tertiary structure

The refinement process utilized the GalaxyRefine tool, determining that model 1 emerged as the most effectively refined template according to multiple parameters: RMSD (0.549), GDT-HA (0.9222), MolProbity (1.498), Poor rotamers (0.2), Clash score (7.1), and Rama favored (97.5). The ProSA-web online tool assessed model quality, which reported a Z-score of − 5.76 for the crude model and − 6.23 for the refined model (Fig. 8).

Fig. 8
figure 8

Validation of the 3D structure of ROP34 protein using Ramachandran plot. A The Z-score plot for 3D structure of predicted vaccine before refinement was assessed to be -5.76. B The Z-score plot for 3D structure of predicted vaccine after refinement was assessed to be -6.23. The analysis of Ramachandran plot using SWISS-MODEL server in initial model (C), the model after refinement (D)

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3.7 Conformational and linear B cell epitopes

The ABCpred server anticipated the linear B-cell epitopes with a 16-mer length, where higher scores indicate greater prediction reliability (Table 2). Additionally, B-cell specific epitopes were identified considering various physico-chemical factors by the ProtScale and Bcepred servers (Fig. 9 and Table 3). Also, Tables 4 and 5 present the BCPREDs and SVMTriP results. The average scores for beta-turn, antigenicity, hydrophilicity, Bepipred linear epitope prediction, surface accessibility and flexibility of the ROP34 protein, as determined by the IEDB online tool, were respectively 1.019, 1.866, 0.971, 0.522, 1.000, and 1.006 (Fig. 10). B-cell epitopes possessing proper solubility in water with appropriate antigenicity and negative allergenicity with ABCpred, BCPREDs, and SVMTriP were the followings, respectively: KGRGRGAQTHEGNHDR (VaxiJen score: 1.8559), ADNAEVPAAKSPAGKTGGAG (VaxiJen score: 1.2008), and GADLLAERQRARLEAREKAD (VaxiJen score: 0.6615). When predicting conformational B cell epitopes via ElliPro, sixteen discontinuous B cell epitopes were identified: 4 residues (score: 0.909), 27 residues (score: 0.89), 8 residues (score: 0.874), 8 residues (score: 0.881), 47 residues (score: 0.827), 6 residues (score: 0.759), 9 residues (score: 0.778), 5 residues (score: 0.752), 16 residues (score: 0.732), 5 residues (score: 0.689), 31 residues (score: 0.696), 19 residues (score: 0.682), 5 residues (score: 0.522), 45 residues (score: 0.576), 6 residues (score: 0.502), and 4 residues (score: 0.501) (Table 6 and Figs. 10, 11).

Table 2 Predicted B-cell epitope using ABCpred server
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Fig. 9
figure 9

Linear B-cell epitopes of ROP34 protein sequence based on A percent of accessible residues, B average flexibility, C beta turn and D hydrophobicity

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Table 3 The predicted B-cell epitopes, using physico-chemical properties based on Bcepred server
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Table 4 BCPreds predictions of linear B-cell epitopes from ROP34 protein
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Table 5 B-cell linear epitopes predicted using SVMTriP server
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Fig. 10
figure 10

Propensity scale plots of ROP34 protein. A Bepipred linear epitope prediction. B Antigenicity. C Beta-turn. D Surface accessibility. E Hydrophilicity. F Flexibility. On the graphs, the Y-axes depicts for each residue the correspondent score (averaged in the specified window), while the X-axes depicts the residue positions in the sequence. The tables provide values of calculated scores for each residue. The larger score for the residues might be interpreted as that the residue might have a higher probability to be part of epitope (those residues are colored in yellow on the graphs). Green color (below the threshold) indicate the unfavorable regions related to the properties of interest

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Table 6 Predicted discontinuous B-cell epitope residues of the ROP34 sequence
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Fig. 11
figure 11

Predicted conformational B-cell epitopes of T. gondii ROP34by ElliPro tool of IEDB Analysis Resource

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3.8 Predicting MHC-specific epitopes

Tables 7 and 8 comprehensively list the anticipated MHC-II (15-mer) and MHC-I (10-mer) epitopes, considering the estimated IC50 values for peptide binding to mouse-specific alleles. Related immunogenicity, antigenicity, IL-4 and IFN-γ induction are also indicated. According to the MHC-II outputs and deeper monitoring process concerning immunogenicity, DNFLVSPEGHIVVSD (score: 1.4, immunogenicity: 1.0273) was the most immunogenic epitope amongst other epitopes. Also, according to the MHC-I outputs and deeper monitoring process concerning immunogenicity, EEARSLDFVL (score: 0.51, immunogenicity: 0.03668) was the most immunogenic epitope amongst other epitopes.

Table 7 Percentile rank for ROP34 binding to MHC-I molecules
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Table 8 Percentile rank for ROP34 binding to MHC-II molecules, antigenicity, IFN-γ and IL-4 induction
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3.9 CTL epitope anticipation

According to the results from the CTLpred server, Table 9 contains ten top-ranking 9-mer CTL epitopes of the T. gondii ROP34 protein that were anticipated.

Table 9 CTL epitopes predicted with CTLpred server
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3.10 Immune simulation profile

Based on the C-ImmSim server output, good humoral responses were elicited upon ROP34 protein administration as a vaccine candidate, particularly after the third injection. In this sense, upon antigenic exposure, adequate titers of IgM (~ 300,000), IgG1 (~ 300,000), and a combination of both [IgG + IgM] (~ 620,000) were predicted (Fig. 12A). Moreover, a high level of the Th-associated cytokine IFN-γ (~ 2,000,000 ng/ml) was provoked by the ROP34 protein of Toxoplasma (Fig. 12L). After ROP34 was administered as a vaccine candidate, the Th-cells started to duplicate (approximately five days post exposure), after which active T-CD4+ cells were present for approximately 350 days (Fig. 12E, F). A similar trend was detected for T-CD8+ cells (T-cytotoxic), which exhibited increasing activity for several weeks post exposure (Fig. 12G, H). The total count of natural killer (NK) cells increased for approximately ten days after antigen exposure, leading to the secretion of IFN-γ and the killing of tachyzoite-infected cells (Fig. 12K, L). More details are illustrated in Fig. 12A–L.

Fig. 12
figure 12

silico immune simulation. A immunoglobulin production in response to ROP34; B, C B lymphocytes population; D plasma B lymphocytes count sub-divided per isotype (IgM, IgG1 and IgG2); E, F TH cell (CD4 +) population; G, H TC cell (CD8 +) population; I dendritic cells per state; J macrophage population per state; K NK cell population; and L level of cytokines production (ng/ml) by ROP34

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4 Discussion

Toxoplasmosis presents a worldwide zoonotic danger with notable public health consequences, affecting pregnant women as well as individuals with weakened immune systems who are at a high risk of infection [6, 9, 55,56,57,58]. The host’s immune condition enables tachyzoite penetration into different nucleated cells. In those with a healthy immune system, the parasites persist in tissue cysts as bradyzoite stages, potentially leading to opportunistic infections if immunity weakens [59]. Hence, it is essential to devise immunoprophylactic approaches to prevent both immediate and prolonged infections [60]. Vaccination stands out as a prominent prevention strategy, known for improving global living standards [61]. Consequently, vaccination is considered the most logical and effective approach to combat toxoplasmosis. The initial step towards successful vaccine production involves identifying the parasite's potential highly immunoprotective antigens [62]. An essential aspect of creating an effective protein-associated vaccine is employing bioinformatics techniques to assess the antigen’s attributes. Bioinformatics is vital in the realm of vaccine development as it forecasts protein structures, functions, and additional biological traits [63]. The core objective of bioinformatics is to advance our comprehension of biological mechanisms and enhance healthcare by pioneering innovative vaccine designs [64]. Rhoptry is a distinctive club-shaped organelle found in T. gondii. Only these apical secretory organelles are common to all parasitic apicomplexans [65]. Rhoptry proteins are essential for the survival of parasites within host cells and play a role in the various stages of their invasion [20, 65, 66]. Because of this, ROPs have gained attention lately and are being considered as potential toxoplasmosis vaccine candidates. According to earlier articles, several ROPs showed positive and promising results [67, 68]. However, the structure and epitopes of ROP34 remain not well-defined.

According to ProtParam results, ROP34 is an appropriate immunogenic compound, with a MW of 61.60149 KDa (antigens with a MW of greater than 5–10 KDa are regarded as strong immunogens) [8]. However, the instability index of ROP34 was calculated to be 49.66. The research also illustrated the enhanced thermotolerance of ROP34, boasting an AI score of 73.98, suggesting that the protein exhibits increased stability over different temperatures. The GRAVY score of − 0.554 indicates that ROP34 is hydrophilic, making it better suited for water-based environments. The isoelectric point (pI) of 6.29 suggests that the protein could be effectively purified using isoelectric focusing and ion-exchange chromatography. These fundamental biochemical characteristics are beneficial for forthcoming extraction and purification procedures in experimental studies. ANTIGENpro and VaxiJen analyses confirmed the protein's immunogenicity, while AlgPred and SOLpro servers indicated that the protein is non-allergenic with a predicted insolubility of 0.598154. Despite its insolubility, the protein can be re-dissolved by adjusting detergent, ionic strength, pH, or dilution during purification assays to enhance protein yield [69]. The extinction coefficient of ROP34, which gauges the substance’s light absorption efficiency at a specific wavelength, was established at 29,005 M−1 cm−1 in water at 280 nm. The secretory apparatus and the existence of signal peptides impact the output of recombinant protein manufacturing. The latter, also known as leader sequences, are typically short peptides located at the N-terminus of newly produced proteins, containing codes associated with the protein secretion pathway and target site. As per the findings, ROP34 features a conventional SPI/Sec signal peptide with a probability of 0.9878. The SPI/Sec signal peptides are conventional secretory signals that are cleaved by Signal Peptidase I and conveyed by the Sec translocon [70]. Proteins synthesized in their crude form commonly undergo diverse enzymatic alterations like phosphorylation and acylation, recognized as PTMs [71]. The existence of PTMs in eukaryotic cells, encompassing parasites, like T. gondii, is vital in determining the suitable expression system for creating recombinant proteins [72]. In this research, we utilized GPS-PAIL and NetPhos 3.1 servers to forecast PTM locations within the ROP34 protein sequence. A total of 44 PTM sites were identified, consisting of 40 phosphorylation sites and 4 acylation sites, which notably influence the protein’s biological role. Furthermore, we verified N- and O-linked glycosylation sites, which are crucial PTMs [18]. Considering these results, when aiming to produce the T. gondii ROP34 protein together with other desired proteins in a recombinant manner, it is essential to take into account the presence of phosphorylation, N-linked glycosylation, and acetylation areas. Given this situation, it is advisable to prioritize eukaryotic expression systems like mammals, yeast, and insects over bacterial systems. No potential transmembrane domain aids in antigen presentation, eliciting both cellular and humoral immune responses, ensuring a swift reaction [8]. Generally, the organization of hydrogen bonds between amino carboxyl oxygen and hydrogen atoms in a polypeptide chain dictates the secondary structure, predominantly comprising β-structures and α-helices [73]. Furthermore, the distinct interactions and bonds within a protein molecule govern its tertiary structure. The existence of beta-turn and alpha-helix within the protein configuration, coupled with significant hydrogen-bond energy, assists in upholding protein conformation and facilitates effective interaction with antibodies [16]. As per the GOR IV and PSIPRED servers, the secondary structure of the protein consists of 36.17% alpha helix, 52.80% random coil, and 11.03% extended strand. Subsequently, the most suitable model from 50 templates of tertiary structures was chosen for additional examination. The GalaxyRefine server reconstructed and optimized side-chains within the selected 3D model, notably enhancing the total quality by molecular dynamics simulation [74]. The refined model was chosen based on several quality factors, including RMSD (0.549), GDT-HA (0.9222), MolProbity (1.498), Poor rotamers (0.2), Clash score (7.1), and Rama favored (97.5). GDT-HA assesses the protein model’s general quality, whereas RMSD gauges the variance in bond lengths and angles between the initial and enhanced models. A reduced RMSD suggests that the refined model closely resembles the initial structure. As per Chen et al., “a structure with a MolProbity score lower than its crystallographic resolution is considered superior in quality compared to an average structure at that resolution” [75, 76]. The clash score indicates steric clashes within the refined model, influenced by the resolution of the protein structure. The limited ability of specific residues to rotate within their side chains leads to suboptimal rotamers. The outcomes from ProSA-web additionally emphasized improvements in the general quality of the refined model in contrast to the original model. The innate immune response pathways to deal with T. gondii establish the foundation for the subsequent triggering of adaptive immunity, which includes both humoral and cellular immune reactions [77]. One benefit of in silico vaccination approaches is their ability to directly stimulate strong immunity, efficiently controlling the parasite. This procedure entails pinpointing epitopes for B-cells, CTLs, and MHC-binding regions of a specific protein using various online resources [19, 78]. The ABCpred server, employing an ANN-oriented fixed-length pattern, forecasted linear B-cell epitopes with an accuracy of 65.93%. Bcepred anticipated continuous B-cell epitopes by considering factors like polarity, hydrophilicity, flexibility, accessibility, turns, and antigenic propensity, achieving accuracies ranging from 58.7% [45, 46]. The SVMTriP Database estimated the B-cell epitopes depending on the Tri-peptide correlation and Propensity values [48]. Another important step in the bioinformatic analysis of a vaccine candidate is the determination of conformational epitopes to improve antigen–antibody interaction. Therefore, the ElliPro tool has been well adopted. Protozoa induce humoral and cellular immune responses in the host. Immunoglobulins, especially IgG, inhibit the binding of parasite adhesions and enhance cellular phagocytosis through the process of opsonization. Note that the most protective immune mechanism against toxoplasmosis is through CD4+ and CD8+ T cells and IFN-γ cytokines, which together limit parasite invasion and reproduction [60, 79]. The IEDB online tool revealed epitopes with increased binding affinity to MHC-II and MHC-I. As mentioned earlier, lower IC50 scores (or percentile ranks) indicate a higher level of affinity. Furthermore, the CTLpred server identified a total of 10 CTL-specific epitopes, crucial for the design of a toxoplasmosis vaccine. The combined and consensus prediction techniques of CTLpred exhibit higher accuracy compared to the ANN and SVM alternatives[52]. We have also shown that the ROP34 protein can elicit adequate humoral and cell-mediated immune reactions, considering the antibody titers and cytokine levels induced by the administration of the ROP34 protein, as evidenced by the C-ImmSim web server. Although our study was only done in silico, the results of previous studies on this gene, similar to our study, show that ROPs can increase survival in mice. DNA vaccine encoding MIC3 and ROP18 of T. gondii immunized in mice demonstrated that mice immunized with pROP18-MIC3 elicited stronger humoral and Th1 cellular immune responses, as well as longer survival times against toxoplasmosis [80]. Another study which was done on GRA7 and ROP1 of T. gondii, the results showed increased IgG2a titers, IFN-γ and TNF-α production, survival duration, and cyst reduction rate [81].

Numerous publications discuss ROP-based vaccines that utilized a combination of bioinformatics online servers and various softwares to predict potential B and T cell epitopes, resulting in an excellent vaccine candidate to prevent toxoplasmosis. Nevertheless, there is insufficient evidence to support the protective effectiveness of certain of them in animal models [82,83,84,85,86,87]. It is strongly advised that more research be done in the future using both in silico and in vivo methods to assess the potency of protein as a likely vaccine candidate, since the immunogenicity of the predicted sequences by various bioinformatics approaches should be confirmed in an appropriate mouse model.

5 Conclusion

Computer-based technologies have afforded humankind an opportunity to more effectively address health issues encountered, including enhanced strategies for preventing infectious diseases. Toxoplasmosis represents a significant global concern, especially for pregnant individuals and those with weakened immune systems. Innovative bioinformatics tools have facilitated a deeper comprehension of the immunoprotective domains within antigenic molecules through immunosense methodologies, contributing to improved vaccine design against diverse infectious pathogens. In this context, we present insightful bioinformatics insights regarding the Toxoplasma ROP34 protein, demonstrating its potential as a future vaccine choice in in silico multi-epitope-associated vaccinology studies. Nevertheless, empirical evaluation of the effectiveness and strength of these candidate proteins, in conjunction with many antigenic epitopes, substances, and/or formulations is recommended.