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Journal of Genetic Engineering and Biotechnology
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Immunoinformatics analysis of Brucella melitensis to approach a suitable vaccine against brucellosis

Journal of Genetic Engineering and Biotechnology volume 21, Article number: 152 (2023) Cite this article

Abstract

Background

Brucellosis caused by B. melitensis is one of the most important common diseases between humans and livestock. Currently, live attenuated vaccines are used for this disease, which causes many problems, and unfortunately, there is no effective vaccine for human brucellosis. The aim of our research was to design a recombinant vaccine containing potential immunogenic epitopes against B. melitensis.

Methods

In this study, using immunoinformatics approaches, 3 antigens Omp31, Omp25, and Omp28 were identified and the amino acid sequence of the selected antigens was determined in NCBI. Signal peptides were predicted by SignaIP-5.0 server. To predict B-cell epitopes from ABCpred and Bcepred servers, to predict MHC-I epitopes from RANKPEP and SYFPEITHI servers, to predict MHC-II epitopes from RANKPEP and MHCPred servers, and to predict CTL epitopes were used from the CTLPred server. Potentially immunogenic final epitopes were joined by flexible linkers. Finally, allergenicity (AllerTOP 2.0 server), antigenicity (Vaxijen server), physicochemical properties (ProtParam server), solubility (Protein-sol server), secondary (PSIPRED and GRO4 servers) and tertiary structure (I-TASSER server), refinement (GalaxyWEB server), validation (ProSA-web, Molprobity, and ERRAT servers), and optimization of the codon sequence (JCat server) of the structure of the multi-epitope vaccine were analyzed.

Results

The analysis of immunoinformatics tools showed that the designed vaccine has high quality, acceptable physicochemical properties, and can induce humoral and cellular immune responses against B. melitensis bacteria. In addition, the high expression level of recombinant antigens in the E. coli host was observed through in silico simulation.

Conclusion

According to the results in silico, the designed vaccine can be a suitable candidate to fight brucellosis and in vitro and in vivo studies are needed to evaluate the research of this study.

Background

Brucella are facultative intracellular and gram-negative coccobacillus bacteria, which causes brucellosis in a variety of animal species and humans. Brucella melitensis, Brucella abortus, and Brucella suis are the maximum malignant species in order of pathogenicity [1].

Brucellosis is a global zoonotic disease which endemic in plenty countries, including the Latin America, Africa, central Asia, and Middle East [2]. This disease not only causes problems for humans, but also causes great damage to livestock and agriculture. Features of the disease with chronic infections with symptoms are such as arthritis, undulating osteomyelitis, and fever in humans, as well as with reduced fertility and abortion in animals [3,4,5].

Therefore, it is very important to strengthen the diagnosis and treatment of this disease [6, 7]. Accordingly, a novel vaccine aimed at stimulating the immune system’s response has a high potential for success. But unfortunately, an effective and no side effects vaccine for human brucellosis is not available yet [8, 9]. All available animal vaccines are based on live weakened strains of Brucella (Rev1, S19, and RB51), which can be potentially effective but are infectious to humans and cause abortion in pregnant animals [10, 11].

Due to the limitations and disadvantages of live, attenuated vaccines, a new treatment method is needed. Peptide vaccines based on multiple epitopes have advantages compared to live attenuated vaccines, because the production of these novel vaccines is relatively fast and cheap, and they are also safe for the host [12, 13]. Research has shown that multi-epitope vaccines containing immune cell epitopes are able to stimulate protective immunity against different diseases [14,15,16]. A useful peptide-based vaccine should include T-cell epitopes and B-cell epitopes to stimulate numerous humoral and cellular immunity system response [17,18,19].

The clue to important brucellosis diagnosis and treatment is to find efficient antigen. Hence, the development of efficient antigens is crucial for improving performance the immune system response of brucellosis multi-epitope vaccines and the sensitivity and specificity of diagnosis and treatment methods [20, 21].

Nevertheless, the recognition of antigenic epitopes as novel vaccine candidates for designing recombinant vaccines is a costly and time-consuming method. Recently, due to the advances in structure and immunoinformatics vaccinology, the prediction of epitopes for T-lymphocytes and B-lymphocytes helps researchers to identify specific peptides [22, 23]. In this work, using previous studies and immunoinformatics tools, three antigens for the new recombinant vaccine against brucellosis including 31 kDa outer membrane protein (Omp31), outer membrane protein 25 (Omp25), and outer membrane protein 28 (Omp28) were selected and evaluated. Antigens in the outer membranes of bacteria (OMPs) play an essential role in stimulating the immune system [24]. The characteristics of selected antigens are as follows:

Omp31 is a play an essential role in conferring defense against Brucella and main membrane protein of Brucella. This antigen is a significant virulence factor of Brucella and is of high potential as an antigenic protein for vaccination [25].

Studies have shown that Omp25 protein is highly conserved in different species of Brucella. Omp25 is highly immunogenic and leads to an inflammatory response in brucellosis by activating MAPK signaling pathway [26].

Researchers use suitable adjuvants to induce effective immunity in the vaccine. Adjuvants can increase the immunogenicity of antigens and create effective and long-lasting immune responses [27, 28]. Therefore, Omp28 protein was used. Omp28 is considered as an important outer membrane protein of Brucella and it has also been reported that Omp28 peptide with CpG oligonucleotide as an adjuvant can induce an immune response, indicating that this protein stimulates the immune system [29].

Immunoinformatics or computational immunology is the interface between computer science and experimental immunology, which represents the use of computational methods and resources to understand immunological information [30]. Therefore, to confirm the availability of the recombinant structure, the physicochemical properties, allergenicity, antigenicity, solubility, secondary, and tertiary structures of the vaccine were analyzed with different immunoinformatics servers. The results showed that the designed recombinant construct can be used to fight B. melitensis.

Methods

Summarized steps of the designing multi-epitope vaccine are presented as flowchart shown in Fig. 1.

Fig. 1
figure 1

Schematic representation of multi-epitope vaccine design against B. melitensis

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Sequence analysis

The complete amino acid sequences of Omp31 (accession ACS50328.1), Omp25 (accession AEF59022), and Omp28 (accession AEF59021) were retrieved from the National Centre for Biotechnology Information (NCBI) at www.ncbi.nlm.nih.gov in FASTA format [31].

Prediction of signal peptide

The SignalP-5.0 server (https://services.healthtech.dtu.dk/service.php?SignalP-5.0) provides a resource for predicting signal peptide cleavage sites in proteins. This server uses convolutional and recurrent (LSTM) neural networks to identify sequence motifs of different lengths [32].

Prediction of B-cell epitopes

The B-cell epitopes of Omp31, Omp25, and Omp28 antigens were predicted using ABCpred (http://crdd.osdd.net/raghava/abcpred/) and Bcepred (http://crdd.osdd.net/raghava/bcepred/) servers [33, 34]. Prediction of B-cell epitopes in ABCpred server using artificial neural network technique [35]. Bcepred server to predict B-cell epitopes using physico-chemical properties. The results of Bcepred server are presented in graphic and tabular format. This server predicts epitopes with 58.7% accuracy and 2.38 threshold [36].

Prediction of MHC-I epitopes

The MHC-I epitopes of Omp31, Omp25, and Omp28 antigens were predicted by RANKPEP (http://imed.med.ucm.es/Tools/rankpep.html) and SYFPEITHI (http://syfpeithi.de/) server [37]. The RANKPEP servers rank all possible epitopes from an input antigen using position-specific scoring matrices (PSSM). MHC-I-restricted epitopes are predicted at a threshold of 2–3% of high-scoring peptides [38]. SYFPEITHI server uses binding status scoring qualitative prediction methods for T-cell epitope prediction [39].

Prediction of MHC-II epitopes

The MHC-II epitopes of Omp31, Omp25, and Omp28 antigens were predicted by MHCPred V.2.0. (http://www.ddg-pharmfac.net/mhcpred/MHCPred/) and RANKPEP (http://imed.med.ucm.es/Tools/rankpep.html) servers. The RANKPEP server ranks all possible epitopes from an input antigen using position-specific scoring matrices (PSSM). MHC-II-restricted epitopes are predicted at a threshold of 4–6% of high-scoring peptides [38]. MHCPred uses an additive method to predict MHC-II epitopes as well as transporter-associated processing (TAP). The order of epitopes is arranged based on IC50 values. If the IC50 value is higher than 5000, the peptide does not bind to MHC molecules. Therefore, epitopes with lower IC50 values are selected [40].

Prediction of CTL epitopes

The CTL (cytotoxic T lymphocyte) epitopes of Omp31, Omp25, and Omp28 antigens was predicted using CTLPred (http://crdd.osdd.net/raghava/ctlpred/applying) server. This server predicts CTL epitopes based on artificial neural network (SVM) and support vector machine (ANN). CTLpred server accuracy is 75.8% [41].

Epitope selection and construction of novel vaccine

The results were compared and regions with the highest score and with the maximum overlap that induce the cellular and humoral immune responses were identified and finally the selected epitopes were joined by linkers.

Linkers

The final selected epitopes need a certain degree of flexibility; in this research, two flexible linkers (GSGSGS and GGGGS) were used to connect the epitopes.

Evaluation of antigenicity and allergenicity of the novel vaccine

Recombinant vaccine was experiment for antigenicity using VaxiJen v2.0 server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with a threshold value ≥ 0.4. This server’s approach for antigen prediction is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors with amino acid features [42]. Allertop v2.0 server (https://www.ddg-pharmfac.net/AllerTOP/) with threshold value of − 0.4 was used to prevent allergic reactions to recombinant vaccine epitopes. The approach of this server is based on converting ACC of protein sequence into uniform vectors of equal length [43].

Physicochemical characterization and solubility novel vaccine

The ProtParam server) https://web.expasy.org/protparam/ (generates physico-chemical parameters for an amino acid sequence. The computed parameters include the grand average of hydropathicity (GRAVY), instability index, aliphatic index, extinction coefficient, atomic composition, estimated half-life, amino acid composition, theoretical pI, and molecular weight [44].

The Protein-sol server (https://protein-sol.manchester.ac.uk/) was used to analyze the solubility of the recombinant antigen. This server is a web based suite of theoretical calculations and predictive algorithms for understanding protein solubility and stability [45].

Protein secondary structure prediction

PSIPRED web server (http://bioinf.cs.ucl.ac.uk/psipred/) was used to predict the second structure. This server uses a very stringent cross-validation method to evaluate the performance of the method. The Q3 score averages 81.6% [46].

GOR IV server (https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_gor4.html) was used to predict secondary structure novel multi-epitopes construct [47].

Protein tertiary structure prediction

I-TASSER server (https://zhanggroup.org/I-TASSER/) was used to predict the 3D structure and function of recombinant antigen. This server uses a hierarchical method for prediction. The evaluation parameter of this server to choose the best model is based on C-score. C-score is a confidence score for estimating the quality of the predicted 3D structures; the score range of this server is (− 5, 2), where a C-score with a higher score indicates a higher quality model [48, 49].

Refinement of the 3D modeled structure

GalaxyRefine server (http://galaxy.seoklab.org/cgi-bin/submit.cgi) is used to refine the best 3D model of I-TASSER server. This server provides more accurate structures for determining the experimental structure, functional study, and molecular design [50].

Validation of the 3D modeled structure

Three-dimensional refined recombinant vaccine structures were validated by ProSAweb, Molprobity, and ERRAT servers. The ProSAweb server at https://prosa.services.came.sbg.ac.at/prosa.php calculates the overall quality score by checking it out the atomic peculiarities of the model. The ProSA-web z-score is depicted on a plan, which contains the z-score of the experimental structures entrusted in PDB. This server only needs Cα atoms to evaluate low-resolution structures [51]. Molprobity server (http://molprobity.biochem.duke.edu) was used for the Ramachandran plot [52]. Calculate phi/psi angles (ϕ, ψ) for any antigenic protein residue, and the residuals include favored regions, allowed regions, and outlier regions [53]. Also, the ERRAT tool (https://saves.mbi.ucla.edu/) was used. This tool is used to verifying protein structures determined by crystallography [54].

Optimization and in silico cloning

The amino acid sequence of the recombinant vaccine was converted to DNA sequence (Reverse Translate) using SMS (Sequence Manipulation Suite) server (https://www.bioinformatics.org/sms2/) [55]. The JCAT server (http://www.jcat.de/) was used to optimize and increase the expression (avoiding cleavage sites for restriction enzymes, predicting GC content, and codon adaption index score (CAI) of the recombinant vaccine protein in the E. coli K12 system [56]. In the end, to clone the recombinant vaccine sequence in the pET-26b vector, HindIII and BamHI restriction sites were added for the N and C terminal of the sequence, respectively.

Results

Sequence analysis

In the present study, Omp31, Omp25, and Omp28 proteins with 240, 213, and 250 amino acids, respectively, were selected as immunogenic proteins for recombinant vaccine design. The sequences of these proteins were obtained from the NCBI database.

Prediction of signal peptide

SignalP-5.0 server was used to predict the signal peptides of Omp31, Omp25, and Omp28 proteins. The signal peptide sequence of Omp31, Omp25, and Omp28 was MKSVILASIAAMFATSAMA, MRTLKSLVIVSAALLPFSATAFA, and MNTRASNFLAASFSTIMLVGAFSLPAFA, respectively (Fig. 2). The signal peptide sequences were removed from the epitope prediction of the 3 selected proteins.

Fig. 2
figure 2

By SignalP-5.0 server, the peptide signal of selected antigens was checked. SP (Sec/SPI), type of signal peptide predicted; CS, the cleavage sits; Other, the probability that the sequence does not have any kind of signal peptide. The signal peptide sequence of Omp31 (a), Omp25 (b), and Omp28 (c) was MKSVILASIAAMFATSAMA, MRTLKSLVIVSAALLPFSATAFA, and MNTRASNFLAASFSTIMLVGAFSLPAFA, respectively

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Prediction of B-cell epitopes

Three selected antigens were predicted B-cell epitopes using Bcepred server. The amino acid sequence was analyzed based on properties such as hydrophilicity, flexibility, antigenic propensity, and exposed surface of this server. The results of this server are shown as graphical result and overlap display in Fig. 3.

Fig. 3
figure 3

B-cell epitopes of Omp31 (a), Omp25 (b), and Omp28 (c) antigens were predicted using Bcepred server. The results of this server are shown as graphical result based on features such as hydrophilicity, flexibility, antigenic propensity, and exposed surface

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To further ensure the obtained results, ABCpred server was used to predict B-cell epitopes. The results of this server are shown in Table 1.

Table 1 Results of prediction of B-cell epitopes using ABCpred server are shown
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Prediction of MHC-I epitopes

RANKPEP and SYFPEITHI servers were used to predict MHC-I epitopes of antigens. The results of SYFPEITHI and RANKPEP servers for selecting the best epitopes of Omp31, Omp25, and Omp28 antigens are shown in Table 2.

Table 2 The prediction results of MHC-I epitopes using RANKPEP and SYFPEITHI servers are shown
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Prediction of MHC-II epitopes

MHCPred V.2.0. and RANKPEP servers were used to predict MHC-II epitopes of antigens. The results of MHCPred V.2.0. and RANKPEP servers for selecting the best epitopes of Omp31, Omp25, and Omp28 antigens are shown in Table 3.

Table 3 The prediction results of MHC-II epitopes using RANKPEP and MHCPred servers are shown
Full size table

Prediction of CTL epitopes

The CTLPred server was used to predict the potential CTL epitopes. The results of this server for Omp31, Omp25, and Omp28 antigens are shown in Table 4.

Table 4 Results of prediction of CTL epitopes using CTLPred server are shown
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Epitope selection and construction of novel vaccine

Based on B-cell, MHC-I, MHC-II, and CTL epitopes, which have high overlap and good score, thirty-one epitopes of Omp31, Omp25, and Omp28 antigens were selected as the final regions. The epitopes selected from each antigen were fused by GSGSGS and GGGGS linkers. Finally, the recombinant sequence including epitopes and linkers is shown in Fig. 4.

Fig. 4
figure 4

A representation of the amino acid sequence of the recombinant vaccine

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Evaluation of antigenicity and allergenicity of the novel vaccine

The results obtained by the AllerTOP 2.0 server showed that the designed vaccine is non-allergenic. The probability of antigenicity of the vaccine was predicted by the Vaxijen server to be 1.4616% at a threshold of 0.4%, which means that our vaccine can stimulate the immune system.

Physicochemical characterization and solubility novel vaccine

The results of ProtParam and protein-sol servers are shown in Table 5. Analysis of the ProtParam server showed that the recombinant vaccine could be classified as a stable protein. The negative value of the GRAVY (grand average of hydropathy) indicates the non-polarity of this recombinant protein. The high value of the aliphatic index of the recombinant protein showed that protein is thermo-stable over a wide temperature range. The analysis of the protein-sol server showed that the target protein is soluble (Fig. 5).

Table 5 Analysis of physicochemical properties of recombinant vaccine
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Fig. 5
figure 5

The analysis of the Protein-sol server showed that the target recombinant vaccine is soluble. The solubility of the vaccine construct was shown to be 0.546 compared to 0.45 of the population average solubility of E. coli

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Protein secondary structure prediction

The graphical representation of secondary structure specification by PSIPRED server is shown in Fig. 6. Secondary structure specifications result by GRO4 server included 27.51% extended strand, 8.90% alpha helix, and 63.59% random coil (Fig. 7).

Fig. 6
figure 6

Graphic representation of the characteristics of the second structure of the recombinant vaccine. Alpha-helix residues, coil residues, and beta-strand residues are shown in pink, gray, and yellow, respectively

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Fig. 7
figure 7

Secondary structure specifications are shown by the GRO4 server. Alpha helixs, extended strands, and beta turns are shown in blue, red, and green, respectively

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Protein tertiary structure prediction

Five models were predicted by I-TASSER server for tertiary structure. The predicted models had C-score values in the range of − 1.43 to − 3.59. Since the C-score with a higher score indicates a higher quality model, then model one with a C-score of − 1.43 was selected (Fig. 8).

Fig. 8
figure 8

The 3D model of the vaccine construct obtained after homology modeling on I-TASSER

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Refinement and validation of the 3D modeled structure

The predicted 3D structure is refined by the GalaxyWEB server. The refined model was validated by ProSA-web, Molprobity, and ERRAT servers. The ProSA-web server was checked for the recombinant vaccine and the z-score value of − 3.47 was provided (Fig. 9a). The Ramachandran plot for the recombinant vaccine was examined and showed that 79.3% residues in most favored regions, 17.0% residues in additional allowed regions, 1.3% residues in generously allowed regions, and 2.4% residues in disallowed regions (Fig. 9b). The ERRAT tool showed a quality score of 61.842% (Fig. 9c).

Fig. 9
figure 9

a The z-score value of the recombinant vaccine is − 3.47, which was obtained by the ProSA-web server. b The 3D structure of the purified recombinant vaccine was evaluated by Molprobity server: 79.3% residues in most favored regions, 17.0% residues in additional allowed regions, 1.3% residues in generously allowed regions, and 2.4% residues in disallowed regions. c The quality score value of the recombinant vaccine is 61.842%, which was obtained by the ERRAT server

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Optimization and in silico cloning

The JCat server improved the nucleotide sequence of the recombinant vaccine and changed the CAI-value of the pasted sequence from 0.5 to 1.0 and the GC-content of the pasted sequence from 65.21 to 51.29%. These results indicate the excellent potential of high expression of the recombinant vaccine in the E. coli host (Fig. 10).

Fig. 10
figure 10

The results of JCat server improved the nucleotide sequence of the recombinant vaccine and changed the GC-content to about 51.29% and CAI value of 1.0

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Discussion

Brucellosis is one of the common diseases between humans and animals, which is associated with fever in humans and abortion in animals [57]. Approximately 500,000 cases of brucellosis are reported annually worldwide. The spread of this disease is increasing due to tourist trips, trade, and immigration [58]. Lack of effective human vaccine, long treatment period, relatively low efficiency of animal vaccines, and the process of prevention and treatment of this disease have faced serious problems. Accordingly, there is an urgent need for new approaches or effective recombinant vaccines to fight against Brucella [59, 60].

Currently, human vaccine against this disease is not available and the vaccines that are used are live attenuated vaccines to immunize animals, but these vaccines have problems such as abortion in livestock, disease in humans, and production of antibodies that make it difficult to diagnose infected animals from vaccinated animals [61].

Therefore, trying to produce an effective vaccine to prevent this infection is inevitable. Studies show that in vaccine design, the use of several different antigens in the vaccine composition can be more effective in its performance [62, 63]. In this research, we used different immunoinformatics tools to design a multi-epitope recombinant construct. According to previous research, Omp31, Omp25, and Omp28 antigens were selected for use in this study, because they are highly conserved and effective in creating an immune response [64, 65].

In the first step of this study, using in silico tools, we identified the immunogenic epitopes of 3 antigens and removed the signal peptide sequences from the final antigen sequences.

Previous studies show that in order to increase the effectiveness of Brucella vaccines, they should stimulate humoral and cellular immune responses [66]. Therefore, the second step in this study is to predict the potential epitopes of B cells, MHC-I, MHC-II, and CTL, which were predicted by online servers (ABCpred, Bcepred, RANKPEP, SYFPEITHI, MHCPred, and CTLPred). Then, the epitopes with the highest score and the most overlap (thirty-one epitopes) were selected and fused together by flexible linkers (GSGSGS and GGGGS). Linkers are an important component of multi-epitope fusion antigens, increase stability in the structure of recombinant antigens, and improve biological activity and increase expression efficiency [67].

The third step of this study was the examination of the immunological characteristics and the physical and chemical characteristics of the recombinant construct, which was analyzed based on immunoinformatics methods. The obtained results showed that the recombinant construct is a strong antigen and non-allergenic and had an acceptable solubility percentage. The results of physicochemical properties by ProtParam tool showed that this multi-epitope protein is classified as a stable protein.

The fourth step of this study was to investigate the secondary and tertiary structure of the recombinant vaccine. Studies on secondary structures showed 27.51% extended strand, 8.90% alpha helix, and 63.59% random coil. The results of the three-dimensional structure of the recombinant vaccine by the I-TASSER server showed that model 1 is the best model to choose among the 5 models, because model 1 has an estimated TM score of 0.54 ± 0.15 (TM score over 0.5 always indicates the correct model) and C-score of − 1.43 (the highest C-score among the 5 models).

The fifth step of this study was to modify the 3D model by GalaxyWEB server. And after refining the 3D structure, the model was validated by ProSA-web, Molprobity, and ERRAT servers. From the ProSA-web server, the z-score shows the overall quality of the model. Its value is displayed in the graph (z-score − 3.47), which includes the z-scores of all experimental protein chains determined in the recombinant protein structure. The Ramachandran plot showed 79.3% residues in most favored regions, 17.0% residues in additional allowed regions, 1.3% residues in generously allowed regions, and 2.4% residues in disallowed regions. The results of the Ramachandran plot show that the quality of the 3D structure is acceptable. Finally, the ERRAT tool has been used for non-bonded interactions between different types of atoms. The higher the scores in ERRAT, the higher the quality of the 3D structure. The accepted range for the model is higher than 50. The quality score of recombinant protein is 61.842%.

In the final steps of this study, we optimized our recombinant nucleotide sequence to increase the expression of multi-epitope vaccine in E. coli. The codon compatibility index (CAI) of the sequence changed from 0.5 to 1.0 and the GC content of the sequence changed from 65.21 to 51.29%.

The results of immunoinformatics analysis showed that the designed multi-epitope construct was a suitable antigen and has the ability to be tested as a vaccine candidate.

Conclusion

In the present study, using in silico tools, a multi-epitope recombinant vaccine against B. melitensis was designed. The purpose of using this method is to save time and money as much as possible. Based on the results of immunoinformatics, the designed construction has virtuous structural, suitable physiochemical characteristics, high antigenicity, good stability, and appropriate solubility, as well as non-allergenic. Therefore, this recombinant vaccine can be used as a suitable candidate to fight against brucellosis infection. But in order to confirm the results of the present studies, the analysis of immunogenicity in in vitro and in vivo tests is very necessary.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author.

References

  1. Aliyev J, Alakbarova M, Garayusifova A, Omarov A, Aliyeva S, Fretin D, Godfroid J (2022) Identification and molecular characterization of Brucella abortus and Brucella melitensis isolated from milk in cattle in Azerbaijan. BMC Vet Res 18(1):71. https://doi.org/10.1186/s12917-022-03155-1

  2. Jokar M, Rahmanian V, Golestani N, Raziee Y, Farhoodi M (2023) The Global Seroprevalence of Equine Brucellosis: A Systematic Review and Meta-analysis Based on Publications From 1990 to 2022. J Equine Vet Sci 123:104227. https://doi.org/10.1016/j.jevs.2023.104227

  3. Aparicio ED (2013) Epidemiology of brucellosis in domestic animals caused by Brucella melitensis, Brucella suis and Brucella abortus. Rev Sci Tech 32(1):53–60

    Article  MathSciNet  Google Scholar 

  4. Galińska EM, Zagórski J (2013) Brucellosis in humans--etiology, diagnostics, clinical forms. Ann Agric Environ Med 20(2):233–8

  5. Dean AS, Crump L, Greter H, Schelling E, Zinsstag J (2012) Global burden of human brucellosis: a systematic review of disease frequency. PLoS Negl Trop Dis 6(10):e1865

    Article  Google Scholar 

  6. Alp E, Doganay M (2008) Current therapeutic strategy in spinal brucellosis. Int J Infect Dis 12(6):573–577

    Article  Google Scholar 

  7. Bashir R, Al-Kawi MZ, Harder EJ, Jinkins J (1985) Nervous system brucellosis: diagnosis and treatment. Neurology 35(11):1576

    Article  Google Scholar 

  8. Sarkar B, Ullah MA, Araf Y, Das S, Hosen MJ (2021) Blueprint of epitope-based multivalent and multipathogenic vaccines: targeted against the dengue and zika viruses. J Biomol Struct Dyn 39(18):6882–902. https://doi.org/10.1080/07391102.2020.1804456

  9. Zhang F, Li Z, Jia B, Zhu Y, Pang P, Zhang C et al (2019) The immunogenicity of OMP31 peptides and its protection against Brucella melitensis infection in mice. Sci Rep 9(1):1–7

    Google Scholar 

  10. Wang Z, Wu Q (2013) Research progress in live attenuated Brucella vaccine development. Curr Pharm Biotechnol 14(10):887–896

    Article  Google Scholar 

  11. Mailybayeva A, Yespembetov B, Ryskeldinova S, Zinina N, Sansyzbay A, Renukaradhya GJ et al (2017) Improved influenza viral vector based Brucella abortus vaccine induces robust B and T-cell responses and protection against Brucella melitensis infection in pregnant sheep and goats. PLoS ONE 12(10):e0186484

    Article  Google Scholar 

  12. Sadeghi Z, Fasihi-Ramandi M, Bouzari S (2019) Evaluation of immunogenicity of novel multi-epitope subunit vaccines in combination with poly I: C against Brucella melitensis and Brucella abortus infection. Int Immunopharmacol 75:105829

    Article  Google Scholar 

  13. Jorge S, Dellagostin OA (2017) The development of veterinary vaccines: a review of traditional methods and modern biotechnology approaches. Biotechnology Research and Innovation 1(1):6–13. https://doi.org/10.1016/J.BIORI.2017.10.001

  14. Skwarczynski M, Toth I (2014) Recent advances in peptide-based subunit nanovaccines. Nanomedicine (Lond) 9(17):2657–69. https://doi.org/10.2217/nnm.14.187

  15. Ben-Yedidia T, Arnon R (2005) Towards an epitope-based human vaccine for influenza. Hum Vaccin 1(3):95–101. https://doi.org/10.4161/hv.1.3.1851

  16. Dariushnejad H, Ghorbanzadeh V, Akbari S, Hashemzadeh P (2021) Designing a Multi-epitope Peptide Vaccine Against COVID-19 Variants Utilizing In-silico Tools. Iran J Med Microbiol 15(5):592–605 https://doi.org/10.30699/ijmm.15.5.592

  17. Hashemzadeh P, Ghorbanzadeh V, Lashgarian HE, et al (2020) Harnessing Bioinformatic Approaches to Design Novel Multi-epitope Subunit Vaccine Against Leishmania infantum. Int J Pept Res Ther 26:1417–28. https://doi.org/10.1007/s10989-019-09949-6

  18. Karimi Rouzbahani A, Kheirandish F, Hashemzadeh P (2022) Bioinformatics Analysis to Design a Multi-epitope-based Peptide Vaccine to Combat Leishmania major. Iran J Med Microbiol 16(5):430–46. http://ijmm.ir/article-1-1596-en.html

  19. Hashemzadeh P, Karimi Rouzbahani A, Bandehpour M, Kheirandish F, Dariushnejad H, Mohamadi M (2020) Designing a recombinant multiepitope vaccine against Leishmania donovani based on immunoinformatics approaches. Minerva Biotechnol 32:52–7. https://doi.org/10.23736/S1120-4826.20.02610-5

  20. Yousefi S, Abbassi-Daloii T, Sekhavati MH, Tahmoorespur M (2018) Evaluation of immune responses induced by polymeric OMP25-BLS Brucella antigen. Microb Pathog 115:50–56. https://doi.org/10.1016/j.micpath.2017.12.045

    Article  Google Scholar 

  21. Elbehiry A, Aldubaib M, Marzouk E, Abalkhail A, Almuzaini AM, Rawway M, Alghamdi A, Alqarni A, Aldawsari M, Draz A (2023) "The Development of Diagnostic and Vaccine Strategies for Early Detection and Control of Human Brucellosis, Particularly in Endemic Areas". Vaccines 11(3):654. https://doi.org/10.3390/vaccines11030654

  22. Enayatkhani M, Hasaniazad M, Faezi S, Gouklani H, Davoodian P, Ahmadi N, et al (2020) Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study. J Biomol Struct Dyn 1–16. https://doi.org/10.1080/07391102.2020.1756411

  23. El-Manzalawy Y, Honavar V (2010) Recent advances in B-cell epitope prediction methods. Immunome Res 6 Suppl 2(Suppl 2):S2–S. https://doi.org/10.1186/1745-7580-6-S2-S2

  24. Dubray G, Charriaut C (1983) Evidence of three major polypeptide species and two major polysaccharide species in the Brucella outer membrane. Ann Rech Vet 14(3):311–8

  25. Cassataro J, Pasquevich K, Bruno L, Wallach JC, Fossati CA, Baldi PC (2004) Antibody reactivity to Omp31 from Brucella melitensis in human and animal infections by smooth and rough Brucellae. Clin Diagn Lab Immunol 11(1):111–114

    Google Scholar 

  26. Zhu H, Jiao H, Nie X, Li B, Xu K, Pang F, Cao R, Zhu S, Yang X, Zhang Z, Peng D, Li Y, Li G, Huang H, Chen C, Du L , Wang F (2018) Alterations of microRNAs and their predicted targeting mRNAs expression in RAW264.7 macrophages infected with Omp25 mutant Brucella melitensis. Innate Immun. 24(6):382–9. https://doi.org/10.1177/1753425918792298

  27. Dariushnejad H, Ghorbanzadeh V, Akbari S, Hashemzadeh P (2022) Design of a Novel Recombinant Multi-Epitope Vaccine against Triple-Negative Breast Cancer. Iran Biomed J 26(2):160–74. https://doi.org/10.52547/ibj.26.2.160

  28. Dariushnejad H, Ghorbanzadeh V, Hashemzadeh P (2021) Prediction of B- and T-cell epitopes using in-silico approaches: a solution to the development of recombinant vaccines against COVID-19. Minerva Biotechnol Biomol Res 33:36–42. https://doi.org/10.23736/S2724-542X.20.02652-X

  29. Kaushik P, Singh DK, Kumar SV, Tiwari AK, Shukla G, Dayal S, Chaudhuri P (2010) Protection of mice against Brucella abortus 544 challenge by vaccination with recombinant OMP28 adjuvanted with CpG oligonucleotides. Vet Res Commun 34(2):119–32. https://doi.org/10.1007/s11259-009-9337-x

  30. Rawat SS, Keshri AK, Kaur R, Prasad A (2023) "Immunoinformatics Approaches for Vaccine Design: A Fast and Secure Strategy for Successful Vaccine Development". Vaccines 11(2):221. https://doi.org/10.3390/vaccines11020221

  31. Sherry ST, Ward M-H, Kholodov M, Baker J, Phan L, Smigielski EM et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308–311

    Article  Google Scholar 

  32. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, von Heijne G, Nielsen H (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37(4):420–3. https://doi.org/10.1038/s41587-019-0036-z

  33. Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–8

    Article  Google Scholar 

  34. Saha S, Raghava GPS (2004) BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties. In: Nicosia G, Cutello V, Bentley PJ, Timmis J, (eds). Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_16

  35. Hashemzadeh P, Ghorbanzadeh V, Valizadeh Otaghsara SM, et al (2020) Novel Predicted B-Cell Epitopes of PSMA for Development of Prostate Cancer Vaccine. Int J Pept Res Ther 26:1523–5. https://doi.org/10.1007/s10989-019-09954-9

  36. Dariushnejad H, Ghorbanzadeh V, Akbari S, Hashemzadeh P (2022) Design of a novel recombinant multi-epitope vaccine against triple-negative breast cancer. Iran Biomed J 26(2):160–174. https://doi.org/10.52547/ibj.26.2.160

    Article  Google Scholar 

  37. Rammensee H-G, Bachmann J, Emmerich NPN, Bachor OA, Stevanović S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50(3–4):213–219

    Article  Google Scholar 

  38. Reche PA, Glutting JP, Zhang H, Reinherz EL (2004) Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 56(6):405–19. https://doi.org/10.1007/s00251-004-0709-7

  39. Manijeh M, Mehrnaz K, Violaine M, Hassan M, Abbas J, Mohammad R (2013) In silico design of discontinuous peptides representative of B and T-cell epitopes from HER2-ECD as potential novel cancer peptide vaccines. Asian Pac J Cancer Prev 14(10):5973–5981. https://doi.org/10.7314/apjcp.2013.14.10.5973

    Article  Google Scholar 

  40. Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHCPred: A server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 31(13):3621–4. https://doi.org/10.1093/nar/gkg510

  41. Bhasin M, Raghava G (2004) Prediction of CTL epitopes using QM SVM and ANN techniques. Vaccine 22(23–24):3195–3204

    Article  Google Scholar 

  42. Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8(1):4

    Article  Google Scholar 

  43. Dimitrov I, Bangov I, Flower DR, Doytchinova I (2014) AllerTOP v. 2—a server for in silico prediction of allergens. J Mol Model 20(6):2278

    Article  Google Scholar 

  44. Gasteiger E, et al (2005) Protein Identification and Analysis Tools on the ExPASy Server. In: Walker JM, (eds). The Proteomics Protocols Handbook. Springer Protocols Handbooks. Humana Press. https://doi.org/10.1385/1-59259-890-0:571

  45. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J (2017) Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 33(19):3098–100. https://doi.org/10.1093/bioinformatics/btx345

  46. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202

    Article  Google Scholar 

  47. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A (2005) GOR V server for protein secondary structure prediction. Bioinformatics 21(11):2787–2788

    Article  Google Scholar 

  48. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015) The I-TASSER Suite: protein structure and function prediction. Nat Methods 12(1):7–8. https://doi.org/10.1038/nmeth.3213

  49. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40. https://doi.org/10.1186/1471-2105-9-40

  50. Heo L, Park H, Seok C (2013) GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res 41(W1):W384–W388

    Article  Google Scholar 

  51. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35(suppl_2):W407–W10

    Article  Google Scholar 

  52. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB 3rd, Snoeyink J, Richardson JS, Richardson DC (2007) MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 35(Web Server issue):W375–83. https://doi.org/10.1093/nar/gkm216

  53. Lovell SC, Davis IW, Arendall WB III, De Bakker PI, Word JM, Prisant MG et al (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50(3):437–50

    Article  Google Scholar 

  54. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–9. https://doi.org/10.1002/pro.5560020916

  55. Stothard P (2000) The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. Biotechniques 28(6):1102, 1104. https://doi.org/10.2144/00286ir01

  56. Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, Jahn D (2005) JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 33(Web Server issue):W526–31. https://doi.org/10.1093/nar/gki376

  57. Godfroid J, Nielsen K, Saegerman C (2010) Diagnosis of brucellosis in livestock and wildlife. Croat Med J 51(4):296–305. https://doi.org/10.3325/cmj.2010.51.296

  58. Golshani M, Buozari S (2017) A review of brucellosis in Iran: epidemiology, risk factors, diagnosis, control, and prevention. Iran Biomed J 21(6):349–359. https://doi.org/10.18869/acadpub.ibj.21.6.349

    Article  Google Scholar 

  59. Thakur SD, Kumar R, Thapliyal DC (2002) Human brucellosis: review of an under-diagnosed animal transmitted disease. J Commun Dis 34(4):287–301

  60. Ficht TA, Kahl-McDonagh MM, Arenas-Gamboa AM, Rice-Ficht AC (2009) Brucellosis: the case for live, attenuated vaccines. Vaccine 27 Suppl 4(Suppl 4):D40–3. https://doi.org/10.1016/j.vaccine.2009.08.058

  61. Tibor A, Jacques I, Guilloteau L, Verger JM, Grayon M, Wansard V, Letesson JJ (1998) Effect of P39 gene deletion in live Brucella vaccine strains on residual virulence and protective activity in mice. Infect Immun 66(11):4–5561. https://doi.org/10.1128/IAI.66.11.5561-5564.1998

  62. Skwarczynski M, Toth I (2016) Peptide-based synthetic vaccines. Chem Sci 7(2):842–54. https://doi.org/10.1039/c5sc03892h

  63. Ulmer JB, Valley U, Rappuoli R (2006) Vaccine manufacturing: challenges and solutions. Nat Biotechnol 24(11):1377–83. https://doi.org/10.1038/nbt1261.

  64. Yao M, Liu M, Chen X, Li J, Li Y, Wei YR, Liu Y, Yang KL, Duan X, Shao W, Sun X, Fan X, Sun S, Tian L, Yin D, Sun M (2022) Comparison of BP26, Omp25 and Omp31 and a Multiepitope-Based Fusion Protein in the Serological Detection of Canine Brucellosis. Infect Drug Resist 15:5301–8. https://doi.org/10.2147/IDR.S374432

  65. Ahmed IM, Khairani-Bejo S, Hassan L, Bahaman AR, Omar AR (2015) Serological diagnostic potential of recombinant outer membrane proteins (rOMPs) from Brucella melitensis in mouse model using indirect enzyme-linked immunosorbent assay. BMC Vet Res 11:275. https://doi.org/10.1186/s12917-015-0587-2

  66. He Y, Rappuoli R, De Groot AS, Chen RT (2010) Emerging vaccine informatics. J Biomed Biotechnol 2010:218590. https://doi.org/10.1155/2010/218590

  67. Chen X, Zaro JL, Shen WC (2013) Fusion protein linkers: properties, design and functionality. Adv Drug Deliv Rev 65(10):1357–69. https://doi.org/10.1016/j.addr.2012.09.039

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  1. Department of Medical Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Lorestan, Iran

    Pejman Hashemzadeh & Hossein Khoshkhabar

  2. Department of Immunology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Lorestan, Iran

    Saba Asgari nezhad

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PH selected the study idea and reviewed and edited the final version. PH AND SA contributed to the original analysis and reviewed and edited the manuscript. PH and HKH contributed to the original analysis and writing the original draft. All authors have read and approved the article.

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Hashemzadeh, P., nezhad, S.A. & Khoshkhabar, H. Immunoinformatics analysis of Brucella melitensis to approach a suitable vaccine against brucellosis. J Genet Eng Biotechnol 21, 152 (2023). https://doi.org/10.1186/s43141-023-00614-6

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