Design and evaluation of a novel multi-epitope antigen for evaluate the diagnostic immunity responses against Leishmania infantum infection

Design and evaluation of a novel multi-epitope antigen for evaluate the diagnostic immunity responses against Leishmania infantum infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and evaluation of a novel multi-epitope antigen for evaluate the diagnostic immunity responses against Leishmania infantum infection Pejman Hashemzadeh, Mojgan Bandehpour, Farnaz Kheirandish, Hassan Dariushnejad, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143767/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Leishmania infantum is the causative agent of visceral leishmaniasis in the Mediterranean region. The diagnosis of complex visceral leishmaniasis and delays in the diagnosis of the infection are associated with the death of patients. Proper diagnosis of infection is an important measure in controlling and preventing the disease. However, studies have shown that the accuracy of antigens used in current diagnostic tests is insufficient, for this reason, researchers are trying to identify multi-epitope antigens as diagnostic markers to increase the specificity and sensitivity of diagnostic tests. In this study, the design and expression of Leishmania infantum multi-epitope antigens were carried out in two parts of the structure design using bioinformatics tools and the laboratory part for the production of the recombinant protein. Materials and Methods The aim of this study was to design and computationally analyze and express Leishmania infantum multi-epitope antigens. In this study, nine antigenic proteins (CPB, H1, KMP11, GP63, HASPB, A2, K39, LACK, and PSA) were selected. Bioinformatics analyzes such as prediction of immune cell epitopes, design of recombinant structure, antigenicity, allergenicity, evaluation of physicochemical properties, solubility, prediction of secondary structure and tertiary structure, refinement and validation of 3D model structure and finally in silico cloning optimization of protein construct were performed. After synthesis of the designed recombinant gene fusion sequence in pUC57 cloning vector, its subcloning was performed in pET26b prokaryotic expression vector using BamHI/ HindIII restriction enzymes. The expression of recombinant multi-epitope antigen was performed in E. coli B (BL21) strain using IPTG inducer and confirmed by SDS-PAGE and western blotting techniques. Results The results of computational analysis showed that the complete structure, which is suitable for immunogenicity and is non-allergenic, was successfully cloned into pET-26b and expressed as a complete protein. Conclusion Finally, the protein was approved. Based on the expression of recombinant proteins and bioinformatics analysis, this structure can be studied in mouse models and its safety can be evaluated. Visceral Leishmaniasis Bioinformatics Epitope prediction Recombinant antigens Western blotting Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction The disease known as leishmaniasis is brought on by the parasite Leishmania , an essential intracellular protozoan. Leishmaniasis poses a threat to more than 350 million individuals in 98 countries, and 1.2 million new cases are recorded each year (Pal, Ejeta et al. 2022 , Saini, Joshi et al. 2022 ). The most important clinical manifestations of this disease include visceral leishmaniasis (VL), cutaneous leishmaniasis (CL) and mucocutaneous leishmaniasis (MCL). Leishmania infantum ( L. infantum ) is the cause of visceral leishmaniasis (VL) known as kala-azar in the Mediterranean region. Clinical symptoms of VL include paleness, fever, splenomegaly, hepatomegaly and lymphadenopathy. Delay in identifying VL in patients can be associated with increased mortality (Scarpini, Dondi et al. 2022 , Costa, Chang et al. 2023 ). Therefore, accurate diagnosis is one of the vital elements in controlling and preventing this disease. Unfortunately, VL is difficult to diagnose because of clinical similarities between VL and other diseases including malaria, typhoid, and tuberculosis, as well as parasite isolation in the spleen, bone marrow, or lymph nodes. Based on this, researchers are always looking for a simple, fast and sensitive method to be used in this field (Herrera, Castillo et al. 2019 , Solimando, Coniglio et al. 2022 ). The high sensitivity and specificity of serological tests has been made possible by technological advances in recombinant antigens used as reagents for serological diagnosis of VL (Zhou, Chen et al. 2022 ). The detection of VL has been made possible by a number of high-performance recombinant proteins, but more work is still required to find the right antigens and achieve high-potency performance (Dias, Machado et al. 2023 ). Currently, various bioinformatics tools are successfully used in the biological field. These tools are used to identify the epitopes of adaptive immune cells in protein analysis (Hashemzadeh, Nezhad et al. 2023 ). In this study, using bioinformatics tools, 9 proteins of L. infantum , which were highly immunogenic antigens, were used in the design of multi-epitope antigens. The selected proteins are as follows: Glycoprotein 63 (GP63) is the main surface glycoprotein of parasite that plays an important role in invasion of parasite to macrophage (Gupta, Das et al. 2022 ). Kinetoplastid membrane protein-11 (KMP-11); due to its wide distribution in parasitic kinetoplastid, it is called this name and is the strong stimulation of immunity cell responses (Kumari, Mahajan et al. 2022 ). Cysteine protease I (CPB); CPs in Leishmania play a role in survival of parasite within macrophage. This role is in such a way that the growth of parasite is stopped in the presence of inhibitors of these enzymes (Rawat, Roy et al. 2021 ). Nuclear Protein Histone H1 (H1); this antigen induces the protection surface against Leishmania infection (Vakili, Nezafat et al. 2022 ). Protein A2 (A2) plays a major role in parasitic survival in visceral organs of host mammals (Jusi, Oliveira et al. 2015 ). Hydrophilic Acylated Surface Protein B (HASPB), this protein is considered as an immunogen antigen in the host (MacLean, Price et al. 2016 ). Kinesin-like protein K39 (K39); this protein is protected among Leishmania species (Reiter-Owona, Rehkaemper-Schaefer et al. 2016 ). Leishmania homologue of receptors for activated C kinase (LACK); LACK protein in host is considered as an immunogen antigen (Fernandez, Carrillo et al. 2018 ). Promastigote Surface Antigen-2 (PSA-2) plays a role in resistance to slippery as well as adhesion to macrophage and invasion through complement receptor (Petitdidier, Pagniez et al. 2016 ). After choosing the antigenic proteins, the appropriate bioinformatics tools were used to analyse the nine antigenic proteins of L. infantum in order to create multi-epitope antigens for evaluating the immune responses against VL. Then, this designed recombinant construct was synthesized and subcloned into the expression vector pET-26b. The expression of multi-epitope structure in recombinant E. coli was investigated in relation to IPTG concentration and culture time before and after induction. The recombinant protein was identified by SDS-PAGE and western blot. Methods 2.1. Bioinformatics studies 2.1.1. A collection of Leishmania infantum protein sequences The complete sequence of antigenic proteins including GP63 ( Gene ID: 5067052 ), KMP11 (Gene ID: 5073122), CPB (Gene ID: 5066588 ), H1 (Gene ID: 5070068), A2 (Gene ID: 5069008 ), HASPB (Gene ID: 5069221 ), K39 ( Gene ID: 5067624 ), LACK (Gene ID: 5070509 ), PSA (Gene ID: 5071243 ) was obtained from the National Center for Biotechnology Information (NCBI) ) https://www.ncbi.nlm.nih.gov/gene/?term= ) and stored in the FASTA format (Maglott, Ostell et al. 2005 ). 2.1.2. Prediction of MHC-II epitopes In order to predict the MHC-II epitopes, three different servers of IEDB, ProPred, and RANKPEP were used. In order to predict the MHC-II connection epitopes in IEDB server ( http://tools.iedb.org/mhcii/ ) between several proposed methods, the recommended IEDB method was used (Kim, Ponomarenko et al. 2012 ). RANKPEP server ( http://imed.med.ucm.es/Tools/rankpep.html ) is based on prediction of the MHC-II connective epitopes, PSSMs (Position Specific Scoring Matrices). In this prediction, the binding threshold is equal to 4–6% (Reche, Glutting et al. 2002 ). Graphical tool ProPred ( https://webs.iiitd.edu.in/raghava/propred/index.html ) was used to predict the MHC-II connection areas in sequence of antigenic proteins. This server uses the matrix-based prediction algorithm (Singh and Raghava 2001 ). 2.1.3. Prediction of B-cell epitopes B-cell epitopes are antigenic determinants in the surface of pathogens that are associated with B-cell receptors (Greenbaum, Andersen et al. 2007 ). There are a lot of tools for structure-based sequence, but the tools are limited to predict B-cell epitopes (Saha, Bhasin et al. 2005 ). In this research, three servers of IEDB, BCpred, ABCpred were used to predict B-cell epitopes. IEDB server ( http://tools.iedb.org/bcell/ ) is a set of prediction methods of linear B-cell epitopes based on sequential features of antigen using amino acid and HMMs scales, which predicts B-cell epitopes with connection threshold of 0.350 based on methods, such as Emini surface accessibility, Karplus and Schulz flexibility, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity, Bepipred linear epitope prediction (Parker, Guo et al. 1986 , Larsen, Lund et al. 2006 ). Web-based ABCpred server ( http://crdd.osdd.net/raghava/abcpred/ABC_submission.html ) allows to predict the linear B-cell epitopes using artificial neural network. Then, based on the resulting score, the epitopes are ranked. ABCpred server with precision of 65.93% and default threshold of 0.51 predicts the linear B-cell epitopes (Rubinstein, Mayrose et al. 2009 ). BCpred sever ( http://crdd.osdd.net/raghava/bcepred/bcepred_submission.html ) predicts the linear B-cell epitopes based on physicochemical groups instead of a unit feature. BCpred is a method to predict the linear B-cell epitopes using SVM machine learning method. The precision of the server is 74.57% and its specificity is 75% (Saha and Raghava 2004 ). 2.1.4. Multi-epitope antigens construction The prediction results of immune system epitopes were compared and the areas with maximum overlapping that causes humoral immunity response and cellular immunity response were specified, and finally, the selected epitopes were linked to each other by linkers GGSG, SSAG, GGGS, GGAG. 2.1.5. Antigenicity prediction In order to evaluate the antigenicity of final multi-epitope antigens, the ANTIGENpro sever was selected. ANTIGENpro ( http://scratch.proteomics.ics.uci.edu/ ) is used to predict whether this protein is a protective antigen or not. ANTIGENpro with precision of 82% and default threshold of 0.4. The results of this server are based on analysis of micro dataset of protein (Hashemzadeh, Ghorbanzadeh et al. 2019 ). 2.1.6. Allergenicity prediction In order to evaluate the allergenicity of designed multi-epitope antigens, two servers of AllerTOP 2.0 and AlgPred were used. AlgPred ( http://crdd.osdd.net/raghava/algpred/ ) is the dataset used for this sever consisted of 578 allergens and 700 non-allergens. The precision of this server is 84%, and in order to evaluate the allergenicity, the motif-based method of MEME/MAST software was used. The sensitivity and specificity of this server are 93.94% and 33.34%, respectively (Saha and Raghava 2006 ). AllerTOP 2.0 in ( https://www.ddg-pharmfac.net/AllerTOP/ ) is another server that is based on auto cross covariance (ACC). This server contains 2210 known allergens of various species and 2210 non-allergens of the same species. This sever can predict allergens and non-allergens with precision of 85.3% by k nearest neighbors (kNN) algorithm (Dimitrov, Bangov et al. 2014 ). 2.1.7. Physicochemical properties evaluation and solubility prediction Physical and chemical features of multi-epitope antigens designed by Protparam ( https://web.expasy.org/protparam/ ) were studied (Gasteiger, Hoogland et al. 2005 ). The computed physicochemical parameters include amino acid composition, theoretical pI, molecular weight (MW), instability index, in vitro and in vivo half-life, grand average of hydropathicity (GRAVY), and aliphatic index. In order to assess the final multi-epitope antigens solubility, Solpro server ( http://scratch.proteomics.ics.uci.edu/ ) was used. Solpro server is a two-stage (support vector machine) SVM-based architecture, the prediction accuracy of its result is estimated over 74% (Magnan, Randall et al. 2009). 2.1.8. Secondary structure prediction PSIPRED server ( http://bioinf.cs.ucl.ac.uk/psipred/ ) is one of the accurate methods to predict the secondary structure. Prediction results are presented as text and graphics and are able to achieve the Q3 mean score of 76.5% (McGuffin, Bryson et al. 2000 ). GOR IV server ( https://npsa-prabi.ibcp.fr/cgi-bin/secpred_gor4.pl ) uses GOR method, which is one of the most popular prediction schemes of secondary structure. Prediction of GOR method is improved by 55–64.4% (Garnier, Gibrat et al. 1996 ). 2.1.9. Tertiary structure prediction I-TASSER ( https://zhanglab.ccmb.med.umich.edu/I-TASSER/ ) was used to predict the 3D structure of protein. The quality of the proposed model is estimated by I-TASSER using C-score. A model with high reliability has a higher C-score (Yang, Yan et al. 2015 ). YASARA software was used to visualize 3D models. 2.1.10. Refinement of the 3D modeled structure Using GalaxyRefine server ( http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE ), the 3D multi-epitope model was very likely to be refined. Mild relaxation technique refines lateral chains while loops and secondary structure parts are refined by the violator technique (Shin, Lee et al. 2014 ). 2.1.11. Validation of the 3D structure ERRAT, Verify3D, and RAMPAGE servers were used to confirm the refined multi-epitope structure. The ERRAT server ( http://services.mbi.ucla.edu/ERRAT/ ) is a novel approach based on atomic interactions to distinguish between proper and wrong regions of distinct protein structures. Most of the distinct atoms have random distributions as a result of model creation flaws, which may be separated from the proper distributions using statistical techniques (Colovos and Yeates 1993 ). Verify3D ( http://servicesn.mbi.ucla.edu/Verify3D/ ) is a program that validates 3D structure of multi-epitope antigens by measuring the compatibility of 3D structure model with its sequences (Lüthy, Bowie et al. 1992 ), and because phi-psi criteria are acknowledged as a key component of validating protein structure, the RAMPAGE server (Ramachandran Plot Assessment) ( http://mordred.bioc.cam.ac.uk/rapper/rampage.php ) calculates the rotation angle phi-psi for each root in the protein. Additionally, the roots are divided into three categories: outlier, allowed, and favoured regions (Lovell, Davis et al. 2003 ). 2.1.12. Codon optimization and in silico cloning Reverse translation and codon optimization were done using SMS, JCAT, and GenScript servers to guarantee protein production at high surface in E. coli . SMS was used for the reverse translation ( https://bioinformatics.org/sms/ ). and JCAT, found online at http://www.jcat.de/ , carried out codon optimization. Codon analysis was performed using the GenScript service, which assesses important DNA sequence characteristics such codon frequency distribution (CFD). (Stothard 2000 , Grote, Hiller et al. 2005 ). Finally, for DNA sequence cloning compatible with the final multi-epitope structure in pET-26b vector, BamHI and HindIII cut sites on N and C terminals were designed, respectively. 2.2. Experimental studies 2.2.1. Subcloning of the multi-epitope antigens fusion in the expression vector The multi-epitope structure fusion was created in the pUC57 cloning vector by Bio Magic Gene Company following bioinformatics analysis. The pET-26b prokaryotic expression vector was subcloned with the gene construct of the multi-epitope antigens. To do this, the multi-epitope antigens fusion was enzymatically digested using the restriction enzymes BamHI/HindIII from the vector pUC57. Additionally, BamHI/HindIII restriction enzymes were used on the pET-26b plasmid. T4 DNA ligase was used to introduce the recombinant gene fragment into the pET-26b plasmid that had been broken down. The competent cells of the E. coli strain TOP10 were given the fusion product, and the transformed cells were grown to yield the recombinant plasmids. 2.2.2. Colony PCR and enzymatic digestion to confirm cloning Since the pET-26b plasmid lacks a screening mechanism, the growing colonies of transformed cells were chosen at random in order to extract the plasmid and verify the cloning. Colony PCR was used to confirm cloning. In the following step, BamHI and HindIII enzymes were used to cleave the plasmids whose recombination was verified by colony PCR. 2.2.3. Expression of multi-epitope antigens E. coli strain (BL21) was transformed with pET-26b plasmid carrying the recombinant gene by heat shock in order to express the multi-epitope antigens. The single colony was then grown for 16 hours at 37°C in bacterial liquid culture media. In the following step, isopropyl-beta-D-thiogalactopyranoside (IPTG) was used to stimulate the bacteria that had developed. when the absorbance at 600 nm wavelength surpassed a certain level. In this case, 1 mM IPTG was employed as the concentration. Both before and after the induction, the recombinant gene was expressed at 37°C. Cell sediments were collected, then electrophoresed on a 12.5% SDS-PAGE gel for analysis. Additionally, western blot analysis utilizing anti-histidine antibody coupled to H2O2 peroxidase and diaminobenzidine (DAB) substrate was used to demonstrate the production of the recombinant multi-epitope antigen. Ethical approval The present study was approved by The Ethics Committee of Lorestan University of Medical Sciences (IR.LUMS.REC.1398.190). Results 3.1. Bioinformatics studies 3.1.1. The whole procedures used for multi-epitope construction design is demonstrated in Fig. 1. Sequences related to nine antigenic peptides were received from NCBI server. 3.1.2. Prediction of MHC-II epitopes HLA-DR was selected for parameters of locus MHC-II, and DRB1*01:01 and DRB1*01:02 were adjusted as α and β chains based on data calculated from Allele frequency net (AFND) for Iranian population http://www.allelefrequencies.net/hla6006a.asp (González-Galarza, Takeshita et al. 2014). In order to predict MHC-II epitopes, three different servers (IEDB, ProPred, and RANKPEP were used, the results of which are shown in Table 1. Table 1: Prediction of MHC II epitopes related to 9 antigenic proteins by 3 different servers Target Antigens Start–end position Sequences GP63 15 TO 29 AARLVRLAAAGAAVI 235 TO 249 PAVGVINIPAANIAS 296 TO 310 INSSTAVAKAREQYG 76 TO 90 LPYVTLDTAAAADRR 610 TO 624 LGMVLSLMALVVVWL KMP-11 20 TO 34 NRKMQEQNAKFFADK 48 TO 62 YEKFERMIKEHTEKF 70 TO 84 SEHFKQKFAELLEQQ 74 TO 88 KQKFAELLEQQKAAQ CPB 18 TO 32 VLRILSLTSRRAAAV 21 TO 35 ILSLTSRRAAAVKDR 88 TO 102 AGALVMGTALLTESA 102 TO 116 ADEGATTTSHSHASH 474 TO 488 NSPSSAQSPQRRVLS H1 4 TO 18 DSAVAALSAAMTSPQ 27 TO 41 KTAAKKAAAKKAGAK 32 TO 46 KAAAKKAGAKKAGAK 73 TO 87 KVAKKVAKKPAKKAA A2 16 TO 30 VAAVLALSASAEPHK 150 TO 164 PQSVGPLSVGPLSVG 174 TO 188 GPQSVGPLSVGPLSV 473 TO 487 PLSVGLQAVDVSPVS HASPB 26 TO 40 ANHRGAAGVPPKHAG 133 TO 147 KEDGRTQKNDGDGPK 201 TO 223 GPKEDENLQQNDGNAQQNDGNAQEKNEDGH 223 TO 237 HNVGDGANGNEDGND K39 68 TO 82 DSEALRGQLEEANAE 105 TO 119 EALRGQLEEANAEKE 360 TO 374 KEDSEALRGQLEEAN 682 TO 696 KEDNEALRGQLEKTT LACK2 69 TO 84 SCVSLAHATDYALTA S 101 TO 115 RKFLKHTKDVLAVAF 247 TO 271 RFWMCVATERSLSVY DLESKAVIAE PSA 23 TO 40 NDMVITDMNAAAVAFFGW 64 TO 78 NTVTLMAHLANSTDV 264 TO 278 CDAFGTVHRMTASMT 378 TO 392 RDLMDLAKARKVRLI 3.1.3. Prediction of B-Cell epitopes Linear B-cell epitopes were selected by three servers of BCpred, IEDB, ABCpred, the results of which are shown in Table 2. Table 2: Prediction of B-cell epitopes related to 9 antigenic proteins by 3 different servers Target Antigens Start–end position Sequences GP63 63 TO 72 RHHTAPGAVS 84 TO 98 AAAADRRPGSAPTVV 192 TO 203 KVPPAHITEGFS 300 TO 311 TAVAKAREQYGC 319 TO 330 IEDQGGAGSAGS KMP-11 18 TO 24 EFNRKMQ 20 TO 26 NRKMQEQ 30 TO 47 FFADKPDESTLSPEMKEH 48 TO 73 YEKFERMIKEHTEKFNKKMHEHSEHF CPB 29 TO 88 AAAVKDRAKAAAAAATPSGLSKKFSHPSLSSSFERSGAGGTLSKRGSPESTAGACDSDGA 100 TO 112 ESADEGATTTSHS 114 TO 124 ASHMLHAPGGC 457 TO 483 SSSWRPISSWRPIPAASERATSANSPSSAQSPQ H1 14 TO 47 MTSPQKSPRSSPKKTAAKKAAAKKAGAKKAGAKK 30 TO 53 AKKAAAKKAGAKKAGAKKAVRKVA 68 TO 79 KKPAKKVAKKVA 78 TO 94 VAKKPAKKAAKKPAKKA 25 TO 35 SAEPHKAAVDV 37 TO 40 PLSV 42 TO 67 VGPLSVGPQSVGPLSVGPQSVGPLS 86 TO 183 VGPLSVGPQSVGPLSVGPQAVGPLSVGPQSVGPLSVDVGPQAVGPQSVGPLSVGPQSVGPLSVGPQSVGPLSVGPLSVGPQSVGSLSVGPQSVGPLSV HASPB 77 TO 88 KEDGHTQKNDGD 121 TO 134 DGRTQKNDGDGPKE 130 TO 152 DGPKEDG RTQKNDGDGPKEDGRT K39 41 TO 73 LEEANAEKERLQSELEEKGSEAEAAKEDSEALR 335 TO 367 LEEANAEKERLQSELEEKGSEAAAAKEDSEALR 370 TO 382 LEEANAEKERLQS LACK2 32 TO 57 TSRDGTAISWKANPDRHSVDSDYGLP 70 TO 104 CVSLAHATDYALTASWDRSIRMWDLRNGQCQRKFL 259 TO 269 SVYDLES KAVI 272 TO 281 LTPDGAKPSE PSA 83 TO 90 QTTRDPHA 89 TO 102 HATVVAWTILPIRL 180 TO 190 LGRPKKPNANQ 186 TO 206 PNANQSLKRILPRLQEVLEKE 3.1.4. Multi-epitope antigens construction 18 epitopes from 9 antigenic proteins were selected as areas with maximum overlapping between B-cell and MHC-II epitopes (Table 3). The epitopes selected from each protein were combined by linkers GGSG, SSAG, GGGS, GGAG. Construction of final recombinant antigen contains 461 amino acid roots that are shown in Fig. 2. Table 3: Eighteen epitopes were selected as the final epitope resulted from 9 antigenic proteins based on the results of several servers. Antigen Start–end position Sequences GP63 70-90 AVSAVGLPYVTLDTAAAADRR 300-320 NSSTAVAKAREQYGCDTLEYL CPB 100-120 ESADEGATTTSHSHASHMLHA 460-480 WRPIPAASERATSANSPSSAQ A2 20-50 LALSASAEPHKAAVDVGPLSV 140-160 PQSVGPLSVGPQSVGPLSVGP HASPB 130-150 DDGGPKEDGHTQKNDGDGPKE 200-220 DGDGPKEDGRTQKNDGDGPKE K39 50-70 RLQSELEEKGSEAEAAKEDSE 360-380 KEDSEALRGQLEEANAEKERL KMP11 20-40 NRKMQEQNAKFFADKPDESTL 50-70 KFERMIKEHTEKFNKKMHEHS LACK2 70-90 CVSLAHATDYALTASWDRSIR 260-280 VYDLESKAVIAELTPDGAKPS PAS 80-100 VVIQTTRDPHATVVAWTILPI 180-200 LGRPKKPNANQSLKRILPRLQ H1 30-50 AKKAAAKKAGAKKAGAKKAVR 70-90 PAKKVAKKVAKKPAKKAAKKP 3.1.5. Allergenicity and antigenicity evaluation The results obtained by AlgPred and AllerTOP 2.0 servers showed that the designed structure is non-allergic. Prediction of antigenicity of multi-epitope antigens was conducted by ANTIGENpro 0.931519, which means that our multi-epitope antigens can stimulate the body humoral and cellular immunity responses. 3.1.6. Physicochemical parameters and protein solubility evaluation The molecular weight, number of amino acids, PI theory, aliphatic index, Grand average of hydropathicity (GRAVY), total number of negative residues (Asp + Glu), total number of positive residues (Arg + Lys), and total number of residues in both the positive and negative categories were obtained using the ProtParam server. Additionally, the probability of multi-epitope antigens solubility was determined using the Solpro server and is shown in Table 4. Table 4: Physicochemical properties and solubility of designed structure of multi-epitope antigens Physicochemical properties Result Number of amino acids 461 Molecular weight 46355.10 Theoretical Pi 8.96 Total number of negatively charged residues (Asp + Glu) 56 Total number of positively charged residues (Arg + Lys) 62 Total number of atoms 6451 instability index (II) 38.51 Aliphatic index 56.88 Grand average of hydropathicity (GRAVY) -0.734 Solubility 0.939126 3.1.7. Secondary structure analysis Secondary structure predicted by PSIPRED server was shown in Fig. 3. The results of GOR IV server showed that our designed protein consisted of 34.06% alphahelix, 13.45% extended strand, 52.49% random coil which are the main elements of secondary structure. 3.1.8. Tertiary structure analysis Five models with C-score value (-5 to 2) were proposed by I - TASSER server. The model with higher C-score is the best model: therefore, the model one with C-score value of -0.82 was selected for further assessments (Fig.4). 3.1.9. 3D structure validation and refinement of the 3D structure Selected model for validation stage was refined by GalaxyRefine server. Ramachandran analysis was also performed before and after refinement processes. In the initial model, the analysis results of Ramachandran plot showed that the number of residues in favored and allowed region and outlier region were 256 (55.8%), 132 (28.8%) and 71 (15.5 %), respectively. After refinement of 3D model, the analysis results of Ramachandran showed that the number of residues in favored and allowed region and outlier region were 371 (80.8%), 63 (13.7%) and 25 (5.4 %), respectively. Moreover, the potential errors and quality of initial and refined 3D model were evaluated by ERRAT and Verify 3D servers. The results of ERRAT showed that the overall quality factor of initial 3D model is 77.6699. Based on Verify 3D score 69.85% of residues had an average 3D-1D score greater than 0.2. After refinement of 3D model, the results obtained from ERRAT coefficient and Verify 3D score were 61.017% and 61.17%, respectively (Fig. 5). According to mentioned results, the quality of 3D structure is improved after refinement. 3.1.10. Codon optimization and in silico cloning The DNA sequence was supplied to the JCAT server after the reverse translation of the protein via the SMS server in order to track important factors that influence the protein expression surface in the E. coli host. According to the JCAT server results, the optimized nucleotide sequence's CAI was 1.0, the optimum value for expression in the host E. coli strain. Additionally, the average GC concentration of DNA sequences was 52.5%, although the optimal range for GC content is between 30% and 70%. The expression of proteins will suffer from any outlier area. The CFD value for the 100% complete gene sequence is evaluated by the GenScript server. The efficiency of transcription and translation is decreased by CFD by 30%. Finally, the findings demonstrated that the host's ideal DNA sequence was present at the highest level. 3.2. Experimental studies 3.2.1. Confirmation of subcloning of the multi-epitope antigens fusion in pET-26b expression vector In the first step, we cultured one of the clones of the puc57R recombinant plasmid. The plasmid extracted from this culture was digested with BamHI and HindIII enzymes. After enzymatic digestion, the separation of the recombinant gene fragment from the cloning vector was observed through electrophoresis in agarose gel (Fig. 6). The separated fragment was purified from the gel using a purification kit. In the second step, plasmid pET-26b was cultured. The plasmid extracted from this culture was digested with BamHI and HindIII enzymes and purified. In the third step, the recombinant gene fragment was added to the digested pET-26b plasmid by T4 DNA ligase enzyme. The fusion product was transfected into competent cells of E. coli strain TOP10. The transformed cells were cultured in order to increase the recombinant plasmids. 3.2.2. Colony PCR and enzymatic digestion to confirm cloning The recombinant gene construct was successfully subcloned into the pET-26b expression vector. By using colony PCR and enzymatic digestion, the existence of recombinant gene fusion in the pET-26b vector was discovered. The plasmid in the colonies was utilized as a template in the Colony PCR technique so that the recombinant gene could be amplified using primers that were unique to it. Following PCR product electrophoresis, the 400 bp fragment confirmed the insertion fragment synthesis process on the pET26b vector (Fig. 7). The recombinant gene fragment should be acquired from the digestion of the recombinant plasmid with these two enzymes as the cutting sites of BamHI and HindIII enzymes are at both ends of the recombinant gene. As a result, the BamHI and HindIII enzymes were used to cut the recombinant plasmids that were verified by the Colony PCR technique. The end result of this digestion was visible on an agarose gel as a band of 1383 bp (insert) and a band associated with the linear plasmid pET-26b (Fig. 8). 3.2.3. Expression of multi-epitope antigens The recombinant vector was transferred to the E. coli BL21 expression host after the cloning of the recombinant gene in the pET-26b plasmid vector was verified. Two pET-26b colonies with recombinant genes, two pET-26b colonies devoid of recombinant genes, and two colonies of BL21 bacterial cells were assessed among the acquired colonies. The expression of the recombinant gene before and after induction was examined on a 12.5% SDS-PAGE gel following the cultivation of the colonies and induction with IPTG (1 mM final concentration). At 37 °C after induction, 1 mM IPTG concentration, and 16 hours after induction, the recombinant protein successfully expressed itself. A 46 kDa band associated with the production of the recombinant protein was identified using SDS-PAGE analysis. Fig. 9 depicts the expression results of six colonies both before and after induction. Additionally, a western blot analysis utilizing an anti-His antibody verified the recombinant protein's identification (Fig. 10). Discussion VL is characterized by irregular bouts of fever and substantial weight loss that is often associated with hepatosplenomegaly, which gradually leads to anemia and death caused by bacterial, viral or bleeding secondary infections. Early diagnosis and proper management can control this disease (Tosyali, Allahverdiyev et al. 2021). Since anti- Leishmania drugs are limited and frequently associated with side effects, the most important step in VL control is to use effective strategies, which is possible with access to affordable and rapid diagnostic tests in endemic disease regions so that physicians can make precise therapeutic decisions. Nonetheless, there is no method with acceptable efficiency for the diagnosis of visceral leishmaniasis (Hagos, Kiros et al. 2024). Accurate methods that are cost-effective and easy to use are very important in the diagnosis and control of visceral leishmaniasis. Serological tests are one of the quick, easy and effective methods for diagnosing this disease (Faria, de Castro Veloso et al. 2015). In most VL diagnostic tests, crude antigens are used. Unfortunately, the use of crude antigen is associated with many problems. To overcome this problem, biotechnological advances in the use of effective epitopes can standardize diagnostic tests and increase the sensitivity and specificity of these tests (Farahmand, Nahrevanian et al. 2018). Recently, a number of bioinformatics tools have been effectively implemented in biological domains. On the one hand, these instruments decrease the time and expense required for the detection of T- and B-cell epitopes, and on the other, they improve the precision of research. In addition, bioinformatics methodologies for the design of multi-epitope structures are economical and save time (Mousavi, Mostafavi-Pour et al. 2017, Vakili, Eslami et al. 2018). In this study, nine immunogenic antigens PSA2, LACK, K39, HASPB, A2, H1, CPB, KMP-11, GP63, were used for epitope selection. Gp63 has a proteases activity, which is expressed in both promastigote and amastigote forms. It could be applied as a pathogenic factor in the first phase of infection. This antigen can create immunity responses of CD4 + T cells and stimulate the expression of cytokines associated with Th1 and protect the intra-cell parasite, which is known as an indicator of protected immunity in Leishmaniasis (Devsani, Vemula et al. 2023). KMP11 is an immunogenic antigen expressed in both promastigote and amastigote forms. This gene is wholly safeguarded, and its product stimulates the humoral and cellular immune systems (Karunathilake, Alles et al. 2024). HASPB is a Leishmania membrane protein that is expressed in both promastigote and amastigote forms of parasite. HASPB protein is highly immunogen and its antibodies are rapidly increased in serum of CL and VL patients and lead to prolonged immunity against Leishmania . HASPB protein is present in all Leishmania species and is identified as the main immunity protein of Leishmania (Kordi, Basmenj et al. 2023). CPB are low-family enzymes, and extensive research indicates that they are effective in parasite reproduction and disease development. They manifest themselves most fully in amastigote form. CPB is the most important virulence factor because it suppresses the host's immune responses and aids in immune evasion (Elmahallawy and Alkhaldi 2021). H1, histones are the proteins available in core of eukaryote cells. DNA strands are wrapped around histone proteins that forms the nucleosome. The studies showed that there is anti-histone antibody H1 in serum of patients with Leishmaniasis. Histone H1 is a highly immunogenic protein, which is highly expressed in both promastigote and amastigote stages of Leishmania species (Hashemzadeh, Karimi Rouzbahani et al. 2020). Leishmania A2 protein is mainly expressed in amastigote. Gene A2 is one of the major factors of disseminating to viscera of L. donovani and L. infantum . The specific A2 antibodies have been identified in 90% of the serum specimens of VL patients, which confirms its expression in human begins. Additionally, A2 protein has led to a considerable immunity infection related to both humoral and cellular immunity responses against Leishmania (Editors 2019). LACK is a 36 kDa protein found in the promastigote and amastigote forms of the Leishmania parasite. The LACK protein plays a crucial role in regulating the immune response against Leishmania , which makes it particularly essential. This gene has been deemed suitable for producing recombinant antigen because LACK induces a rapid immune response against parasites (Gomes, Souza et al. 2022). K39 is a member of kinesin family with 39 amino acids. This antigen is mainly expressed by amastigote and has high specificity and sensitivity in diagnosis of VL (Sanchez, Celeste et al. 2020). PSA-2 is the factor for strong Th1 immunity response in human and protects mice in experimental infection and effectively stimulate the immunity system (Kaushal, Naik et al. 2023). In this investigation, a multi-epitope structure comprised of MHC-II and B-cell epitopes of L. infantum antigens was designed using bioinformatics tools. There are three varieties of linkers used to connect integrated proteins: flexible linkers, rigid linkers, and cleavable linkers. -helical structures provide a relatively rigid structure for the rigid linkers. In many instances, flexible linkers separate functional domains more effectively. By altering the number of copies, the connector length can be modified to achieve the desired distance between domains. Therefore, rigid linkers are chosen when the spatial separation of domains is essential for the stability or biocompatibility of integrated proteins. (Chen, Zaro et al. 2013). In the present study, a rigid linker is used for connecting B and T-cell epitopes. Immunological properties of the designed structure showed that it is a strong immunogenic and non-allergic. The physicochemical properties of the recombinant protein showed that the molecular weight is about 46 kDa, which is suitable for expression in E. coli . Also, the designed protein has a suitable half-life in laboratory conditions and in the body and cells of mammals. The instability index of a suitable protein is less than 40, the instability index of our protein is 38.51. Studying the Protein secondary structure: 34.06% of the designed protein has an alpha helix structure. According to previous research, alpha helix structures in peptides play a significant role in stability (Finkelstein, Badretdinov et al. 1991). Also, subsequently, the optimal fragment was chosen based on the studies conducted on the third structure of the protein by the I-TASSER software. Following numerous analyses of intrinsic protein disorders, we concluded that the designed recombinant protein exhibits few intrinsic protein disorders and can be effective. After design, the production of this protein must be optimized in the expression host. Jcat server was used to optimize codons in order to enhance the expression of recombinant protein and increase its expression in the E. coli K-12 expression host. This server predicted high expression for the E. coli host with a CAI greater than 97%. The designed protein was expressed using a pET-26b vector. In addition, E. coli BL21 served as the bacterial host for this vector's protein expression. Due to genetic engineering, various E. coli organisms with the T7 RNA polymerase coding gene in their chromosomes are used today. T7 is an effective promoter for the expression process. As it was found in the research, after the addition of IPTG, it induced protein expression. According to the experiment, the target protein was well expressed and for its purification, His-tag sequence was designed and used at the end of the recombinant structure. Finally, the weight of the target protein was determined to be about 46 kDa by western blotting technique. Conclusion After bioinformatic analysis and obtaining new data for the design of recombinant protein consisting of 9 antigenic proteins, we concluded that our designed construct is likely to elicit an appropriate protective immune response in in silico conditions. In this regard, in the future, this recombinant structure should be investigated in vivo. Considering the synthesis, expression, and purification of this recombinant construct, as well as the verification of its qualities as a prospective recombinant antigen. This structure has passed the in vivo and immunogenicity tests and is suitable for further evaluation. Once we know the outcomes of the immunogenicity investigation, we can go forward with the development of this recombinant antigen for the diagnosis of visceral leishmaniasis. Declarations Acknowledgments The authors appreciate Deputy of Research and Technology, Lorestan University of Medical Sciences, Khorramabad, Iran. This article is derived from the Master's thesis of the first author, Department of Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran. Conflict of interest The authors declare that there is no conflict of interest. Funding This research was financially supported by Lorestan University of Medical Sciences, Khorramabad, Iran. Hereby the authors appreciate all the people who helped in this research. References Chen, X., J. L. Zaro and W.-C. Shen (2013). "Fusion protein linkers: property, design and functionality." Advanced drug delivery reviews 65 (10): 1357-1369. Colovos, C. and T. O. Yeates (1993). "Verification of protein structures: patterns of nonbonded atomic interactions." Protein science 2 (9): 1511-1519. Costa, C. H., K.-P. Chang, D. L. Costa and F. V. M. Cunha (2023). "From infection to death: An overview of the pathogenesis of visceral leishmaniasis." Pathogens 12 (7): 969. Devsani, N., D. Vemula and V. Bhandari (2023). 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arian","middleName":"Karimi","lastName":"Rouzbahani","suffix":""}],"badges":[],"createdAt":"2024-03-21 13:14:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4143767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4143767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55319407,"identity":"a29d6c78-f173-4747-bf05-1c55e34ae7f5","added_by":"auto","created_at":"2024-04-25 15:59:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16294,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagram of the summary of the approaches that were employed for \u003cem\u003ein silico\u003c/em\u003edesigning the multi-epitope construct.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/406848b3640e9112767da434.png"},{"id":55319415,"identity":"42fe6335-e023-4be7-ac54-ca1e551beef6","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88454,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the designed final structure of multi-epitope antigens\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/78645161bb4bfbcf6af4ffbd.png"},{"id":55319408,"identity":"0e2f1097-0765-42bb-8e75-398443d32124","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128763,"visible":true,"origin":"","legend":"\u003cp\u003eSecondary structure of multi-epitope antigens using PSIPRED server\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/a03bbbb63a47feddaa3853ba.png"},{"id":55319411,"identity":"2a7bd25f-1334-4d28-9667-2defb69673b0","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168211,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of 3D structure of multi-epitope antigens by I-TASSER server\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/870ee7d86cca823dad511fbc.png"},{"id":55320319,"identity":"0f08913a-bf33-4c58-8865-b3c838bbb364","added_by":"auto","created_at":"2024-04-25 16:07:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":568294,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of 3D structure of final structure of multi-epitope antigens\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/bc2d0b36407b2beaefe27c5e.png"},{"id":55320318,"identity":"6a6f5901-5031-4164-b27c-c3a74c4b7504","added_by":"auto","created_at":"2024-04-25 16:07:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96981,"visible":true,"origin":"","legend":"\u003cp\u003e1% Agarose gel electrophoresis: Lane 1: 100 bp DNA ladder marker. Lane 2: Digested by pUC57R recombinant plasmid by HindIII and BamHI.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/ed313cc7b6047ef93604a046.png"},{"id":55319413,"identity":"d591f315-6405-48ef-ac4a-3e9968bf280b","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49022,"visible":true,"origin":"","legend":"\u003cp\u003eConfirmation of the entry of the recombinant gene into pET-26b plasmid using PCR: Lane 1, 2: The 400-bp as PCR product of pET-26b recombinant. Lane 3: 100 bp DNA ladder marker.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/8eb9f8897e3355834d92ca47.png"},{"id":55319409,"identity":"26cd8127-4b30-4de2-ad86-00aa535e6cb0","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":106432,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of recombinant plasmid pET-26b using enzyme digestion: Lane 1: pET-26b recombinant plasmid digested with HindIII and BamHI. Lane 2:100-bp DNA ladder marker.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/d79d28d73579cf101587d468.png"},{"id":55319414,"identity":"049f4627-08f5-4ea3-bf7b-4579755a9fc5","added_by":"auto","created_at":"2024-04-25 15:59:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":145102,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression result of 6 colonies before and after induction on SDS-PAGE gel using a concentration of 1 mM IPTG: column 1: \u003cem\u003eE. coli\u003c/em\u003e BL2 bacterial culture sediment before induction, column 2: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment after induction, column 3: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment containing pET-26b vector before induction, column 4: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment containing pET-26b vector after induction, column 5: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment containing pET-26b vector with recombinant gene after induction, column 6: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment containing pET-26b vector with recombinant gene before induction, column 7: protein size indicator.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/e8c5e5ccc6af01c3daea3d38.png"},{"id":55320320,"identity":"fe139e88-68f2-4345-bbd1-db7b28ac604e","added_by":"auto","created_at":"2024-04-25 16:07:52","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":81499,"visible":true,"origin":"","legend":"\u003cp\u003eWestern blot analysis of the recombinant protein expressed in the strain: \u003cem\u003eE. coli\u003c/em\u003e BL21, column 1: protein size indicator, column 2: sediment of \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture containing pET-26b vector together with the recombinant gene after induction, column 3: sediment \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture containing pET-26b vector with recombinant gene before induction, column 4: \u003cem\u003eE. coli\u003c/em\u003eBL21 bacterial culture sediment containing pET-26b vector after induction, column 5: \u003cem\u003eE. coli\u003c/em\u003e BL21 bacterial culture sediment containing vector pET-26b before induction, column 6: E. coli BL21 bacterial culture sediment after induction, column 7: \u003cem\u003eE. coli\u003c/em\u003e BL2 bacterial culture sediment before induction.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/21161a1e763115c4d37959ec.png"},{"id":59504213,"identity":"0cbe81b9-8a25-470e-9278-e173bd30e656","added_by":"auto","created_at":"2024-07-02 14:48:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2719401,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4143767/v1/f4cb0765-0836-481f-b0b7-26fa7fcbc291.pdf"}],"financialInterests":"","formattedTitle":"Design and evaluation of a novel multi-epitope antigen for evaluate the diagnostic immunity responses against Leishmania infantum infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe disease known as leishmaniasis is brought on by the parasite \u003cem\u003eLeishmania\u003c/em\u003e, an essential intracellular protozoan. Leishmaniasis poses a threat to more than 350\u0026nbsp;million individuals in 98 countries, and 1.2\u0026nbsp;million new cases are recorded each year (Pal, Ejeta et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Saini, Joshi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most important clinical manifestations of this disease include visceral leishmaniasis (VL), cutaneous leishmaniasis (CL) and mucocutaneous leishmaniasis (MCL). \u003cem\u003eLeishmania infantum\u003c/em\u003e (\u003cem\u003eL. infantum\u003c/em\u003e) is the cause of visceral leishmaniasis (VL) known as kala-azar in the Mediterranean region. Clinical symptoms of VL include paleness, fever, splenomegaly, hepatomegaly and lymphadenopathy. Delay in identifying VL in patients can be associated with increased mortality (Scarpini, Dondi et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Costa, Chang et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, accurate diagnosis is one of the vital elements in controlling and preventing this disease.\u003c/p\u003e \u003cp\u003eUnfortunately, VL is difficult to diagnose because of clinical similarities between VL and other diseases including malaria, typhoid, and tuberculosis, as well as parasite isolation in the spleen, bone marrow, or lymph nodes. Based on this, researchers are always looking for a simple, fast and sensitive method to be used in this field (Herrera, Castillo et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Solimando, Coniglio et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high sensitivity and specificity of serological tests has been made possible by technological advances in recombinant antigens used as reagents for serological diagnosis of VL (Zhou, Chen et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The detection of VL has been made possible by a number of high-performance recombinant proteins, but more work is still required to find the right antigens and achieve high-potency performance (Dias, Machado et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, various bioinformatics tools are successfully used in the biological field. These tools are used to identify the epitopes of adaptive immune cells in protein analysis (Hashemzadeh, Nezhad et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, using bioinformatics tools, 9 proteins of \u003cem\u003eL. infantum\u003c/em\u003e, which were highly immunogenic antigens, were used in the design of multi-epitope antigens. The selected proteins are as follows:\u003c/p\u003e \u003cp\u003eGlycoprotein 63 (GP63) is the main surface glycoprotein of parasite that plays an important role in invasion of parasite to macrophage (Gupta, Das et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Kinetoplastid membrane protein-11 (KMP-11); due to its wide distribution in parasitic kinetoplastid, it is called this name and is the strong stimulation of immunity cell responses (Kumari, Mahajan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cysteine protease I (CPB); CPs in \u003cem\u003eLeishmania\u003c/em\u003e play a role in survival of parasite within macrophage. This role is in such a way that the growth of parasite is stopped in the presence of inhibitors of these enzymes (Rawat, Roy et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nuclear Protein Histone H1 (H1); this antigen induces the protection surface against \u003cem\u003eLeishmania\u003c/em\u003e infection (Vakili, Nezafat et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Protein A2 (A2) plays a major role in parasitic survival in visceral organs of host mammals (Jusi, Oliveira et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hydrophilic Acylated Surface Protein B (HASPB), this protein is considered as an immunogen antigen in the host (MacLean, Price et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Kinesin-like protein K39 (K39); this protein is protected among \u003cem\u003eLeishmania\u003c/em\u003e species (Reiter-Owona, Rehkaemper-Schaefer et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eLeishmania\u003c/em\u003e homologue of receptors for activated C kinase (LACK); LACK protein in host is considered as an immunogen antigen (Fernandez, Carrillo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Promastigote Surface Antigen-2 (PSA-2) plays a role in resistance to slippery as well as adhesion to macrophage and invasion through complement receptor (Petitdidier, Pagniez et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter choosing the antigenic proteins, the appropriate bioinformatics tools were used to analyse the nine antigenic proteins of \u003cem\u003eL. infantum\u003c/em\u003e in order to create multi-epitope antigens for evaluating the immune responses against VL. Then, this designed recombinant construct was synthesized and subcloned into the expression vector pET-26b. The expression of multi-epitope structure in recombinant E. coli was investigated in relation to IPTG concentration and culture time before and after induction. The recombinant protein was identified by SDS-PAGE and western blot.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Bioinformatics studies\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. A collection of \u003cem\u003eLeishmania infantum\u003c/em\u003e protein sequences\u003c/h2\u003e \u003cp\u003eThe complete sequence of antigenic proteins including GP63 ( Gene ID: 5067052 ), KMP11 (Gene ID: 5073122), CPB (Gene ID: 5066588 ), H1 (Gene ID: 5070068), A2 (Gene ID: 5069008 ), HASPB (Gene ID: 5069221 ), K39 ( Gene ID: 5067624 ), LACK (Gene ID: 5070509 ), PSA (Gene ID: 5071243 ) was obtained from the National Center for Biotechnology Information (NCBI) )\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/?term=\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/?term=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and stored in the FASTA format (Maglott, Ostell et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Prediction of MHC-II epitopes\u003c/h2\u003e \u003cp\u003eIn order to predict the MHC-II epitopes, three different servers of IEDB, ProPred, and RANKPEP were used.\u003c/p\u003e \u003cp\u003eIn order to predict the MHC-II connection epitopes in IEDB server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhcii/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhcii/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) between several proposed methods, the recommended IEDB method was used (Kim, Ponomarenko et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRANKPEP server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://imed.med.ucm.es/Tools/rankpep.html\u003c/span\u003e\u003cspan address=\"http://imed.med.ucm.es/Tools/rankpep.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is based on prediction of the MHC-II connective epitopes, PSSMs (Position Specific Scoring Matrices). In this prediction, the binding threshold is equal to 4\u0026ndash;6% (Reche, Glutting et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGraphical tool ProPred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/propred/index.html\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/propred/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the MHC-II connection areas in sequence of antigenic proteins. This server uses the matrix-based prediction algorithm (Singh and Raghava \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Prediction of B-cell epitopes\u003c/h2\u003e \u003cp\u003eB-cell epitopes are antigenic determinants in the surface of pathogens that are associated with B-cell receptors (Greenbaum, Andersen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). There are a lot of tools for structure-based sequence, but the tools are limited to predict B-cell epitopes (Saha, Bhasin et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In this research, three servers of IEDB, BCpred, ABCpred were used to predict B-cell epitopes.\u003c/p\u003e \u003cp\u003eIEDB server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/bcell/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/bcell/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a set of prediction methods of linear B-cell epitopes based on sequential features of antigen using amino acid and HMMs scales, which predicts B-cell epitopes with connection threshold of 0.350 based on methods, such as Emini surface accessibility, Karplus and Schulz flexibility, Kolaskar and Tongaonkar antigenicity, Parker hydrophilicity, Bepipred linear epitope prediction (Parker, Guo et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, Larsen, Lund et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeb-based ABCpred server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://crdd.osdd.net/raghava/abcpred/ABC_submission.html\u003c/span\u003e\u003cspan address=\"http://crdd.osdd.net/raghava/abcpred/ABC_submission.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) allows to predict the linear B-cell epitopes using artificial neural network. Then, based on the resulting score, the epitopes are ranked. ABCpred server with precision of 65.93% and default threshold of 0.51 predicts the linear B-cell epitopes (Rubinstein, Mayrose et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBCpred sever (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://crdd.osdd.net/raghava/bcepred/bcepred_submission.html\u003c/span\u003e\u003cspan address=\"http://crdd.osdd.net/raghava/bcepred/bcepred_submission.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) predicts the linear B-cell epitopes based on physicochemical groups instead of a unit feature. BCpred is a method to predict the linear B-cell epitopes using SVM machine learning method. The precision of the server is 74.57% and its specificity is 75% (Saha and Raghava \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4. Multi-epitope antigens construction\u003c/h2\u003e \u003cp\u003eThe prediction results of immune system epitopes were compared and the areas with maximum overlapping that causes humoral immunity response and cellular immunity response were specified, and finally, the selected epitopes were linked to each other by linkers GGSG, SSAG, GGGS, GGAG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5. Antigenicity prediction\u003c/h2\u003e \u003cp\u003eIn order to evaluate the antigenicity of final multi-epitope antigens, the ANTIGENpro sever was selected. ANTIGENpro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://scratch.proteomics.ics.uci.edu/\u003c/span\u003e\u003cspan address=\"http://scratch.proteomics.ics.uci.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is used to predict whether this protein is a protective antigen or not.\u003c/p\u003e \u003cp\u003eANTIGENpro with precision of 82% and default threshold of 0.4. The results of this server are based on analysis of micro dataset of protein (Hashemzadeh, Ghorbanzadeh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.6. Allergenicity prediction\u003c/h2\u003e \u003cp\u003eIn order to evaluate the allergenicity of designed multi-epitope antigens, two servers of AllerTOP 2.0 and AlgPred were used.\u003c/p\u003e \u003cp\u003eAlgPred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://crdd.osdd.net/raghava/algpred/\u003c/span\u003e\u003cspan address=\"http://crdd.osdd.net/raghava/algpred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is the dataset used for this sever consisted of 578 allergens and 700 non-allergens. The precision of this server is 84%, and in order to evaluate the allergenicity, the motif-based method of MEME/MAST software was used. The sensitivity and specificity of this server are 93.94% and 33.34%, respectively (Saha and Raghava \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAllerTOP 2.0 in (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ddg-pharmfac.net/AllerTOP/\u003c/span\u003e\u003cspan address=\"https://www.ddg-pharmfac.net/AllerTOP/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is another server that is based on auto cross covariance (ACC). This server contains 2210 known allergens of various species and 2210 non-allergens of the same species. This sever can predict allergens and non-allergens with precision of 85.3% by k nearest neighbors (kNN) algorithm (Dimitrov, Bangov et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.1.7. Physicochemical properties evaluation and solubility prediction\u003c/h2\u003e \u003cp\u003ePhysical and chemical features of multi-epitope antigens designed by Protparam (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were studied (Gasteiger, Hoogland et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The computed physicochemical parameters include amino acid composition, theoretical pI, molecular weight (MW), instability index, in vitro and in vivo half-life, grand average of hydropathicity (GRAVY), and aliphatic index.\u003c/p\u003e \u003cp\u003eIn order to assess the final multi-epitope antigens solubility, Solpro server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://scratch.proteomics.ics.uci.edu/\u003c/span\u003e\u003cspan address=\"http://scratch.proteomics.ics.uci.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used. Solpro server is a two-stage (support vector machine) SVM-based architecture, the prediction accuracy of its result is estimated over 74% (Magnan, Randall et al. 2009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.1.8. Secondary structure prediction\u003c/h2\u003e \u003cp\u003ePSIPRED server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinf.cs.ucl.ac.uk/psipred/\u003c/span\u003e\u003cspan address=\"http://bioinf.cs.ucl.ac.uk/psipred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is one of the accurate methods to predict the secondary structure. Prediction results are presented as text and graphics and are able to achieve the Q3 mean score of 76.5% (McGuffin, Bryson et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGOR IV server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa-prabi.ibcp.fr/cgi-bin/secpred_gor4.pl\u003c/span\u003e\u003cspan address=\"https://npsa-prabi.ibcp.fr/cgi-bin/secpred_gor4.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) uses GOR method, which is one of the most popular prediction schemes of secondary structure. Prediction of GOR method is improved by 55\u0026ndash;64.4% (Garnier, Gibrat et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.1.9. Tertiary structure prediction\u003c/h2\u003e \u003cp\u003eI-TASSER (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zhanglab.ccmb.med.umich.edu/I-TASSER/\u003c/span\u003e\u003cspan address=\"https://zhanglab.ccmb.med.umich.edu/I-TASSER/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the 3D structure of protein. The quality of the proposed model is estimated by I-TASSER using C-score. A model with high reliability has a higher C-score (Yang, Yan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). YASARA software was used to visualize 3D models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.1.10. Refinement of the 3D modeled structure\u003c/h2\u003e \u003cp\u003eUsing GalaxyRefine server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\u003c/span\u003e\u003cspan address=\"http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the 3D multi-epitope model was very likely to be refined. Mild relaxation technique refines lateral chains while loops and secondary structure parts are refined by the violator technique (Shin, Lee et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.1.11. Validation of the 3D structure\u003c/h2\u003e \u003cp\u003eERRAT, Verify3D, and RAMPAGE servers were used to confirm the refined multi-epitope structure. The ERRAT server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://services.mbi.ucla.edu/ERRAT/\u003c/span\u003e\u003cspan address=\"http://services.mbi.ucla.edu/ERRAT/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) is a novel approach based on atomic interactions to distinguish between proper and wrong regions of distinct protein structures. Most of the distinct atoms have random distributions as a result of model creation flaws, which may be separated from the proper distributions using statistical techniques (Colovos and Yeates \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Verify3D (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://servicesn.mbi.ucla.edu/Verify3D/\u003c/span\u003e\u003cspan address=\"http://servicesn.mbi.ucla.edu/Verify3D/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a program that validates 3D structure of multi-epitope antigens by measuring the compatibility of 3D structure model with its sequences (L\u0026uuml;thy, Bowie et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), and because phi-psi criteria are acknowledged as a key component of validating protein structure, the RAMPAGE server (Ramachandran Plot Assessment) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mordred.bioc.cam.ac.uk/rapper/rampage.php\u003c/span\u003e\u003cspan address=\"http://mordred.bioc.cam.ac.uk/rapper/rampage.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) calculates the rotation angle phi-psi for each root in the protein. Additionally, the roots are divided into three categories: outlier, allowed, and favoured regions (Lovell, Davis et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.1.12. Codon optimization and \u003cem\u003ein silico\u003c/em\u003e cloning\u003c/h2\u003e \u003cp\u003eReverse translation and codon optimization were done using SMS, JCAT, and GenScript servers to guarantee protein production at high surface in \u003cem\u003eE. coli\u003c/em\u003e. SMS was used for the reverse translation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.org/sms/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.org/sms/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). and JCAT, found online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jcat.de/\u003c/span\u003e\u003cspan address=\"http://www.jcat.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, carried out codon optimization. Codon analysis was performed using the GenScript service, which assesses important DNA sequence characteristics such codon frequency distribution (CFD). (Stothard \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Grote, Hiller et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Finally, for DNA sequence cloning compatible with the final multi-epitope structure in pET-26b vector, BamHI and HindIII cut sites on N and C terminals were designed, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental studies\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Subcloning of the multi-epitope antigens fusion in the expression vector\u003c/h2\u003e \u003cp\u003eThe multi-epitope structure fusion was created in the pUC57 cloning vector by Bio Magic Gene Company following bioinformatics analysis. The pET-26b prokaryotic expression vector was subcloned with the gene construct of the multi-epitope antigens. To do this, the multi-epitope antigens fusion was enzymatically digested using the restriction enzymes BamHI/HindIII from the vector pUC57. Additionally, BamHI/HindIII restriction enzymes were used on the pET-26b plasmid. T4 DNA ligase was used to introduce the recombinant gene fragment into the pET-26b plasmid that had been broken down. The competent cells of the \u003cem\u003eE. coli\u003c/em\u003e strain TOP10 were given the fusion product, and the transformed cells were grown to yield the recombinant plasmids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Colony PCR and enzymatic digestion to confirm cloning\u003c/h2\u003e \u003cp\u003eSince the pET-26b plasmid lacks a screening mechanism, the growing colonies of transformed cells were chosen at random in order to extract the plasmid and verify the cloning. Colony PCR was used to confirm cloning. In the following step, BamHI and HindIII enzymes were used to cleave the plasmids whose recombination was verified by colony PCR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Expression of multi-epitope antigens\u003c/h2\u003e \u003cp\u003e \u003cem\u003eE. coli\u003c/em\u003e strain (BL21) was transformed with pET-26b plasmid carrying the recombinant gene by heat shock in order to express the multi-epitope antigens. The single colony was then grown for 16 hours at 37\u0026deg;C in bacterial liquid culture media. In the following step, isopropyl-beta-D-thiogalactopyranoside (IPTG) was used to stimulate the bacteria that had developed. when the absorbance at 600 nm wavelength surpassed a certain level. In this case, 1 mM IPTG was employed as the concentration. Both before and after the induction, the recombinant gene was expressed at 37\u0026deg;C. Cell sediments were collected, then electrophoresed on a 12.5% SDS-PAGE gel for analysis. Additionally, western blot analysis utilizing anti-histidine antibody coupled to H2O2 peroxidase and diaminobenzidine (DAB) substrate was used to demonstrate the production of the recombinant multi-epitope antigen.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by The Ethics Committee of Lorestan University of Medical Sciences (IR.LUMS.REC.1398.190).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. \u003cstrong\u003eBioinformatics studies\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.1.1. The whole procedures used for multi-epitope construction design is demonstrated in Fig. 1. Sequences related to nine antigenic peptides were received from NCBI server.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.2. Prediction of MHC-II epitopes\u003c/p\u003e\n\u003cp\u003eHLA-DR was selected for parameters of locus MHC-II, and DRB1*01:01 and DRB1*01:02 were adjusted as \u0026alpha; and \u0026beta; chains based on data calculated from Allele frequency net (AFND) for Iranian population\u0026nbsp;\u003cp\u003ehttp://www.allelefrequencies.net/hla6006a.asp\u003c/p\u003e(Gonz\u0026aacute;lez-Galarza, Takeshita et al. 2014). In order to predict MHC-II epitopes, three different servers (IEDB, ProPred, and\u0026nbsp;RANKPEP were used, the results of which are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1: Prediction of MHC II epitopes related to 9 antigenic proteins by 3 different servers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"390\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Antigens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u0026ndash;end position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e15 TO 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAARLVRLAAAGAAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e235 TO 249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003ePAVGVINIPAANIAS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e296 TO 310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eINSSTAVAKAREQYG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e76 TO 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eLPYVTLDTAAAADRR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e610 TO 624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eLGMVLSLMALVVVWL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMP-11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e20 TO 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNRKMQEQNAKFFADK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e48 TO 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eYEKFERMIKEHTEKF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e70 TO 84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eSEHFKQKFAELLEQQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e74 TO 88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKQKFAELLEQQKAAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e18 TO 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eVLRILSLTSRRAAAV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e21 TO 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eILSLTSRRAAAVKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e88 TO 102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eAGALVMGTALLTESA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e102 TO 116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eADEGATTTSHSHASH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e474 TO 488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eNSPSSAQSPQRRVLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e4 TO 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eDSAVAALSAAMTSPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e27 TO 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKTAAKKAAAKKAGAK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e32 TO 46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKAAAKKAGAKKAGAK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e73 TO 87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKVAKKVAKKPAKKAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e16 TO 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eVAAVLALSASAEPHK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e150 TO 164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003ePQSVGPLSVGPLSVG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e174 TO 188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eGPQSVGPLSVGPLSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e473 TO 487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003ePLSVGLQAVDVSPVS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHASPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e26 TO 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eANHRGAAGVPPKHAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e133 TO 147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKEDGRTQKNDGDGPK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e201 TO 223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eGPKEDENLQQNDGNAQQNDGNAQEKNEDGH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e223 TO 237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eHNVGDGANGNEDGND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e68 TO 82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eDSEALRGQLEEANAE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e105 TO 119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eEALRGQLEEANAEKE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e360 TO 374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKEDSEALRGQLEEAN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e682 TO 696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eKEDNEALRGQLEKTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLACK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e69 TO 84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eSCVSLAHATDYALTA S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e101 TO 115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eRKFLKHTKDVLAVAF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e247 TO 271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eRFWMCVATERSLSVY DLESKAVIAE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.692307692307693%\" valign=\"top\"\u003e\n \u003cp\u003e23 TO 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.23076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNDMVITDMNAAAVAFFGW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e64 TO 78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eNTVTLMAHLANSTDV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e264 TO 278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eCDAFGTVHRMTASMT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e378 TO 392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\" valign=\"top\"\u003e\n \u003cp\u003eRDLMDLAKARKVRLI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1.3. Prediction of B-Cell epitopes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLinear B-cell epitopes were selected by three servers of BCpred, IEDB, ABCpred, the results of which are shown in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Prediction of B-cell epitopes related to 9 antigenic proteins by 3 different servers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Antigens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u0026ndash;end position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e63 TO 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eRHHTAPGAVS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e84 TO 98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eAAAADRRPGSAPTVV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e192 TO 203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eKVPPAHITEGFS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e300 TO 311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eTAVAKAREQYGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e319 TO 330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eIEDQGGAGSAGS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMP-11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e18 TO 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eEFNRKMQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e20 TO 26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eNRKMQEQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e30 TO 47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eFFADKPDESTLSPEMKEH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e48 TO \u0026nbsp; \u0026nbsp; \u0026nbsp;73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eYEKFERMIKEHTEKFNKKMHEHSEHF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e29 TO 88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eAAAVKDRAKAAAAAATPSGLSKKFSHPSLSSSFERSGAGGTLSKRGSPESTAGACDSDGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e100 TO 112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eESADEGATTTSHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e114 TO \u0026nbsp;124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eASHMLHAPGGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e457 TO \u0026nbsp; \u0026nbsp; 483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eSSSWRPISSWRPIPAASERATSANSPSSAQSPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e14 TO 47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eMTSPQKSPRSSPKKTAAKKAAAKKAGAKKAGAKK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e30 TO 53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eAKKAAAKKAGAKKAGAKKAVRKVA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e68 TO 79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eKKPAKKVAKKVA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e78 TO \u0026nbsp; \u0026nbsp; 94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eVAKKPAKKAAKKPAKKA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e25 TO 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eSAEPHKAAVDV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e37 TO 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003ePLSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e42 TO 67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eVGPLSVGPQSVGPLSVGPQSVGPLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e86 TO 183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eVGPLSVGPQSVGPLSVGPQAVGPLSVGPQSVGPLSVDVGPQAVGPQSVGPLSVGPQSVGPLSVGPQSVGPLSVGPLSVGPQSVGSLSVGPQSVGPLSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHASPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e77 TO 88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eKEDGHTQKNDGD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e121 TO 134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eDGRTQKNDGDGPKE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e130 TO 152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eDGPKEDG RTQKNDGDGPKEDGRT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e41 TO 73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eLEEANAEKERLQSELEEKGSEAEAAKEDSEALR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e335 TO 367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eLEEANAEKERLQSELEEKGSEAAAAKEDSEALR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e370 TO 382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eLEEANAEKERLQS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLACK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e32 TO 57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eTSRDGTAISWKANPDRHSVDSDYGLP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e70 TO 104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eCVSLAHATDYALTASWDRSIRMWDLRNGQCQRKFL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e259 TO 269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eSVYDLES KAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e272 TO 281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eLTPDGAKPSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\" valign=\"top\"\u003e\n \u003cp\u003e83 TO 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.01030927835052%\" valign=\"top\"\u003e\n \u003cp\u003eQTTRDPHA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e89 TO 102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eHATVVAWTILPIRL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e180 TO 190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003eLGRPKKPNANQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.73170731707317%\" valign=\"top\"\u003e\n \u003cp\u003e186 TO 206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.26829268292683%\" valign=\"top\"\u003e\n \u003cp\u003ePNANQSLKRILPRLQEVLEKE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1.4. Multi-epitope antigens construction\u003c/p\u003e\n\u003cp\u003e18 epitopes from 9 antigenic proteins were selected as areas with maximum overlapping between\u0026nbsp;B-cell and MHC-II epitopes (Table 3). The epitopes selected from each protein were combined by linkers GGSG, SSAG, GGGS, GGAG. Construction of final recombinant antigen contains 461 amino acid roots that are shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003eTable 3: Eighteen epitopes were selected as the final epitope resulted from 9 antigenic proteins based on the results of several servers.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u0026ndash;end position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGP63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e70-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eAVSAVGLPYVTLDTAAAADRR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e300-320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eNSSTAVAKAREQYGCDTLEYL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e100-120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eESADEGATTTSHSHASHMLHA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e460-480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eWRPIPAASERATSANSPSSAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e20-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eLALSASAEPHKAAVDVGPLSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e140-160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003ePQSVGPLSVGPQSVGPLSVGP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHASPB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e130-150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eDDGGPKEDGHTQKNDGDGPKE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e200-220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eDGDGPKEDGRTQKNDGDGPKE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e50-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eRLQSELEEKGSEAEAAKEDSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e360-380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eKEDSEALRGQLEEANAEKERL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKMP11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e20-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eNRKMQEQNAKFFADKPDESTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e50-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eKFERMIKEHTEKFNKKMHEHS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLACK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e70-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eCVSLAHATDYALTASWDRSIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e260-280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eVYDLESKAVIAELTPDGAKPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e80-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eVVIQTTRDPHATVVAWTILPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e180-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eLGRPKKPNANQSLKRILPRLQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.3125%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003e30-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.125%\" valign=\"top\"\u003e\n \u003cp\u003eAKKAAAKKAGAKKAGAKKAVR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e70-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"top\"\u003e\n \u003cp\u003ePAKKVAKKVAKKPAKKAAKKP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.1.5. Allergenicity and antigenicity evaluation\u003c/p\u003e\n\u003cp\u003eThe results obtained by\u0026nbsp;AlgPred and AllerTOP 2.0\u0026nbsp;servers showed that the designed structure is non-allergic. Prediction of antigenicity of multi-epitope antigens was conducted by ANTIGENpro 0.931519, which means that our multi-epitope antigens can stimulate the body humoral and cellular immunity responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.6. Physicochemical parameters and protein solubility evaluation\u003c/p\u003e\n\u003cp\u003eThe molecular weight, number of amino acids, PI theory, aliphatic index, Grand average of hydropathicity (GRAVY), total number of negative residues (Asp + Glu), total number of positive residues (Arg + Lys), and total number of residues in both the positive and negative categories were obtained using the ProtParam server. Additionally, the probability of multi-epitope antigens solubility was determined using the Solpro server and is shown in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4: Physicochemical properties and solubility of designed structure of multi-epitope antigens\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"372\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysicochemical properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of amino acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eMolecular weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e46355.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eTheoretical Pi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e8.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eTotal number of negatively charged residues (Asp + Glu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eTotal number of positively charged residues (Arg + Lys)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eTotal number of atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e6451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003einstability index (II)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e38.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eAliphatic index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e56.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eGrand average of hydropathicity (GRAVY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e-0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"82.25806451612904%\" valign=\"top\"\u003e\n \u003cp\u003eSolubility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.741935483870968%\" valign=\"top\"\u003e\n \u003cp\u003e0.939126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.7. Secondary structure analysis\u003c/p\u003e\n\u003cp\u003eSecondary structure predicted by PSIPRED server was shown in Fig. 3. The results of GOR IV server showed that our designed protein consisted of 34.06% alphahelix, 13.45% extended strand, 52.49% random coil which are the main elements of secondary structure.\u003c/p\u003e\n\u003cp\u003e3.1.8. Tertiary structure analysis\u003c/p\u003e\n\u003cp\u003eFive models with C-score value (-5 to 2) were proposed by I - TASSER server. The model with higher C-score is the best model: therefore, the model one with C-score value of -0.82 was selected for further assessments (Fig.4).\u003c/p\u003e\n\u003cp\u003e3.1.9. 3D structure validation and refinement of the 3D structure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSelected model for validation stage was refined by GalaxyRefine server. Ramachandran analysis was also performed before and after refinement processes. In the initial model, the analysis results of Ramachandran plot showed that the number of residues in favored and allowed region and outlier region were 256 (55.8%), 132 (28.8%) and 71 (15.5 %), respectively. After refinement of 3D model, the analysis results of Ramachandran showed that the number of residues in favored and allowed region and outlier region were 371 (80.8%), 63 (13.7%) and 25 (5.4 %), respectively. Moreover, the potential errors and quality of initial and refined 3D model were evaluated by ERRAT and Verify 3D servers. The results of ERRAT showed that the overall quality factor of initial 3D model is 77.6699. Based on Verify 3D score 69.85% of residues had an average 3D-1D score greater than 0.2. After refinement of 3D model, the results obtained from ERRAT coefficient and Verify 3D score were 61.017% and 61.17%, respectively (Fig. 5).\u003c/p\u003e\n\u003cp\u003eAccording to mentioned results, the quality of 3D structure is improved after refinement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.1.10. Codon optimization and \u003cem\u003ein silico\u003c/em\u003e cloning\u003c/p\u003e\n\u003cp\u003eThe DNA sequence was supplied to the JCAT server after the reverse translation of the protein via the SMS server in order to track important factors that influence the protein expression surface in the \u003cem\u003eE. coli\u003c/em\u003e host. According to the JCAT server results, the optimized nucleotide sequence\u0026apos;s CAI was 1.0, the optimum value for expression in the host \u003cem\u003eE. coli\u003c/em\u003e strain. Additionally, the average GC concentration of DNA sequences was 52.5%, although the optimal range for GC content is between 30% and 70%. The expression of proteins will suffer from any outlier area. The CFD value for the 100% complete gene sequence is evaluated by the GenScript server. The efficiency of transcription and translation is decreased by CFD by 30%. Finally, the findings demonstrated that the host\u0026apos;s ideal DNA sequence was present at the highest level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Experimental studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2.1. Confirmation of subcloning of the multi-epitope antigens fusion in pET-26b expression vector\u003c/p\u003e\n\u003cp\u003eIn the first step, we cultured one of the clones of the puc57R recombinant plasmid. The plasmid extracted from this culture was digested with BamHI and HindIII enzymes. After enzymatic digestion, the separation of the recombinant gene fragment from the cloning vector was observed through electrophoresis in agarose gel (Fig. 6). The separated fragment was purified from the gel using a purification kit. In the second step, plasmid pET-26b was cultured. The plasmid extracted from this culture was digested with BamHI and HindIII enzymes and purified. In the third step, the recombinant gene fragment was added to the digested pET-26b plasmid by T4 DNA ligase enzyme. The fusion product was transfected into competent cells of \u003cem\u003eE. coli\u003c/em\u003e strain TOP10. The transformed cells were cultured in order to increase the recombinant plasmids.\u003c/p\u003e\n\u003cp\u003e3.2.2. Colony PCR and enzymatic digestion to confirm cloning\u003c/p\u003e\n\u003cp\u003eThe recombinant gene construct was successfully subcloned into the pET-26b expression vector. By using colony PCR and enzymatic digestion, the existence of recombinant gene fusion in the pET-26b vector was discovered.\u003c/p\u003e\n\u003cp\u003eThe plasmid in the colonies was utilized as a template in the Colony PCR technique so that the recombinant gene could be amplified using primers that were unique to it. Following PCR product electrophoresis, the 400 bp fragment confirmed the insertion fragment synthesis process on the pET26b vector (Fig. 7).\u003c/p\u003e\n\u003cp\u003eThe recombinant gene fragment should be acquired from the digestion of the recombinant plasmid with these two enzymes as the cutting sites of BamHI and HindIII enzymes are at both ends of the recombinant gene. As a result, the BamHI and HindIII enzymes were used to cut the recombinant plasmids that were verified by the Colony PCR technique. The end result of this digestion was visible on an agarose gel as a band of 1383 bp (insert) and a band associated with the linear plasmid pET-26b (Fig. 8).\u003c/p\u003e\n\u003cp\u003e3.2.3. Expression of multi-epitope antigens\u003c/p\u003e\n\u003cp\u003eThe recombinant vector was transferred to the \u003cem\u003eE. coli\u003c/em\u003e BL21 expression host after the cloning of the recombinant gene in the pET-26b plasmid vector was verified. Two pET-26b colonies with recombinant genes, two pET-26b colonies devoid of recombinant genes, and two colonies of BL21 bacterial cells were assessed among the acquired colonies. The expression of the recombinant gene before and after induction was examined on a 12.5% SDS-PAGE gel following the cultivation of the colonies and induction with IPTG (1 mM final concentration). At 37 \u0026deg;C after induction, 1 mM IPTG concentration, and 16 hours after induction, the recombinant protein successfully expressed itself. A 46 kDa band associated with the production of the recombinant protein was identified using SDS-PAGE analysis. Fig. 9 depicts the expression results of six colonies both before and after induction. Additionally, a western blot analysis utilizing an anti-His antibody verified the recombinant protein\u0026apos;s identification (Fig. 10).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eVL is characterized by irregular bouts of\u0026nbsp;fever and substantial weight loss that is often associated with hepatosplenomegaly, which gradually leads to anemia and death caused by bacterial, viral or bleeding secondary infections. Early diagnosis and proper management can control this disease\u0026nbsp;(Tosyali, Allahverdiyev et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince anti-\u003cem\u003eLeishmania\u003c/em\u003e drugs are limited and frequently associated with side effects, the most important step in VL control is to use effective strategies, which is possible with access to affordable and rapid diagnostic tests in endemic disease regions so that physicians can make precise therapeutic decisions. Nonetheless, there is no method with acceptable efficiency for the diagnosis of visceral leishmaniasis\u0026nbsp;(Hagos, Kiros et al. 2024). Accurate methods that are cost-effective and easy to use are very important in the diagnosis and control of visceral leishmaniasis. Serological tests are one of the quick, easy and effective methods for diagnosing this disease\u0026nbsp;(Faria, de Castro Veloso et al. 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn most VL diagnostic tests, crude antigens are used. Unfortunately, the use of crude antigen is associated with many problems. To overcome this problem, biotechnological advances in the use of effective epitopes can standardize diagnostic tests and increase the sensitivity and specificity of these tests\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(Farahmand, Nahrevanian et al. 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, a number of bioinformatics tools have been effectively implemented in biological domains. On the one hand, these instruments decrease the time and expense required for the detection of T- and B-cell epitopes, and on the other, they improve the precision of research. In addition, bioinformatics methodologies for the design of multi-epitope structures are economical and save time\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(Mousavi, Mostafavi-Pour et al. 2017, Vakili, Eslami et al. 2018).\u003c/p\u003e\n\u003cp\u003eIn this study, nine immunogenic antigens PSA2, LACK, K39, HASPB, A2, H1, CPB, KMP-11, GP63, were used for epitope selection. Gp63 has a proteases activity, which is expressed in both promastigote and amastigote forms. It could be applied as a pathogenic factor in the first phase of infection. This antigen can create immunity responses of CD4 + T cells and stimulate the expression of cytokines associated with Th1 and protect the intra-cell parasite, which is known as an indicator of protected immunity in Leishmaniasis (Devsani, Vemula et al. 2023). KMP11 is an immunogenic antigen expressed in both promastigote and amastigote forms. This gene is wholly safeguarded, and its product stimulates the humoral and cellular immune systems (Karunathilake, Alles et al. 2024). HASPB is a \u003cem\u003eLeishmania\u0026nbsp;\u003c/em\u003emembrane protein that is expressed in both promastigote and amastigote forms of parasite. HASPB protein is highly immunogen and its antibodies are rapidly increased in serum of CL and VL patients and lead to prolonged immunity against \u003cem\u003eLeishmania\u003c/em\u003e. HASPB protein is present in all \u003cem\u003eLeishmania\u003c/em\u003e species and is identified as the main immunity protein of \u003cem\u003eLeishmania\u0026nbsp;\u003c/em\u003e(Kordi, Basmenj et al. 2023). CPB are low-family enzymes, and extensive research indicates that they are effective in parasite reproduction and disease development. They manifest themselves most fully in amastigote form. CPB is the most important virulence factor because it suppresses the host\u0026apos;s immune responses and aids in immune evasion (Elmahallawy and Alkhaldi 2021). H1, histones are the proteins available in core of eukaryote cells. DNA strands are wrapped around histone proteins that forms the nucleosome. The studies showed that there is anti-histone antibody H1 in serum of patients with Leishmaniasis. Histone H1 is a highly immunogenic protein, which is highly expressed in both promastigote and amastigote stages of \u003cem\u003eLeishmania\u003c/em\u003e species (Hashemzadeh, Karimi Rouzbahani et al. 2020). \u003cem\u003eLeishmania\u003c/em\u003e A2 protein is mainly expressed in amastigote. Gene A2 is one of the major factors of disseminating to viscera of \u003cem\u003eL. donovani\u003c/em\u003e and \u003cem\u003eL. infantum\u003c/em\u003e. The specific A2 antibodies have been identified in 90% of the serum specimens of VL patients, which confirms its expression in human begins. Additionally, A2 protein has led to a considerable immunity infection related to both humoral and cellular immunity responses against \u003cem\u003eLeishmania\u0026nbsp;\u003c/em\u003e(Editors 2019). LACK is a 36 kDa protein found in the promastigote and amastigote forms of the \u003cem\u003eLeishmania\u003c/em\u003e parasite. The LACK protein plays a crucial role in regulating the immune response against \u003cem\u003eLeishmania\u003c/em\u003e, which makes it particularly essential. This gene has been deemed suitable for producing recombinant antigen because LACK induces a rapid immune response against parasites (Gomes, Souza et al. 2022). \u0026nbsp;K39\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eis a member of kinesin family with 39 amino acids. This antigen is mainly expressed by amastigote and has high specificity and sensitivity in diagnosis of VL (Sanchez, Celeste et al. 2020). \u0026nbsp;PSA-2 is the factor for strong Th1 immunity response in human and protects mice in experimental infection and effectively stimulate the immunity system (Kaushal, Naik et al. 2023). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this investigation, a multi-epitope structure comprised of MHC-II and B-cell epitopes of \u003cem\u003eL. infantum\u003c/em\u003e antigens was designed using bioinformatics tools. There are three varieties of linkers used to connect integrated proteins: flexible linkers, rigid linkers, and cleavable linkers. -helical structures provide a relatively rigid structure for the rigid linkers. In many instances, flexible linkers separate functional domains more effectively. By altering the number of copies, the connector length can be modified to achieve the desired distance between domains. Therefore, rigid linkers are chosen when the spatial separation of domains is essential for the stability or biocompatibility of integrated proteins. (Chen, Zaro et al. 2013). In the present study, a rigid linker is used for connecting B and T-cell epitopes. Immunological properties of the designed structure showed that it is a strong immunogenic and non-allergic. The physicochemical properties of the recombinant protein showed that the molecular weight is about 46 kDa, which is suitable for expression in \u003cem\u003eE. coli\u003c/em\u003e\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e Also, the designed protein has a suitable half-life in laboratory conditions and in the body and cells of mammals. The instability index of a suitable protein is less than 40, the instability index of our protein is 38.51. Studying the Protein secondary structure: 34.06% of the designed protein has an alpha helix structure. According to previous research, alpha helix structures in peptides play a significant role in stability (Finkelstein, Badretdinov et al. 1991). Also, subsequently, the optimal fragment was chosen based on the studies conducted on the third structure of the protein by the I-TASSER software. Following numerous analyses of intrinsic protein disorders, we concluded that the designed recombinant protein exhibits few intrinsic protein disorders and can be effective. After design, the production of this protein must be optimized in the expression host. Jcat server was used to optimize codons in order to enhance the expression of recombinant protein and increase its expression in the \u003cem\u003eE. coli\u003c/em\u003e K-12 expression host. This server predicted high expression for the E. coli host with a CAI greater than 97%. The designed protein was expressed using a pET-26b vector. In addition, \u003cem\u003eE. coli\u003c/em\u003e BL21 served as the bacterial host for this vector\u0026apos;s protein expression. Due to genetic engineering, various \u003cem\u003eE. coli\u003c/em\u003e organisms with the T7 RNA polymerase coding gene in their chromosomes are used today. T7 is an effective promoter for the expression process. As it was found in the research, after the addition of IPTG, it induced protein expression. According to the experiment, the target protein was well expressed and for its purification, His-tag sequence was designed and used at the end of the recombinant structure. Finally, the weight of the target protein was determined to be about 46 kDa by western blotting technique. \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAfter bioinformatic analysis and obtaining new data for the design of recombinant protein consisting of 9 antigenic proteins, we concluded that our designed construct is likely to elicit an appropriate protective immune response in \u003cem\u003ein silico\u003c/em\u003e conditions. In this regard, in the future, this recombinant structure should be investigated in vivo. Considering the synthesis, expression, and purification of this recombinant construct, as well as the verification of its qualities as a prospective recombinant antigen. This structure has passed the in vivo and immunogenicity tests and is suitable for further evaluation. Once we know the outcomes of the immunogenicity investigation, we can go forward with the development of this recombinant antigen for the diagnosis of visceral leishmaniasis.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate Deputy of Research and Technology, Lorestan University of Medical Sciences, Khorramabad, Iran. This article is derived from the Master\u0026apos;s thesis of the first author, Department of Biotechnology, School of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by Lorestan University of Medical Sciences, Khorramabad, Iran.\u0026nbsp;Hereby the authors appreciate all the people who helped in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChen, X., J. L. Zaro and W.-C. 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Xiao (2022). \u0026quot;A promising tool in serological diagnosis: current research Progress of antigenic epitopes in infectious diseases.\u0026quot; \u003cu\u003ePathogens\u003c/u\u003e \u003cstrong\u003e11\u003c/strong\u003e(10): 1095.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Visceral Leishmaniasis, Bioinformatics, Epitope prediction, Recombinant antigens, Western blotting","lastPublishedDoi":"10.21203/rs.3.rs-4143767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eLeishmania infantum\u003c/em\u003e is the causative agent of visceral leishmaniasis in the Mediterranean region. The diagnosis of complex visceral leishmaniasis and delays in the diagnosis of the infection are associated with the death of patients. Proper diagnosis of infection is an important measure in controlling and preventing the disease. However, studies have shown that the accuracy of antigens used in current diagnostic tests is insufficient, for this reason, researchers are trying to identify multi-epitope antigens as diagnostic markers to increase the specificity and sensitivity of diagnostic tests. In this study, the design and expression of \u003cem\u003eLeishmania infantum\u003c/em\u003e multi-epitope antigens were carried out in two parts of the structure design using bioinformatics tools and the laboratory part for the production of the recombinant protein.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe aim of this study was to design and computationally analyze and express \u003cem\u003eLeishmania infantum\u003c/em\u003e multi-epitope antigens. In this study, nine antigenic proteins (CPB, H1, KMP11, GP63, HASPB, A2, K39, LACK, and PSA) were selected. Bioinformatics \u003cem\u003eanalyzes\u003c/em\u003e such as prediction of immune cell epitopes, design of recombinant structure, antigenicity, allergenicity, evaluation of physicochemical properties, solubility, prediction of secondary structure and tertiary structure, refinement and validation of 3D model structure and finally in silico cloning optimization of protein construct were performed. After synthesis of the designed recombinant gene fusion sequence in pUC57 cloning vector, its subcloning was performed in pET26b prokaryotic expression vector using BamHI/ HindIII restriction enzymes. The expression of recombinant multi-epitope antigen was performed in \u003cem\u003eE. coli\u003c/em\u003e B (BL21) strain using IPTG inducer and confirmed by SDS-PAGE and western blotting techniques.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of computational analysis showed that the complete structure, which is suitable for immunogenicity and is non-allergenic, was successfully cloned into pET-26b and expressed as a complete protein.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFinally, the protein was approved. Based on the expression of recombinant proteins and bioinformatics analysis, this structure can be studied in mouse models and its safety can be evaluated.\u003c/p\u003e","manuscriptTitle":"Design and evaluation of a novel multi-epitope antigen for evaluate the diagnostic immunity responses against Leishmania infantum infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 15:59:47","doi":"10.21203/rs.3.rs-4143767/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"591ddae9-6ce4-4f4d-8193-79c3325d9867","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T14:40:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 15:59:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4143767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143767","identity":"rs-4143767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}