In silico characterization and heterologous expression of the recombinant YkuE protein from Corynebacterium pseudotuberculosis for immunobiological purpose

In silico characterization and heterologous expression of the recombinant YkuE protein from Corynebacterium pseudotuberculosis for immunobiological purpose | 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 In silico characterization and heterologous expression of the recombinant YkuE protein from Corynebacterium pseudotuberculosis for immunobiological purpose Ana Karolina Melo Oliveira Barros, Augusto César Silva Ribeiro, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9012757/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 Caseous lymphadenitis (CLA), caused by Corynebacterium pseudotuberculosis , remains a major challenge for small ruminant production due to the limited efficacy and questionable safety of currently available vaccines, as well as the inefficacy of antibiotic treatment once abscesses are established. These limitations reinforce the need for novel antigens that may improve both prophylactic and diagnostic strategies. In this study, the YkuE protein, previously identified as a promising antigen through reverse vaccinology, was comprehensively characterized using in silico immunoinformatics approaches and experimental validation. The computational analyses predicted YkuE as a stable, non-allergenic, and moderately antigenic protein containing multiple B- and T-cell epitopes, supporting its immunogenic potential. Structural modeling and refinement confirmed the reliability of the three-dimensional structure, while molecular docking analysis revealed a strong interaction between YkuE and Toll-like receptor 2 (TLR2), suggesting its involvement in innate immune recognition. The ykuE gene was cloned into the pET28a expression vector and heterologously expressed Escherichia coli BL21 Star (DE3). The recombinant YkuE protein was predominantly recovered in the insoluble fraction and exhibited an apparent molecular mass of approximately 37 kDa. Purified rYkuE was specifically recognized by sera from sheep and goats naturally infected with C. pseudotuberculosis , while no reactivity was observed with negative sera. Together, these findings highlight recognition by YkuE as a naturally targeted antigen in infected small ruminants and support its potential application in the development of recombinant vaccines and immunodiagnostic tools for CLA control. Caseous lymphadenitis Escherichia coli Immunoinformatics metallophosphoesterase recombinant protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Over the past decades, the development of recombinant subunit vaccines has emerged as a promising strategy due to their superior safety and antigenic specificity compared with traditional formulations (Van Herck et al. 2021 ). These vaccines rely on purified proteins or antigenic fragments that are capable of eliciting protective immune responses without the risks associated with attenuated or inactivated microorganisms (Hou et al. 2023 ). The integration of bioinformatics tools has greatly accelerated the identification of novel vaccine candidates, enabling the rational selection of immunogenic proteins directly from pathogen genomes (Chatterjee et al. 2021 ; Rawal et al. 2021 ). Such approaches have been extensively applied to Corynebacterium pseudotuberculosis (Araújo et al. 2019; Rezende et al. 2016 ; Trost et al. 2010 ), the etiological agent of caseous lymphadenitis (CLA), a chronic infectious disease characterized by pyogranulomatous lesions that mainly affect the lymph nodes and internal organs of small ruminants (Yitagesu et al. 2020 ). CLA has no effective treatment, as antibiotic therapy is often unable to penetrate the encapsulated abscesses and has poor management control (Khanamir et al. 2023 ; Aftabuzzaman and Cho 2021 ; Washburn et al. 2009 ). Although commercial vaccines are available, they provide partial protection and fail to prevent abscess formation, emphasizing the need for more effective antigens (de Pinho et al. 2021 ; Dorella et al. 2009 ). Advances in computational biology have enhanced the efficiency of antigen discovery and characterization (Basmenj et al. 2025 ). Another bioinformatics approach that has emerged as a valuable tool in the search for subunit vaccine targets is reverse vaccinology, which enables the identification of candidate antigens directly from pathogen genome analysis (Finco and Rappuoli 2014 ). In 2019, Araújo et al. identified new immunogenic targets in the core genome of 65 different strains of C. pseudotuberculosis after transcriptomic screening (RNA-seq) under abiotic stress conditions by reverse vaccinology. Among the predicted antigens, the ykuE gene, which encodes a metallophosphoesterase, was identified as one of the most promising vaccine candidates against CLA. The YkuE protein, previously described in Bacillus subtilis , is involved in bacterial stress responses and plays a crucial role in adaptation to hostile environments (Monteferrante et al. 2012 ). Its presence in C. pseudotuberculosis suggests a potential role in resistance to host defense mechanisms, making it a promising target for vaccine development. Therefore, YkuE could trigger both innate and adaptive immune responses, contributing to bacterial clearance before the establishment of infection. In this context, the present study aimed to characterize and analyze the interaction with the immune system in silico of the recombinant YkuE protein of C. pseudotuberculosis. In addition, the YkuE protein was heterologously expressed in E. coli , and its antigenicity was evaluated in goat and sheep to assess its ability to be recognized by infected animals. Materials and methods Protein Sequence The amino acid sequence of the CP1002_0198 protein, also known as the YkuE protein (NCBI Accession: ADL20101), was recovered in FASTA format from the NCBI GenBank database (available at: https://www.ncbi.nlm.nih.gov/ ). The sequence was then analyzed using the SignalP 6.0 program (available at: https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) to predict the presence of a signal peptide. This machine learning–based software identifies and cleaves short amino acid regions that direct proteins to specific cellular compartments (Teufel et al. 2022 ). Topological Analysis The YkuE protein was previously described as a membrane-associated protein by Araújo et al. (2019). The topology of the YkuE protein was predicted using the DeepTMHMM 1.0 server ( https://services.healthtech.dtu.dk/services/DeepTMHMM-1.0/ ). This deep-learning–based model identifies transmembrane proteins containing α-helical and β-barrel domains and predicts their localization and orientation within the cellular membrane (Hallgren et al. 2022). Three-Dimensional Structure Modeling and Refinement The three-dimensional structure of the YkuE protein was predicted using AlphaFold2 through Google Colab ( https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb ). This software performs multiple sequence alignments with structural databases to predict protein conformation, and its reliability is evaluated using the predicted Local Distance Difference Test (pLDDT), which measures the confidence in each residue prediction (Mirdita et al. 2022). The generated model was further refined using GalaxyRefine ( https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE ) , a tool that improves the structural quality of proteins through side-chain reconstruction and successive relaxations, including both mild and aggressive refinements. This process aims to obtain lower-energy conformations and enhance model stability (Heo, Park, and Seok 2013 ). Structural validation was performed using the Ramachandran Plot Server ( https://www.ramplot.in/ ) , which evaluates the torsion angles φ (phi) and ψ (psi) of amino acid residues. The resulting Ramachandran plot indicates whether residues occupy energetically favorable conformations or present significant deviations (Chen et al. 2009 ). Epitope Prediction Linear B-cell epitopes were first predicted using the ABCPred server ( https://webs.iiitd.edu.in/raghava/abcpred/ ). This tool employs a recurrent neural network (Jordan-type RNN) to identify epitopes in peptide sequences of 10–20 amino acids. A threshold of 0.9 was used for epitope selection (Saha and Raghava 2006 ). Subsequently, the BCPred server ( http://ailab-projects2.ist.psu.edu/bcpred/ ) was applied. This classifier is based on Support Vector Machine (SVM) learning coupled with a subsequence kernel, which identifies linear epitopes of fixed 20-amino-acid length using a 0.9 threshold for filtering (El-Manzalawy, Dobbs and Honavar 2008 ). Additionally, B-cell epitope prediction from the protein’s three-dimensional structure was performed using ElliPro ( http://tools.iedb.org/ellipro/ ). This software uses a modified version of Thornton’s method to predict continuous (linear) epitopes and a clustering algorithm to identify discontinuous (conformational) ones. Only continuous epitopes were considered in this study, applying default parameters and a 0.5 threshold (Ponomarenko et al. 2008 ). T-cell epitopes were predicted using the IEDB Tepitool ( http://tools.iedb.org/tepitool/ ) , which estimates peptide interactions with class I and II major histocompatibility complex (MHC) molecules. The tool integrates multiple prediction algorithms to assess binding affinity across hundreds of alleles from several species, including humans, cattle, mice and goats (Paul et al. 2016 ). The NetMHCpan 4.1 EL method was used for MHC binding prediction (Reynisson et al. 2020 ). For class I MHC epitopes, the bovine model was adopted as a reference for the caprine species, considering the alleles BoLA-1:02801, BoLA-2:01601, BoLA-2:02201, BoLA-3:03601, BoLA-3:00101, BoLA-3:01001, BoLA-3:01101, and BoLA-6:01501, with epitopes ranging from 8 to 10 amino acids. For class II MHC epitopes, the murine model was used, including the alleles H2-IAb, H2-IAd, H2-IAk, H2-IAq, H2-IAs, H2-IAu, H2-IEd and H2-IEk, to predict epitopes between 12 and 18 residues. Epitopes with scores ≤ 0.5 and percentile ranks below 1 were selected. In silico Immunological Analyses The predicted epitopes and the YkuE protein were subjected to immunological analyses to evaluate their antigenicity and allergenicity. Antigenicity prediction was performed using VaxiJen 2.0 ( https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) , which is based on the Auto-Cross Covariance (ACC) transformation method (Hellberg et al., 1987 ). This alignment-independent approach analyzes physicochemical properties of amino acids to predict antigenicity, achieving accuracies between 70% and 89% (Doytchinova and Flower 2007 ). A threshold of 0.4 was applied for classification. Allergenicity prediction was conducted using AlgPred 2.0 ( https://webs.iiitd.edu.in/raghava/algpred2/batch.html ) , which integrates multiple strategies including BLAST-based homology search, IgE epitope motif identification, and machine-learning-based physicochemical property analysis. The hybrid method of the tool was applied using a 0.3 threshold to maximize specificity and predictive accuracy. In addition, the physicochemical properties of YkuE were evaluated using ProtParam ( https://web.expasy.org/protparam/ ) , which calculates several biochemical parameters, including molecular weight, isoelectric point, amino acid composition, in vitro and in vivo half-life, instability index, aliphatic index, and the Grand Average of Hydropathicity (GRAVY) (Gasteiger et al. 2005 ). The solubility of the YkuE protein was assessed using the SoluProt 1.0 program ( https://loschmidt.chemi.muni.cz/soluprot ) , which predicts the likelihood of a protein being soluble when expressed in the Escherichia coli system. In this method, scores above 0.5 indicate high solubility, whereas values below 0.5 suggest a tendency toward insolubility (Hon et al. 2021). Molecular Docking and Interaction Analysis To assess the immunological potential of YkuE, molecular docking was performed with the immune receptor Toll-like receptor 2 (TLR2). The three-dimensional structure of TLR2 (PDB ID: 3A7B) was obtained from the Protein Data Bank ( https://www.rcsb.org/ ). Docking simulations were carried out using ClusPro 2.0 ( https://cluspro.bu.edu/home.php ) , which conducts rigid-body docking through the PIPER algorithm. The receptor remains fixed while the ligand explores billions of possible conformations, evaluated based on electrostatic and desolvation energies. The 1,000 lowest-energy poses are clustered, and the centers of the most populated clusters are selected as representative models, which are then refined by energy minimization (Kozakov et al. 2017 ). Protein–receptor complexes were visualized using UCSF ChimeraX ( https://www.cgl.ucsf.edu/chimerax/ ) (Pettersen et al. 2020 ). The molecular interactions were analyzed using LigPlot+ v2.2 ( https://www.ebi.ac.uk/thornton-srv/software/LigPlus/ ) , which generates two-dimensional diagrams from three-dimensional coordinates, highlighting hydrogen bonds and hydrophobic contacts. LigPlot+ enables a detailed visualization of protein–ligand complementarity and supports the overlay of multiple complexes for comparative analysis (Laskowski and Swindells 2011 ). Strains and culture conditions Four different Escherichia coli strains were used as expression hosts to evaluate which one provided the best recombinant protein yield: E. coli BL21 (DE3) Star, Rosetta, BL21 (DE3) pLysS, and BL21 (DE3) pLysE. All strains were cultured in liquid Luria–Bertani (LB; 10 mg tryptone, 5 mg yeast extract, and 5 mg NaCl) medium or on LB agar plates for 16 hours at 37°C. When required, LB medium was supplemented with kanamycin (100 µg/mL; Sigma-Aldrich, Saint Louis, USA). Chloramphenicol (100 µg/mL; Sigma-Aldrich, Saint Louis, USA) was added to the culture of the Rosetta, BL21 (DE3) pLysS, and BL21 (DE3) pLysE strains. Cloning, solubility tests, expression, and purification of recombinant protein The DNA sequence corresponding to the ykuE gene from Corynebacterium pseudotuberculosis (Gene ID: 12299865) was obtained from the GenBank database (www.ncbi.nlm.nih.gov ). The gene was synthesized and cloned into the pET28a expression vector by GenOne Biotechnologies (Rio de Janeiro, Brazil), with the restriction enzymes NdeI and HindIII , resulting in the plasmid pET28a/ ykuE (Fig. 1 ). The plasmid was resuspended according to the manufacturer’s instructions and stored at -20°C until use. The plasmid was introduced by electroporation with 1 pulse of 1.8 kV into the E. coli expression strains using MicroPulser Electroporator (Bio-Rad Laboratories, Inc., California, USA). To assess the solubility of the recombinant proteins, bacterial cells were harvested by centrifugation, and the pellet was resuspended in a 1× TE buffer (10 mM Tris-HCl, pH 8.0; 1 mM EDTA). Samples were then subjected to sonication cycles for cell lysis, followed by centrifugation at 14.000 rpm for 5 minutes. After centrifugation, 500 µL of the supernatant was collected for analysis, while the pellet was resuspended again in a 1× TE buffer. Both the soluble and insoluble fractions were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to determine the solubility profile of the recombinant proteins. The expression of recombinant protein on a small scale was induced after all the cultures reached an exponential growth phase in LB medium containing kanamycin by adding 1 mM Isopropyl β-D-1-thiogalactopyranidase (IPTG) to each culture and maintaining it under constant stirring in an orbital shaker for 3 hours at 37°C. A 15% SDS-PAGE was performed to evaluate which E. coli strain showed the highest expression level of the recombinant YkuE protein, allowing the selection of the best strain to be used for expression on a large scale and further purification steps. For rYKUE purification, the culture was centrifuged, and the resulting pellet was resuspended in a lysis buffer containing 50 mM NaH₂PO₄, 300 mM NaCl, 20 mM imidazole, and 8 M urea, supplemented with 100 µg/mL of lysozyme. The suspension was subjected to sonication and kept under constant agitation at 4°C for 16 hours. Recombinant proteins were subsequently purified using affinity chromatography with a HisTrap™ Sepharose nickel column (GE Healthcare, USA), followed by dialysis using SnakeSkin™ Dialysis Tubing (Thermo Fisher Scientific, Waltham, MA, USA). Purity was determined using a 1% SDS-PAGE gel, and the concentration was determined by the BCA kit (Pierce, USA) Western Blot Analysis To assess the identity and antigenicity of the recombinant protein YkuE, western blot analyses were conducted. To identity, the purified rYkuE protein was mixed with SDS loading buffer (100 mM Tris-HCl, pH 6.8, 100 mM 2-mercaptoethanol, 4% (w/v) SDS, 0.2% (w/v) bromophenol blue, and 20% (v/v) glycerol) and incubated at 95°C for 10 minutes. The samples were then separated by electrophoresis on a 12% SDS-PAGE gel. Following electrophoresis, the protein was transferred to a nitrocellulose membrane (GE Healthcare, USA) using a Mini Trans-Blot™ Cell System (Bio-Rad, Hercules, CA). The membrane was blocked with PBS-milk powder solution (5%), incubated at 37°C for 30 minutes, and washed 3 times with PBS containing 0.05% Tween 20 (PBS-T). Later, the membrane was incubated with a monoclonal anti-6× His antibody (Sigma–Aldrich, USA) diluted 1:2000 in PBS-T for 1 hour at 37°C. Reactive bands were visualized using a solution containing 3,3′-diaminobenzidine (DAB), 0.3% nickel sulfate, 50 mM Tris-HCl buffer (pH 7.6), and H 2 O 2 . Regarding antigenicity, in the western blotting performed, after blocking, the membrane was washed three times and then incubated with the serum. The serum of sheep and goat were diluted 1:100 in PBS-T and added to the blot for 1 hour at 37°C. After three additional washes with PBS-T, anti-goat and anti-sheep peroxidase-conjugated antibodies diluted 1:4000 in PBS-T were added and incubated for 1 hour at 37°C. Reactive protein bands were also visualized using DAB solution. Positive control sera were obtained from four sheep and four goats presenting clinical CLA, confirmed by microbiological isolation of Corynebacterium pseudotuberculosis . Negative control sera were collected from four sheep and four goats (under 6 months old) that tested negative in an ELISA using secreted proteins from C. pseudotuberculosis and originating from farms with no history of CLA (non-endemic areas). Results Topological Analysis The analysis revealed that all amino acid residues of YkuE are located in the intracellular region, classifying it as a globular-type protein. Three-Dimensional Structure Modeling and Refinement The models presenting the highest pLDDT scores—an indicator of local confidence in structural prediction—were selected for further analysis. For YkuE, model 1 showed the highest pLDDT value (89.9). The chosen model was subsequently refined using GalaxyRefine and validated for stereochemical quality through the Ramachandran Plot Server, resulting in the final three-dimensional structure shown in Fig. 2 . Refinement significantly improved the quality of the three-dimensional model of the YkuE protein. A reduction in clash score from 29.7 to 8.1 was observed, along with an increase in the percentage of residues in favored regions of the Ramachandran plot (Rama favored) from 95.2% to 98.1%. These results indicate enhanced stereochemical and conformational stability of the refined structure. Epitopes prediction As shown in Table 1 , the analysis resulted in the prediction of a total of 162 epitopes for the YkuE protein. Among these, 87 epitopes were predicted for MHC class I and 58 for MHC class II by TepiTool, highlighting the potential of YkuE as a promising immunogenic target. Table 1 Number of predicted epitopes for the YkuE protein using different computational tools. B-cell epitopes were predicted with ABCpred, BCpred, and ElliPro, while T-cell epitopes were identified using MHC-I and MHC-II prediction tools. The total represents the sum of epitopes detected by each method for the protein Program YKUE ABCpred 5 BCpred 4 Ellipro - linear 8 MHC-I 87 MHC-II 58 Total 162 In silico Immunological Analyses The antigenicity and allergenicity analyses of the predicted epitopes and the YkuE protein indicated a strong immunological potential. As shown in Supplementary Table 1 52 .4% of the predicted epitopes exhibited antigenic potential, presenting scores higher than 0.4, while 47.5% had lower scores and were therefore considered non-antigenic. Despite the high proportion of antigenic epitopes, only 22.2% were predicted as non-allergenic (scores below 0.3), whereas 77.8% were classified as potentially allergenic. Table 2 summarizes the five YkuE epitopes that displayed both high antigenicity and non-allergenic profiles, suggesting their relevance as promising immunogenic targets. Table 2 The five top-ranked predicted epitopes for the YkuE protein by different computational tools (IEDB). Epitopes were selected based on the highest antigenicity (VaxiJen) scores and classification as non-allergenic (AllerTop) Epitopes Program Antigenicity Allergenicity FELKIVEL MHC-I 1.9616 (Probable ANTIGEN) 0.26 (Non-Allergen) MVNPFLYLF MHC-I 1.7193 (Probable ANTIGEN) 0.28 (Non-Allergen) SNFELKIVEL MHC-I 1.5353 (Probable ANTIGEN) 0.26 (Non-Allergen) ARHEFQINHVRIA MHC-II 1.2085 (Probable ANTIGEN) 0.3 (Non-Allergen) MVNPFLYL MHC-I 1.1987 (Probable ANTIGEN). 0.3 (Non-Allergen) As shown in Table 2 , among the five predicted epitopes with the highest antigenicity scores and non-allergenic profiles derived from the YkuE protein, four were predicted to bind to MHC class I molecules, and one to MHC class II (epitope ARHEFQINHVRIA). We also observe that FELKIVEL epitope, predicted for MHC class I, exhibited the highest antigenicity score (1.9616) and an allergenicity score of 0.26. In contrast, the MVNPFLYL epitope, also predicted for MHC class I, showed the lowest antigenicity score (1.1987) among the five, with an allergenicity score of 0.3. Table 3 summarizes the immunological analyses for antigenicity and allergenicity, as well as solubility and physicochemical properties of the YkuE protein. Table 3 Analysis of antigenicity, allergenicity, solubility, and physicochemical properties of the YkuE protein. Parameters evaluated included molecular weight, isoelectric point (pI), instability index, hydrophobicity, and predicted half-life Parameters YKUE Antigenicity 0.4062 (Probable ANTIGEN) Allergenicity 0.03 (Non-Allergen) Solubility Good solubility in water Solubility in E. coli 0.189 (high insolubility) Amino acid 312 Molecular weight 34,82318 kDa Isoelectric point (pI) 9,06 Instability index 33.51 GRAVY -0,184 (hidrofílic) in vitro half-life 30 hours in vivo half-life - yeast > 20 hours in vivo half-life - E. coli > 10 hours As shown in Table 3 , YkuE protein exhibited moderate antigenicity, in addition to being classified as non-allergenic and water-soluble. The antigenicity score was 0.4062, and the allergenicity score was 0.03. Physicochemical analysis revealed that the YkuE protein consists of 312 amino acids, with a molecular weight of approximately 34.82 kDa and a theoretical isoelectric point (pI) of 9.06. The instability index of 33.51 suggests that the protein is stable under biological conditions. The GRAVY value of − 0.184 indicates a hydrophilic character. The estimated half-life was approximately 30 hours in vitro, over 20 hours in yeast, and over 10 hours in Escherichia coli , supporting its overall structural stability. Molecular Docking and Interaction Analysis Following the generation of the three-dimensional structures of YkuE and the TLR2 receptor, molecular docking was performed to evaluate the interaction between both proteins. Among the models generated by ClusPro under the balanced parameter, the cluster 6 model (Table 4 ) was selected due to its conformation being visually compatible with a potential biological interaction. The resulting structure, along with the predicted molecular interactions visualized using LigPlot+, is shown in Fig. 3 . Table 4 Results of the molecular docking between the TLR2 receptor and the YKUE protein obtained using the ClusPro server. The identified cluster, the number of cluster members, the representative structure (cluster center and lowest-energy conformation), and the corresponding weighted scores are shown, with more negative values indicating more favorable interactions Cluster Members Representative Weighted Score 6 34 Center -1200.5 Lowest Energy -1207.4 The molecular interaction analysis, illustrated in Fig. 3 , revealed a significant number of hydrogen bonds and hydrophobic interactions between the YkuE protein and the TLR2 receptor. Among the main YkuE residues involved in hydrogen bond formation are Asn79, Arg40, Phe293, Val2, and Lys4. These residues interact with several amino acids of TLR2, including Lys383, Gln357, Glu375, Tyr332, Ser354, and Arg87, totaling 17 hydrogen bond interactions. In addition, an extensive network of hydrophobic interactions was observed, involving residues such as Leu147, Val255, Met140, Ser333, Tyr336, Gly31, and Glu321, among others. These findings indicate the formation of a well-defined binding interface between YkuE and the TLR2, suggesting a specific and stable molecular recognition process. Cloning, solubility tests, expression, and purification of recombinant protein The analysis, shown in Fig. 4 , revealed a distinct band corresponding to approximately 37 kDa in pellet fraction of the BL21 Star (DE3) culture, indicating overexpression of the recombinant protein in the insoluble fraction. In contrast, no overexpression band equivalent to 37 kDa was detected in either the soluble or insoluble fractions of the Rosetta, pLysS and pLysE, suggesting that protein expression in these strains was minimal or absent. So, BL21 Star (DE3) was the most efficient strain for rYkuE expression, and protein was predominantly found in the insoluble fraction (pellet). The large scale of rYkuE’s expression resulted in a yield of 17,68 mg/L after purification. Western Blot Analysis The Western blot, using a monoclonal anti-6×His antibody, confirmed the identity of the rYkuE protein (Fig. 5 ), which detected a reactive band at the expected molecular weight of 37 kDa. A comparative analysis with the rCP40 protein (40 kDa) supported the confirmation of the recombinant protein’s identity. In the antigenicity assessment by Western blot, the rYkuE protein was recognized by sera from goats and sheep naturally infected with C. pseudotuberculosis , indicating that the rYkuE preserved epitopes from the native protein (Fig. 6 ). No reactivity was observed with negative CLA sera. Discussion The high prevalence of Corynebacterium pseudotuberculosis infection in lambs, combined with considerable economic losses associated with CLA, underscores the urgent need for more effective control and preventive strategies (Dorella et al. 2009 ). This is the first report describing the heterologous expression of the YkuE protein of C. pseudotuberculosis in E. coli and its recognition by sera from sheep and goats naturally infected with CLA, highlighting its potential as a diagnostic or vaccine target. Monteferrante et al. ( 2012 ) demonstrated that YkuE, a metallophosphoesterase, is specifically targeted to the cell wall through the twin-arginine translocation (Tat) pathway, a mechanism responsible for exporting folded proteins across the cytoplasmic membrane in Gram-positive bacteria, Bacillus subtilis . This localization suggests that YkuE is exposed or associated with the bacterial surface, where it may interact with host immune components. The surface accessibility of YkuE makes it a biologically relevant target for immune recognition and, consequently, a promising candidate for vaccine development. In the context of C. pseudotuberculosis , a pathogen that relies heavily on cell wall-associated factors for host colonization and persistence (Raynal et al. 2018 ), proteins exported via the Tat pathway may play essential roles in virulence and immune evasion. Our in silico analyses predicted YkuE as a cytoplasmic and globular protein. However, previous studies in Bacillus species have reported that YkuE is exported via the Tat pathway and associated with the cell wall. Taken together, these observations suggest that, although YkuE may be primarily intracellular in C. pseudotuberculosis , its conserved structure and potential release during bacterial lysis or alternative secretion mechanisms may contribute to its recognition by the host immune system. Recent advances in bioinformatics have transformed the process of vaccine development, enabling the direct identification of promising antigens from pathogen genomes, reducing experimental costs and time while providing a rational basis for antigen selection (Gloanec et al. 2025 ). In this context, bioinformatic analyses were applied to the YkuE protein of C. pseudotuberculosis to explore its potential as a vaccine antigen and guide subsequent experimental evaluations. Araújo et al. (2019) predicted YkuE as a membrane-associated protein. However, our topological analysis predicted YkuE as a globular and intracellular protein, consistent with the metallophosphoesterase proposed function, suggesting a participation in cellular stress responses instead of being a membrane-associated enzyme. This discrepancy can be attributed to the fact that the software version used by Araújo in the previous study is outdated. When the same prediction tool is applied using its older version, the result reproduces the membrane localization reported by Araújo et al. (2019). Nevertheless, the updated version incorporates improved algorithms and databases and consistently classifies YkuE as a cytoplasmic and globular protein. The globular conformation observed here suggests that YkuE is likely localized in the cytoplasmic compartment. Nonetheless, it may still contribute to host immune recognition through alternative mechanisms, such as the release during bacterial lysis or the cross-presentation of conserved epitopes. Epitope prediction revealed that YkuE harbors multiple regions capable of eliciting both B- and T-cell responses. While B-cell epitopes are essential for the induction of humoral immunity, T lymphocytes play a fundamental role in stimulating cellular immune responses, acting directly against intracellular or facultatively intracellular pathogens such as C. pseudotuberculosis (Dorella et al. 2009 ). Among 162 identified epitopes, several presented high antigenicity scores and were predicted to bind MHC class I and II molecules, suggesting that this protein could activate both humoral and cellular immune responses. The antigenicity and allergenicity analyses reinforced these findings, indicating that YkuE is moderately antigenic and non-allergenic, properties that are desirable for a subunit vaccine antigen. Toll-like receptors (TLRs) play a central role in pathogen recognition by detecting conserved microbial structures known as pathogen-associated molecular patterns (PAMPs), which initiate downstream signaling that leads to the production of cytokines and interferons (Carter et al. 2025 ). This mechanism establishes a crucial link between innate detection and adaptive immune activation. Thus, our docking analysis demonstrated a strong interaction between YkuE and TLR2, indicating that YkuE may participate in host immune recognition through TLR-mediated pathways. TLR2 primarily signals through the MyD88-dependent pathway, leading to the recruitment of IRAK kinases and TRAF6, followed by activation of the NF-κB and MAPK pathways and subsequent transcription of pro-inflammatory mediators, including TNF-α, IL-6, IL-1β, and IL-12, that favor Th1-type immune responses (Kawai and Akira 2011 ; Akira et al. 2001 ). These signaling events favor macrophage activation and the development of Th1-type immune responses, which are particularly important for controlling intracellular and facultative intracellular pathogens (Iwasaki and Medzhitov 2015 ). Ott, Möller, and Burkovski ( 2022 ) have shown that TLR2 recognizes components of Corynebacterium diphtheriae and activates similar inflammatory pathways, triggering MyD88-dependent signaling cascades that culminate in inflammatory cytokine production, reinforcing its role as a key mediator in the recognition of Corynebacterium species. Accordingly, the interaction observed between the YkuE protein and TLR2 in our in silico analyses suggests that this antigen may be effectively recognized by the innate immune system, contributing to the activation of protective mechanisms against Corynebacterium pseudotuberculosis infection. The heterologous expression of recombinant proteins in Escherichia coli is a widely employed strategy for structural and immunological characterization studies of candidate antigens (Pouresmaeil and Azizi-Dargahlou 2023 ). This approach enables the production of sufficient amounts of purified protein for experimental analyses and facilitates in vitro and in vivo assessments of antigenicity and immunogenicity. In our study, we identified E. coli BL21 Star (DE3) as the most efficient strain for the heterologous expression of the YkuE protein. This strain produced a strong rYkue expression, predominantly in the pellet fraction. The superior performance of the BL21 Star (DE3) strain in rYkuE expression may be explained by key physiological features that distinguish this lineage from other E. coli expression hosts. BL21-derived strains are widely used for recombinant protein production due to their reduced proteolytic activity, which minimizes degradation of heterologous proteins during expression (İncir and Kaplan 2024 ). More specifically, BL21 Star can carry a mutation in the rne gene, which encodes RNase E, resulting in enhanced mRNA stability and, consequently, higher transcript abundance, leading to increased protein synthesis (Carpousis 2007 ). Although in silico analyses indicated that the recombinant protein exhibited favorable solubility characteristics, experimental assays revealed that it was predominantly expressed in the insoluble fraction. This discrepancy may be partially explained by codon usage bias among different prokaryotic organisms (Botzman and Margalit 2011 ). The heterologous expression of genes derived from organisms with distinct genomic compositions often results in translational inefficiencies, since the preferred codons of the original organism may not be efficiently recognized by the host’s translational machinery, particularly when they correspond to rare codons in E. coli (Sharp et al. 1988 ; Sharp and Li 1986 ; Gouy and Gautier 1982 ). Additionally, C. pseudotuberculosis is a Gram-positive bacterium, whereas E. coli is a Gram-negative bacterium; therefore, differences in cell wall architecture, protein folding environments, and secretion systems may further influence the expression and solubility of heterologous proteins. Such structural and physiological disparities can compromise proper protein folding during synthesis, promoting the formation of inclusion bodies, insoluble aggregates commonly observed in recombinant expression systems (İncir and Kaplan 2024 ; Plotkin and Kudla 2010 ). The molecular weight predicted in silico for the native YkuE protein of C. pseudotuberculosis was approximately 34 kDa, which is consistent with the theoretical size expected from its amino acid sequence. However, during heterologous expression in E. coli , the recombinant YkuE was observed to migrate at around 37 kDa on SDS-PAGE. This apparent increase in molecular mass can be attributed to modifications introduced by the expression system, particularly the additional sequences encoded by the pET vector and the incorporation of the polyhistidine tag at the N-terminus. Evidence from the literature also supports this interpretation. According to Shilling et al. (2020), the pET28a plasmid incorporates modules encoding an N-terminal poly-histidine tag (His₆) and a thrombin cleavage site, thereby introducing additional amino acid residues into the recombinant protein, which consequently increases the size of the protein. The recognition of the recombinant YkuE protein by sera from naturally infected sheep and goats demonstrates that the host immune system naturally targets this antigen during C. pseudotuberculosis infection. When compared with previous studies using recombinant antigens (Barral et al. 2022 , 2019 ; Silva et al. 2021 ), similar patterns of recognition were observed, suggesting that rYkuE contains conserved and immunodominant epitopes capable of inducing antibody responses across species, thereby supporting its potential role as a broadly recognized antigen. Our findings highlight the relevance of rYkuE as a promising antigen for advancing recombinant vaccine development and diagnostic approaches against Corynebacterium pseudotuberculosis . Through bioinformatic characterization, efficient heterologous expression, and antigenicity analyses, rYkuE demonstrated strong recognition by sera from naturally infected sheep and goats, indicating that this protein is targeted by the host immune response during infection. This evidence underscores its potential applicability in both prophylactic and diagnostic contexts. Future investigations should prioritize the evaluation of rYkuE in CLA vaccine formulations and immunodiagnostic platforms. Declarations Acknowledgements The authors gratefully acknowledge CAPES for providing scholarships to some of the authors, CNPq for financial support through project no. 408137/2023-1 (CNPq/MCTI Call No. 10/2023), and Instituto Sabiá for additional financial support. Competing interests None of the authors has any potential financial conflict of interest related to this manuscript. Author contribution FSBB and AKMOB conceived the experiments. AKMOB, ACSR, GRO, TVSA and LMPM conducted the experiments. PLFF performed the in silico analyses. AKMOB and PLFF performed the data analysis and wrote the manuscript. SB, FSBB, DVCL and BMFV critically revised and helped in writing the work. All authors read and approved the final manuscript. 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Journal of the American Veterinary Medical Association 234(9):1162–1166. https://doi.org/10.2460/javma.234.9.1162 Yitagesu E, Alemnew E, Olani A, Asfaw T, Demis C (2020) Survival Analysis of Clinical Cases of Caseous Lymphadenitis of Goats in North Shoa, Ethiopia. Veterinary Medicine International 2020:1–8. https://doi.org/10.1155/2020/8822997 Additional Declarations No competing interests reported. Supplementary Files SuplementaryTable.docx AUTHORSCHECKLIST.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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software\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.pET28aykuEplasmidmapgeneratedusingtheSnapGenesoftware..png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/c1113ce3e1cd4eb1a2689760.png"},{"id":105908075,"identity":"17f79a9d-6502-4a4f-8da6-74930dae62af","added_by":"auto","created_at":"2026-04-01 10:34:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1044321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRefined three-dimensional structures of the YkuE protein and corresponding Ramachandran plots.\u003c/strong\u003e Subfigure (a) shows the modeled structure of YkuE. Subfigure (b) presents the Ramachandran plots used for stereochemical assessment of structural quality\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/4e701404c233bfd14f39ac89.png"},{"id":105908133,"identity":"15f1bf5b-818e-4d22-baca-ac81c9dcbe3f","added_by":"auto","created_at":"2026-04-01 10:35:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3475221,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis between the YkuE protein and the TLR2 receptor. \u003c/strong\u003eSubfigure (a) shows the protein–receptor complex model generated by ClusPro. Subfigure (b) presents the molecular interactions identified, highlighting hydrogen bonds and hydrophobic contacts established between the residues of the protein and the receptor\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/026abb5d040a002b6f6b044a.png"},{"id":105908034,"identity":"8c895f4f-3e84-4756-ae31-f24b96a4a48a","added_by":"auto","created_at":"2026-04-01 10:34:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":828343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA 15% SDS-PAGE gel of the lysates of different strains of Escherichia coli showing the small-scale expression and solubility test of the recombinant YkuE protein in different E. coli strains. \u003c/strong\u003eMW: PageRuler Prestained Protein Ladder (Thermo Fisher Scientific, Waltham, MA, USA); SN: supernatant; PE: pellet; NI: non-induced; NT: non-transformed. (A) Results obtained for BL21 (DE3) Star and Rosetta strains. (B) Results obtained for BL21 (DE3) pLysS and BL21 (DE3) pLysE strains\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/f3f83fe8709aabae48bb75e2.png"},{"id":105908051,"identity":"f2a19bb7-1044-4935-8f68-748a4c23c1c0","added_by":"auto","created_at":"2026-04-01 10:34:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWestern blot using a monoclonal anti-6 × His antibody to confirm rYkuE identity showing a band of approximately 37 kDa.\u003c/strong\u003e Lanes: [MW] PageRuler Prestained Protein Ladder (Thermo Fisher Scientific, Waltham, MA, USA); [1] rCP40; [2] YkuE.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/2c823e99515cec208a12edf1.png"},{"id":105908135,"identity":"76147687-d9a2-47bd-bd12-e916274619d5","added_by":"auto","created_at":"2026-04-01 10:35:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWestern blot to determine the rYkuE antigenicity.\u003c/strong\u003e (A) Sheep sera: CLA-positive (+) and CLA-negative (−) animals. (B) Goat sera: CLA-positive (+) and CLA-negative (−) animals. MW: PageRuler Prestained Protein Ladder(Thermo Fisher Scientific, Waltham, MA, USA)\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/1c82b447ffeba9d7c25c2760.png"},{"id":109500246,"identity":"20787d8d-b7a3-4421-aa32-b7a5e982eeec","added_by":"auto","created_at":"2026-05-18 21:54:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6161232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/4a93e35b-76fc-489b-8d9c-9c13a8d14708.pdf"},{"id":105907936,"identity":"16d28283-d236-4e45-b390-59e460eb9350","added_by":"auto","created_at":"2026-04-01 10:34:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28183,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/4d3526018fea3cde18c7596e.docx"},{"id":105908077,"identity":"454cb2f7-ecc3-4ced-affc-37b45cce4844","added_by":"auto","created_at":"2026-04-01 10:34:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21675,"visible":true,"origin":"","legend":"","description":"","filename":"AUTHORSCHECKLIST.docx","url":"https://assets-eu.researchsquare.com/files/rs-9012757/v1/fbd8b122ec9e456c3b9fec0f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico characterization and heterologous expression of the recombinant YkuE protein from Corynebacterium pseudotuberculosis for immunobiological purpose","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the past decades, the development of recombinant subunit vaccines has emerged as a promising strategy due to their superior safety and antigenic specificity compared with traditional formulations (Van Herck et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These vaccines rely on purified proteins or antigenic fragments that are capable of eliciting protective immune responses without the risks associated with attenuated or inactivated microorganisms (Hou et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of bioinformatics tools has greatly accelerated the identification of novel vaccine candidates, enabling the rational selection of immunogenic proteins directly from pathogen genomes (Chatterjee et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rawal et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such approaches have been extensively applied to \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e (Ara\u0026uacute;jo et al. 2019; Rezende et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Trost et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the etiological agent of caseous lymphadenitis (CLA), a chronic infectious disease characterized by pyogranulomatous lesions that mainly affect the lymph nodes and internal organs of small ruminants (Yitagesu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). CLA has no effective treatment, as antibiotic therapy is often unable to penetrate the encapsulated abscesses and has poor management control (Khanamir et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Aftabuzzaman and Cho \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Washburn et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Although commercial vaccines are available, they provide partial protection and fail to prevent abscess formation, emphasizing the need for more effective antigens (de Pinho et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dorella et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvances in computational biology have enhanced the efficiency of antigen discovery and characterization (Basmenj et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Another bioinformatics approach that has emerged as a valuable tool in the search for subunit vaccine targets is reverse vaccinology, which enables the identification of candidate antigens directly from pathogen genome analysis (Finco and Rappuoli \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2019, Ara\u0026uacute;jo et al. identified new immunogenic targets in the core genome of 65 different strains of \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e after transcriptomic screening (RNA-seq) under abiotic stress conditions by reverse vaccinology. Among the predicted antigens, the \u003cem\u003eykuE\u003c/em\u003e gene, which encodes a metallophosphoesterase, was identified as one of the most promising vaccine candidates against CLA. The YkuE protein, previously described in \u003cem\u003eBacillus subtilis\u003c/em\u003e, is involved in bacterial stress responses and plays a crucial role in adaptation to hostile environments (Monteferrante et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Its presence in \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e suggests a potential role in resistance to host defense mechanisms, making it a promising target for vaccine development. Therefore, YkuE could trigger both innate and adaptive immune responses, contributing to bacterial clearance before the establishment of infection.\u003c/p\u003e \u003cp\u003eIn this context, the present study aimed to characterize and analyze the interaction with the immune system \u003cem\u003ein silico\u003c/em\u003e of the recombinant YkuE protein of \u003cem\u003eC. pseudotuberculosis.\u003c/em\u003e In addition, the YkuE protein was heterologously expressed in \u003cem\u003eE. coli\u003c/em\u003e, and its antigenicity was evaluated in goat and sheep to assess its ability to be recognized by infected animals.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProtein Sequence\u003c/h2\u003e \u003cp\u003eThe amino acid sequence of the CP1002_0198 protein, also known as the YkuE protein (NCBI Accession: ADL20101), was recovered in FASTA format from the NCBI GenBank database (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The sequence was then analyzed using the SignalP 6.0 program (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/SignalP-6.0/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/SignalP-6.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to predict the presence of a signal peptide. This machine learning\u0026ndash;based software identifies and cleaves short amino acid regions that direct proteins to specific cellular compartments (Teufel et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTopological Analysis\u003c/h3\u003e\n\u003cp\u003eThe YkuE protein was previously described as a membrane-associated protein by Ara\u0026uacute;jo et al. (2019). The topology of the YkuE protein was predicted using the DeepTMHMM 1.0 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/DeepTMHMM-1.0/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/DeepTMHMM-1.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This deep-learning\u0026ndash;based model identifies transmembrane proteins containing α-helical and β-barrel domains and predicts their localization and orientation within the cellular membrane (Hallgren et al. 2022).\u003c/p\u003e\n\u003ch3\u003eThree-Dimensional Structure Modeling and Refinement\u003c/h3\u003e\n\u003cp\u003eThe three-dimensional structure of the YkuE protein was predicted using AlphaFold2 through Google Colab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb\u003c/span\u003e\u003cspan address=\"https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This software performs multiple sequence alignments with structural databases to predict protein conformation, and its reliability is evaluated using the predicted Local Distance Difference Test (pLDDT), which measures the confidence in each residue prediction (Mirdita et al. 2022).\u003c/p\u003e \u003cp\u003eThe generated model was further refined using GalaxyRefine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\u003c/span\u003e\u003cspan address=\"https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=REFINE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a tool that improves the structural quality of proteins through side-chain reconstruction and successive relaxations, including both mild and aggressive refinements. This process aims to obtain lower-energy conformations and enhance model stability (Heo, Park, and Seok \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural validation was performed using the Ramachandran Plot Server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ramplot.in/\u003c/span\u003e\u003cspan address=\"https://www.ramplot.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which evaluates the torsion angles φ (phi) and ψ (psi) of amino acid residues. The resulting Ramachandran plot indicates whether residues occupy energetically favorable conformations or present significant deviations (Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEpitope Prediction\u003c/h3\u003e\n\u003cp\u003eLinear B-cell epitopes were first predicted using the ABCPred server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/abcpred/\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/abcpred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This tool employs a recurrent neural network (Jordan-type RNN) to identify epitopes in peptide sequences of 10\u0026ndash;20 amino acids. A threshold of 0.9 was used for epitope selection (Saha and Raghava \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, the BCPred server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ailab-projects2.ist.psu.edu/bcpred/\u003c/span\u003e\u003cspan address=\"http://ailab-projects2.ist.psu.edu/bcpred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied. This classifier is based on Support Vector Machine (SVM) learning coupled with a subsequence kernel, which identifies linear epitopes of fixed 20-amino-acid length using a 0.9 threshold for filtering (El-Manzalawy, Dobbs and Honavar \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, B-cell epitope prediction from the protein\u0026rsquo;s three-dimensional structure was performed using ElliPro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/ellipro/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/ellipro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This software uses a modified version of Thornton\u0026rsquo;s method to predict continuous (linear) epitopes and a clustering algorithm to identify discontinuous (conformational) ones. Only continuous epitopes were considered in this study, applying default parameters and a 0.5 threshold (Ponomarenko et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eT-cell epitopes were predicted using the IEDB Tepitool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/tepitool/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/tepitool/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which estimates peptide interactions with class I and II major histocompatibility complex (MHC) molecules. The tool integrates multiple prediction algorithms to assess binding affinity across hundreds of alleles from several species, including humans, cattle, mice and goats (Paul et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The NetMHCpan 4.1 EL method was used for MHC binding prediction (Reynisson et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor class I MHC epitopes, the bovine model was adopted as a reference for the caprine species, considering the alleles BoLA-1:02801, BoLA-2:01601, BoLA-2:02201, BoLA-3:03601, BoLA-3:00101, BoLA-3:01001, BoLA-3:01101, and BoLA-6:01501, with epitopes ranging from 8 to 10 amino acids. For class II MHC epitopes, the murine model was used, including the alleles H2-IAb, H2-IAd, H2-IAk, H2-IAq, H2-IAs, H2-IAu, H2-IEd and H2-IEk, to predict epitopes between 12 and 18 residues. Epitopes with scores\u0026thinsp;\u0026le;\u0026thinsp;0.5 and percentile ranks below 1 were selected.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eImmunological Analyses\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe predicted epitopes and the YkuE protein were subjected to immunological analyses to evaluate their antigenicity and allergenicity. Antigenicity prediction was performed using VaxiJen 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\u003c/span\u003e\u003cspan address=\"https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which is based on the Auto-Cross Covariance (ACC) transformation method (Hellberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). This alignment-independent approach analyzes physicochemical properties of amino acids to predict antigenicity, achieving accuracies between 70% and 89% (Doytchinova and Flower \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A threshold of 0.4 was applied for classification.\u003c/p\u003e \u003cp\u003eAllergenicity prediction was conducted using AlgPred 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/algpred2/batch.html\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/algpred2/batch.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which integrates multiple strategies including BLAST-based homology search, IgE epitope motif identification, and machine-learning-based physicochemical property analysis. The hybrid method of the tool was applied using a 0.3 threshold to maximize specificity and predictive accuracy. In addition, the physicochemical properties of YkuE were evaluated using 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\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which calculates several biochemical parameters, including molecular weight, isoelectric point, amino acid composition, in vitro and in vivo half-life, instability index, aliphatic index, and the Grand Average of Hydropathicity (GRAVY) (Gasteiger et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe solubility of the YkuE protein was assessed using the SoluProt 1.0 program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://loschmidt.chemi.muni.cz/soluprot\u003c/span\u003e\u003cspan address=\"https://loschmidt.chemi.muni.cz/soluprot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which predicts the likelihood of a protein being soluble when expressed in the Escherichia coli system. In this method, scores above 0.5 indicate high solubility, whereas values below 0.5 suggest a tendency toward insolubility (Hon et al. 2021).\u003c/p\u003e\n\u003ch3\u003eMolecular Docking and Interaction Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the immunological potential of YkuE, molecular docking was performed with the immune receptor Toll-like receptor 2 (TLR2). The three-dimensional structure of TLR2 (PDB ID: 3A7B) was obtained from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Docking simulations were carried out using ClusPro 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cluspro.bu.edu/home.php\u003c/span\u003e\u003cspan address=\"https://cluspro.bu.edu/home.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which conducts rigid-body docking through the PIPER algorithm. The receptor remains fixed while the ligand explores billions of possible conformations, evaluated based on electrostatic and desolvation energies. The 1,000 lowest-energy poses are clustered, and the centers of the most populated clusters are selected as representative models, which are then refined by energy minimization (Kozakov et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Protein\u0026ndash;receptor complexes were visualized using UCSF ChimeraX (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cgl.ucsf.edu/chimerax/\u003c/span\u003e\u003cspan address=\"https://www.cgl.ucsf.edu/chimerax/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Pettersen et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The molecular interactions were analyzed using LigPlot+ v2.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/thornton-srv/software/LigPlus/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/thornton-srv/software/LigPlus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, which generates two-dimensional diagrams from three-dimensional coordinates, highlighting hydrogen bonds and hydrophobic contacts. LigPlot+ enables a detailed visualization of protein\u0026ndash;ligand complementarity and supports the overlay of multiple complexes for comparative analysis (Laskowski and Swindells \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrains and culture conditions\u003c/h2\u003e \u003cp\u003eFour different \u003cem\u003eEscherichia coli\u003c/em\u003e strains were used as expression hosts to evaluate which one provided the best recombinant protein yield: \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) Star, Rosetta, BL21 (DE3) pLysS, and BL21 (DE3) pLysE. All strains were cultured in liquid Luria\u0026ndash;Bertani (LB; 10 mg tryptone, 5 mg yeast extract, and 5 mg NaCl) medium or on LB agar plates for 16 hours at 37\u0026deg;C. When required, LB medium was supplemented with kanamycin (100 \u0026micro;g/mL; Sigma-Aldrich, Saint Louis, USA). Chloramphenicol (100 \u0026micro;g/mL; Sigma-Aldrich, Saint Louis, USA) was added to the culture of the Rosetta, BL21 (DE3) pLysS, and BL21 (DE3) pLysE strains.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCloning, solubility tests, expression, and purification of recombinant protein\u003c/h3\u003e\n\u003cp\u003eThe DNA sequence corresponding to the \u003cem\u003eykuE\u003c/em\u003e gene from \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e (Gene ID: 12299865) was obtained from the GenBank database (www.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The gene was synthesized and cloned into the pET28a expression vector by GenOne Biotechnologies (Rio de Janeiro, Brazil), with the restriction enzymes \u003cem\u003eNdeI\u003c/em\u003e and \u003cem\u003eHindIII\u003c/em\u003e, resulting in the plasmid pET28a/\u003cem\u003eykuE\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The plasmid was resuspended according to the manufacturer\u0026rsquo;s instructions and stored at -20\u0026deg;C until use.\u003c/p\u003e\u003cp\u003eThe plasmid was introduced by electroporation with 1 pulse of 1.8 kV into the \u003cem\u003eE. coli\u003c/em\u003e expression strains using MicroPulser Electroporator (Bio-Rad Laboratories, Inc., California, USA).\u003c/p\u003e \u003cp\u003eTo assess the solubility of the recombinant proteins, bacterial cells were harvested by centrifugation, and the pellet was resuspended in a 1\u0026times; TE buffer (10 mM Tris-HCl, pH 8.0; 1 mM EDTA). Samples were then subjected to sonication cycles for cell lysis, followed by centrifugation at 14.000 rpm for 5 minutes. After centrifugation, 500 \u0026micro;L of the supernatant was collected for analysis, while the pellet was resuspended again in a 1\u0026times; TE buffer. Both the soluble and insoluble fractions were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to determine the solubility profile of the recombinant proteins.\u003c/p\u003e \u003cp\u003eThe expression of recombinant protein on a small scale was induced after all the cultures reached an exponential growth phase in LB medium containing kanamycin by adding 1 mM Isopropyl β-D-1-thiogalactopyranidase (IPTG) to each culture and maintaining it under constant stirring in an orbital shaker for 3 hours at 37\u0026deg;C. A 15% SDS-PAGE was performed to evaluate which \u003cem\u003eE. coli\u003c/em\u003e strain showed the highest expression level of the recombinant YkuE protein, allowing the selection of the best strain to be used for expression on a large scale and further purification steps. For rYKUE purification, the culture was centrifuged, and the resulting pellet was resuspended in a lysis buffer containing 50 mM NaH₂PO₄, 300 mM NaCl, 20 mM imidazole, and 8 M urea, supplemented with 100 \u0026micro;g/mL of lysozyme. The suspension was subjected to sonication and kept under constant agitation at 4\u0026deg;C for 16 hours. Recombinant proteins were subsequently purified using affinity chromatography with a HisTrap\u0026trade; Sepharose nickel column (GE Healthcare, USA), followed by dialysis using SnakeSkin\u0026trade; Dialysis Tubing (Thermo Fisher Scientific, Waltham, MA, USA). Purity was determined using a 1% SDS-PAGE gel, and the concentration was determined by the BCA kit (Pierce, USA)\u003c/p\u003e\n\u003ch3\u003eWestern Blot Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the identity and antigenicity of the recombinant protein YkuE, western blot analyses were conducted. To identity, the purified rYkuE protein was mixed with SDS loading buffer (100 mM Tris-HCl, pH 6.8, 100 mM 2-mercaptoethanol, 4% (w/v) SDS, 0.2% (w/v) bromophenol blue, and 20% (v/v) glycerol) and incubated at 95\u0026deg;C for 10 minutes. The samples were then separated by electrophoresis on a 12% SDS-PAGE gel. Following electrophoresis, the protein was transferred to a nitrocellulose membrane (GE Healthcare, USA) using a Mini Trans-Blot\u0026trade; Cell System (Bio-Rad, Hercules, CA). The membrane was blocked with PBS-milk powder solution (5%), incubated at 37\u0026deg;C for 30 minutes, and washed 3 times with PBS containing 0.05% Tween 20 (PBS-T). Later, the membrane was incubated with a monoclonal anti-6\u0026times; His antibody (Sigma\u0026ndash;Aldrich, USA) diluted 1:2000 in PBS-T for 1 hour at 37\u0026deg;C. Reactive bands were visualized using a solution containing 3,3\u0026prime;-diaminobenzidine (DAB), 0.3% nickel sulfate, 50 mM Tris-HCl buffer (pH 7.6), and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eRegarding antigenicity, in the western blotting performed, after blocking, the membrane was washed three times and then incubated with the serum. The serum of sheep and goat were diluted 1:100 in PBS-T and added to the blot for 1 hour at 37\u0026deg;C. After three additional washes with PBS-T, anti-goat and anti-sheep peroxidase-conjugated antibodies diluted 1:4000 in PBS-T were added and incubated for 1 hour at 37\u0026deg;C. Reactive protein bands were also visualized using DAB solution. Positive control sera were obtained from four sheep and four goats presenting clinical CLA, confirmed by microbiological isolation of \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e. Negative control sera were collected from four sheep and four goats (under 6 months old) that tested negative in an ELISA using secreted proteins from \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e and originating from farms with no history of CLA (non-endemic areas).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTopological Analysis\u003c/h2\u003e \u003cp\u003eThe analysis revealed that all amino acid residues of YkuE are located in the intracellular region, classifying it as a globular-type protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThree-Dimensional Structure Modeling and Refinement\u003c/h2\u003e \u003cp\u003eThe models presenting the highest pLDDT scores\u0026mdash;an indicator of local confidence in structural prediction\u0026mdash;were selected for further analysis. For YkuE, model 1 showed the highest pLDDT value (89.9). The chosen model was subsequently refined using GalaxyRefine and validated for stereochemical quality through the Ramachandran Plot Server, resulting in the final three-dimensional structure shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRefinement significantly improved the quality of the three-dimensional model of the YkuE protein. A reduction in clash score from 29.7 to 8.1 was observed, along with an increase in the percentage of residues in favored regions of the Ramachandran plot (Rama favored) from 95.2% to 98.1%. These results indicate enhanced stereochemical and conformational stability of the refined structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEpitopes prediction\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the analysis resulted in the prediction of a total of 162 epitopes for the YkuE protein. Among these, 87 epitopes were predicted for MHC class I and 58 for MHC class II by TepiTool, highlighting the potential of YkuE as a promising immunogenic target.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eNumber of predicted epitopes for the YkuE protein using different computational tools.\u003c/b\u003e B-cell epitopes were predicted with ABCpred, BCpred, and ElliPro, while T-cell epitopes were identified using MHC-I and MHC-II prediction tools. The total represents the sum of epitopes detected by each method for the protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYKUE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCpred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCpred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEllipro - linear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHC-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eImmunological Analyses\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe antigenicity and allergenicity analyses of the predicted epitopes and the YkuE protein indicated a strong immunological potential. As shown in \u003cb\u003eSupplementary Table\u0026nbsp;1 52\u003c/b\u003e.4% of the predicted epitopes exhibited antigenic potential, presenting scores higher than 0.4, while 47.5% had lower scores and were therefore considered non-antigenic.\u003c/p\u003e \u003cp\u003eDespite the high proportion of antigenic epitopes, only 22.2% were predicted as non-allergenic (scores below 0.3), whereas 77.8% were classified as potentially allergenic. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the five YkuE epitopes that displayed both high antigenicity and non-allergenic profiles, suggesting their relevance as promising immunogenic targets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe five top-ranked predicted epitopes for the YkuE protein by different computational tools (IEDB).\u003c/b\u003e Epitopes were selected based on the highest antigenicity (VaxiJen) scores and classification as non-allergenic (AllerTop)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpitopes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProgram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntigenicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllergenicity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFELKIVEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9616 (Probable ANTIGEN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVNPFLYLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7193 (Probable ANTIGEN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNFELKIVEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5353 (Probable ANTIGEN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARHEFQINHVRIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHC-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2085 (Probable ANTIGEN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVNPFLYL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMHC-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1987 (Probable ANTIGEN).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, among the five predicted epitopes with the highest antigenicity scores and non-allergenic profiles derived from the YkuE protein, four were predicted to bind to MHC class I molecules, and one to MHC class II (epitope ARHEFQINHVRIA). We also observe that FELKIVEL epitope, predicted for MHC class I, exhibited the highest antigenicity score (1.9616) and an allergenicity score of 0.26. In contrast, the MVNPFLYL epitope, also predicted for MHC class I, showed the lowest antigenicity score (1.1987) among the five, with an allergenicity score of 0.3.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the immunological analyses for antigenicity and allergenicity, as well as solubility and physicochemical properties of the YkuE protein.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAnalysis of antigenicity, allergenicity, solubility, and physicochemical properties of the YkuE protein.\u003c/b\u003e Parameters evaluated included molecular weight, isoelectric point (pI), instability index, hydrophobicity, and predicted half-life\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYKUE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntigenicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4062 (Probable ANTIGEN)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergenicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (Non-Allergen)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolubility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood solubility in water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolubility in \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.189 (high insolubility)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34,82318 kDa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoelectric point (pI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstability index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRAVY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,184 (hidrof\u0026iacute;lic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ein vitro\u003c/em\u003e half-life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ein vivo\u003c/em\u003e half-life - yeast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ein vivo\u003c/em\u003e half-life - \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, YkuE protein exhibited moderate antigenicity, in addition to being classified as non-allergenic and water-soluble. The antigenicity score was 0.4062, and the allergenicity score was 0.03.\u003c/p\u003e \u003cp\u003ePhysicochemical analysis revealed that the YkuE protein consists of 312 amino acids, with a molecular weight of approximately 34.82 kDa and a theoretical isoelectric point (pI) of 9.06. The instability index of 33.51 suggests that the protein is stable under biological conditions. The GRAVY value of \u0026minus;\u0026thinsp;0.184 indicates a hydrophilic character. The estimated half-life was approximately 30 hours in vitro, over 20 hours in yeast, and over 10 hours in \u003cem\u003eEscherichia coli\u003c/em\u003e, supporting its overall structural stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking and Interaction Analysis\u003c/h2\u003e \u003cp\u003eFollowing the generation of the three-dimensional structures of YkuE and the TLR2 receptor, molecular docking was performed to evaluate the interaction between both proteins. Among the models generated by ClusPro under the \u003cem\u003ebalanced\u003c/em\u003e parameter, the cluster 6 model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was selected due to its conformation being visually compatible with a potential biological interaction. The resulting structure, along with the predicted molecular interactions visualized using LigPlot+, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eResults of the molecular docking between the TLR2 receptor and the YKUE protein obtained using the ClusPro server.\u003c/b\u003e The identified cluster, the number of cluster members, the representative structure (cluster center and lowest-energy conformation), and the corresponding weighted scores are shown, with more negative values indicating more favorable interactions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresentative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1200.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLowest Energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1207.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe molecular interaction analysis, illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealed a significant number of hydrogen bonds and hydrophobic interactions between the YkuE protein and the TLR2 receptor. Among the main YkuE residues involved in hydrogen bond formation are Asn79, Arg40, Phe293, Val2, and Lys4. These residues interact with several amino acids of TLR2, including Lys383, Gln357, Glu375, Tyr332, Ser354, and Arg87, totaling 17 hydrogen bond interactions.\u003c/p\u003e \u003cp\u003eIn addition, an extensive network of hydrophobic interactions was observed, involving residues such as Leu147, Val255, Met140, Ser333, Tyr336, Gly31, and Glu321, among others. These findings indicate the formation of a well-defined binding interface between YkuE and the TLR2, suggesting a specific and stable molecular recognition process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCloning, solubility tests, expression, and purification of recombinant protein\u003c/h2\u003e \u003cp\u003eThe analysis, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, revealed a distinct band corresponding to approximately 37 kDa in pellet fraction of the BL21 Star (DE3) culture, indicating overexpression of the recombinant protein in the insoluble fraction. In contrast, no overexpression band equivalent to 37 kDa was detected in either the soluble or insoluble fractions of the Rosetta, pLysS and pLysE, suggesting that protein expression in these strains was minimal or absent. So, BL21 Star (DE3) was the most efficient strain for rYkuE expression, and protein was predominantly found in the insoluble fraction (pellet). The large scale of rYkuE\u0026rsquo;s expression resulted in a yield of 17,68 mg/L after purification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eThe Western blot, using a monoclonal anti-6\u0026times;His antibody, confirmed the identity of the rYkuE protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which detected a reactive band at the expected molecular weight of 37 kDa. A comparative analysis with the rCP40 protein (40 kDa) supported the confirmation of the recombinant protein\u0026rsquo;s identity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the antigenicity assessment by Western blot, the rYkuE protein was recognized by sera from goats and sheep naturally infected with \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e, indicating that the rYkuE preserved epitopes from the native protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). No reactivity was observed with negative CLA sera.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe high prevalence of \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e infection in lambs, combined with considerable economic losses associated with CLA, underscores the urgent need for more effective control and preventive strategies (Dorella et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This is the first report describing the heterologous expression of the YkuE protein of \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e in \u003cem\u003eE. coli\u003c/em\u003e and its recognition by sera from sheep and goats naturally infected with CLA, highlighting its potential as a diagnostic or vaccine target.\u003c/p\u003e \u003cp\u003eMonteferrante et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) demonstrated that YkuE, a metallophosphoesterase, is specifically targeted to the cell wall through the twin-arginine translocation (Tat) pathway, a mechanism responsible for exporting folded proteins across the cytoplasmic membrane in Gram-positive bacteria, \u003cem\u003eBacillus subtilis\u003c/em\u003e. This localization suggests that YkuE is exposed or associated with the bacterial surface, where it may interact with host immune components. The surface accessibility of YkuE makes it a biologically relevant target for immune recognition and, consequently, a promising candidate for vaccine development.\u003c/p\u003e \u003cp\u003eIn the context of \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e, a pathogen that relies heavily on cell wall-associated factors for host colonization and persistence (Raynal et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), proteins exported via the Tat pathway may play essential roles in virulence and immune evasion. Our \u003cem\u003ein silico\u003c/em\u003e analyses predicted YkuE as a cytoplasmic and globular protein. However, previous studies in \u003cem\u003eBacillus\u003c/em\u003e species have reported that YkuE is exported via the Tat pathway and associated with the cell wall. Taken together, these observations suggest that, although YkuE may be primarily intracellular in \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e, its conserved structure and potential release during bacterial lysis or alternative secretion mechanisms may contribute to its recognition by the host immune system.\u003c/p\u003e \u003cp\u003eRecent advances in bioinformatics have transformed the process of vaccine development, enabling the direct identification of promising antigens from pathogen genomes, reducing experimental costs and time while providing a rational basis for antigen selection (Gloanec et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this context, bioinformatic analyses were applied to the YkuE protein of \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e to explore its potential as a vaccine antigen and guide subsequent experimental evaluations.\u003c/p\u003e \u003cp\u003eAra\u0026uacute;jo et al. (2019) predicted YkuE as a membrane-associated protein. However, our topological analysis predicted YkuE as a globular and intracellular protein, consistent with the metallophosphoesterase proposed function, suggesting a participation in cellular stress responses instead of being a membrane-associated enzyme. This discrepancy can be attributed to the fact that the software version used by Ara\u0026uacute;jo in the previous study is outdated. When the same prediction tool is applied using its older version, the result reproduces the membrane localization reported by Ara\u0026uacute;jo et al. (2019). Nevertheless, the updated version incorporates improved algorithms and databases and consistently classifies YkuE as a cytoplasmic and globular protein. The globular conformation observed here suggests that YkuE is likely localized in the cytoplasmic compartment. Nonetheless, it may still contribute to host immune recognition through alternative mechanisms, such as the release during bacterial lysis or the cross-presentation of conserved epitopes.\u003c/p\u003e \u003cp\u003eEpitope prediction revealed that YkuE harbors multiple regions capable of eliciting both B- and T-cell responses. While B-cell epitopes are essential for the induction of humoral immunity, T lymphocytes play a fundamental role in stimulating cellular immune responses, acting directly against intracellular or facultatively intracellular pathogens such as \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e (Dorella et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Among 162 identified epitopes, several presented high antigenicity scores and were predicted to bind MHC class I and II molecules, suggesting that this protein could activate both humoral and cellular immune responses. The antigenicity and allergenicity analyses reinforced these findings, indicating that YkuE is moderately antigenic and non-allergenic, properties that are desirable for a subunit vaccine antigen.\u003c/p\u003e \u003cp\u003eToll-like receptors (TLRs) play a central role in pathogen recognition by detecting conserved microbial structures known as pathogen-associated molecular patterns (PAMPs), which initiate downstream signaling that leads to the production of cytokines and interferons (Carter et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This mechanism establishes a crucial link between innate detection and adaptive immune activation. Thus, our docking analysis demonstrated a strong interaction between YkuE and TLR2, indicating that YkuE may participate in host immune recognition through TLR-mediated pathways. TLR2 primarily signals through the MyD88-dependent pathway, leading to the recruitment of IRAK kinases and TRAF6, followed by activation of the NF-κB and MAPK pathways and subsequent transcription of pro-inflammatory mediators, including TNF-α, IL-6, IL-1β, and IL-12, that favor Th1-type immune responses (Kawai and Akira \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Akira et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These signaling events favor macrophage activation and the development of Th1-type immune responses, which are particularly important for controlling intracellular and facultative intracellular pathogens (Iwasaki and Medzhitov \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOtt, M\u0026ouml;ller, and Burkovski (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have shown that TLR2 recognizes components of \u003cem\u003eCorynebacterium diphtheriae\u003c/em\u003e and activates similar inflammatory pathways, triggering MyD88-dependent signaling cascades that culminate in inflammatory cytokine production, reinforcing its role as a key mediator in the recognition of \u003cem\u003eCorynebacterium\u003c/em\u003e species. Accordingly, the interaction observed between the YkuE protein and TLR2 in our \u003cem\u003ein silico\u003c/em\u003e analyses suggests that this antigen may be effectively recognized by the innate immune system, contributing to the activation of protective mechanisms against \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e infection.\u003c/p\u003e \u003cp\u003eThe heterologous expression of recombinant proteins in \u003cem\u003eEscherichia coli\u003c/em\u003e is a widely employed strategy for structural and immunological characterization studies of candidate antigens (Pouresmaeil and Azizi-Dargahlou \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This approach enables the production of sufficient amounts of purified protein for experimental analyses and facilitates in vitro and in vivo assessments of antigenicity and immunogenicity. In our study, we identified \u003cem\u003eE. coli\u003c/em\u003e BL21 Star (DE3) as the most efficient strain for the heterologous expression of the YkuE protein. This strain produced a strong rYkue expression, predominantly in the pellet fraction.\u003c/p\u003e \u003cp\u003eThe superior performance of the BL21 Star (DE3) strain in rYkuE expression may be explained by key physiological features that distinguish this lineage from other \u003cem\u003eE. coli\u003c/em\u003e expression hosts. BL21-derived strains are widely used for recombinant protein production due to their reduced proteolytic activity, which minimizes degradation of heterologous proteins during expression (İncir and Kaplan \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More specifically, BL21 Star can carry a mutation in the \u003cem\u003erne\u003c/em\u003e gene, which encodes RNase E, resulting in enhanced mRNA stability and, consequently, higher transcript abundance, leading to increased protein synthesis (Carpousis \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough \u003cem\u003ein silico\u003c/em\u003e analyses indicated that the recombinant protein exhibited favorable solubility characteristics, experimental assays revealed that it was predominantly expressed in the insoluble fraction. This discrepancy may be partially explained by codon usage bias among different prokaryotic organisms (Botzman and Margalit \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The heterologous expression of genes derived from organisms with distinct genomic compositions often results in translational inefficiencies, since the preferred codons of the original organism may not be efficiently recognized by the host\u0026rsquo;s translational machinery, particularly when they correspond to rare codons in \u003cem\u003eE. coli\u003c/em\u003e (Sharp et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sharp and Li \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Gouy and Gautier \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e is a Gram-positive bacterium, whereas \u003cem\u003eE. coli\u003c/em\u003e is a Gram-negative bacterium; therefore, differences in cell wall architecture, protein folding environments, and secretion systems may further influence the expression and solubility of heterologous proteins. Such structural and physiological disparities can compromise proper protein folding during synthesis, promoting the formation of inclusion bodies, insoluble aggregates commonly observed in recombinant expression systems (İncir and Kaplan \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Plotkin and Kudla \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe molecular weight predicted \u003cem\u003ein silico\u003c/em\u003e for the native YkuE protein of \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e was approximately 34 kDa, which is consistent with the theoretical size expected from its amino acid sequence. However, during heterologous expression in \u003cem\u003eE. coli\u003c/em\u003e, the recombinant YkuE was observed to migrate at around 37 kDa on SDS-PAGE. This apparent increase in molecular mass can be attributed to modifications introduced by the expression system, particularly the additional sequences encoded by the pET vector and the incorporation of the polyhistidine tag at the N-terminus. Evidence from the literature also supports this interpretation. According to Shilling et al. (2020), the pET28a plasmid incorporates modules encoding an N-terminal poly-histidine tag (His₆) and a thrombin cleavage site, thereby introducing additional amino acid residues into the recombinant protein, which consequently increases the size of the protein.\u003c/p\u003e \u003cp\u003eThe recognition of the recombinant YkuE protein by sera from naturally infected sheep and goats demonstrates that the host immune system naturally targets this antigen during \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e infection. When compared with previous studies using recombinant antigens (Barral et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), similar patterns of recognition were observed, suggesting that rYkuE contains conserved and immunodominant epitopes capable of inducing antibody responses across species, thereby supporting its potential role as a broadly recognized antigen.\u003c/p\u003e \u003cp\u003eOur findings highlight the relevance of rYkuE as a promising antigen for advancing recombinant vaccine development and diagnostic approaches against \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e. Through bioinformatic characterization, efficient heterologous expression, and antigenicity analyses, rYkuE demonstrated strong recognition by sera from naturally infected sheep and goats, indicating that this protein is targeted by the host immune response during infection. This evidence underscores its potential applicability in both prophylactic and diagnostic contexts. Future investigations should prioritize the evaluation of rYkuE in CLA vaccine formulations and immunodiagnostic platforms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge CAPES for providing scholarships to some of the authors, CNPq for financial support through project no. 408137/2023-1 (CNPq/MCTI Call No. 10/2023), and Instituto Sabi\u0026aacute; for additional financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors has any potential financial conflict of interest related to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFSBB and AKMOB conceived the experiments. AKMOB, ACSR, GRO, TVSA and LMPM conducted the experiments. PLFF performed the \u003cem\u003ein silico\u003c/em\u003e analyses. AKMOB and PLFF performed the data analysis and wrote the manuscript. SB, FSBB, DVCL and BMFV critically revised and helped in writing the work. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAftabuzzaman M, Cho Y (2021) Recent perspectives on caseous lymphadenitis caused by Corynebacterium pseudotuberculosis in goats-A review. Korean Journal of Veterinary Service 44(2):61\u0026ndash;71. https://doi.org/https://doi.org/10.7853/kjvs.2021.44.2.61\u003c/li\u003e\n\u003cli\u003eAkira S, Takeda K, Kaisho T (2001) Toll-like receptors: critical proteins linking innate and acquired immunity. 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BMC Genomics 11(1). https://doi.org/10.1186/1471-2164-11-728\u003c/li\u003e\n\u003cli\u003eVan Herck S, Feng B, Tang L (2021) Delivery of STING agonists for adjuvanting subunit vaccines. Advanced Drug Delivery Reviews 179:114020. https://doi.org/10.1016/j.addr.2021.114020\u003c/li\u003e\n\u003cli\u003eWashburn KE, Bissett WT, Fajt VR, Libal MC, Fosgate GT, Miga JA, Rockey KM (2009) Comparison of three treatment regimens for sheep and goats with caseous lymphadenitis. Journal of the American Veterinary Medical Association 234(9):1162\u0026ndash;1166. https://doi.org/10.2460/javma.234.9.1162\u003c/li\u003e\n\u003cli\u003eYitagesu E, Alemnew E, Olani A, Asfaw T, Demis C (2020) Survival Analysis of Clinical Cases of Caseous Lymphadenitis of Goats in North Shoa, Ethiopia. Veterinary Medicine International 2020:1\u0026ndash;8. https://doi.org/10.1155/2020/8822997\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":"Caseous lymphadenitis, Escherichia coli, Immunoinformatics, metallophosphoesterase, recombinant protein","lastPublishedDoi":"10.21203/rs.3.rs-9012757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9012757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCaseous lymphadenitis (CLA), caused by \u003cem\u003eCorynebacterium pseudotuberculosis\u003c/em\u003e, remains a major challenge for small ruminant production due to the limited efficacy and questionable safety of currently available vaccines, as well as the inefficacy of antibiotic treatment once abscesses are established. These limitations reinforce the need for novel antigens that may improve both prophylactic and diagnostic strategies. In this study, the YkuE protein, previously identified as a promising antigen through reverse vaccinology, was comprehensively characterized using \u003cem\u003ein silico\u003c/em\u003e immunoinformatics approaches and experimental validation. The computational analyses predicted YkuE as a stable, non-allergenic, and moderately antigenic protein containing multiple B- and T-cell epitopes, supporting its immunogenic potential. Structural modeling and refinement confirmed the reliability of the three-dimensional structure, while molecular docking analysis revealed a strong interaction between YkuE and Toll-like receptor 2 (TLR2), suggesting its involvement in innate immune recognition. The \u003cem\u003eykuE\u003c/em\u003e gene was cloned into the pET28a expression vector and heterologously expressed \u003cem\u003eEscherichia coli\u003c/em\u003e BL21 Star (DE3). The recombinant YkuE protein was predominantly recovered in the insoluble fraction and exhibited an apparent molecular mass of approximately 37 kDa. Purified rYkuE was specifically recognized by sera from sheep and goats naturally infected with \u003cem\u003eC. pseudotuberculosis\u003c/em\u003e, while no reactivity was observed with negative sera. Together, these findings highlight recognition by YkuE as a naturally targeted antigen in infected small ruminants and support its potential application in the development of recombinant vaccines and immunodiagnostic tools for CLA control.\u003c/p\u003e","manuscriptTitle":"In silico characterization and heterologous expression of the recombinant YkuE protein from Corynebacterium pseudotuberculosis for immunobiological purpose","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 10:14:40","doi":"10.21203/rs.3.rs-9012757/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":"bb2d85c4-9a55-4f21-b390-61aad60a7c77","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T21:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 10:14:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9012757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9012757","identity":"rs-9012757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}