Designing a next-generation self-amplifying mRNA vaccine against Hepatitis E Virus 1 (HEV-1): A reverse vaccinology and immunoinformatics approach

Designing a next-generation self-amplifying mRNA vaccine against Hepatitis E Virus 1 (HEV-1): A reverse vaccinology and immunoinformatics approach | 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 Article Designing a next-generation self-amplifying mRNA vaccine against Hepatitis E Virus 1 (HEV-1): A reverse vaccinology and immunoinformatics approach Mohammad Mahdi Abolhosseini, Samaneh Hashemi, Ghazal Ghaznavi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7572829/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 Hepatitis E virus 1 (HEV-1) is a worldwide public health issue that induces acute viral hepatitis as well as high mortality, especially among pregnant women and immunocompromised subjects. The World Health Organization has characterized Hepatitis E virus as an emerging pathogen for a vaccine design that has become most important for its elimination. Currently, there is only one available vaccine for hepatitis E that is exclusively administered in China. Another potential vaccine, the recombinant Hepatitis E virus vaccine, has not yet been approved for commercial use. Bioinformatics methods have shown potential for the development of vaccine candidates, particularly Hepatitis E virus. mRNA technology has received great interest because it can induce both B and T cell immune responses, is safe, and is a cell-free manufacturing process. Self-amplifying RNA-based vaccines, which code for the antigen of interest as well as proteins enabling the duplication of RNA, result in enhanced antigen expression as well as equivalent immune responses to those of mRNA-based vaccines. This study seeks to design a novel self-amplifying mRNA anti-HEV vaccine using immunoinformatic tools based on the ORF2 antigen. We predicted antigenic, non-toxic, and non-allergic epitopes of B and T cells and assembled the selected epitopes with appropriate linkers for mRNA vaccine construction. Bioinformatics data, including physicochemical properties, 2D, 3D structural modeling, validation and refinement analyzes, and mRNA secondary structure, illustrated the stability and quality of the designed vaccine. Molecular docking and simulation indicated effectively engaging the immune system and eliciting a strong immune response by TLR4. Immune simulations demonstrate a robust initial immune response, with a gradual increase in levels of immunoglobulins, cytokines, cytotoxic T cells, and helper T cell populations following a single dose administration. However, this in silico study presents a promising self-amplifying mRNA vaccine candidate against HEV, which requires further evaluation and validation through additional tests, such as preclinical and clinical trials. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Microbiology Self-amplifying mRNA Vaccine Reverse vaccinology Immunoinformatics Hepatitis E Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Hepatitis E Virus (HEV) presents a significant global challenge to public health, especially within countries with limited financial resources, and is responsible for the occurrence of acute viral hepatitis 1 , 2 . HEV represents 20 million instances of disease yearly, as expressed by The World Health Organization (WHO). Although the infection is normally self-restricting in most patients, intense infection in pregnant women might cause extreme clinical results with high mortality rates of up to 30% 3 . Immunocompromised patients who undergo organ transplants tend to develop chronic HEV infection 4 . HEV is mostly transmitted via fecal-oral transmission. Approximately, less than 50% of the global population is living in HEV-endemic areas 5 . The World Health Organization (WHO) has declared Hepatitis E Virus as an emerging pathogen, a disease causing high mortality, and economic losses in several developing countries. Consequently, the search for a vaccine that is effective has been intensified in order to eliminate the disease 6 . The WHO has launched a comprehensive effort to eradicate viral hepatitis by 2030. However, it is worth noting that the global health community still does not fully recognize the burden caused by hepatitis E. The goal of eliminating viral hepatitis is unattainable without addressing the issue of areas with high rates of diseases 7 . HEV is a small virus without an envelope and has positive-sense single-stranded RNA (ssRNA) infection, with a diameter of 27–34 nm 8 . Currently, 8 genotypes of HEV have been identified, 4 of which account for the majority of infections (HEV1-4) 9 . Types 3 and 4 are zoonotic, accounting for most cases of chronic infection in humans 10 . Types 1 and 2 only infect humans, causing acute hepatitis 11 . While human-to-human spread of HEV-1 and HEV-2 is unusual in individual and epidemic cases, prenatal transmission from mother to fetus is well-recognized 12 , 13 . The genome is organized into three Open Reading Frames (ORFs) (i.e. ORF1-3), along with some non-coding regions. The ORF1 functions in the viral replication and protein modification via a special RNA polymerase. ORF2 encodes capsid protein, which facilitates viral binding to the host cell membrane and is responsible for the selection of neutralizing antibodies. ORF3 involves in morphogenesis and release of the complete virus. Among these, ORF2 has shown to stimulate host immune response significantly more than other ORFs. This makes ORF2 an efficient candidate for biomedical research purposes 14 – 16 . The efficacy of antiviral drugs in the treatment of hepatitis E is limited, which necessitates the development of a vaccine 17 . Vaccination is the healthiest and most effective method for the prevention and control of HEV, so vaccination would be the focal point of the most effective strategy for its prevention and control. Two of the numerous vaccine candidates stood out and proceeded to human clinical testing 18 . Currently, the only available vaccination for HEV is Hecolin®, which is solely distributed and administered within the territorial boundaries of China. The vaccination currently being examined is a preventive strategy that employs the capsid protein derived from HEV genotype 1. However, there is still a lack of certainty regarding the effectiveness of this intervention in protecting both humans and domestic animals from zoonotic HEVs 19 . With the assistance of Glaxo, Smith, and Kline, an additional vaccination for HEV known as recombinant HEV (rHEV) was developed. However, this specific vaccine has not been approved for commercial use, and the specific reasons for this decision remain unknown 20 . Immunoinformatics may provide a better way to assign function to uncharacterized genes in developing vaccines against pathogens that infect immunocompromised patients. The best approach to screening epitopes for design of candidate vaccines, particularly for HEV, has been immunoinformatics 21 . The utilization of computational tools and resources in immunoinformatic analysis has significantly contributed to the understanding of immune responses in various antigenic locations 22 . One of the attractive platforms for designing vaccines using bioinformatics is mRNA vaccines, which have garnered significant scientific interest during the pandemic of viral diseases such as Covid-19. RNA vaccines can elicit responses from both B cells and T cells, while combining safety with a fast, cell-free production process 23 . The production of RNA vaccines is technically simple, fast, economically viable, without cell and animal ingredients, and highly adaptable 24 . RNA vaccines can be produced with various platforms: Laboratory-synthesized mRNA sequences containing only antigen(s) of interest or self-amplifying RNA molecules (Sa-RNA). Sa-RNA not only encodes the specific antigen but also encodes the proteins responsible for facilitating the replication of RNA vaccines 25 . In a self-amplifying RNA vaccine construct, a selected antigen is substituted for the open reading frame expressing viral structural proteins, while the viral replicase component remains a critical element. This protein facilitates intracellular RNA amplification post vaccination 26 . As a result of amplifying the RNA encoding antigen in the sa-RNA vaccine, higher amounts of antigen will be expressed than from mRNA, leading to similar immune responses 25 . Here, we aim to design a novel sa-RNA vaccine against HEV-1 based on the ORF2 antigen using principles of reverse vaccinology and an immunoinformatic approach. 2 Materials and methods The steps in designing a candidate self-amplifying mRNA vaccine against HEV-1 are depicted in Fig 1. 2.1 Obtaining protein sequence data from databases The amino acid sequences of Hepatitis E Virus ORF2 (ID: P29326) as vaccine candidate and Mycobacterium tuberculosis heat shock protein 70 (Hsp70) (ID: O53673) as adjuvant were retrieved from the Protein Knowledge Base (UniProtKB) (www.uniprot.org) and stored in FASTA format. 2.2 Immuno-informatics analysis 2.2.1 Linear B-cell epitope prediction. ABCPred was employed to detect potential B cell epitopes located within ORF2. This tool, accessible at http://crdd.osdd.net/raghava/abcpred/, utilizes a Recurrent Neural Network (RNN) to forecast linear B-cell epitopes, achieving a prediction accuracy of 65.93% 27 . The threshold value was 0.51. 2.2.2 MHC-I epitope prediction. The amino acid sequence of the ORF2 protein was examined through the IEDB MHC-I web server (http://tools.iedb.org/mhci/) to pinpoint peptide-binding epitopes associated with MHC class I. This platform utilizes Artificial Neural Networks (ANN) to classify peptides as strong or weak binders for each HLA allele, considering their binding affinity (nM) and percentile rankings. The IEDB's prediction accuracy for MHC-I epitope identification stands at 95% 28 . 2.2.3 MHC-II epitope prediction The amino acid sequence of the ORF2 protein was submitted to the IEDB MHC-II web server (http://tools.iedb.org/mhcii/) to determine peptide-binding epitopes associated with MHC class II. This platform utilizes an Artificial Neural Network (ANN) algorithm to assess the binding affinity of peptides (measured in nM) and their corresponding percentile rankings, classifying them as either strong or weak binders for each HLA allele. Peptides that receive lower percentile rankings indicate a greater binding affinity. 2.2.4 Antigenicity prediction for the selected epitopes The antigenic properties of the chosen epitopes were assessed through Vaxijen 2.0, a complementary online resource (http://www.jenner.ac.uk/VaxiJen). This platform employs the auto- and cross-covariance (ACC) technique to convert target protein sequences into corresponding amino acid sequence vectors 29 . 2.2.5 .Allergenicity and toxicity prediction of selected epitopes The selected epitopes in FASTA format were evaluated for their allergenic potential using the AllerCatPro 2.0 server (https://allercatpro.bii.a-star.edu.sg/), which has an accuracy rate of 84% [30]. Furthermore, the toxicity of these epitopes was examined utilizing ToxinPred, accessible at (http://crdd.osdd.net/raghava/toxinpred/).Selection of epitopes and vaccine constructionSelection of epitopes and vaccine construction. 2.3 Selection of epitopes and vaccine construction The top-ranking predicted epitopes were selected for the vaccine design process. Various components were incorporated into the construct, including the 5ʹm7G cap, alpha globin 5ʹ UTR, Kozak sequence, replicase sequence (nonstructural proteins NSP1-4), subgenomic promoter, signal peptide, and the gene of interest, which consisted of Hsp70 as an adjuvant alongside linkers and epitopes (B-cell, MHC-II, MHC-I), respectively . Additional elements integrated were the beta-globin 3ʹ UTR and a poly A tail ranging from 120–150 nucleotides, respectively. B-cell epitopes were connected via GPGPG linkers, while MHC-II and MHC-I epitopes were linked using AAY linkers. 2.4 Codon optimization and evaluation of mRNA secondary structure for the designed vaccine To enhance codon usage and back-translate the vaccine construct, the amino acid sequence was analyzed using the JCat server (https://www.jcat.de/), which aims to improve expression efficiency in E. coli (strain K12). This server also assesses various nucleic acid sequence parameters, including the codon adaptation index (CAI) and the percentage of GC content 30-32 . Following this optimization, the refined nucleic acid sequence of the vaccine was uploaded to the RNAFold server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) to forecast the secondary structure of the mRNA vaccine. RNAFold estimates the Minimum Free Energy (MFE) of mRNA configurations with an accuracy of 70%, utilizing standard energy parameter settings. 2.5 Physicochemical properties ProtParam (http://web.expasy.org/protparam/) was utilized to analyze the physicochemical characteristics of the developed vaccine, encompassing aspects such as amino acid composition, molecular weight, isoelectric point, instability index, and aliphatic index 33 . Additionally, the solubility of the vaccine's protein sequence was assessed using SolPro (http://scratch.proteomics.ics.uci.edu) 34 . 2.6 Protein secondary structure modeling The secondary structure of the synthesized protein was analyzed using SOPMA, accessible at (https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html.). The SOPMA web tool demonstrates a prediction accuracy of 69.5%. 2.7 Protein tertiary structure modeling Three distinct servers were utilized to forecast the tertiary structure of the developed vaccine: I-TASSER, available at (https://zhanggroup.org/I-TASSER/), trRosetta available at (https://yanglab.qd.sdu.edu.cn/trRosetta/), and GalaxyTBM, which is available at (https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=TBM). Each of these servers generated five unique tertiary models for the vaccine. The resulting models were saved as PDB files for subsequent analysis. 2.8 Validation and refinement of tertiary structure Validation of the constructed vaccine was done using (i) ERRAT, which determines the overall quality factor of structure of the constructed vaccine based on a variety of crystallographic structures, (ii) Verify3D, which is utilized for assessing model-sequence consistency using 3D profile verification with a threshold of 80%, and (iii) PROCHECK, which evaluates the quality of the given protein and geometric analysis of residues based on ramachandran plot. ERRAT, Verify3D, and Procheck are available at SAVES v6.0 (https://saves.mbi.ucla.edu/). The ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php) was used as a server to evaluate the three-dimensional structure of proteins based on atomic coordinates of the model to be evaluated and overall quality score for a particular input structure, namely Z-Score. The best 3D structure candidate was selected and submitted to GalaxyRefine module from GalaxyWEB server (https://galaxy.seoklab.org/). GalaxyRefine returns five refined output models based on the initial structure within 1-2 hours post submission, with mild relaxation and energy minimization for model 1, and aggressive relaxation for models 2-5. 2.9 Molecular docking of vaccine with TLR4 Human Toll-Like Receptor 4 in complex with MD2 (TLR4-MD2) (PDB ID: 3FXI) was considered as recipients for the designed vaccine. BIOVIA Discovery Studio 2019 was utilized to visualize the PDB structures. Before docking, water molecules and miscellaneous ligands were removed from input PDB structures and the modified structures were saved as new PDB files. Then, ClusPro 2.0 server (https://cluspro.bu.edu/home.php) was used to analyze the interaction between the receptor and designed vaccine structure as a ligand. The server requires only two files in Protein Data Bank format for basic use. 2.10 Molecular dynamics simulation Fast simulation and normal mode analysis of the flexibility of TLR4 in complex with the designed vaccine was performed using and iMODS (http://imods.chaconlab.org) 35 . A protein PDB code or a protein structure in the PDB format is the data needed. Here, as the input for the quick flexibility simulation, the selected docked vaccine-TLR4 and complex was used. 2.11 Simulation of Immune Responses · To predict the immune response generated by vaccine dosage, the amino acid sequence of the candidate vaccine was submitted to C-ImmSim available at (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php). A single dose simulation was performed with the rest of the parameters set to default values. 3 Results 3.1 Prediction of B-Cell epitopes Epitopes consisting of 16 amino acids in the ORF2 protein were chosen for inclusion in the final vaccine construct. Table 1 presents five B-cell epitopes that received the highest scores. Table 1. B-cell epitopes predictd by ABCPred Rank Sequence Start position Score 1 TEASNYAQYRVARATI 177 0.92 2 RVRQPARPLGSAWRDQ 75 0.91 2 SSTARHRLRRGADGTA 324 0.91 3 TTKAGYPYNYNTTASD 552 0.9 3.2 Prediction of MHC-I epitopes 9-mer MHC-I epitopes were predicted using IEDB server with the prediction method set as NetMHCPan_el, and the predicted epitopes were sorted based on percentile rank. Of the predicted epitopes, the first five with highest peptide scores and lowest percentile ranks were selected and are shown in Table 2. Table 2. Predicted 9-mer MHC-I binding epitopes, predicted using IEDB No Length Peptide HLA Score Percentile Rank 1 9 APSRPFSVL HLA-B*07:02 0.995419 0.01 2 9 RPRPILLLL HLA-B*07:02 0.995331 0.01 3 9 GEIGRGIAL HLA-B*40:01 0.989468 0.01 4 9 STSPLTSSV HLA-A*68:02 0.977366 0.01 3.3 Prediction of MHC-II epitopes 15-mer MHC-II epitopes were predicted using IEDB server with the prediction method set as NetMHCIIPan_el, and the predicted epitopes were sorted based on percentile rank. Of the predicted epitopes, the first five with highest peptide scores and lowest percentile ranks were selected and are shown in Table 3. Table 3. Predicted 15-mer MHC-II binding epitopes, predicted using IEDB No Peptide HLA Score Rank 1 HPTNPFAPDVTAAAG HLA-DRB1*04:01 0.9861 0.01 2 ATIRYRPLVPNAVGG HLA-DRB1*01:01 0.9904 0.01 3 PTNPFAPDVTAAAGA HLA-DRB1*04:01 0.9907 0.01 4 TIRYRPLVPNAVGGY HLA-DRB1*01:01 0.991 0.01 5 TNPFAPDVTAAAGAG HLA-DRB1*04:01 0.9917 0.01 3.4 Antigenicity, Allergenicity, and toxicity of selected epitopes Based on results obtained from ToxinPred, all selected epitopes were identified as non-toxin. Allergenicity of the selected epitopes was evaluated using two servers, i.e. AllerCatPro. Most selected epitopes were identified as non-allergen. All selected epitopes were identified as probable antigens, based on results from VaxiJen 2.0. 3.5 Physiochemical properties and solubility of the designed vaccine Protparam was used to evaluate the physiochemical properties of the designed vaccine. According to the results, the vaccine construct has a molecular weight of 54.1kD and an isoelectric point of 5.57. The construct has a half-life of 100 hours in mammalian reticulocytes, more than 20 hours in yeast cells, and more than 10 hours in E. coli . Furthermore, the instability index of the construct is 29.33, which means that the protein is stable. The GRAVY index was calculated -0.376. Aliphatic index was 73.42. The vaccine construct is soluble upon overexpression in E. coli , with a probability of 89.5% according to the results obtained by SOLPro server. 3.6 Secondary structure evaluation of the designed vaccine To predict the secondary structure of the designed vaccine, the sequence of the designed construct was submitted to SOPMA server. Based on the results, the secondary structure of the designed protein consists of 33.78% alpha helix, 18.23% extended strand, 41.07% random coil. Data is shown in Fig 3. 3.7 Evaluation of tertiary structure, validation, and refinement Three servers, i.e. trRosetta, I-TASSER, and GalaxyTBM were used to predict the tertiary structure of the designed vaccine. Each server provides five output structures which were stored in PDB format for further analyses. The best model of predicted 3D structures from trRosetta predicted models was selected and selected for further evaluations (Fig 4). Based on validation data, the best model was chosen and was submitted to GalaxyRefine for structure refinement. After refinement, the quality of the resulting structure was slightly improved. The refined model had an ERRAT value of 96.52 (Fig 4A), a Verify3D score of 63.15 (Fig 4B), and a ProSA Z-score of -6.96 (Fig 4D), showing good overall quality of the designed vaccine. Additionally, all the amino acids are in the favorable regions of the Ramachandran plot with no amino acids in disallowed or generously favored regions (Fig 4C). 3.8 Codon optimization and prediction of mRNA secondary structure The amino acid sequence of the vaccine construct was back-translated and codon-optimized using the JCAT server (https://www.jcat.de/) to ensure efficient expression in E. coli (strain K12). The optimization process calculated key metrics to evaluate the suitability of the designed sequence for bacterial expression. The codon adaptation index (CAI) of the optimized sequence was determined to be 1.0, indicating a high level of compatibility with the codon usage preferences of E. coli. Additionally, the GC content of the optimized sequence was calculated as 55.79%, reflecting its structural stability and expression efficiency. This value was compared with the GC content of E. coli (strain K12), which is 50.73%, to ensure compatibility and alignment with the host's genomic environment. These metrics collectively validate that the optimized vaccine sequence is tailored for robust and efficient expression in E. coli . The mRNA sequence was then submitted to the RNAFold web server for secondary structure prediction. RNAFold uses energy-based modeling to evaluate the folding patterns of RNA and calculates key parameters such as Minimum Free Energy (MFE), centroid MFE, and ensemble diversity. The predicted secondary structure displayed an optimal MFE of -517.10 kcal/mol, indicating a stable mRNA structure suitable for efficient translation (Fig 6A). The centroid MFE was calculated to be -330.74 kcal/mol, further supporting the mRNA construct's structural integrity (Fig 6B). Additionally, the free energy of the thermodynamic ensemble is measured at -542.13 kcal/mol. These values reflect the variability within the predicted ensemble of RNA structures. The results suggest that the designed mRNA vaccine possesses appropriate structural characteristics, balancing stability and translational efficiency. A mountain plot illustrating the MFE structure, the thermodynamic ensemble of RNA structures, and the centroid structure is provided in Fig 8. Furthermore, the positional entropy for each nucleotide position is displayed. 3.9 Protein-protein interaction analyses ClusPro2.0 was used for the evaluation of PPI and molecular docking. The refined structure of the designed vaccine was used as a ligand for TLR4-MD2. The models were visualized using standalone software Accelrys Discovery Studio 2019. Among the 30 output docking results, the first model showed the lowest energy (-1287.9) among 28 cluster members ( Fig 9). 3.10 Molecular dynamics simulation The iMODS server was employed for normal mode analysis of the docked vaccine-TLR4 complex. The analysis revealed an eigenvalue of 2.356911e-07, reflecting the energy required for the deformation of the complex. The docked vaccine-TLR complex exhibited relative deformability. The B-factor, which is associated with the thermal stability of proteins, represents the displacement of atoms around their conformational equilibrium. The elastic-network model demonstrated flexibility patterns in the lectin–spike protein complex, visualizing atom pairs linked by springs and categorizing their stiffness. Stiffer springs were marked in dark grey, with dots symbolizing individual springs and grey regions indicating higher stiffness. Overall, the docked complex displayed strong binding stability, relative deformities and rigidity. 3.11 Immune Simulation The C-ImmSim server enables rapid evaluation of vaccine immunogenicity through computational simulations, streamlining the screening process for candidate vaccines. Simulation outcomes revealed heightened B-cell activity within 15 days of a single stimulation, resulting in notable increases in B-cell populations and antibody levels, including IgG1 + IgG2, IgM, and combined IgG + IgM. Cytokine release patterns indicated substantial secretion of IFN-γ, followed by IL-2, reflecting intense activation during the initial 0–15-day period before reaching saturation. The simulation demonstrated a strong immune response induction, suggesting that a single dose of the vaccine can effectively enhance immunoglobulin production and stimulate high levels of IL-2 and IFN-γ post-injection. 4 Discussion The HEV virus results in over 70,000 deaths each year, contributing to a total of 1.3 million fatalities globally. This disease is one of the leading health concerns, and there remains a lack of an effective and accessible vaccine 36 , 37 . The computational design of self-amplifying mRNA vaccines targeting HEV represents a significant advancement in vaccine development, particularly in enhancing the immune response to infectious diseases 38 . mRNA vaccines have revolutionized the prevention of infectious diseases by offering rapid, adaptable, and highly effective immunization solutions. Extensive research into mRNA design and delivery has resulted in vaccines that can be quickly modified to combat emerging pathogens, as demonstrated by the swift deployment of COVID-19 vaccines 39 . A multi-epitope vaccine targeting specific HEV proteins demonstrated the efficacy of this technology in stimulating immune responses through design processes, including structural validation 40 . The HEV genome consists of three consistently conserved open reading frames (ORFs): ORF1, ORF2, and ORF3. The ORF2 protein of HEV serves as the main antigenic component of the virus and is a key target for vaccine development. They are essential for recognition as they form antibody complexes that can affect viral clearance and disease progression. Its capacity to provoke a robust immune response makes it the leading candidate for HEV vaccine design, protecting against infection 38 , 41 . In a previous study, immunoinformatics was employed to design a multi-epitope vaccine targeting the ORF2 and ORF3 proteins of HEV. ORF2 is essential for neutralizing antibody responses, whereas ORF3, a viroporin, is involved in viral release and immune modulation 42 . Another study utilized deep learning algorithms in combination with immunoinformatics to enhance epitope selection, aiming for wider cross-genotype coverage. This study focused on the ORF2 protein, which is known for its high conservation across HEV genotypes 43 . This study identified high rank B-cell epitopes within the ORF2 protein of HEV. B-cell epitopes recognized by the surface receptors of B-cell lymphocytes can trigger a specific humoral response. These high-ranking epitopes show significant promise for inclusion in mRNA vaccines, likely enhancing their ability to elicit a robust and specific immune response against HEV. 9-mer MHC-I epitopes and 15-mer MHC-II epitopes with strongest binding affinities were selected. MHC-I selected epitopes were expected to induce strong cytotoxic T cell responses and MHC-II selected epitopes are promising candidates for vaccine construction, as they will likely stimulate helper T-cell response, respectively. The antigenicity and non-toxic characteristics of the selected epitopes imply their potential to trigger an immune response without leading to negative effects. Finally, using appropriate linkers, the final construct of the candidate vaccine was designed based on these selected epitopes. Also, adjuvants were incorporated into the vaccine construct using the EAAAK linker, for a strong immunological response. In addition, the entire vaccine structure was examined in terms of allergenicity and antigenicity, and the results showed that our designed vaccine structure was both non-allergenic and had the necessary antigenicity. An isoelectric point of 5.57 suggest that the construct is acidic nature. Additionally, its long half-life in various biological systems, indicates that the construct remains relatively stable in vivo . The instability index 29.33 further supports this stability, as values below 40 indicate a stable protein. A GRAVY index of -0.376 indicates a hydrophilic nature, which could enhance solubility and practical expression in host systems. A high aliphatic index of 73.42 suggests excellent thermostability, which is advantageous for storage and large-scale production. A predicted solubility of 89.5% when overexpressed in E. coli is a promising sign of its potential for recombinant production. The results of this study underscore the effectiveness of the codon optimization strategy utilized to enhance the bacterial expression of the designed vaccine construct in Escherichia coli (strain K12). The Codon Adaptation Index (CAI) value of 1.0 reflects the optimized sequence's perfect compatibility with the host organism's codon usage preferences, which is pivotal for achieving maximal translational efficiency and protein yield. Previous research emphasizes the significance of a high CAI value in ensuring successful heterologous gene expression in E. coli 44 . Furthermore, the GC content of 55.79% aligns well with the native genomic GC content of E. coli (50.73%), which minimizes the likelihood of issues related to codon usage bias and enhances structural stability. Similar studies have reported that optimized GC content is critical for balancing stability and translational efficiency in bacterial hosts 45 . The predicted secondary structure of the mRNA, as determined by RNAFold, complements our findings. The Minimum Free Energy (MFE) of our predicted structure indicates robust structural stability, which is essential for efficient translation. Additionally, the thermodynamic parameters, including centroid MFE and ensemble diversity, illustrate the dynamic structural flexibility of the mRNA construct. These parameters contribute to its ability to balance stability and adaptability during translation—a trait corroborated by studies investigating RNA secondary structure and its impact on translation efficiency 46 . While the zero frequency of the MFE structure in the ensemble might seem unusual, it highlights the diversity within the ensemble and emphasizes the need for further investigation into its functional implications. Overall, the findings validate the tailored approach employed in designing the vaccine construct. The synergistic interplay of optimized codon usage, balanced GC content, and mRNA structural characteristics illustrates its robustness and potential for efficient expression in E. coli . However, further experimental validation is required to assess the translational efficacy and immunogenic potential of the expressed vaccine. Analysis of the secondary structure of the designed vaccine construct revealed a well-balanced structural composition that is essential for protein stability, folding, and functionality. Prediction of the tertiary structure and refinement of the designed vaccine construct suggested a well-validated and stable model. Utilizing multiple prediction servers increased the reliability of the structural model, offering various structural conformations for further validation. The ERRAT value indicates strong structural reliability, and the Verify3D score confirms excellent compatibility between the atomic model and its sequence. Furthermore, the ProsaWeb z-score reinforces the overall quality of the structure, suggesting proper folding and stability. Protein-protein and interaction analysis conducted with ClusPro2.0 reveal that the designed vaccine has strong binding potential with TLR4-MD2, an essential immune receptor. This binding affinity suggests that the vaccine construct can effectively engage the immune system and elicit a strong immune response. Normal mode analysis (NMA) of the docked vaccine-TLR4 complex, carried out using the iMODs server, offered valuable insights into the structural flexibility and stability of the system. The eigenvalue indicated that the complex required minimal energy for deformation, reflecting an overall stable conformation. The deformability graph revealed that most residues showed low flexibility, with only a few regions displaying slight fluctuations, likely corresponding to loop regions or unstructured domains. The B-factor plot provides additional evidence for the structural stability of the docked complex, as the calculated fluctuations correspond well with the expected atomic movements in a stable protein-protein interaction. This relationship indicated that the complex maintained its conformational balance and preserved its structural integrity under physiological conditions. The elastic network model revealed specific flexibility patterns within the complex structure. Stiffer springs in critical regions imply strong interactions between the vaccine and TLR4, further supporting the stability of the complex. Immunogenicity evaluation using the C-ImmSim server provided a comprehensive insight into the immune response triggered by the designed vaccine. Based on the simulation results, B-cell activity peaked 15 days after stimulation. A substantial increase in B cells and elevated immunoglobulin levels, particularly IgG1, IgG2, IgM, and IgG + IgM, indicates a successful humoral immune response. 40 . Additionally, cytokine profiling revealed a strong induction of IFN-γ, a critical marker of cellular immunity, followed by a significant increase in IL-2 production. Interleukin-2 (IL-2) concentrations reach their maximum levels early in the immune response, facilitating the proliferation and differentiation of T-cells, which is essential for maintaining a robust immune reaction. Additionally, other cytokines, including IL-4, tumor necrosis factor-alpha (TNF-α), and IL-10 indicate well-regulated activation of pro-inflammatory and regulatory pathways, thereby promoting effective immune modulation. Overall, the multi-epitope peptide vaccine (MEPV) against HEV showed strong humoral and cellular immune reactions. 5 Conclusion Hepatitis E is a chronic and life-threatening disease in humans. Given the proven efficacy of next-generation vaccines in combating various diseases, this study utilized an mRNA-based vaccine with self-amplifying properties. The designed vaccine incorporated multiple epitopes derived from ORF2. The results demonstrated that the vaccine elicited both humoral and cellular immune responses, highlighting its potential as a promising candidate for further development in the prevention and treatment of Hepatitis E. However, in future research, it is necessary to conduct in vitro and in vivo studies to confirm the safety and efficacy of this potential vaccine. Declarations 6 Competing interests The authors declare no competing interests. 7 Data availability The Authers assures that the date used in the manuscript is available and any required data for queries will be provided at any time. Dr. Amir Atapour will be the primary contact for any future data-related issues. 8 Funding There is no funding for this manuscript. 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10:19:51","extension":"html","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138894,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/0fd59bfe7897e5fcea978912.html"},{"id":92707989,"identity":"f23f304f-a4f6-4b8a-9227-39238231a232","added_by":"auto","created_at":"2025-10-03 10:19:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":893183,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the stages of \u003cem\u003ein silico\u003c/em\u003e design of a candidate sa-mRNA vaccine against HEV-1\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/bd0f063b256fff3423118afb.png"},{"id":92708810,"identity":"a5abb0bd-c4e5-4ad5-b75c-30b36a03e15d","added_by":"auto","created_at":"2025-10-03 10:27:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":589389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the design of a candidate self-amplifying mRNA (sa-mRNA) vaccine against Hepatitis E Virus genotype 1 (HEV-1). \u003c/strong\u003e(A) The construct includes a 5′ m7G cap, 5′ untranslated region (UTR), a Kozak sequence for translational initiation, and replicase genes (NSP1–NSP4) enabling RNA self-amplification. (B) The gene of interest (GOI) encodes a multi-epitope immunogen comprising Hsp70, B-cell, HTL and CTL epitopes linked with suitable EAAAK, GPGPG, and AAY linkers, respectively. The design concludes with a 3′ UTR and poly (A) tail to enhance mRNA stability and translational efficiency.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/a79b7b31ef8d8c315d8c35f6.png"},{"id":92709045,"identity":"ee3d5340-e271-4b8a-9912-a52f2ea9556b","added_by":"auto","created_at":"2025-10-03 10:35:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1968774,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSecondary structure prediction of the designed vaccine construct using SOPMA\u003c/strong\u003e. (A) Graphical representation of predicted secondary structural elements along the amino acid sequence, showing alpha helices (red), beta sheets (blue), turns (purple). (B) Line plot illustrating the distribution and frequency of each secondary structure element across the length of the construct.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/10f713644bbba18f48d1b7e2.png"},{"id":92708811,"identity":"1250dd0c-0f6a-4699-9098-08be3791cda7","added_by":"auto","created_at":"2025-10-03 10:27:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1044367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe best model of 3D structure of the designed vaccine\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/2962a76acb6fb76fd98b01e7.png"},{"id":92707993,"identity":"65c29e0f-c1ca-4534-bccb-95a37d4fba3d","added_by":"auto","created_at":"2025-10-03 10:19:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6697897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of predicted 3D structure of designed vaccine\u003c/strong\u003e; (A) The ERRAT validation which assesses the reliability of the protein model by detecting structural errors and highlighting regions that may require refinement, (B) Verify 3D validation which assesses the compatibility of the protein structure with its sequence by evaluating residue-specific 3D environment profiles, (C) PROCHECK validation which assesses the stereochemical quality of the protein structure, using the Ramachandran plot to highlight favored and disallowed regions of backbone dihedral angles, (C) ProSA-Web validation which evaluates the overall quality of the protein structure by comparing it to known experimental structures and identifying potential errors based on statistical analysis.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/b9dd5bf714d24e832e737a8e.png"},{"id":92707998,"identity":"987632f6-def3-4bae-b8df-df8b67f6163e","added_by":"auto","created_at":"2025-10-03 10:19:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7707869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDot-bracket depiction of secondary structure prediction of designed mRNA vaccine. \u003c/strong\u003e(A) The optimal secondary structure in dot-bracket notation with a minimum free energy of -517.10 kcal/mol; (B\u003cstrong\u003e) \u003c/strong\u003eThe centroid secondary structure in dot-bracket notation with a minimum free energy of -330.74 kcal/mol.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/4a533739a2b3852c96b1a768.png"},{"id":92708001,"identity":"c73a7918-2a29-478b-93b4-7c533d8af706","added_by":"auto","created_at":"2025-10-03 10:19:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5245952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted secondary structures of designed mRNA vaccine. (\u003c/strong\u003eA) The predicted MFE secondary structure. (\u003cstrong\u003eB\u003c/strong\u003e) The expected centroid secondary structure\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/6a1e2f42c8eb582251bae3db.png"},{"id":92708006,"identity":"66e645f4-16ed-4a2c-97d0-bc7d44c7af5a","added_by":"auto","created_at":"2025-10-03 10:19:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5231536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnergy predictions of designed mRNA vaccine.\u003c/strong\u003e (A) Mountain plot depicting the minimum free energy (MFE) structure, the thermodynamic ensemble of RNA structures, and the centroid structure. (B) The positional entropy for each nucleotide position.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/c3de0ae435e98c6480a86d7a.png"},{"id":92709051,"identity":"2aba40ec-53a8-4b19-9d37-ebc2175b1d38","added_by":"auto","created_at":"2025-10-03 10:35:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4001765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis of the designed vaccine with the TLR4-MD2 complex using ClusPro2.0\u003c/strong\u003e. The vaccine structure showed in green (A) was docked as a ligand to the TLR4-MD2 receptor complex, comprising chains B (blue) and C (yellow).\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/98b24150fda551545cf497d4.png"},{"id":92708824,"identity":"93e3d738-f798-4995-a941-cf46a14105b3","added_by":"auto","created_at":"2025-10-03 10:27:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3914369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNormal mode analysis (NMA) of the docked vaccine–TLR4 complex performed using the iMODS server.\u003c/strong\u003e (A) Cross-correlation matrix illustrating residue-residue motion correlations. Red and blue indicate correlated and anti-correlated movements, respectively. (B) Elastic network model showing atom pairs connected by springs; darker regions represent stiffer connections.(C) Plot of the eigenvalue distribution, with the first eigenvalue (2.356911e-07) indicating the minimal energy required for structural deformation.(D) Deformability graph highlighting local flexibility at each atom index, showing low deformability across the complex.(E) Variance associated with each normal mode, illustrating cumulative contributions of the first 20 modes.(F) B-factor plot comparing theoretical (NMA) and experimental (PDB) fluctuations, indicating regions of relative rigidity and flexibility.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/fb79c528ac815bc3624feeab.png"},{"id":92708017,"identity":"15cfd8e8-df20-4f18-9618-96460eac79e8","added_by":"auto","created_at":"2025-10-03 10:19:51","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":5596676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune response simulation of the vaccine using the C-ImmSim server. \u003c/strong\u003e(A) Simulated antibody titers show a rapid rise in immunoglobulin levels, particularly IgG1 + IgG2, IgM, and combined IgG + IgM, peaking within the first 15 days post-injection.(B) Cytokine profiles demonstrate a strong immune response with pronounced IFN-γ and IL-2 secretion during the initial phase, indicating robust activation of the immune system. (C) Cytotoxic T-cell population, showing maximum cytotoxic activity between days 10-15. (D) Helper T-cell population peaking at day 10.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/8383243c62d9a68376b7d225.png"},{"id":92830822,"identity":"f200be31-2bda-458c-8e89-286d834a15e6","added_by":"auto","created_at":"2025-10-06 06:17:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40664666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7572829/v1/3fd47cce-84e4-42e6-9240-89f7b9329de7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Designing a next-generation self-amplifying mRNA vaccine against Hepatitis E Virus 1 (HEV-1): A reverse vaccinology and immunoinformatics approach","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e\u003cem\u003eHepatitis E Virus\u003c/em\u003e (HEV) presents a significant global challenge to public health, especially within countries with limited financial resources, and is responsible for the occurrence of acute viral hepatitis \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. HEV represents 20\u0026nbsp;million instances of disease yearly, as expressed by The World Health Organization (WHO). Although the infection is normally self-restricting in most patients, intense infection in pregnant women might cause extreme clinical results with high mortality rates of up to 30% \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImmunocompromised patients who undergo organ transplants tend to develop chronic HEV infection \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. HEV is mostly transmitted via fecal-oral transmission. Approximately, less than 50% of the global population is living in HEV-endemic areas \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) has declared \u003cem\u003eHepatitis E Virus\u003c/em\u003e as an emerging pathogen, a disease causing high mortality, and economic losses in several developing countries. Consequently, the search for a vaccine that is effective has been intensified in order to eliminate the disease \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The WHO has launched a comprehensive effort to eradicate viral hepatitis by 2030. However, it is worth noting that the global health community still does not fully recognize the burden caused by hepatitis E. The goal of eliminating viral hepatitis is unattainable without addressing the issue of areas with high rates of diseases \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHEV is a small virus without an envelope and has positive-sense single-stranded RNA (ssRNA) infection, with a diameter of 27\u0026ndash;34 nm \u003csup\u003e8\u003c/sup\u003e. Currently, 8 genotypes of HEV have been identified, 4 of which account for the majority of infections (HEV1-4) \u003csup\u003e9\u003c/sup\u003e. Types 3 and 4 are zoonotic, accounting for most cases of chronic infection in humans \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Types 1 and 2 only infect humans, causing acute hepatitis \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While human-to-human spread of HEV-1 and HEV-2 is unusual in individual and epidemic cases, prenatal transmission from mother to fetus is well-recognized \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe genome is organized into three Open Reading Frames (ORFs) (i.e. ORF1-3), along with some non-coding regions. The \u003cem\u003eORF1\u003c/em\u003e functions in the viral replication and protein modification via a special RNA polymerase. \u003cem\u003eORF2\u003c/em\u003e encodes capsid protein, which facilitates viral binding to the host cell membrane and is responsible for the selection of neutralizing antibodies. \u003cem\u003eORF3\u003c/em\u003e involves in morphogenesis and release of the complete virus. Among these, \u003cem\u003eORF2\u003c/em\u003e has shown to stimulate host immune response significantly more than other ORFs. This makes \u003cem\u003eORF2\u003c/em\u003e an efficient candidate for biomedical research purposes \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe efficacy of antiviral drugs in the treatment of hepatitis E is limited, which necessitates the development of a vaccine \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Vaccination is the healthiest and most effective method for the prevention and control of HEV, so vaccination would be the focal point of the most effective strategy for its prevention and control. Two of the numerous vaccine candidates stood out and proceeded to human clinical testing \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, the only available vaccination for HEV is Hecolin\u0026reg;, which is solely distributed and administered within the territorial boundaries of China. The vaccination currently being examined is a preventive strategy that employs the capsid protein derived from HEV genotype 1. However, there is still a lack of certainty regarding the effectiveness of this intervention in protecting both humans and domestic animals from zoonotic HEVs \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWith the assistance of Glaxo, Smith, and Kline, an additional vaccination for HEV known as recombinant HEV (rHEV) was developed. However, this specific vaccine has not been approved for commercial use, and the specific reasons for this decision remain unknown \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eImmunoinformatics may provide a better way to assign function to uncharacterized genes in developing vaccines against pathogens that infect immunocompromised patients. The best approach to screening epitopes for design of candidate vaccines, particularly for HEV, has been immunoinformatics \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The utilization of computational tools and resources in immunoinformatic analysis has significantly contributed to the understanding of immune responses in various antigenic locations \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne of the attractive platforms for designing vaccines using bioinformatics is mRNA vaccines, which have garnered significant scientific interest during the pandemic of viral diseases such as Covid-19. RNA vaccines can elicit responses from both B cells and T cells, while combining safety with a fast, cell-free production process \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The production of RNA vaccines is technically simple, fast, economically viable, without cell and animal ingredients, and highly adaptable \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRNA vaccines can be produced with various platforms: Laboratory-synthesized mRNA sequences containing only antigen(s) of interest or self-amplifying RNA molecules (Sa-RNA). Sa-RNA not only encodes the specific antigen but also encodes the proteins responsible for facilitating the replication of RNA vaccines \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In a self-amplifying RNA vaccine construct, a selected antigen is substituted for the open reading frame expressing viral structural proteins, while the viral replicase component remains a critical element. This protein facilitates intracellular RNA amplification post vaccination \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. As a result of amplifying the RNA encoding antigen in the sa-RNA vaccine, higher amounts of antigen will be expressed than from mRNA, leading to similar immune responses \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we aim to design a novel sa-RNA vaccine against HEV-1 based on the ORF2 antigen using principles of reverse vaccinology and an immunoinformatic approach.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003eThe steps in designing a candidate self-amplifying mRNA vaccine against HEV-1 are depicted in Fig 1.\u003c/p\u003e\n\u003ch2\u003e2.1 Obtaining protein sequence data from databases\u003c/h2\u003e\n\u003cp\u003eThe amino acid sequences of \u003cem\u003eHepatitis E Virus ORF2\u003c/em\u003e (ID: P29326) as vaccine candidate and Mycobacterium tuberculosis heat shock protein 70 (Hsp70) (ID: O53673) as adjuvant were retrieved from the Protein Knowledge Base (UniProtKB) (www.uniprot.org) and stored in FASTA format.\u003c/p\u003e\n\u003ch2\u003e2.2 Immuno-informatics analysis\u003c/h2\u003e\n\u003ch3\u003e2.2.1 Linear B-cell epitope prediction.\u003c/h3\u003e\n\u003cp\u003eABCPred was employed to detect potential B cell epitopes located within ORF2. This tool, accessible at http://crdd.osdd.net/raghava/abcpred/, utilizes a Recurrent Neural Network (RNN) to forecast linear B-cell epitopes, achieving a prediction accuracy of 65.93% \u003csup\u003e27\u003c/sup\u003e. The threshold value was 0.51.\u003c/p\u003e\n\u003ch3\u003e2.2.2 MHC-I epitope prediction.\u003c/h3\u003e\n\u003cp\u003eThe amino acid sequence of the ORF2 protein was examined through the IEDB MHC-I web server (http://tools.iedb.org/mhci/) to pinpoint peptide-binding epitopes associated with MHC class I. This platform utilizes Artificial Neural Networks (ANN) to classify peptides as strong or weak binders for each HLA allele, considering their binding affinity (nM) and percentile rankings. The IEDB\u0026apos;s prediction accuracy for MHC-I epitope identification stands at 95% \u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e2.2.3 MHC-II epitope prediction\u003c/h3\u003e\n\u003cp\u003eThe amino acid sequence of the ORF2 protein was submitted to the IEDB MHC-II web server (http://tools.iedb.org/mhcii/) to determine peptide-binding epitopes associated with MHC class II. This platform utilizes an Artificial Neural Network (ANN) algorithm to assess the binding affinity of peptides (measured in nM) and their corresponding percentile rankings, classifying them as either strong or weak binders for each HLA allele. Peptides that receive lower percentile rankings indicate a greater binding affinity.\u003c/p\u003e\n\u003ch3\u003e2.2.4 Antigenicity prediction for the selected epitopes\u003c/h3\u003e\n\u003cp\u003eThe antigenic properties of the chosen epitopes were assessed through Vaxijen 2.0, a complementary online resource (http://www.jenner.ac.uk/VaxiJen). This platform employs the auto- and cross-covariance (ACC) technique to convert target protein sequences into corresponding amino acid sequence vectors \u003csup\u003e29\u003c/sup\u003e. \u003c/p\u003e\n\u003ch3\u003e2.2.5 .Allergenicity and toxicity prediction of selected epitopes\u003c/h3\u003e\n\u003cp\u003eThe selected epitopes in FASTA format were evaluated for their allergenic potential using the AllerCatPro 2.0 server (https://allercatpro.bii.a-star.edu.sg/), which has an accuracy rate of 84% [30]. Furthermore, the toxicity of these epitopes was examined utilizing ToxinPred, accessible at (http://crdd.osdd.net/raghava/toxinpred/).Selection of epitopes and vaccine constructionSelection of epitopes and vaccine construction.\u003c/p\u003e\n\u003ch2\u003e2.3 Selection of epitopes and vaccine construction\u003c/h2\u003e\n\u003cp\u003eThe top-ranking predicted epitopes were selected for the vaccine design process. Various components were incorporated into the construct, including the 5ʹm7G cap, alpha globin 5ʹ UTR, Kozak sequence, replicase sequence (nonstructural proteins NSP1-4), subgenomic promoter, signal peptide, and the gene of interest, \u003cstrong\u003ewhich consisted of Hsp70 as an adjuvant\u003c/strong\u003e alongside linkers and epitopes (B-cell, MHC-II, MHC-I), \u003cstrong\u003erespectively\u003c/strong\u003e. Additional elements integrated were the beta-globin 3ʹ UTR and a poly A tail ranging from 120\u0026ndash;150 nucleotides, respectively.\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eB-cell epitopes were connected via GPGPG linkers, while MHC-II and MHC-I epitopes were linked using AAY linkers.\u003c/p\u003e\n\u003ch2\u003e2.4 Codon optimization and evaluation of mRNA secondary structure for the designed vaccine\u003c/h2\u003e\n\u003cp\u003eTo enhance codon usage and back-translate the vaccine construct, the amino acid sequence was analyzed using the JCat server (https://www.jcat.de/), which aims to improve expression efficiency in \u003cem\u003eE. coli\u003c/em\u003e (strain K12). This server also assesses various nucleic acid sequence parameters, including the codon adaptation index (CAI) and the percentage of GC content \u003csup\u003e30-32\u003c/sup\u003e. Following this optimization, the refined nucleic acid sequence of the vaccine was uploaded to the RNAFold server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) to forecast the secondary structure of the mRNA vaccine. RNAFold estimates the Minimum Free Energy (MFE) of mRNA configurations with an accuracy of 70%, utilizing standard energy parameter settings. \u003c/p\u003e\n\u003ch2\u003e2.5 Physicochemical properties\u003c/h2\u003e\n\u003cp\u003eProtParam (http://web.expasy.org/protparam/) was utilized to analyze the physicochemical characteristics of the developed vaccine, encompassing aspects such as amino acid composition, molecular weight, isoelectric point, instability index, and aliphatic index \u003csup\u003e33\u003c/sup\u003e. Additionally, the solubility of the vaccine\u0026apos;s protein sequence was assessed using SolPro (http://scratch.proteomics.ics.uci.edu) \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e2.6 Protein secondary structure modeling\u003c/h2\u003e\n\u003cp\u003eThe secondary structure of the synthesized protein was analyzed using SOPMA, accessible at (https://npsa-prabi.ibcp.fr/NPSA/npsa_sopma.html.). The SOPMA web tool demonstrates a prediction accuracy of 69.5%.\u003c/p\u003e\n\u003ch2\u003e2.7 Protein tertiary structure modeling\u003c/h2\u003e\n\u003cp\u003eThree distinct servers were utilized to forecast the tertiary structure of the developed vaccine: I-TASSER, available at (https://zhanggroup.org/I-TASSER/), trRosetta available at (https://yanglab.qd.sdu.edu.cn/trRosetta/), and GalaxyTBM, which is available at (https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=TBM). Each of these servers generated five unique tertiary models for the vaccine. The resulting models were saved as PDB files for subsequent analysis.\u003c/p\u003e\n\u003ch2\u003e2.8 Validation and refinement of tertiary structure\u003c/h2\u003e\n\u003cp\u003eValidation of the constructed vaccine was done using (i) ERRAT, which determines the overall quality factor of structure of the constructed vaccine based on a variety of crystallographic structures, (ii) Verify3D, which is utilized for assessing model-sequence consistency using 3D profile verification with a threshold of 80%, and (iii) PROCHECK, which evaluates the quality of the given protein and geometric analysis of residues based on ramachandran plot. ERRAT, Verify3D, and Procheck are available at SAVES v6.0 (https://saves.mbi.ucla.edu/).\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php) was used as a server to evaluate the three-dimensional structure of proteins based on atomic coordinates of the model to be evaluated and overall quality score for a particular input structure, namely Z-Score. The best 3D structure candidate was selected and submitted to GalaxyRefine module from GalaxyWEB server\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e(https://galaxy.seoklab.org/). GalaxyRefine returns\u003cspan dir=\"RTL\"\u003e \u003c/span\u003efive refined output models based on the initial structure within 1-2 hours post submission, with mild relaxation and energy minimization for model 1, and aggressive relaxation for models 2-5.\u003c/p\u003e\n\u003ch2\u003e2.9 Molecular docking of vaccine with TLR4\u003c/h2\u003e\n\u003cp\u003eHuman Toll-Like Receptor 4 in complex with MD2 (TLR4-MD2) (PDB ID: 3FXI) was considered as recipients for the designed vaccine. BIOVIA Discovery Studio 2019 was utilized to visualize the PDB structures. Before docking, water molecules and miscellaneous ligands were removed from input PDB structures and the modified structures were saved as new PDB files. Then, ClusPro 2.0 server (https://cluspro.bu.edu/home.php) was used to analyze the interaction between the receptor and designed vaccine structure as a ligand.\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eThe server requires only two files in Protein Data Bank format for basic use. \u003c/p\u003e\n\u003ch2\u003e2.10 Molecular dynamics simulation\u003c/h2\u003e\n\u003cp\u003eFast simulation and normal mode analysis of the flexibility of TLR4 in complex with the designed vaccine was performed using and iMODS (http://imods.chaconlab.org) \u003csup\u003e35\u003c/sup\u003e. A protein PDB code or a protein structure in the PDB format is the data needed. Here, as the input for the quick flexibility simulation, the selected docked vaccine-TLR4 and complex was used.\u003c/p\u003e\n\u003ch2\u003e2.11 Simulation of Immune Responses\u003c/h2\u003e\n\u003cp\u003e\u0026middot; To predict the immune response generated by vaccine dosage, the amino acid sequence of the candidate vaccine was submitted to C-ImmSim available at (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php). A single dose simulation was performed with the rest of the parameters set to default values.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Prediction of B-Cell epitopes\u003c/h2\u003e\n\u003cp\u003eEpitopes consisting of 16 amino acids in the ORF2 protein were chosen for inclusion in the final vaccine construct. Table 1 presents five B-cell epitopes that received the highest scores.\u003c/p\u003e\n\u003cp\u003eTable 1. B-cell epitopes predictd by ABCPred\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTEASNYAQYRVARATI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRVRQPARPLGSAWRDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSSTARHRLRRGADGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTTKAGYPYNYNTTASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.2 Prediction of MHC-I epitopes\u003c/h2\u003e\n\u003cp\u003e9-mer MHC-I epitopes were predicted using IEDB server with the prediction method set as NetMHCPan_el, and the predicted epitopes were sorted based on percentile rank. Of the predicted epitopes, the first five with highest peptide scores and lowest percentile ranks were selected and are shown in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Predicted 9-mer MHC-I binding epitopes, predicted using IEDB\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentile Rank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPSRPFSVL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-B*07:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.995419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRPRPILLLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-B*07:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.995331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGEIGRGIAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-B*40:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.989468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTSPLTSSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-A*68:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.977366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.3 Prediction of MHC-II epitopes\u003c/h2\u003e\n\u003cp\u003e15-mer MHC-II epitopes were predicted using IEDB server with the prediction method set as NetMHCIIPan_el, and the predicted epitopes were sorted based on percentile rank. Of the predicted epitopes, the first five with highest peptide scores and lowest percentile ranks were selected and are shown in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3. Predicted 15-mer MHC-II binding epitopes, predicted using IEDB\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeptide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHPTNPFAPDVTAAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eATIRYRPLVPNAVGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePTNPFAPDVTAAAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTIRYRPLVPNAVGGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNPFAPDVTAAAGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.4 Antigenicity, Allergenicity, and toxicity of selected epitopes\u003c/h2\u003e\n\u003cp\u003eBased on results obtained from ToxinPred, all selected epitopes were identified as non-toxin. Allergenicity of the selected epitopes was evaluated using two servers, i.e. AllerCatPro. Most selected epitopes were identified as non-allergen. All selected epitopes were identified as probable antigens, based on results from VaxiJen 2.0.\u003c/p\u003e\n\u003ch2\u003e3.5 Physiochemical properties and solubility of the designed vaccine\u003c/h2\u003e\n\u003cp\u003eProtparam was used to evaluate the physiochemical properties of the designed vaccine. According to the results, the vaccine construct has a molecular weight of 54.1kD and an isoelectric point of 5.57. The construct has a half-life of 100 hours in mammalian reticulocytes, more than 20 hours in yeast cells, and more than 10 hours in \u003cem\u003eE. coli\u003c/em\u003e. Furthermore, the instability index of the construct is 29.33, which means that the protein is stable. The GRAVY index was calculated -0.376. Aliphatic index was 73.42. The vaccine construct is soluble upon overexpression in \u003cem\u003eE. coli\u003c/em\u003e, with a probability of 89.5% according to the results obtained by SOLPro server.\u003c/p\u003e\n\u003ch2\u003e3.6 Secondary structure evaluation of the designed vaccine\u003c/h2\u003e\n\u003cp\u003eTo predict the secondary structure of the designed vaccine, the sequence of the designed construct was submitted to SOPMA server. Based on the results, the secondary structure of the designed protein consists of 33.78% alpha helix, 18.23% extended strand, 41.07% random coil. Data is shown in Fig 3.\u003c/p\u003e\n\u003ch2\u003e3.7 Evaluation of tertiary structure, validation, and refinement\u003c/h2\u003e\n\u003cp\u003eThree servers, i.e. trRosetta, I-TASSER, and GalaxyTBM\u0026nbsp;were used to predict the tertiary structure of the designed vaccine. Each server provides five output structures which were stored in PDB format for further analyses. The best model of predicted 3D structures from trRosetta predicted models was selected and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eselected for further evaluations (Fig 4).\u003c/p\u003e\n\u003cp\u003eBased on validation data, the best model was chosen\u0026nbsp;and was submitted to GalaxyRefine for structure refinement. After refinement, the quality of the resulting structure was slightly improved. The refined model had an ERRAT value of 96.52 (Fig 4A), a Verify3D score of 63.15 (Fig 4B), and a ProSA Z-score of -6.96 (Fig 4D), showing good overall quality of the designed vaccine. Additionally, all the amino acids are in the favorable regions of the Ramachandran plot with no amino acids in disallowed or generously favored regions (Fig 4C).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.8 Codon optimization and prediction of mRNA secondary structure\u003c/h2\u003e\n\u003cp\u003eThe amino acid sequence of the vaccine construct was back-translated and codon-optimized using the JCAT server (https://www.jcat.de/) to ensure efficient expression in \u003cem\u003eE. coli\u003c/em\u003e (strain K12). The optimization process calculated key metrics to evaluate the suitability of the designed sequence for bacterial expression. The codon adaptation index (CAI) of the optimized sequence was determined to be 1.0, indicating a high level of compatibility with the codon usage preferences of \u003cem\u003eE. coli.\u003c/em\u003e Additionally, the GC content of the optimized sequence was calculated as 55.79%, reflecting its structural stability and expression efficiency. This value was compared with the GC content of E. coli (strain K12), which is 50.73%, to ensure compatibility and alignment with the host's genomic environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese metrics collectively validate that the optimized vaccine sequence is tailored for robust and efficient expression in \u003cem\u003eE. coli\u003c/em\u003e. The mRNA sequence was then submitted to the RNAFold web server for secondary structure prediction. RNAFold uses energy-based modeling to evaluate the folding patterns of RNA and calculates key parameters such as Minimum Free Energy (MFE), centroid MFE, and ensemble diversity. The predicted secondary structure displayed an optimal MFE of -517.10 kcal/mol, indicating a stable mRNA structure suitable for efficient translation (Fig 6A). The centroid MFE was calculated to be -330.74 kcal/mol, further supporting the mRNA construct's structural integrity (Fig 6B). Additionally, the free energy of the thermodynamic ensemble is measured at -542.13 kcal/mol. These values reflect the variability within the predicted ensemble of RNA structures. The results suggest that the designed mRNA vaccine possesses appropriate structural characteristics, balancing stability and translational efficiency.\u003c/p\u003e\n\u003cp\u003eA mountain plot illustrating the MFE structure, the thermodynamic ensemble of RNA structures, and the centroid structure is provided in Fig 8. Furthermore, the positional entropy for each nucleotide position is displayed.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.9 Protein-protein interaction analyses\u003c/h2\u003e\n\u003cp\u003eClusPro2.0 was used for the evaluation of PPI and molecular docking. The refined structure of the designed vaccine was used as a ligand for TLR4-MD2. The models were visualized using standalone software Accelrys Discovery Studio 2019. Among the 30 output docking results, the first model showed the lowest energy (-1287.9) among 28 cluster members (\u003cstrong\u003eFig 9).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.10 Molecular dynamics simulation\u003c/h2\u003e\n\u003cp\u003eThe iMODS server was employed for normal mode analysis of the docked vaccine-TLR4 complex. The analysis revealed an eigenvalue of 2.356911e-07, reflecting the energy required for the deformation of the complex. The docked vaccine-TLR complex exhibited relative deformability. The B-factor, which is associated with the thermal stability of proteins, represents the displacement of atoms around their conformational equilibrium. The elastic-network model demonstrated flexibility patterns in the lectin–spike protein complex, visualizing atom pairs linked by springs and categorizing their stiffness. Stiffer springs were marked in dark grey, with dots symbolizing individual springs and grey regions indicating higher stiffness. Overall, the docked complex displayed strong binding stability, relative deformities and rigidity.\u003c/p\u003e\n\u003ch2\u003e3.11 Immune Simulation\u003c/h2\u003e\n\u003cp\u003eThe C-ImmSim server enables rapid evaluation of vaccine immunogenicity through computational simulations, streamlining the screening process for candidate vaccines. Simulation outcomes revealed heightened B-cell activity within 15 days of a single stimulation, resulting in notable increases in B-cell populations and antibody levels, including IgG1 + IgG2, IgM, and combined IgG + IgM. Cytokine release patterns indicated substantial secretion of IFN-γ, followed by IL-2, reflecting intense activation during the initial 0–15-day period before reaching saturation. The simulation demonstrated a strong immune response induction, suggesting that a single dose of the vaccine can effectively enhance immunoglobulin production and stimulate high levels of IL-2 and IFN-γ post-injection.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe HEV virus results in over 70,000 deaths each year, contributing to a total of 1.3\u0026nbsp;million fatalities globally. This disease is one of the leading health concerns, and there remains a lack of an effective and accessible vaccine \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The computational design of self-amplifying mRNA vaccines targeting HEV represents a significant advancement in vaccine development, particularly in enhancing the immune response to infectious diseases \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003emRNA vaccines have revolutionized the prevention of infectious diseases by offering rapid, adaptable, and highly effective immunization solutions. Extensive research into mRNA design and delivery has resulted in vaccines that can be quickly modified to combat emerging pathogens, as demonstrated by the swift deployment of COVID-19 vaccines \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA multi-epitope vaccine targeting specific HEV proteins demonstrated the efficacy of this technology in stimulating immune responses through design processes, including structural validation \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The HEV genome consists of three consistently conserved open reading frames (ORFs): ORF1, ORF2, and ORF3. The ORF2 protein of HEV serves as the main antigenic component of the virus and is a key target for vaccine development. They are essential for recognition as they form antibody complexes that can affect viral clearance and disease progression. Its capacity to provoke a robust immune response makes it the leading candidate for HEV vaccine design, protecting against infection \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn a previous study, immunoinformatics was employed to design a multi-epitope vaccine targeting the ORF2 and ORF3 proteins of HEV. ORF2 is essential for neutralizing antibody responses, whereas ORF3, a viroporin, is involved in viral release and immune modulation \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Another study utilized deep learning algorithms in combination with immunoinformatics to enhance epitope selection, aiming for wider cross-genotype coverage. This study focused on the ORF2 protein, which is known for its high conservation across HEV genotypes \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study identified high rank B-cell epitopes within the ORF2 protein of HEV. B-cell epitopes recognized by the surface receptors of B-cell lymphocytes can trigger a specific humoral response. These high-ranking epitopes show significant promise for inclusion in mRNA vaccines, likely enhancing their ability to elicit a robust and specific immune response against HEV.\u003c/p\u003e\u003cp\u003e9-mer MHC-I epitopes and 15-mer MHC-II epitopes with strongest binding affinities were selected. MHC-I selected epitopes were expected to induce strong cytotoxic T cell responses and MHC-II selected epitopes are promising candidates for vaccine construction, as they will likely stimulate helper T-cell response, respectively. The antigenicity and non-toxic characteristics of the selected epitopes imply their potential to trigger an immune response without leading to negative effects. Finally, using appropriate linkers, the final construct of the candidate vaccine was designed based on these selected epitopes. Also, adjuvants were incorporated into the vaccine construct using the EAAAK linker, for a strong immunological response. In addition, the entire vaccine structure was examined in terms of allergenicity and antigenicity, and the results showed that our designed vaccine structure was both non-allergenic and had the necessary antigenicity.\u003c/p\u003e\u003cp\u003eAn isoelectric point of 5.57 suggest that the construct is acidic nature. Additionally, its long half-life in various biological systems, indicates that the construct remains relatively stable \u003cem\u003ein vivo\u003c/em\u003e. The instability index 29.33 further supports this stability, as values below 40 indicate a stable protein. A GRAVY index of -0.376 indicates a hydrophilic nature, which could enhance solubility and practical expression in host systems. A high aliphatic index of 73.42 suggests excellent thermostability, which is advantageous for storage and large-scale production. A predicted solubility of 89.5% when overexpressed in \u003cem\u003eE. coli\u003c/em\u003e is a promising sign of its potential for recombinant production.\u003c/p\u003e\u003cp\u003eThe results of this study underscore the effectiveness of the codon optimization strategy utilized to enhance the bacterial expression of the designed vaccine construct in \u003cem\u003eEscherichia coli\u003c/em\u003e (strain K12). The Codon Adaptation Index (CAI) value of 1.0 reflects the optimized sequence's perfect compatibility with the host organism's codon usage preferences, which is pivotal for achieving maximal translational efficiency and protein yield. Previous research emphasizes the significance of a high CAI value in ensuring successful heterologous gene expression in \u003cem\u003eE. coli\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Furthermore, the GC content of 55.79% aligns well with the native genomic GC content of \u003cem\u003eE. coli\u003c/em\u003e (50.73%), which minimizes the likelihood of issues related to codon usage bias and enhances structural stability. Similar studies have reported that optimized GC content is critical for balancing stability and translational efficiency in bacterial hosts \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe predicted secondary structure of the mRNA, as determined by RNAFold, complements our findings. The Minimum Free Energy (MFE) of our predicted structure indicates robust structural stability, which is essential for efficient translation. Additionally, the thermodynamic parameters, including centroid MFE and ensemble diversity, illustrate the dynamic structural flexibility of the mRNA construct. These parameters contribute to its ability to balance stability and adaptability during translation\u0026mdash;a trait corroborated by studies investigating RNA secondary structure and its impact on translation efficiency \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. While the zero frequency of the MFE structure in the ensemble might seem unusual, it highlights the diversity within the ensemble and emphasizes the need for further investigation into its functional implications.\u003c/p\u003e\u003cp\u003eOverall, the findings validate the tailored approach employed in designing the vaccine construct. The synergistic interplay of optimized codon usage, balanced GC content, and mRNA structural characteristics illustrates its robustness and potential for efficient expression in \u003cem\u003eE. coli\u003c/em\u003e. However, further experimental validation is required to assess the translational efficacy and immunogenic potential of the expressed vaccine.\u003c/p\u003e\u003cp\u003eAnalysis of the secondary structure of the designed vaccine construct revealed a well-balanced structural composition that is essential for protein stability, folding, and functionality.\u003c/p\u003e\u003cp\u003ePrediction of the tertiary structure and refinement of the designed vaccine construct suggested a well-validated and stable model. Utilizing multiple prediction servers increased the reliability of the structural model, offering various structural conformations for further validation. The ERRAT value indicates strong structural reliability, and the Verify3D score confirms excellent compatibility between the atomic model and its sequence. Furthermore, the ProsaWeb z-score reinforces the overall quality of the structure, suggesting proper folding and stability.\u003c/p\u003e\u003cp\u003eProtein-protein and interaction analysis conducted with ClusPro2.0 reveal that the designed vaccine has strong binding potential with TLR4-MD2, an essential immune receptor. This binding affinity suggests that the vaccine construct can effectively engage the immune system and elicit a strong immune response.\u003c/p\u003e\u003cp\u003eNormal mode analysis (NMA) of the docked vaccine-TLR4 complex, carried out using the iMODs server, offered valuable insights into the structural flexibility and stability of the system. The eigenvalue indicated that the complex required minimal energy for deformation, reflecting an overall stable conformation. The deformability graph revealed that most residues showed low flexibility, with only a few regions displaying slight fluctuations, likely corresponding to loop regions or unstructured domains.\u003c/p\u003e\u003cp\u003eThe B-factor plot provides additional evidence for the structural stability of the docked complex, as the calculated fluctuations correspond well with the expected atomic movements in a stable protein-protein interaction. This relationship indicated that the complex maintained its conformational balance and preserved its structural integrity under physiological conditions. The elastic network model revealed specific flexibility patterns within the complex structure. Stiffer springs in critical regions imply strong interactions between the vaccine and TLR4, further supporting the stability of the complex.\u003c/p\u003e\u003cp\u003eImmunogenicity evaluation using the C-ImmSim server provided a comprehensive insight into the immune response triggered by the designed vaccine. Based on the simulation results, B-cell activity peaked 15 days after stimulation. A substantial increase in B cells and elevated immunoglobulin levels, particularly IgG1, IgG2, IgM, and IgG\u0026thinsp;+\u0026thinsp;IgM, indicates a successful humoral immune response. \u003csup\u003e40\u003c/sup\u003e. Additionally, cytokine profiling revealed a strong induction of IFN-γ, a critical marker of cellular immunity, followed by a significant increase in IL-2 production. Interleukin-2 (IL-2) concentrations reach their maximum levels early in the immune response, facilitating the proliferation and differentiation of T-cells, which is essential for maintaining a robust immune reaction. Additionally, other cytokines, including IL-4, tumor necrosis factor-alpha (TNF-α), and IL-10 indicate well-regulated activation of pro-inflammatory and regulatory pathways, thereby promoting effective immune modulation. Overall, the multi-epitope peptide vaccine (MEPV) against HEV showed strong humoral and cellular immune reactions.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eHepatitis E is a chronic and life-threatening disease in humans. Given the proven efficacy of next-generation vaccines in combating various diseases, this study utilized an mRNA-based vaccine with self-amplifying properties. The designed vaccine incorporated multiple epitopes derived from ORF2. The results demonstrated that the vaccine elicited both humoral and cellular immune responses, highlighting its potential as a promising candidate for further development in the prevention and treatment of Hepatitis E. However, in future research, it is necessary to conduct \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies to confirm the safety and efficacy of this potential vaccine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e6 Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e7 \u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authers assures that the date used in the manuscript is available and any required data for queries will be provided at any time. Dr. Amir Atapour will be the primary contact for any future data-related issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8 Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding for this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu, X., Chen, P., Lin, H., Hao, X. \u0026amp; Liang, Z. 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D.\u003cem\u003e et al.\u003c/em\u003e Comparative Analysis of Codon Optimization Tools: Advancing toward a Multi-Criteria Framework for Synthetic Gene Design. \u003cem\u003eJ. Microbiol. Biotechnol.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1-11, doi:10.4014/jmb.2411.11066 (2025).\u003c/li\u003e\n\u003cli\u003eLin, J., Chen, Y., Zhang, Y., Lin, H. \u0026amp; Ouyang, Z. Deciphering the role of RNA structure in translation efficiency. \u003cem\u003eBMC bioinformatics\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 559, doi:10.1186/s12859-022-05037-7 (2022).\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":"Self-amplifying mRNA, Vaccine, Reverse vaccinology, Immunoinformatics, Hepatitis E","lastPublishedDoi":"10.21203/rs.3.rs-7572829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7572829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eHepatitis E virus 1\u003c/em\u003e (HEV-1) is a worldwide public health issue that induces acute viral hepatitis as well as high mortality, especially among pregnant women and immunocompromised subjects. The World Health Organization has characterized Hepatitis E virus as an emerging pathogen for a vaccine design that has become most important for its elimination. Currently, there is only one available vaccine for hepatitis E that is exclusively administered in China. Another potential vaccine, the recombinant Hepatitis E virus vaccine, has not yet been approved for commercial use. Bioinformatics methods have shown potential for the development of vaccine candidates, particularly Hepatitis E virus. mRNA technology has received great interest because it can induce both B and T cell immune responses, is safe, and is a cell-free manufacturing process. Self-amplifying RNA-based vaccines, which code for the antigen of interest as well as proteins enabling the duplication of RNA, result in enhanced antigen expression as well as equivalent immune responses to those of mRNA-based vaccines. This study seeks to design a novel self-amplifying mRNA anti-HEV vaccine using immunoinformatic tools based on the ORF2 antigen. We predicted antigenic, non-toxic, and non-allergic epitopes of B and T cells and assembled the selected epitopes with appropriate linkers for mRNA vaccine construction. Bioinformatics data, including physicochemical properties, 2D, 3D structural modeling, validation and refinement analyzes, and mRNA secondary structure, illustrated the stability and quality of the designed vaccine. Molecular docking and simulation indicated effectively engaging the immune system and eliciting a strong immune response by TLR4. Immune simulations demonstrate a robust initial immune response, with a gradual increase in levels of immunoglobulins, cytokines, cytotoxic T cells, and helper T cell populations following a single dose administration. However, this in silico study presents a promising self-amplifying mRNA vaccine candidate against HEV, which requires further evaluation and validation through additional tests, such as preclinical and clinical trials.\u003c/p\u003e","manuscriptTitle":"Designing a next-generation self-amplifying mRNA vaccine against Hepatitis E Virus 1 (HEV-1): A reverse vaccinology and immunoinformatics approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 10:19:45","doi":"10.21203/rs.3.rs-7572829/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":"68d33e13-8357-467c-9348-38ffb4522f72","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55671808,"name":"Biological sciences/Biotechnology"},{"id":55671809,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55671810,"name":"Biological sciences/Immunology"},{"id":55671811,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-10-06T06:09:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 10:19:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7572829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7572829","identity":"rs-7572829","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}