Immunoinformatics-Driven Design and In Silico Validation of a Multi Epitope Subunit Vaccine Targeting Norovirus

Immunoinformatics-Driven Design and In Silico Validation of a Multi Epitope Subunit Vaccine Targeting Norovirus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Immunoinformatics-Driven Design and In Silico Validation of a Multi Epitope Subunit Vaccine Targeting Norovirus Nitish Kumar R, Kesiya Joy, Parvana Nair This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8606904/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 Norovirus, a non-enveloped, positive-sense single-stranded RNA virus belonging to the Caliciviridae family, is a major causative agent of acute gastroenteritis (AGE) outbreaks worldwide. It is primarily transmitted via the fecal–oral route, with clinical manifestations including abdominal pain, watery diarrhoea, nausea, and vomiting. In the United States alone, norovirus is estimated to cause approximately 19–21 million cases annually. Emerging variants, such as the GII.17 genotype, have been implicated in an increasing number of outbreaks across multiple countries, highlighting the urgent need for effective preventive strategies. In this study, an immunoinformatics-based approach was employed to design and evaluate a multi-epitope subunit vaccine candidate against norovirus. Three viral proteins—capsid protein (UniProt ID: A7YK10), small protein (A7YK11), and polyprotein (A7YK09)—were selected for epitope-based vaccine design. B-cell and T-cell epitopes were predicted using the Immune Epitope Database (IEDB) and subsequently screened for antigenicity (VaxiJen), allergenicity (AllerTOP v2.1), toxicity (ToxinPred), and population coverage. Physicochemical properties were evaluated using ProtParam, and secondary structure analysis was performed to assess the structural feasibility of the vaccine construct. The finalized multi-epitope vaccine construct was further subjected to molecular docking analyses to evaluate its binding affinity with key immune receptors, including MHC class I, MHC class II, and Toll-like receptor 4 (TLR4), providing insight into its potential immunogenic interactions. While the computational analyses indicate that the designed construct is a promising vaccine candidate, further experimental validation, including in vitro expression and in vivo immunogenicity and efficacy studies, will be required to confirm its protective potential. Bioinformatics Immunology Norovirus acute gastroenteritis multi-epitope vaccine immunoinformatics molecular docking immunogenicity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Norovirus (NoV), a non-enveloped, positive sense single-stranded RNA virus classified under the Caliciviridae family, is globally recognized as the leading cause of non-bacterial acute gastroenteritis (AGE). This highly contagious pathogen affects individuals across all age groups and significantly contributes to the worldwide burden of gastrointestinal illnesses and associated mortality (Lanata et al., 2013). Clinical manifestations of Norovirus infection commonly include abdominal pain, watery diarrhoea, nausea, and vomiting, with transmission primarily occurring via the fecal-oral route (Lew et al., 2012). The scale of its impact is substantial; in the United States alone, Norovirus is estimated to be responsible for approximately 19 to 21 million cases annually. The virus poses a particularly serious threat to vulnerable populations, such as infants and the elderly, largely due to its remarkable ease of transmission and its resilience in diverse environmental conditions (Ahmed et al., 2014; Pires et al., 2015). The dynamic nature of Norovirus, exemplified by the recent implication of emerging variants like the GII.17 genotype in an increased number of outbreaks across multiple countries, presents a continuous challenge to public health (Afgan et al., 2018 ). This constant evolution of viral strains directly implies a significant hurdle for traditional vaccine approaches, as antigenic drift can rapidly reduce the efficacy of vaccines designed against specific, static targets. While in silico predictions offer a powerful and rapid initial step in vaccine design, their ultimate utility in addressing the dynamic nature of viral evolution necessitates rigorous experimental validation (Somana et al., 2020). Such empirical confirmation is crucial to ascertain whether a designed construct can indeed provide broad protection against existing and emerging variants, thereby bridging the gap between computational promise and real-world clinical effectiveness. Despite numerous international efforts, a licensed Norovirus vaccine or specific antiviral therapy remains unavailable. This critical gap in treatment and prevention is primarily a consequence of several formidable biological and technical hurdles (Tanaka et al., 2013). The virus exhibits extensive genetic variability, particularly prominent in genogroups GI and GII, with the GII.4 genotype being notably the most common and virulent. This high mutation rate, leading to frequent antigenic variations, is a major factor contributing to recurring global outbreaks and renders traditional immunization strategies, which often target single or limited strains, largely inadequate (Patel et al., 2021). The inherent biological challenges of Norovirus, including the absence of efficient in vitro culture methods and the limited availability of suitable animal models for experimental research, have profoundly impacted vaccine development (Das et al., 2014). These limitations make empirical vaccine development extremely difficult and slow, as it is challenging to propagate the virus for research or test vaccine candidates in relevant biological systems. This problem directly necessitates a strategic shift from traditional immunization approaches towards multivalent or broad-coverage vaccine strategies. Such an approach aims to overcome the virus's extensive genetic diversity by targeting conserved regions across multiple strains, thereby offering the potential for broader and more durable protection (Chen et al. 2022) The pivot to computational methods, therefore, becomes not merely an alternative but a strategic imperative to circumvent these long-standing obstacles, enabling the identification of conserved sequences that can provide broader protection against evolving strains and accelerate the vaccine discovery pipeline. In response to the challenges posed by Norovirus, computational vaccinology and immunoinformatics have emerged as innovative and cost-effective methods for creating multi-epitope vaccines (Atmar et al., 2020). These advanced approaches leverage bioinformatics tools and algorithms to identify conserved sequences across various viral strains, enabling the rational design of vaccine candidates that can elicit robust and lasting immune responses efficiently. The extensive array of in silico tools utilized in modern vaccine design, ranging from epitope prediction databases like IEDB to structural analysis tools such as ProtParam and PSIPRED, and functional prediction platforms for antigenicity, immunogenicity, toxicity, allergenicity, and molecular docking, reflects a significant underlying trend in the field. This increasing reliance on computational biology and immunoinformatics, often termed reverse vaccinology, signifies a paradigm shift towards accelerating the initial phases of vaccine development. This shift is driven by the urgent need for more efficient, cost-effective, and rapid design cycles, particularly for rapidly evolving pathogens like Norovirus (Bartsch et al 2018 ). The systematic application of these tools, from the initial prediction of potential epitopes to the comprehensive validation of vaccine construct properties, establishes a rational design pipeline. This approach moves beyond traditional empirical methods, suggesting that in silico techniques are becoming foundational for identifying promising vaccine candidates before committing to resource-intensive and time-consuming wet-lab experiments. 2. Materials and Methods 2.1 Target Protein Sequence Retrieval and Selection Norovirus protein sequences were retrieved through a systematic database search. The National Center for Biotechnology Information (NCBI) database ( https://www.ncbi.nlm.nih.gov/ ) was used to identify relevant Norovirus genomic information. Based on extensive literature review and antigenic relevance, three viral proteins were selected for vaccine design: the capsid protein, small protein, and polyprotein. Protein sequences corresponding to the Murine norovirus GV/WU24/2005/USA strain (OX = 463715) were obtained in FASTA format from the UniProt database ( https://www.uniprot.org/ ). The selected proteins and their respective UniProt identifiers were A7YK10 (capsid protein), A7YK11 (small protein), and A7YK09 (polyprotein). 2.2 Overall Workflow for Epitope-Based Vaccine Design The selected Norovirus protein sequences were subjected to a comprehensive immunoinformatics pipeline involving epitope prediction, screening, vaccine construct design, structural modeling, molecular docking, immune simulation, and codon optimization. A schematic representation of the overall workflow is shown in Fig. 1 . 2.3 B-Cell Epitope Prediction Linear B-cell epitopes were predicted from the selected protein sequences using the Immune Epitope Database (IEDB) resource. Predicted epitopes were evaluated for their antigenic potential and suitability for inclusion in the vaccine construct. 2.4 T-Cell Epitope Prediction T-cell epitope prediction was performed using IEDB tools to identify peptides capable of binding to major histocompatibility complex (MHC) molecules. Both MHC class I–restricted epitopes (for CD8⁺ T cells) and MHC class II–restricted epitopes (for CD4⁺ T cells) were predicted to ensure the induction of cell-mediated immune responses. 2.5 Cytotoxic T Lymphocyte (CTL) Epitope Prediction CTL epitopes binding to MHC class I molecules were predicted using the IEDB MHC-I binding tool ( http://tools.iedb.org/mhci/ ). Predictions were based on peptide binding affinity, proteasomal C-terminal cleavage efficiency, and transporter associated with antigen processing (TAP) transport potential. Epitopes demonstrating strong binding affinity and favorable processing characteristics were selected for further analysis. 2.6 Helper T Lymphocyte (HTL) Epitope Prediction Helper T lymphocyte (HTL) epitopes were predicted using the IEDB MHC-II binding tool ( http://tools.iedb.org/mhcii/ ). Epitopes with strong binding affinity to commonly occurring human leukocyte antigen (HLA) class II alleles were prioritized to enhance CD4⁺ T-cell–mediated immune responses. 2.7 Population Coverage Analysis Population coverage analysis was conducted using the IEDB-AR v2.22 population coverage tool to evaluate the global applicability of the selected CTL and HTL epitopes. This analysis estimates the proportion of individuals likely to mount an immune response based on the distribution of associated HLA class I and II alleles across different ethnic and geographical populations. 2.8 Vaccine Construct Design A multi-epitope subunit vaccine (MEV) construct was designed by assembling the selected CTL and HTL epitopes using appropriate linker sequences. Epitope selection was based on high antigenicity, non-allergenicity, non-toxicity, conservation across strains, strong HLA binding affinity, and broad population coverage. The construct was designed to optimize immunogenicity, stability, and safety. 2.9 Physicochemical Property Analysis The physicochemical properties of the designed vaccine construct, including molecular weight, theoretical isoelectric point (pI), instability index, aliphatic index, and grand average of hydropathicity (GRAVY), were evaluated using the ProtParam tool. 2.10 Antigenicity and Solubility Prediction Antigenicity of the vaccine construct was assessed using the VaxiJen v2.0 server with a threshold value of 0.4. Protein solubility upon recombinant expression in Escherichia coli was predicted using the SOLUPROT web server to assess feasibility for downstream expression and purification. 2.11 Secondary Structure Prediction Secondary structure prediction of the vaccine construct was performed using the PSIPRED server. The predicted proportions of alpha helices, beta strands, and random coils were analyzed to evaluate structural features relevant to protein stability and epitope presentation. 2.12 Tertiary Structure Prediction and Validation Due to the chimeric nature of the multi-epitope vaccine construct and the absence of suitable homologous templates for conventional homology modeling, tertiary structure prediction was performed using the Robetta web server. Structural validation of the predicted model was conducted using PROCHECK via the SAVES v6.0 server. Ramachandran plot analysis was used to assess stereochemical quality, and the ProSA-web server was employed to evaluate overall model quality and structural stability. 2.13 Molecular Docking Analysis Molecular docking was performed to evaluate interactions between the vaccine construct and key immune receptors, including Toll-like receptor 4 (TLR4), MHC class I, and MHC class II molecules. Docking simulations were carried out using the HADDOCK v2.2 web server. The resulting complexes were visualized using UCSF Chimera, and detailed interaction analysis was performed using the PDBsum database. 2.14 In Silico Immune Simulation In silico immune simulation was performed using the C-ImmSim server ( http://150.146.2.1/C-IMMSIM/index.php ) to predict immune responses elicited by the vaccine construct. Simulations were conducted at time steps 1, 84, and 168, corresponding to three vaccine doses administered at four-week intervals. 2.15 Codon Optimization and In Silico Cloning Codon optimization of the vaccine construct was performed using the Java Codon Adaptation Tool (JCAT) to enhance expression efficiency in E. coli . The optimized nucleotide sequence was subsequently cloned in silico into the pET-28a(+) expression vector using SnapGene software. 3. Results 3.1 Protein Sequence Retrieval The target Norovirus protein sequences were successfully retrieved from the UniProt database. Three proteins were selected for epitope prediction and vaccine design: the capsid protein (UniProt ID: A7YK10), small protein (UniProt ID: A7YK11), and polyprotein (UniProt ID: A7YK09). These sequences formed the basis for subsequent epitope prediction and screening analyses. The complete protein sequences are provided in the supplementary material. 3.2 Epitope Screening and Selection The capsid protein, small protein, and polyprotein sequences were submitted to the Immune Epitope Database (IEDB) server for the prediction of B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes. Predicted epitopes were systematically screened based on multiple immunological and safety parameters, including antigenicity (VaxiJen), immunogenicity (IEDB tools), allergenicity (AllerTOP v2.0), toxicity (ToxinPred), and interferon-gamma (IFN-γ) induction potential (IFNepitope server). Following this comprehensive screening, a total of six B-cell epitopes, six MHC class I (CTL) epitopes, and nine MHC class II (HTL) epitopes were selected for inclusion in the final multi-epitope vaccine construct. All selected epitopes demonstrated antigenicity scores above the recommended threshold (VaxiJen > 0.4), were predicted to be non-toxic and non-allergenic, and showed favorable immunogenic profiles. Additionally, the selected T-cell epitopes were predicted to induce IFN-γ, indicating their potential to elicit robust antiviral cell-mediated immune responses. Tables 1 – 3 summarize the selected B-cell, MHC class I, and MHC class II epitopes along with their respective immunological properties. Table 1 Selected B-cell epitopes SEQUENCE TOXICITY ALLERGENICITY ANTIGENICITY VAXIGEN SCORE HVNGTLLGTTPVSGSWVS NON TOXIN NON-ALLERGEN ANTIGEN 0.5223 PLDLVDGRVRAVPRSVYFFQDVLPEYNDGLL NON TOXIN NON-ALLERGEN ANTIGEN 0.6936 SEDEVNPALL NON TOXIN NON-ALLERGEN ANTIGEN 0.8404 DELVPKQDEKYQK NON TOXIN NON-ALLERGEN ANTIGEN 0.5609 TKGPHPGKPELTPLGA NON TOXIN NON-ALLERGEN ANTIGEN 1.1375 FGTMDAEPTQERSA NON TOXIN NON-ALLERGEN ANTIGEN 0.9676 Table 2 Selected MHC class I (CTL) epitopes SEQUENCE TOXICITY IFN GAMMA ALLERGENICITY ANTIGENICITY VAXIJEN SCORE AVDWSGTRYY NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 0.4291 LPSLRGGSW NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 1.0971 SWVPRLFQL NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 0.5009 SEDPVPALL NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 0.4575 ETLPGHAQR NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 0.4575 SESEDEVNY NON TOXIN POSITIVE NON-ALLERGEN ANTIGEN 0.5439 Table 3 Selected MHC class II (HTL) epitopes SEQUENCE TOXICITY IFN GAMMA ALLERGENICITY ANTIGENICITY VAXIGEN SCORE DIEMLGAQVQAQAQA NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.9669 LGAQVQAQAQAQENA NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.9256 KHDIEMLGAQVQAQA NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.7088 DQAPYQGKVYASLAA NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.4022 DFNFVYLTPPIERTV NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 1.3365 AQDWNVDPQPFIPS NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 1.0250 LKRYGLLPTRADKEE NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.9887 VTAFKAMAADAGIPW NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 0.4609 KRYGLLPTRADKEEG NON-TOXIN POSITIVE NON ALLERGEN ANTIGEN 1.2715 3.3 Population Coverage Analysis Population coverage analysis was performed using the IEDB Population Coverage Tool to evaluate the global distribution of the selected CTL and HTL epitopes in association with their corresponding HLA alleles. The combined set of six CTL and nine HTL epitopes demonstrated an overall worldwide population coverage of 98.96%, indicating that the selected epitopes have the potential to elicit immune responses in a large proportion of the global population. This high coverage supports the suitability of the selected epitopes for inclusion in a broadly applicable multi-epitope vaccine construct. 3.4 Vaccine Construct Design The final multi-epitope vaccine construct was designed by assembling the six selected CTL epitopes and nine HTL epitopes. β-defensin 3, a Toll-like receptor 4 (TLR4) agonist, was incorporated at the N-terminal region as an adjuvant to enhance immune stimulation. The adjuvant was linked to the first CTL epitope using an EAAAK linker to ensure structural rigidity and functional separation. CTL epitopes were joined using AAY linkers, while HTL epitopes were connected using GPGPG linkers. These linkers were selected to facilitate efficient antigen processing and presentation while minimizing the formation of junctional (neo-) epitopes. The final construct comprised 433 amino acids and included a C-terminal 6×His tag to facilitate protein purification. The design strategy supports efficient recombinant expression and purification in Escherichia coli using standard expression systems. 3.5 Physicochemical Properties and Solubility The physicochemical properties of the final vaccine construct were analyzed using the ProtParam tool. The construct consisted of 433 amino acids with a molecular weight of 46,095.07 Da and a theoretical isoelectric point (pI) of 8.88. It contained 51 positively charged residues (Arg + Lys) and 44 negatively charged residues (Asp + Glu). The instability index was calculated to be 29.20, which is below the threshold value of 40, indicating that the protein is predicted to be stable. The aliphatic index was 65.68, suggesting moderate thermostability. The GRAVY score was − 0.576, indicating a hydrophilic nature favorable for solubility and aqueous interactions. The extinction coefficient was estimated to be 65,695 M⁻¹ cm⁻¹ (oxidized form) and 65,320 M⁻¹ cm⁻¹ (reduced form). The predicted half-life was 30 hours in mammalian reticulocytes (in vitro), > 20 hours in yeast (in vivo), and > 10 hours in E. coli (in vivo). Protein solubility analysis using the SOLUproT server yielded a solubility score of 0.894, indicating that the construct is highly soluble and suitable for recombinant expression and purification in E. coli . Collectively, these results support the stability and practical feasibility of the designed vaccine construct. 3.6 Secondary Structure Prediction 3.7 Tertiary Structure Prediction and Validation The tertiary structure of the vaccine construct was predicted using the Robetta server, which generated five initial models. Model 1 was selected and refined using the GalaxyRefine server. Structural validation revealed that 88.9% of residues were located in the most favored regions of the Ramachandran plot, with 9.9% in additionally allowed regions and 0.6% in generously allowed regions, indicating excellent stereochemical quality. The refined model achieved a MolProbity score of 1.55 and a QMEAN Z-score of − 0.97, suggesting compatibility with experimentally determined protein structures of similar size. The global QMEANDisCo score was 0.47 ± 0.05, indicating moderate-to-good local structural reliability. ERRAT analysis yielded a quality score of 90.148, further supporting the robustness of the predicted structure. 3.8 Molecular Docking and Protein–Protein Interactions Protein–protein docking analysis was conducted to evaluate interactions between the vaccine construct and key immune receptors, including TLR4, MHC class I, and MHC class II molecules, using the HADDOCK v2.2 server. The vaccine–TLR4 complex exhibited the most favorable interaction, with a HADDOCK score of − 103.9 ± 11.0. The vaccine–MHC-I and vaccine–MHC-II complexes yielded HADDOCK scores of − 91.4 ± 8.1 and − 76.3 ± 25.5, respectively. Detailed interaction analysis revealed extensive non-bonded contacts, hydrogen bonds, and salt bridges in all docked complexes. For the vaccine–MHC-I complex, 178 non-bonded contacts, seven hydrogen bonds, and four salt bridges were observed. The vaccine–MHC-II complex exhibited 225 non-bonded contacts, 17 hydrogen bonds, and eight salt bridges. The vaccine–TLR4 complex showed 211 non-bonded contacts, 18 hydrogen bonds, and two salt bridges. These interactions suggest stable and specific binding, supporting the immunogenic potential of the vaccine construct. 3.9 In Silico Immune Simulation Analysis Immune simulation using the C-ImmSim server predicted robust immune responses following administration of the vaccine construct. The simulation demonstrated a substantial increase in antibody levels, particularly IgG1 + IgM and IgG + IgM, indicating a strong humoral immune response. Additionally, increased populations of active B cells, helper T cells, and cytotoxic T cells were observed, suggesting the development of immune memory and effective secondary immune responses. The simulation also predicted elevated levels of key cytokines, including IFN-γ, IL-23, IL-10, and IFN-β, which are critical for antiviral immunity. Increased dendritic cell and macrophage populations indicated efficient antigen presentation. These results support the functional immunogenic potential of the vaccine construct in a simulated immune environment. 3.10 Molecular Dynamics Simulation Molecular dynamics (MD) simulation was performed to assess the stability of the vaccine–TLR4 complex over a 100 ns simulation period. The root mean square deviation (RMSD) values remained stable within the range of 1–1.5 Å, indicating structural stability of the complex. Root mean square fluctuation (RMSF) analysis showed that most residues exhibited acceptable fluctuation, with higher flexibility observed in surface-exposed regions, which is typical for protein complexes. These results suggest that the vaccine–TLR4 complex maintains dynamic stability, supporting sustained receptor engagement and downstream immune signaling 3.11 Codon Optimization and In Silico Cloning 3.11.1 Codon Optimization Codon optimization of the vaccine construct was performed using the JCAT server to enhance expression efficiency in E. coli strain K12. The optimized nucleotide sequence comprised 1395 nucleotides, with a GC content of 56.99% and a codon adaptation index (CAI) of 1.0, indicating a high likelihood of efficient expression. 3.11.2 In Silico Cloning The optimized vaccine gene was cloned in silico into the pET-28a(+) expression vector between the XhoI and NdeI restriction sites using SnapGene software. This step confirmed the feasibility of downstream cloning and recombinant expression in E. coli , providing an experimentally ready construct for future wet-lab validation. 4. Discussion The increasing global burden of Norovirus infections, coupled with the continued absence of a licensed vaccine, underscores the urgent need for alternative and efficient vaccine development strategies (Atmar & Estes, 2020 ; Patel et al., 2009 ). In this study, an immunoinformatics-driven framework was employed to design and evaluate a multi-epitope subunit vaccine candidate targeting Norovirus. By integrating conserved antigenic regions from the capsid protein (A7YK10), small protein (A7YK11), and polyprotein (A7YK09), the design strategy aimed to address the extensive genetic diversity and antigenic variability characteristic of Norovirus, which is a major obstacle in conventional vaccine development (Cannon & Vinjé, 2020 ; Chhabra et al., 2019 ). The epitope selection process was guided by stringent immunological and safety criteria, including antigenicity, non-allergenicity, non-toxicity, IFN-γ induction potential, and broad HLA population coverage. The identification of B-cell, CTL, and HTL epitopes with high predicted binding affinities to multiple MHC class I and II alleles suggests the potential to elicit both humoral and cell-mediated immune responses (Vita et al., 2019 ; Azim et al., 2020 ). This prediction is further supported by the high global population coverage of 98.96%, indicating that the selected epitopes may be immunologically relevant across diverse ethnic and geographical populations. Such broad coverage is particularly important for Norovirus, given its global circulation and frequent emergence of new variants (Lo et al., 2023 ; Chen et al., 2024 ). Physicochemical characterization of the vaccine construct revealed favorable properties for stability and expression. The predicted instability index (29.20), aliphatic index (65.68), and hydrophilic GRAVY score (− 0.576) indicate a stable and soluble protein, which is essential for downstream recombinant expression and formulation (Gasteiger et al., 2005 ). Consistent with these findings, solubility prediction using SOLproT suggested a high likelihood of successful expression in Escherichia coli (Magnan et al., 2009 ). These properties support the practical feasibility of producing the vaccine construct using conventional bacterial expression systems. Structural analysis further reinforced the robustness of the designed construct. Secondary structure prediction indicated a predominance of flexible coil regions, which may facilitate effective epitope exposure and immune recognition (Jones, 1999 ). Tertiary structure modeling and validation demonstrated acceptable stereochemical quality, with 88.9% of residues located in the most favored regions of the Ramachandran plot and a high ERRAT quality score, consistent with structurally reliable vaccine candidates reported in similar immunoinformatics-based studies (Ahmad et al., 2020 ; Ahmed & Abid, 2022 ). Maintenance of correct epitope conformation is critical for immune recognition, and the predicted structural integrity of the construct supports its potential immunogenicity. Molecular docking and molecular dynamics simulations provided mechanistic insights into the interaction between the vaccine construct and key immune receptors. Favorable HADDOCK scores and stable interaction profiles were observed for complexes with TLR4, MHC class I, and MHC class II molecules, particularly the vaccine–TLR4 complex. These interactions are essential for efficient antigen presentation and activation of innate and adaptive immune responses (van Zundert et al., 2016 ; Tan & Jiang, 2014 ). The observed stability of the vaccine–TLR4 complex during molecular dynamics simulations further supports the likelihood of sustained immune signaling. The in silico immune simulation results complemented the structural and docking analyses by predicting robust humoral and cellular immune responses, including memory B-cell and T-cell formation and the production of key antiviral cytokines such as IFN-γ. Similar immune response patterns have been reported in previous computational vaccine studies targeting Norovirus and other enteric viruses (Ahmad et al., 2020 ; Matos et al., 2023 ). Together, these findings suggest that the designed multi-epitope construct has the capacity to elicit coordinated immune responses in a simulated biological environment. Despite these promising results, it is important to acknowledge the limitations inherent to computational studies. In silico predictions cannot fully replicate the complexity of host immune responses or account for factors such as post-translational modifications, protein folding dynamics in vivo, and immunoregulatory mechanisms (Das et al., 2021 ). Therefore, experimental validation remains essential. Future studies should focus on in vitro expression and purification of the vaccine construct, followed by immunogenicity assessment using assays such as ELISA, ELISpot, and flow cytometry. In vivo evaluation in suitable animal models will be critical to assess safety, immunogenicity, and protective efficacy (Atmar & Estes, 2020 ). Additionally, testing the construct against a broader range of Norovirus genotypes and exploring alternative adjuvants or delivery platforms may further enhance its protective potential. 5. Conclusion In this study, an immunoinformatics and reverse vaccinology approach was successfully applied to design a rational multi-epitope subunit vaccine candidate against Norovirus. Conserved epitopes derived from the capsid protein, small protein, and polyprotein were systematically identified and assembled into a single construct designed to elicit both humoral and cellular immune responses, addressing the challenge posed by Norovirus genetic diversity (Patel et al., 2009 ; Chhabra et al., 2019 ). Comprehensive in silico analyses demonstrated that the final vaccine construct possesses favorable physicochemical properties, structural stability, and strong interactions with key immune receptors, including MHC class I, MHC class II, and TLR4, consistent with findings from previously reported computational vaccine studies (Ahmad et al., 2020 ; Ahmed & Abid, 2022 ). Immune simulation further supported its potential to induce robust and long-lasting immune responses. Codon optimization and in silico cloning confirmed the feasibility of recombinant expression in Escherichia coli , supporting the construct’s scalability for experimental production (Grote et al., 2005 ). Overall, these findings suggest that the designed multi-epitope vaccine construct represents a promising computational candidate for Norovirus vaccine development. While the results are encouraging, further experimental validation through in vitro and in vivo studies is required to confirm the safety, immunogenicity, and protective efficacy of the construct prior to clinical translation (Atmar & Estes, 2020 ; Chen et al., 2024 ). Declarations Acknowledgement The authors extend their gratitude to Dr. Ramchandra Prasad and Dr. Shanmuga Priya for their valuable guidance and support. Disclosure statement No potential conflict of interest was reported by the authors. Data availability statement The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. ORCID Nitish Kumar R https://orcid.org/0009-0002-2002-1194 Parvana Nair https://orcid.org/0009-0004-2014-8106 References Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Goecks J, Taylor J (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. 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Expert Rev Mol Med 16:e5. https://doi.org/10.1017/erm.2014.6 van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C, Kastritis PL, Karaca E, Bonvin AMJJ (2016) The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes. J Mol Biol 428(4):720–725. https://doi.org/10.1016/j.jmb.2015.09.014 Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Peters B (2019) The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47(D1):D339–D343 Additional Declarations The authors declare no competing interests. Supplementary Files Bcellepitope.xlsx B cell epitopes clonedDNAsequence.fasta.txt DNA sequence.fasta DockedMHC1.pdb vaccine construct + MHC1 docked DockedMHC2.pdb vaccine construct + MHC2 docked DockedTLR4.pdb vaccine construct + TLR4 docked MHC1epitopes.xlsx MHC1 epitopes MHC2epitopes.xlsx MHC2 epitopes Vaccineconstruct.fasta.txt Vaccine construct.fasta Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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2","display":"","copyAsset":false,"role":"figure","size":328928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. \u003c/strong\u003eillustration of overall architecture of the multi-epitope vaccine construct\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/6860c4502ae896502af0bcdb.png"},{"id":100412517,"identity":"37fa442d-2d15-4d7a-a110-a9f48e160915","added_by":"auto","created_at":"2026-01-16 13:14:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":175458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e. depiction of predicted secondary structure of the refined vaccine 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(A) In Ramachandran analysis residues were allocated; the most favored region 88.9%, 22.3%, 6.8%, and 0.4%. (B) QMEANDIsco showing Z-score (4.85) for 3D structure validation\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/337af72080792ac225b0d0eb.png"},{"id":100412445,"identity":"812b7707-bf0e-49a3-af07-680b5c5e05fe","added_by":"auto","created_at":"2026-01-16 13:14:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":595806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6.\u003c/strong\u003e illustration the docked complexes with (A) MHC-I, (B) MHC-II, and (C) TLR4.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/1689015a92a3a993093f4f1c.png"},{"id":100412577,"identity":"c1ab9f5e-616d-4055-b749-8c2dce3bb0ca","added_by":"auto","created_at":"2026-01-16 13:14:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":322704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7. \u003c/strong\u003eThe simulation of the humoral and cellular responses to the full ChRNV22 vaccine (a – b), the vaccine sequence without adjuvants (c – d) and to the adjuvant sequences alone (e – f), in a three doses administration regimen (days 0, 30 and 90). Differences in the simulated immune responses to a live replicating virus with (g – h) or without (i – j) the previous administration of the vaccine in three doses (days 0, 30 and 90, infection at day 36.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/feca799049c5547e55dc676f.png"},{"id":100412295,"identity":"ebabfc5a-748b-4c5d-8521-5f3811dcb4f3","added_by":"auto","created_at":"2026-01-16 13:14:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":136628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8.\u003c/strong\u003e (A) Molecular dynamics simulation of the receptor-vaccine complex. The graph is showing the RMSD of the complex (X-axis = Time in ps and Y-axis = RMSD) (B)The second graph is showing the RMSF of the complex (X-axis = Time in residue and Y-axis = RMSF\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/b60ddc9c00f63dd1dd2f9635.png"},{"id":100412500,"identity":"6b3d0976-e053-4840-95cb-098c7bb5043f","added_by":"auto","created_at":"2026-01-16 13:14:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":375566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9.\u003c/strong\u003e In silico restriction cloning of the final vaccine construct into pET28a(+) expression vector where Red part representing the vaccine insert and black circle showing the vector.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/9df159924f78e860bad7893e.png"},{"id":100423822,"identity":"3d1265c2-f07d-41ad-8889-376a01c02f05","added_by":"auto","created_at":"2026-01-16 14:15:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3275479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/138615fa-89f6-42ef-b239-2c2edce36e77.pdf"},{"id":100412360,"identity":"a12701b6-d895-484a-afd5-d7c8d67750a6","added_by":"auto","created_at":"2026-01-16 13:14:18","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11236,"visible":true,"origin":"","legend":"\u003cp\u003eB cell epitopes\u003c/p\u003e","description":"","filename":"Bcellepitope.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/8bca502dac081de0f0ca770c.xlsx"},{"id":100412556,"identity":"16f9cb2f-b9a2-4515-a82b-7de59a42043b","added_by":"auto","created_at":"2026-01-16 13:14:47","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1374,"visible":true,"origin":"","legend":"\u003cp\u003eDNA sequence.fasta\u003c/p\u003e","description":"","filename":"clonedDNAsequence.fasta.txt","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/ab9ef5a4d4dc27c0962f391b.txt"},{"id":100421980,"identity":"9328eaa4-b9b2-4c91-bd71-807ed8b4c6f7","added_by":"auto","created_at":"2026-01-16 14:04:29","extension":"pdb","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":543396,"visible":true,"origin":"","legend":"\u003cp\u003evaccine construct + MHC1 docked\u003c/p\u003e","description":"","filename":"DockedMHC1.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/735db200b2bcd4963c9f0d91.pdb"},{"id":100412173,"identity":"2268febe-d16b-424e-97dc-fd57b4a800e1","added_by":"auto","created_at":"2026-01-16 13:14:03","extension":"pdb","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":467257,"visible":true,"origin":"","legend":"\u003cp\u003evaccine construct + MHC2 docked\u003c/p\u003e","description":"","filename":"DockedMHC2.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/3db5ec78b0ba6eb6c4efe5c1.pdb"},{"id":100412282,"identity":"c3c21e10-3fcc-4a94-9c3f-4d3d13dfb494","added_by":"auto","created_at":"2026-01-16 13:14:12","extension":"pdb","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":796764,"visible":true,"origin":"","legend":"\u003cp\u003evaccine construct + TLR4 docked\u003c/p\u003e","description":"","filename":"DockedTLR4.pdb","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/b8d2daa1dbd2d41fa3d72d0f.pdb"},{"id":100412576,"identity":"b63d0849-1c8b-4f69-9333-6fa3546096d5","added_by":"auto","created_at":"2026-01-16 13:14:50","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":187419,"visible":true,"origin":"","legend":"\u003cp\u003eMHC1 epitopes\u003c/p\u003e","description":"","filename":"MHC1epitopes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/caf6f691d0d35de37a01bb4f.xlsx"},{"id":100412502,"identity":"3c58f2f7-052e-4e72-bb6d-9648cf2b77a5","added_by":"auto","created_at":"2026-01-16 13:14:39","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":112779,"visible":true,"origin":"","legend":"\u003cp\u003eMHC2 epitopes\u003c/p\u003e","description":"","filename":"MHC2epitopes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/5a68804a2a04b56d616f3b8d.xlsx"},{"id":100412344,"identity":"4c3767ea-efff-47f8-ac64-ef6686441f1d","added_by":"auto","created_at":"2026-01-16 13:14:17","extension":"txt","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":515,"visible":true,"origin":"","legend":"\u003cp\u003eVaccine construct.fasta\u003c/p\u003e","description":"","filename":"Vaccineconstruct.fasta.txt","url":"https://assets-eu.researchsquare.com/files/rs-8606904/v1/8e4cd717f258de2d11f7bc8a.txt"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImmunoinformatics-Driven Design and In Silico Validation of a Multi Epitope Subunit Vaccine Targeting Norovirus\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNorovirus (NoV), a non-enveloped, positive sense single-stranded RNA virus classified under the \u003cem\u003eCaliciviridae\u003c/em\u003e family, is globally recognized as the leading cause of non-bacterial acute gastroenteritis (AGE). This highly contagious pathogen affects individuals across all age groups and significantly contributes to the worldwide burden of gastrointestinal illnesses and associated mortality (Lanata et al., 2013). Clinical manifestations of Norovirus infection commonly include abdominal pain, watery diarrhoea, nausea, and vomiting, with transmission primarily occurring via the fecal-oral route (Lew et al., 2012). The scale of its impact is substantial; in the United States alone, Norovirus is estimated to be responsible for approximately 19 to 21\u0026nbsp;million cases annually. The virus poses a particularly serious threat to vulnerable populations, such as infants and the elderly, largely due to its remarkable ease of transmission and its resilience in diverse environmental conditions (Ahmed et al., 2014; Pires et al., 2015). The dynamic nature of Norovirus, exemplified by the recent implication of emerging variants like the GII.17 genotype in an increased number of outbreaks across multiple countries, presents a continuous challenge to public health (Afgan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This constant evolution of viral strains directly implies a significant hurdle for traditional vaccine approaches, as antigenic drift can rapidly reduce the efficacy of vaccines designed against specific, static targets. While \u003cem\u003ein silico\u003c/em\u003e predictions offer a powerful and rapid initial step in vaccine design, their ultimate utility in addressing the dynamic nature of viral evolution necessitates rigorous experimental validation (Somana et al., 2020). Such empirical confirmation is crucial to ascertain whether a designed construct can indeed provide broad protection against existing and emerging variants, thereby bridging the gap between computational promise and real-world clinical effectiveness. Despite numerous international efforts, a licensed Norovirus vaccine or specific antiviral therapy remains unavailable. This critical gap in treatment and prevention is primarily a consequence of several formidable biological and technical hurdles (Tanaka et al., 2013). The virus exhibits extensive genetic variability, particularly prominent in genogroups GI and GII, with the GII.4 genotype being notably the most common and virulent. This high mutation rate, leading to frequent antigenic variations, is a major factor contributing to recurring global outbreaks and renders traditional immunization strategies, which often target single or limited strains, largely inadequate (Patel et al., 2021). The inherent biological challenges of Norovirus, including the absence of efficient \u003cem\u003ein vitro\u003c/em\u003e culture methods and the limited availability of suitable animal models for experimental research, have profoundly impacted vaccine development (Das et al., 2014). These limitations make empirical vaccine development extremely difficult and slow, as it is challenging to propagate the virus for research or test vaccine candidates in relevant biological systems. This problem directly necessitates a strategic shift from traditional immunization approaches towards multivalent or broad-coverage vaccine strategies. Such an approach aims to overcome the virus's extensive genetic diversity by targeting conserved regions across multiple strains, thereby offering the potential for broader and more durable protection (Chen et al. 2022) The pivot to computational methods, therefore, becomes not merely an alternative but a strategic imperative to circumvent these long-standing obstacles, enabling the identification of conserved sequences that can provide broader protection against evolving strains and accelerate the vaccine discovery pipeline. In response to the challenges posed by Norovirus, computational vaccinology and immunoinformatics have emerged as innovative and cost-effective methods for creating multi-epitope vaccines (Atmar et al., 2020). These advanced approaches leverage bioinformatics tools and algorithms to identify conserved sequences across various viral strains, enabling the rational design of vaccine candidates that can elicit robust and lasting immune responses efficiently. The extensive array of \u003cem\u003ein silico\u003c/em\u003e tools utilized in modern vaccine design, ranging from epitope prediction databases like IEDB to structural analysis tools such as ProtParam and PSIPRED, and functional prediction platforms for antigenicity, immunogenicity, toxicity, allergenicity, and molecular docking, reflects a significant underlying trend in the field. This increasing reliance on computational biology and immunoinformatics, often termed reverse vaccinology, signifies a paradigm shift towards accelerating the initial phases of vaccine development. This shift is driven by the urgent need for more efficient, cost-effective, and rapid design cycles, particularly for rapidly evolving pathogens like Norovirus (Bartsch et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The systematic application of these tools, from the initial prediction of potential epitopes to the comprehensive validation of vaccine construct properties, establishes a rational design pipeline. This approach moves beyond traditional empirical methods, suggesting that \u003cem\u003ein silico\u003c/em\u003e techniques are becoming foundational for identifying promising vaccine candidates before committing to resource-intensive and time-consuming wet-lab experiments.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Target Protein Sequence Retrieval and Selection\u003c/h2\u003e \u003cp\u003eNorovirus protein sequences were retrieved through a systematic database search. The National Center for Biotechnology Information (NCBI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify relevant Norovirus genomic information. Based on extensive literature review and antigenic relevance, three viral proteins were selected for vaccine design: the capsid protein, small protein, and polyprotein. Protein sequences corresponding to the Murine norovirus GV/WU24/2005/USA strain (OX\u0026thinsp;=\u0026thinsp;463715) were obtained in FASTA format from the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The selected proteins and their respective UniProt identifiers were A7YK10 (capsid protein), A7YK11 (small protein), and A7YK09 (polyprotein).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Overall Workflow for Epitope-Based Vaccine Design\u003c/h2\u003e \u003cp\u003eThe selected Norovirus protein sequences were subjected to a comprehensive immunoinformatics pipeline involving epitope prediction, screening, vaccine construct design, structural modeling, molecular docking, immune simulation, and codon optimization. A schematic representation of the overall workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 B-Cell Epitope Prediction\u003c/h2\u003e \u003cp\u003eLinear B-cell epitopes were predicted from the selected protein sequences using the Immune Epitope Database (IEDB) resource. Predicted epitopes were evaluated for their antigenic potential and suitability for inclusion in the vaccine construct.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 T-Cell Epitope Prediction\u003c/h2\u003e \u003cp\u003eT-cell epitope prediction was performed using IEDB tools to identify peptides capable of binding to major histocompatibility complex (MHC) molecules. Both MHC class I\u0026ndash;restricted epitopes (for CD8⁺ T cells) and MHC class II\u0026ndash;restricted epitopes (for CD4⁺ T cells) were predicted to ensure the induction of cell-mediated immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cytotoxic T Lymphocyte (CTL) Epitope Prediction\u003c/h2\u003e \u003cp\u003eCTL epitopes binding to MHC class I molecules were predicted using the IEDB MHC-I binding tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhci/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhci/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Predictions were based on peptide binding affinity, proteasomal C-terminal cleavage efficiency, and transporter associated with antigen processing (TAP) transport potential. Epitopes demonstrating strong binding affinity and favorable processing characteristics were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Helper T Lymphocyte (HTL) Epitope Prediction\u003c/h2\u003e \u003cp\u003eHelper T lymphocyte (HTL) epitopes were predicted using the IEDB MHC-II binding tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhcii/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhcii/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Epitopes with strong binding affinity to commonly occurring human leukocyte antigen (HLA) class II alleles were prioritized to enhance CD4⁺ T-cell\u0026ndash;mediated immune responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Population Coverage Analysis\u003c/h2\u003e \u003cp\u003ePopulation coverage analysis was conducted using the IEDB-AR v2.22 population coverage tool to evaluate the global applicability of the selected CTL and HTL epitopes. This analysis estimates the proportion of individuals likely to mount an immune response based on the distribution of associated HLA class I and II alleles across different ethnic and geographical populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Vaccine Construct Design\u003c/h2\u003e \u003cp\u003eA multi-epitope subunit vaccine (MEV) construct was designed by assembling the selected CTL and HTL epitopes using appropriate linker sequences. Epitope selection was based on high antigenicity, non-allergenicity, non-toxicity, conservation across strains, strong HLA binding affinity, and broad population coverage. The construct was designed to optimize immunogenicity, stability, and safety.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Physicochemical Property Analysis\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the designed vaccine construct, including molecular weight, theoretical isoelectric point (pI), instability index, aliphatic index, and grand average of hydropathicity (GRAVY), were evaluated using the ProtParam tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Antigenicity and Solubility Prediction\u003c/h2\u003e \u003cp\u003eAntigenicity of the vaccine construct was assessed using the VaxiJen v2.0 server with a threshold value of 0.4. Protein solubility upon recombinant expression in \u003cem\u003eEscherichia coli\u003c/em\u003e was predicted using the SOLUPROT web server to assess feasibility for downstream expression and purification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Secondary Structure Prediction\u003c/h2\u003e \u003cp\u003eSecondary structure prediction of the vaccine construct was performed using the PSIPRED server. The predicted proportions of alpha helices, beta strands, and random coils were analyzed to evaluate structural features relevant to protein stability and epitope presentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Tertiary Structure Prediction and Validation\u003c/h2\u003e \u003cp\u003eDue to the chimeric nature of the multi-epitope vaccine construct and the absence of suitable homologous templates for conventional homology modeling, tertiary structure prediction was performed using the Robetta web server. Structural validation of the predicted model was conducted using PROCHECK via the SAVES v6.0 server. Ramachandran plot analysis was used to assess stereochemical quality, and the ProSA-web server was employed to evaluate overall model quality and structural stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Molecular Docking Analysis\u003c/h2\u003e \u003cp\u003eMolecular docking was performed to evaluate interactions between the vaccine construct and key immune receptors, including Toll-like receptor 4 (TLR4), MHC class I, and MHC class II molecules. Docking simulations were carried out using the HADDOCK v2.2 web server. The resulting complexes were visualized using UCSF Chimera, and detailed interaction analysis was performed using the PDBsum database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 In Silico Immune Simulation\u003c/h2\u003e \u003cp\u003eIn silico immune simulation was performed using the C-ImmSim server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://150.146.2.1/C-IMMSIM/index.php\u003c/span\u003e\u003cspan address=\"http://150.146.2.1/C-IMMSIM/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict immune responses elicited by the vaccine construct. Simulations were conducted at time steps 1, 84, and 168, corresponding to three vaccine doses administered at four-week intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Codon Optimization and In Silico Cloning\u003c/h2\u003e \u003cp\u003eCodon optimization of the vaccine construct was performed using the Java Codon Adaptation Tool (JCAT) to enhance expression efficiency in \u003cem\u003eE. coli\u003c/em\u003e. The optimized nucleotide sequence was subsequently cloned in silico into the pET-28a(+) expression vector using SnapGene software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Protein Sequence Retrieval\u003c/h2\u003e \u003cp\u003eThe target Norovirus protein sequences were successfully retrieved from the UniProt database. Three proteins were selected for epitope prediction and vaccine design: the capsid protein (UniProt ID: A7YK10), small protein (UniProt ID: A7YK11), and polyprotein (UniProt ID: A7YK09). These sequences formed the basis for subsequent epitope prediction and screening analyses. The complete protein sequences are provided in the supplementary material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Epitope Screening and Selection\u003c/h2\u003e \u003cp\u003eThe capsid protein, small protein, and polyprotein sequences were submitted to the Immune Epitope Database (IEDB) server for the prediction of B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes. Predicted epitopes were systematically screened based on multiple immunological and safety parameters, including antigenicity (VaxiJen), immunogenicity (IEDB tools), allergenicity (AllerTOP v2.0), toxicity (ToxinPred), and interferon-gamma (IFN-γ) induction potential (IFNepitope server).\u003c/p\u003e \u003cp\u003eFollowing this comprehensive screening, a total of six B-cell epitopes, six MHC class I (CTL) epitopes, and nine MHC class II (HTL) epitopes were selected for inclusion in the final multi-epitope vaccine construct. All selected epitopes demonstrated antigenicity scores above the recommended threshold (VaxiJen\u0026thinsp;\u0026gt;\u0026thinsp;0.4), were predicted to be non-toxic and non-allergenic, and showed favorable immunogenic profiles. Additionally, the selected T-cell epitopes were predicted to induce IFN-γ, indicating their potential to elicit robust antiviral cell-mediated immune responses.\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarize the selected B-cell, MHC class I, and MHC class II epitopes along with their respective immunological properties.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected B-cell epitopes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEQUENCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOXICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALLERGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVAXIGEN SCORE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHVNGTLLGTTPVSGSWVS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLDLVDGRVRAVPRSVYFFQDVLPEYNDGLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEDEVNPALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDELVPKQDEKYQK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTKGPHPGKPELTPLGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGTMDAEPTQERSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected MHC class I (CTL) epitopes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEQUENCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOXICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIFN GAMMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALLERGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVAXIJEN SCORE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAVDWSGTRYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPSLRGGSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWVPRLFQL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEDPVPALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETLPGHAQR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSESEDEVNY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON-ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected MHC class II (HTL) epitopes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEQUENCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOXICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIFN GAMMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALLERGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGENICITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVAXIGEN SCORE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIEMLGAQVQAQAQA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGAQVQAQAQAQENA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKHDIEMLGAQVQAQA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDQAPYQGKVYASLAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFNFVYLTPPIERTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAQDWNVDPQPFIPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLKRYGLLPTRADKEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVTAFKAMAADAGIPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRYGLLPTRADKEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNON-TOXIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOSITIVE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNON ALLERGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANTIGEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Population Coverage Analysis\u003c/h2\u003e \u003cp\u003ePopulation coverage analysis was performed using the IEDB Population Coverage Tool to evaluate the global distribution of the selected CTL and HTL epitopes in association with their corresponding HLA alleles. The combined set of six CTL and nine HTL epitopes demonstrated an overall worldwide population coverage of 98.96%, indicating that the selected epitopes have the potential to elicit immune responses in a large proportion of the global population. This high coverage supports the suitability of the selected epitopes for inclusion in a broadly applicable multi-epitope vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Vaccine Construct Design\u003c/h2\u003e \u003cp\u003eThe final multi-epitope vaccine construct was designed by assembling the six selected CTL epitopes and nine HTL epitopes. β-defensin 3, a Toll-like receptor 4 (TLR4) agonist, was incorporated at the N-terminal region as an adjuvant to enhance immune stimulation. The adjuvant was linked to the first CTL epitope using an EAAAK linker to ensure structural rigidity and functional separation.\u003c/p\u003e \u003cp\u003eCTL epitopes were joined using AAY linkers, while HTL epitopes were connected using GPGPG linkers. These linkers were selected to facilitate efficient antigen processing and presentation while minimizing the formation of junctional (neo-) epitopes. The final construct comprised 433 amino acids and included a C-terminal 6\u0026times;His tag to facilitate protein purification. The design strategy supports efficient recombinant expression and purification in \u003cem\u003eEscherichia coli\u003c/em\u003e using standard expression systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Physicochemical Properties and Solubility\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the final vaccine construct were analyzed using the ProtParam tool. The construct consisted of 433 amino acids with a molecular weight of 46,095.07 Da and a theoretical isoelectric point (pI) of 8.88. It contained 51 positively charged residues (Arg\u0026thinsp;+\u0026thinsp;Lys) and 44 negatively charged residues (Asp\u0026thinsp;+\u0026thinsp;Glu).\u003c/p\u003e \u003cp\u003eThe instability index was calculated to be 29.20, which is below the threshold value of 40, indicating that the protein is predicted to be stable. The aliphatic index was 65.68, suggesting moderate thermostability. The GRAVY score was \u0026minus;\u0026thinsp;0.576, indicating a hydrophilic nature favorable for solubility and aqueous interactions. The extinction coefficient was estimated to be 65,695 M⁻\u0026sup1; cm⁻\u0026sup1; (oxidized form) and 65,320 M⁻\u0026sup1; cm⁻\u0026sup1; (reduced form). The predicted half-life was 30 hours in mammalian reticulocytes (in vitro), \u0026gt;\u0026thinsp;20 hours in yeast (in vivo), and \u0026gt;\u0026thinsp;10 hours in \u003cem\u003eE. coli\u003c/em\u003e (in vivo).\u003c/p\u003e \u003cp\u003eProtein solubility analysis using the SOLUproT server yielded a solubility score of 0.894, indicating that the construct is highly soluble and suitable for recombinant expression and purification in \u003cem\u003eE. coli\u003c/em\u003e. Collectively, these results support the stability and practical feasibility of the designed vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Secondary Structure Prediction\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Tertiary Structure Prediction and Validation\u003c/h2\u003e \u003cp\u003eThe tertiary structure of the vaccine construct was predicted using the Robetta server, which generated five initial models. Model 1 was selected and refined using the GalaxyRefine server. Structural validation revealed that 88.9% of residues were located in the most favored regions of the Ramachandran plot, with 9.9% in additionally allowed regions and 0.6% in generously allowed regions, indicating excellent stereochemical quality.\u003c/p\u003e \u003cp\u003eThe refined model achieved a MolProbity score of 1.55 and a QMEAN Z-score of \u0026minus;\u0026thinsp;0.97, suggesting compatibility with experimentally determined protein structures of similar size. The global QMEANDisCo score was 0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, indicating moderate-to-good local structural reliability. ERRAT analysis yielded a quality score of 90.148, further supporting the robustness of the predicted structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Molecular Docking and Protein\u0026ndash;Protein Interactions\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;protein docking analysis was conducted to evaluate interactions between the vaccine construct and key immune receptors, including TLR4, MHC class I, and MHC class II\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003emolecules, using the HADDOCK v2.2 server. The vaccine\u0026ndash;TLR4 complex exhibited the most favorable interaction, with a HADDOCK score of \u0026minus;\u0026thinsp;103.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0. The vaccine\u0026ndash;MHC-I and vaccine\u0026ndash;MHC-II complexes yielded HADDOCK scores of \u0026minus;\u0026thinsp;91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 and \u0026minus;\u0026thinsp;76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5, respectively.\u003c/p\u003e \u003cp\u003eDetailed interaction analysis revealed extensive non-bonded contacts, hydrogen bonds, and salt bridges in all docked complexes. For the vaccine\u0026ndash;MHC-I complex, 178 non-bonded contacts, seven hydrogen bonds, and four salt bridges were observed. The vaccine\u0026ndash;MHC-II complex exhibited 225 non-bonded contacts, 17 hydrogen bonds, and eight salt bridges. The vaccine\u0026ndash;TLR4 complex showed 211 non-bonded contacts, 18 hydrogen bonds, and two salt bridges. These interactions suggest stable and specific binding, supporting the immunogenic potential of the vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.9 In Silico Immune Simulation Analysis\u003c/h2\u003e \u003cp\u003eImmune simulation using the C-ImmSim server predicted robust immune responses following administration of the vaccine construct. The simulation demonstrated a substantial increase in antibody levels, particularly IgG1\u0026thinsp;+\u0026thinsp;IgM and IgG\u0026thinsp;+\u0026thinsp;IgM, indicating a strong humoral immune response. Additionally, increased populations of active B cells, helper T cells, and cytotoxic T cells were observed, suggesting the development of immune memory and effective secondary immune responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe simulation also predicted elevated levels of key cytokines, including IFN-γ, IL-23, IL-10, and IFN-β, which are critical for antiviral immunity. Increased dendritic cell and macrophage populations indicated efficient antigen presentation. These results support the functional immunogenic potential of the vaccine construct in a simulated immune environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Molecular Dynamics Simulation\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulation was performed to assess the stability of the vaccine\u0026ndash;TLR4 complex over a 100 ns simulation period. The root mean square deviation (RMSD) values remained stable within the range of 1\u0026ndash;1.5 \u0026Aring;, indicating structural stability of the complex. Root mean square fluctuation (RMSF) analysis showed that most residues exhibited acceptable fluctuation, with higher flexibility observed in surface-exposed regions, which is typical for protein complexes. These results suggest that the vaccine\u0026ndash;TLR4 complex maintains dynamic stability, supporting sustained receptor engagement and downstream immune signaling\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Codon Optimization and In Silico Cloning\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.11.1 Codon Optimization\u003c/h2\u003e \u003cp\u003eCodon optimization of the vaccine construct was performed using the JCAT server to enhance expression efficiency in \u003cem\u003eE. coli\u003c/em\u003e strain K12. The optimized nucleotide sequence comprised 1395 nucleotides, with a GC content of 56.99% and a codon adaptation index (CAI) of 1.0, indicating a high likelihood of efficient expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.11.2 In Silico Cloning\u003c/h2\u003e \u003cp\u003eThe optimized vaccine gene was cloned in silico into the pET-28a(+) expression vector between the XhoI and NdeI restriction sites using SnapGene software. This step confirmed the feasibility of downstream cloning and recombinant expression in \u003cem\u003eE. coli\u003c/em\u003e, providing an experimentally ready construct for future wet-lab validation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe increasing global burden of Norovirus infections, coupled with the continued absence of a licensed vaccine, underscores the urgent need for alternative and efficient vaccine development strategies (Atmar \u0026amp; Estes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this study, an immunoinformatics-driven framework was employed to design and evaluate a multi-epitope subunit vaccine candidate targeting Norovirus. By integrating conserved antigenic regions from the capsid protein\u003c/p\u003e \u003cp\u003e(A7YK10), small protein (A7YK11), and polyprotein (A7YK09), the design strategy aimed to address the extensive genetic diversity and antigenic variability characteristic of Norovirus, which is a major obstacle in conventional vaccine development (Cannon \u0026amp; Vinj\u0026eacute;, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chhabra et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe epitope selection process was guided by stringent immunological and safety criteria, including antigenicity, non-allergenicity, non-toxicity, IFN-γ induction potential, and broad HLA population coverage. The identification of B-cell, CTL, and HTL epitopes with high predicted binding affinities to multiple MHC class I and II alleles suggests the potential to elicit both humoral and cell-mediated immune responses (Vita et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Azim et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This prediction is further supported by the high global population coverage of 98.96%, indicating that the selected epitopes may be immunologically relevant across diverse ethnic and geographical populations. Such broad coverage is particularly important for Norovirus, given its global circulation and frequent emergence of new variants (Lo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhysicochemical characterization of the vaccine construct revealed favorable properties for stability and expression. The predicted instability index (29.20), aliphatic index (65.68), and hydrophilic GRAVY score (\u0026minus;\u0026thinsp;0.576) indicate a stable and soluble protein, which is essential for downstream recombinant expression and formulation (Gasteiger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Consistent with these findings, solubility prediction using SOLproT suggested a high likelihood of successful expression in \u003cem\u003eEscherichia coli\u003c/em\u003e (Magnan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These properties support the practical feasibility of producing the vaccine construct using conventional bacterial expression systems.\u003c/p\u003e \u003cp\u003eStructural analysis further reinforced the robustness of the designed construct. Secondary structure prediction indicated a predominance of flexible coil regions, which may facilitate effective epitope exposure and immune recognition (Jones, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Tertiary structure modeling and validation demonstrated acceptable stereochemical quality, with 88.9% of residues located in the most favored regions of the Ramachandran plot and a high ERRAT quality score, consistent with structurally reliable vaccine candidates reported in similar immunoinformatics-based studies (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ahmed \u0026amp; Abid, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Maintenance of correct epitope conformation is critical for immune recognition, and the predicted structural integrity of the construct supports its potential immunogenicity.\u003c/p\u003e \u003cp\u003eMolecular docking and molecular dynamics simulations provided mechanistic insights into the interaction between the vaccine construct and key immune receptors. Favorable HADDOCK scores and stable interaction profiles were observed for complexes with TLR4, MHC class I, and MHC class II molecules, particularly the vaccine\u0026ndash;TLR4 complex. These interactions are essential for efficient antigen presentation and activation of innate and adaptive immune responses (van Zundert et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tan \u0026amp; Jiang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The observed stability of the vaccine\u0026ndash;TLR4 complex during molecular dynamics simulations further supports the likelihood of sustained immune signaling.\u003c/p\u003e \u003cp\u003eThe in silico immune simulation results complemented the structural and docking analyses by predicting robust humoral and cellular immune responses, including memory B-cell and T-cell formation and the production of key antiviral cytokines such as IFN-γ. Similar immune response patterns have been reported in previous computational vaccine studies targeting Norovirus and other enteric viruses (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matos et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Together, these findings suggest that the designed multi-epitope construct has the capacity to elicit coordinated immune responses in a simulated biological environment.\u003c/p\u003e \u003cp\u003eDespite these promising results, it is important to acknowledge the limitations inherent to computational studies. In silico predictions cannot fully replicate the complexity of host immune responses or account for factors such as post-translational modifications, protein folding dynamics in vivo, and immunoregulatory mechanisms (Das et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, experimental validation remains essential. Future studies should focus on in vitro expression and purification of the vaccine construct, followed by immunogenicity assessment using assays such as ELISA, ELISpot, and flow cytometry. In vivo evaluation in suitable animal models will be critical to assess safety, immunogenicity, and protective efficacy (Atmar \u0026amp; Estes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, testing the construct against a broader range of Norovirus genotypes and exploring alternative adjuvants or delivery platforms may further enhance its protective potential.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, an immunoinformatics and reverse vaccinology approach was successfully applied to design a rational multi-epitope subunit vaccine candidate against Norovirus. Conserved epitopes derived from the capsid protein, small protein, and polyprotein were systematically identified and assembled into a single construct designed to elicit both humoral and cellular immune responses, addressing the challenge posed by Norovirus genetic diversity (Patel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chhabra et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComprehensive in silico analyses demonstrated that the final vaccine construct possesses favorable physicochemical properties, structural stability, and strong interactions with key immune receptors, including MHC class I, MHC class II, and TLR4, consistent with findings from previously reported computational vaccine studies (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ahmed \u0026amp; Abid, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Immune simulation further supported its potential to induce robust and long-lasting immune responses. Codon optimization and in silico cloning confirmed the feasibility of recombinant expression in \u003cem\u003eEscherichia coli\u003c/em\u003e, supporting the construct\u0026rsquo;s scalability for experimental production (Grote et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these findings suggest that the designed multi-epitope vaccine construct represents a promising computational candidate for Norovirus vaccine development. While the results are encouraging, further experimental validation through in vitro and in vivo studies is required to confirm the safety, immunogenicity, and protective efficacy of the construct prior to clinical translation (Atmar \u0026amp; Estes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their gratitude to Dr. Ramchandra Prasad and Dr. Shanmuga Priya for their valuable guidance and support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eORCID\u003c/p\u003e\n\u003cp\u003eNitish Kumar R https://orcid.org/0009-0002-2002-1194\u003c/p\u003e\n\u003cp\u003eParvana Nair https://orcid.org/0009-0004-2014-8106\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfgan E, Baker D, Batut B, van den Beek M, Bouvier D, Goecks J, Taylor J (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. 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Nucleic Acids Res 47(D1):D339\u0026ndash;D343\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Garden City University College","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Norovirus, acute gastroenteritis, multi-epitope vaccine, immunoinformatics, molecular docking, immunogenicity","lastPublishedDoi":"10.21203/rs.3.rs-8606904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8606904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNorovirus, a non-enveloped, positive-sense single-stranded RNA virus belonging to the \u003cem\u003eCaliciviridae\u003c/em\u003e family, is a major causative agent of acute gastroenteritis (AGE) outbreaks worldwide. It is primarily transmitted via the fecal\u0026ndash;oral route, with clinical manifestations including abdominal pain, watery diarrhoea, nausea, and vomiting. In the United States alone, norovirus is estimated to cause approximately 19\u0026ndash;21\u0026nbsp;million cases annually. Emerging variants, such as the GII.17 genotype, have been implicated in an increasing number of outbreaks across multiple countries, highlighting the urgent need for effective preventive strategies. In this study, an immunoinformatics-based approach was employed to design and evaluate a multi-epitope subunit vaccine candidate against norovirus. Three viral proteins\u0026mdash;capsid protein (UniProt ID: A7YK10), small protein (A7YK11), and polyprotein (A7YK09)\u0026mdash;were selected for epitope-based vaccine design. B-cell and T-cell epitopes were predicted using the Immune Epitope Database (IEDB) and subsequently screened for antigenicity (VaxiJen), allergenicity (AllerTOP v2.1), toxicity (ToxinPred), and population coverage. Physicochemical properties were evaluated using ProtParam, and secondary structure analysis was performed to assess the structural feasibility of the vaccine construct. The finalized multi-epitope vaccine construct was further subjected to molecular docking analyses to evaluate its binding affinity with key immune receptors, including MHC class I, MHC class II, and Toll-like receptor 4 (TLR4), providing insight into its potential immunogenic interactions. While the computational analyses indicate that the designed construct is a promising vaccine candidate, further experimental validation, including in vitro expression and in vivo immunogenicity and efficacy studies, will be required to confirm its protective potential.\u003c/p\u003e","manuscriptTitle":"Immunoinformatics-Driven Design and In Silico Validation of a Multi Epitope Subunit Vaccine Targeting Norovirus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:24:13","doi":"10.21203/rs.3.rs-8606904/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":"64bae3e7-9bd8-4051-acf7-38fe24029e22","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61173754,"name":"Bioinformatics"},{"id":61173755,"name":"Immunology"}],"tags":[],"updatedAt":"2026-01-16T11:24:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:24:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8606904","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8606904","identity":"rs-8606904","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}