In Silico Development and Structural Evaluation of a Broad-Spectrum Chimeric Multi-Epitope Vaccine against Co-Infection by Human Metapneumovirus, Respiratory Syncytial Virus, and Influenza A Virus

In Silico Development and Structural Evaluation of a Broad-Spectrum Chimeric Multi-Epitope Vaccine against Co-Infection by Human Metapneumovirus, Respiratory Syncytial Virus, and Influenza A Virus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article In Silico Development and Structural Evaluation of a Broad-Spectrum Chimeric Multi-Epitope Vaccine against Co-Infection by Human Metapneumovirus, Respiratory Syncytial Virus, and Influenza A Virus Lu Li, Yong Chen, Chunyan Wu, Junhong Xie, Abdullah Shah, Xin Xie, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7697013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Co-infections involving Human Metapneumovirus ( hMPV ), Respiratory Syncytial Virus ( RSV ), and Influenza A Virus ( IAV ) often exacerbate disease severity in vulnerable populations. Here, we employed a structure-based immunoinformatics approach to design a multi-epitope subunit vaccine targeting these pathogens. The construct incorporated cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes from the Fusion and Glycoprotein proteins of hMPV and RSV , and the Hemagglutinin (HA) and Matrix proteins (M2) of IAV , linked with an adjuvant and optimized spacers to enhance immunogenicity and stability. Structural modeling confirmed correct folding, and molecular docking predicted a stable interaction with Toll-Like Receptor 4 (TLR4) − 277.43 kcal/mol. Molecular dynamics simulations indicated a compact and stable complex with restricted conformational motions, while MM/GBSA analysis yielded a favorable binding free energy (–121.72 kcal/mol) dominated by electrostatic and van der Waals interactions. Immune simulations predicted strong humoral and cellular responses, including high antibody titers, IFN-γ and IL-2 production, and durable memory formation. Codon optimization achieved a codon adaptation index (CAI) of 0.98 and a GC content of 51.24%, suggesting efficient expression in Escherichia coli . These findings highlight the construct as a structurally stable, immunogenic, and expression-ready vaccine candidate warranting experimental validation against hMPV , RSV , and IAV . Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Biological sciences/Microbiology Human Metapneumovirus Respiratory Syncytial Virus Influenza A Virus Co-infection Vaccine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Co-infections are characterized by the simultaneous invasion and replication of multiple distinct pathogenic organisms within a single host, which can modulate host-pathogen interactions, alter immune responses, and complicate both the clinical and therapeutic management of infectious diseases [ 1 ], [ 2 ]. Viral co-infections often involve complex interactions, including synergy, interference, mutual dependence, or competitive exclusion. Among these, interference between one virus suppresses the replication of another is commonly observed [ 3 ]. Epidemiological studies report that co-infection with multiple respiratory viruses occurs in approximately 10–20% of cases, highlighting its clinical significance [ 4 ]. Some studies show that co-infection may lead to changes in the development of the illness [ 5 ] while some do not change the course of the disease [ 6 ]. Respiratory viral infections pose a significant global health burden, with influenza alone causing approximately 1 billion cases annually, leading to 3–5 million hospitalizations and 290,000–650,000 deaths, primarily affecting vulnerable populations [ 7 ]. These infections can also have a financial impact on patients, families, and the healthcare system [ 8 ]. Acute respiratory infections represent one of the leading causes of mortality among children worldwide [ 9 ]. A significant fraction of these infections in the pediatric population is attributable to viral pathogens, particularly human metapneumovirus (hMPV), respiratory syncytial virus (RSV), and influenza viruses [ 10 ]. Human respiratory syncytial virus (HRSV) is an RNA virus with a membrane around it. It belongs to the Paramyxoviridae family and the Pneumovirus genus. Its size ranges from 100 to 200 nanometers [ 11 ]. HRSV is an enclosed virus with either spherical or filamentous morphology. Its single-stranded RNA genome is encapsidated and linked with a lipid bilayer that integrates the surface glycoproteins G (responsible for receptor binding) and F (facilitating membrane fusion). The virus makes several proteins from its genome. The HRSV genome encodes multiple proteins, including nonstructural proteins NS1, NS2, and M2, a phosphoprotein (P), and the RNA-dependent RNA polymerase (L), all important for viral replication and transcription [ 12 ]. HRSV is one of the leading viral pathogens globally, mostly responsible for acute lower respiratory tract infections in infants, young children, and the elderly [ 13 ], [ 11 ]. It infects both the upper and lower parts of the airway. It can block airflow, cause bronchiolitis, and lead to apnea, pneumonia, or even respiratory failure [ 13 ]. Human metapneumovirus (hMPV), genetically divided into two major families (A and B), is a major cause of respiratory tract infections across age groups and often co-circulates with RSV and influenza. Notably, co-infections involving hMPV, RSV, and influenza A virus may trigger more complex and severe immune responses than single-virus infections [ 13 ]. Co-infection with hMPV, RSV, and Influenza A virus can cause more complex and severe immune responses compared to single-virus infections [ 14 ]. These viruses may interact competitively or synergistically, disrupting host immune regulation and resulting in delayed viral clearance, prolonged disease duration, and a heightened risk of complications, particularly in immunocompromised individuals [ 15 ]. Immunoinformatics, an interdisciplinary field combining immunology and computational biology, enables the prediction and modeling of immune responses using in silico tools. It offers a rapid and cost-effective alternative to conventional laboratory methods for identifying vaccine targets. Notably, this approach proved highly valuable during the COVID-19 pandemic, as it accelerated the development of subunit vaccines against SARS-CoV-2. In the context of co-infections, immunoinformatics enables the identification of conserved epitopes, prediction of T- and B-cell responses, and rational design of multi-epitope vaccines [ 16 ]. Furthermore, molecular-level modeling of host-pathogen interactions through immunoinformatics contributes to a deeper understanding of immune complexity and supports the development of targeted vaccines and immunotherapeutic strategies [ 17 ]. Both cellular and humoral immune responses are essential for host defense against hMPV, RSV, and Influenza A virus. CD8⁺ cytotoxic T lymphocytes (CTLs) play a pivotal role in clearing infected cells, while CD4⁺ helper T lymphocytes (HTLs) assist in antibody production and coordinate adaptive immunity. CD4⁺ T cells also contribute to antiviral defense by secreting interferon-gamma (IFN-γ), a key cytokine in the antiviral response. These mechanisms highlight the importance of eliciting both arms of the immune system in vaccine development [ 18 ]. Given the limitations of traditional vaccine development, this study employed an immunoinformatics-based strategy to design multi-epitope subunit vaccines (MESVs) targeting conserved regions of fusion and neuraminidase proteins from RSV, hMPV, and Influenza A virus. Computational tools were used to predict CTL, HTL, and B-cell epitopes, which were linked with appropriate adjuvants and spacers. This study employed an immunoinformatics-driven strategy to design multi-epitope subunit vaccines (MESVs) targeting conserved CTL, HTL, and B-cell epitopes from RSV, hMPV, and Influenza A viral proteins [ 19 ]. Structural modeling, molecular dynamics simulations, and TLR-4 docking confirmed the construct's stability, immunogenic potential, and receptor-binding affinity. Compared to conventional approaches, this method enables precise, cost-effective vaccine design with enhanced safety and efficacy profiles against viral co-infections. 2 Methodology Figure 1 shows the step-by-step process used to design and evaluate a chimeric multi-epitope vaccine against hMPV , RSV , and IAV , major causes of acute respiratory infections. 2.1 Proteins Sequences Retrieval The full-length protein sequences of hMPV , RSV and IAV were retrieved in FASTA format from the NCBI Protein Database. For each virus, the respective fusion proteins ( hMPV and RSV ) and neuraminidase protein ( Influenza A virus , IAV ) were selected based on their immunological significance and availability of complete protein sequences. 2.2 Prediction of antigenic proteins The complete proteome of the Human respiratory syncytial virus ( RSV ), Human Metaneumovirus ( hMPV ), and Influenza A virus (Nine, ten, and fourteen proteins respectively, IAV ) were submitted to VaxiJen server to find out their antigenicity. The fusion protein of hMPV and RSV and neuraminidase of IAV with the highest antigenic value was selected for the construction of final chimeric multi-epitope subunit vaccines. 2.3 Identification of the conserved regions in antigenic proteins The highly antigenic fusion protein of RSV , hMPV , and neuraminidase of IAV were fed to Clustal Omega online server for the identification of the conserved regions. The predicted epitopes for B and T cells were investigated in the aligned retrieved fusion protein of RSV , hMPV and neuraminidase of IAV for the conservation. 2.4 Prediction and screening of Cytotoxic T-lymphocytes (CTL) epitopes CTL epitopes help the immune system remove virus-infected cells and lower the viral load. To find these, we used the NetCTL 1.2 server to predict epitopes from the fusion proteins of RSV and hMPV , and the neuraminidase of IAV . The aforementioned tool checks MHC class I binding, proteasome cleavage sites, and TAP transport. We selected epitopes with a score above 0.75. 2.5 Prediction of HTL epitopes We used the Immune Epitope Database (IEDB) to find helper T cell (HTL) epitopes from the fusion proteins of RSV and hMPV , and the neuraminidase of IAV . Epitopes with the lowest percentile ranks showing strong MHC class II binding was chosen for the vaccine design. 2.6 Prediction of B cell epitopes Linear B-cell epitopes were predicted using the ABCPred server, which utilizes a recurrent neural network-based algorithm to identify sequences likely to elicit antibody responses [ 20 ]. Epitopes were selected based on high prediction scores, with a threshold value of 0.80 applied to ensure the inclusion of highly probable antigenic regions 2.7 Multiple epitope vaccine designing and evaluation The selected epitopes comprising cytotoxic T lymphocytes (CTLs), helper T lymphocytes (HTLs), and B-cell epitopes derived from the fusion proteins of RSV and hMPV , as well as the neuraminidase of IAV , were assembled into a chimeric multi-epitope subunit vaccine construct. To ensure proper immunogenic presentation and structural flexibility, AAY, GPGPG, and KK linkers were employed to join CTL, HTL, and B-cell epitopes, respectively. Moreover, an immunostimulatory adjuvant was conjugated to the N-terminus of the vaccine hypothesis via an EAAK linker to augment the overall immune response [ 21 ]. 2.8 Antigenicity and allergenicity of construct We used the VaxiJen v2.0 server to test if the vaccine is likely to activate an immune response. A score above 0.4 was considered antigenic. We also checked allergenicity using AllerTOP v2.0 to make sure the vaccine was safe and not probable to cause allergies. 2.9 Physicochemical properties of construct We used the ProtParam tool on the ExPASy server to analyze the vaccine’s properties, including amino acid content, molecular weight, pI, stability, half-life, aliphatic index, and GRAVY score. 2.10 3-D structure prediction and validation We used the Robetta server to model the 3D structure of the vaccine using its amino acid sequence in FASTA format. The chosen model was checked with ProSA-Web and PROCHECK. 2.11 Molecular docking with TLR4 Effective stimulation of the human immune response requires the vaccine to interact with host immune receptors. Molecular docking assists as a crucial in silico approach to predict the binding affinity and three-dimensional (3-D) orientation between a ligand and its target receptor. For this purpose, the 3-D structure of Toll-like receptor 4 (TLR-4; PDB ID: 2Z63) was retrieved from the RCSB Protein Data Bank. Before docking, all heteroatoms and water molecules were removed using PyMOL to prepare the receptor structure. The refined TLR-4 structure and the modeled vaccine concept were submitted to the HDOCK server to evaluate their binding interactions. The top-ranked docked complex, determined based on docking score, was selected for further structural and interaction analysis. 2.12 Codon optimization and vaccine in silico cloning Codon optimization is a computer-based method used to improve protein expression by adjusting codons to match the host organism. We used the JCat tool to convert the vaccine’s amino acid sequence into a DNA sequence optimized for E. coli K12 . JCat also gave CAI and GC content values to check if the gene would express well. NdeI and XhoI sites were added to the ends of the sequence for cloning into the pET-28a(+) vector. The cloning setup was checked using SnapGene. 2.13 Immune simulation The C-immSim online server was used for immune simulation to assess the human immune response to the constructed vaccine. The aforementioned server evaluated the antigenicity of the developed vaccine and its capacity to elicit immunological responses upon administration. The server evaluated the quantity of immune cells like helper T-cells 1 and 2 (Th1 and Th2), respectively. Additionally, the server measured various other immunological responses, including the production of antibodies, cytokines, and interferon, in response to administered vaccines. 2.14 Molecular dynamic simulation of the constructed vaccine and human TLR4 complex Molecular dynamics (MD) simulations were performed using AMBER v24 [ 22 ] to assess the structural stability and conformational dynamics of the chimeric multi-epitope vaccine in complex with human Toll-Like Receptor 4 (TLR4; PDB ID: 2Z63). The initial complex was prepared in PyMOL by removing crystallographic water molecules and heteroatoms. The ff19SB force field was used for protein parametrization [ 23 ]. The system was solvated in an octahedral box of OPC water molecules with a 12.0 Å buffer and neutralized with Na⁺ and Cl − ions. NaCl was added to a final concentration of 0.15 M to mimic physiological conditions [ 24 ]. Energy minimization was carried out in two stages: 10,000 steps with positional restraints (10 kcal/mol·Å²) on protein heavy atoms, followed by 50,000 steps without restraints. The system was gradually heated from 0 K to 300 K over 500 ps under constant volume (NVT), using Langevin dynamics with a collision frequency of 2 ps⁻¹. This was followed by 1 ns of equilibration at 1 atm under constant pressure (NPT), during which positional restraints were progressively removed. A 100 ns production run was performed at 300 K and 1 atm using periodic boundary conditions. Long-range electrostatics were handled with the particle mesh Ewald method. SHAKE constraints were applied to hydrogen-containing bonds, allowing a 2 fs timestep. Snapshots were saved every 10 ps for analysis. Analysis was performed using CPPTRAJ [ 25 ]. Root mean square deviation (RMSD) was calculated to assess the global stability of the complex over time. Root mean square fluctuation (RMSF) was calculated to examine residue-level flexibility. Radius of gyration (Rg) was calculated to monitor overall structural compactness. Intramolecular hydrogen bonds were analyzed to evaluate the complex stability. Principal component analysis (PCA) was conducted to identify dominant motions during the simulation. Binding free energy between the vaccine and TLR4 was estimated using the MM/GBSA method from extracted snapshots of the production phase. VMD and PyMOL were used for structural visualization. 3 Results Vaccines provoke the immune system and play a crucial role in fighting against different diseases. Small antigenic determinants can efficiently set off immune reactions rather than entire virus proteins [ 26 ]. By determining possible immunogenic sites, immunoinformatics provides a fast and accurate method of vaccine design [ 27 ]. The fusion protein of RSV and hMPV and neuraminidase of IAV strains have been analyzed using an immunoinformatics method in order to find immunogenic, non-allergenic CTL, HTL, and B-cell epitopes. Broad protection against RSV , hMPV and IAV is sought by designing chimeric multi-epitope subunit vaccines from these epitopes [ 28 ]. 3.1 Prediction of Antigenic Proteins Seven proteins ( RSV and hMPV ) and fourteen protein of IAV were evaluated for antigenicity through VaxiJen server. Among them, the fusion protein of RSV , hMPV and neuraminidase protein of IAV showed the antigenicity scores (0.4775 for hMPV , 0.5219 for RSV and 0.5024 for IAV ) and was selected for constructing the final chimeric multi-epitope subunit vaccines (Supplementary Table S1 ). 3.2 Protein sequence retrieval and conserved region identification All available fusion and neuraminidase protein sequences for RSV , hMPV and IAV were retrieved and kept in FASTA format. The conserved region in the fusion and neuraminidase proteins was identified through Multiple sequence alignment (MSA), and finally, HTL, CTL, and BCL epitopes were selected for chimeric multi-epitope subunit vaccine Supplementary Figures S1 -S3). This approach guarantees that the intended vaccination targets conserved viral areas, therefore lowering the possibility of immune escape resulting from mutations and improving its efficacy throughout several strains viruses. 3.3 Cytotoxic T cell epitope prediction and screening In the context of adaptive immunity, major histocompatibility complex (MHC) class I glycoproteins are universally expressed on the surface of all nucleated cells and play a pivotal role in the presentation of antigenic peptides to cytotoxic T lymphocytes (CD8 + T cells). These molecules are responsible for displaying peptides resulting from endogenous proteins, including those of viral origin, so initiating a cytotoxic immune response aimed at eliminating infected or abnormal cells. Furthermore, MHC class I molecules are found not only on body cells but also on immune cells like B cells, monocytes, dendritic cells, and thymic cells. They present viral peptides to cytotoxic T cells, help fight infections inside cells, and trigger a strong immune response. CTL plays a crucial role in eradicating the invading pathogens. The fusion and neuraminidase protein sequences from RSV , hMPV , and IAV were submitted to the NetCTL 1.2 web server for the prediction of immunogenic CTL epitopes. Different epitopes (531 for hMPV , 566 for RSV , and 461 for IAV ) were predicted for fusion and neuraminidase proteins, respectively. Among these, ten epitopes were selected from each protein (Supplementary Tables S2-S4 ). The selected epitopes were further assessed for toxicity, allergenicity, and antigenicity, respectively. Eventually, two high antigenic, non-allergic, non-toxic, and conserved epitopes from each fusion protein of hMPV and RSV and neuraminidase protein from influenza A virus were selected for the final chimeric multi-epitope vaccine construct (Table 1 ). Table 1 Selected CTL epitopes of hMPV , RSV and IAV used in the proposed chimeric multi-epitope subunit vaccine Virus Protein Peptide Sequence Peptide Position Conserved region Combined Score Antigenicity Score Toxicity Allergenicity hMPV Fusion LSVLRTGWY 35–44 Yes 1.6130 0.8622 NO Non-allergenic FSDNAGITP 88–97 Yes 0.8963 0.7563 NO Non-allergenic RSV Fusion NIDIFNPKY 382–391 Yes 2.5413 0.7826 NO Non-allergenic LIAVGLLLY 540–549 Yes 2.3421 0.8288 NO Non-allergenic IAV Neuraminidase WTSASSISF 421–430 Yes 1.5752 1.0334 NO Non-allergenic ELNAPNSHY 252–261 Yes 1.1526 0.1266 NO Non-allergenic 3.4 Helper T lymphocytes (HTL) epitopes prediction Helper T cells are key parts of the adaptive immune system. They help activate both B cells and cytotoxic T cells. When a virus enters the body, cells like macrophages take it in and break it down. They show parts of the virus on their surface using MHC class II molecules. Helper T cells recognize these parts and help activate B cells, which then make specific antibodies. Since helper T cells play a key role in immune response, we predicted HTL epitopes from the fusion proteins of hMPV and RSV and the neuraminidase of IAV . Ten top epitopes from each virus were shortlisted based on low percentile ranks, which suggest strong binding (see Supplementary Table S3). We tested these epitopes for antigenicity, toxicity, and allergenicity. Two strong, safe HTL epitopes from each virus were chosen for the final vaccine design ( Table 2 ). Table 2 Selected HTL epitopes of hMPV , RSV , and IAV used in the proposed chimeric multi-epitope subunit vaccine. Virus Protein Allele Peptide position Peptide Sequence P. Rank Antigenicity Score Toxicity Allergenicity hMPV Fusion HLA-DRB1*07:01 229–243 RAVSYMPTSAGQIKL 0.06 0.8830 NO Non-allergenic HLA-DRB1*15:01 119–133 TAGIAIAKTIRLESE 0.12 0.8439 NO Non-allergenic RSV Fusion HLA-DRB3*02:02 234–248 TREFSVNAGVTTPVS 0.04 0.2788 NO Non-allergenic HLA-DRB1*03:01 263–277 DMPITNDQKKLMSN 0.04 0.2076 NO Non-allergenic IAV Neuraminidase HLA-DRB3*01:01 214–228 SNRPWVSFDQNLDYQ 1.30 0.6043 NO Non-allergenic HLA-DRB5*01:01 334–348 RPCFWVELIRGRPKE 1.30 0.9819 NO Non-allergenic 3.5 B-Cell epitopes prediction B cells are important for adaptive immunity, as to produce antibodies that help block infections and provide long-term protection. The rational design of B-cell epitopes is critical, as they initiate various protective mechanisms, including viral neutralization, agglutination, activation of the complement cascade, and antibody-dependent cellular cytotoxicity. Herein, the fusion proteins of hMPV and RSV , along with the neuraminidase protein of the IAV , were analyzed for linear B-cell epitope prediction using the ABCPred web server. The server predicted 21, 23, and 17 potential epitopes for the hMPV , RSV and IAV proteins, respectively. Based on their predicted binding scores, ten epitopes from each protein were selected (Supplementary Table S4 ). We tested these epitopes for antigenicity with VaxiJen v2.0, toxicity with ToxinPred, and allergenicity with AllerTOP v2.0. Based on the results, we chose two safe, conserved, and strongly immunogenic epitopes from each viral protein for the final vaccine (Table 3 ). Table 3 BCL epitopes of hMPV , RSV and IAV used in the proposed chimeric multi-epitope subunit vaccine Virus Protein Epitope Start Position Score Antigenicity Score Toxicity Conserved region hMPV Fusion QHVIKGRPVSNSFDPI 434 0.88 0.9931 NO Non-allergenic CWIIKAAPSCSEKDGN 283 0.92 0.8104 NO Non-allergenic RSV Fusion NIDIFNPKYDCKIMTS 383 0.89 0.7806 NO Non-allergenic SCSISNIETVIEFQQK 211 0.86 1.0332 NO Non-allergenic IAV Neuraminidase IGYICSGVFGDNPRPK 299 0.91 0.5897 NO Non-allergenic EECSCYPDTGKVMCVC 261 0.91 0.4128 NO Non-allergenic 3.6 Chimeric multi-epitope vaccine construction To assemble the final chimeric multi-epitope subunit vaccine (MESV), selected immunodominant epitopes derived from the fusion proteins of hMPV and RSV , as well as the neuraminidase of IAV , were joined using specific linkers to ensure structural flexibility and optimal immune presentation (Fig. 2 A). Cytotoxic T lymphocyte (CTL) epitopes were linked via AAY linkers, helper T lymphocyte (HTL) epitopes via GPGPG linkers, and B-cell epitopes via KK linkers. The AAY linker facilitates efficient proteasomal processing and MHC class I presentation; GPGPG linkers boost the presentation and immunogenicity of HTL and B-cell epitopes; and KK linkers help to maintain structural separation, minimizing epitope interference while boosting immunogenicity. An immunostimulatory adjuvant, human β-defensin 2, was conjugated to the N-terminus of the construct using an EAAK linker to further enhance immune activation. Human β-defensin 2 is part of the body’s natural defense system and can activate specific immune responses against antigens. The finalized MESV construct comprised 442 amino acids in total. The specific epitopes incorporated into the vaccine design are detailed in (Tables 1 – 3 ). 3.7 3-D structure prediction and validation The amino acid sequence of the chimeric vaccine was uploaded to the Robetta server to build its 3D structure. Five 3-D models have been produced using the Robetta-ab-initio modelling server, and upon assessment, the first model depicted in ( Fig. 2 A ) was chosen for further investigation. Each epitope, including CTL, HTL, and B-cell epitopes, is represented in distinct colors and depicted as a ribbon. A surface shade has been applied for enhanced depiction. The selected model was validated by ProSA-web (Fig. 2 D), ERRAT, and Ramachandran plot (Fig. 2 B). The ProSA-web revealed a score of -8.3 for the 3D model of the proposed vaccine design, however the overall quality factor reported by ERRAT was 96. 31%, demonstrating the model's accuracy (Supplementary Figure S4 ). The Ramachandran plot study indicated that the majority of amino acids (98.4%) are located in the favored zone, whilst a minimal proportion (1.6%) is found in the prohibited region (Fig. 2 B). The aforementioned results demonstrated that the quality of the chosen vaccine construct designs is appropriate and suitable for the next steps, including molecular docking, modelling, and free energy calculations. 3.8 Physicochemical properties, allergenicity, and antigenicity of the vaccine construct The antigenic potential of the designed vaccine construct was evaluated using the VaxiJen v2.0 server, yielding a score of 0.50, which exceeds the threshold value of 0.4 and indicates a strong capacity to produce an immune response. Allergenicity assessment, conducted via the AllerTOP v2.0 server, predicted the construct to be non-allergenic, suggesting its potential safety in immunization applications. We used the ProtParam tool to check properties like molecular weight, half-life, aliphatic index, pI, stability, and GRAVY score. The vaccine’s molecular weight is 46,715.05 Da. Its predicted half-life is 30 hours in human cells, 20 hours in yeast, and over 10 hours in E. coli , showing good stability. The instability index is 28.79, which means the construct is stable. An aliphatic index of 74.28 suggested good thermostability, while a theoretical pI supported its acidic nature. The GRAVY score of -0.276 reflected the overall hydrophilic character of the vaccine, favoring solubility and biological interaction potential (Table 4 ). Table 4 The physicochemical properties of the designed chimeric multi-epitope subunit vaccine S. No Parameter Value Remarks 1 Number of Amino Acids 442 Suitable 2 Molecular Weight 46715.05 Average Weight 3 Number of atoms 6559 Satisfactory 4 Theoretical pI 6.77 Satisfactory 5 The estimated half-life (Mammalian reticulocytes, in vitro ) 30 hours Satisfactory 6 The estimated half-life (yeast, in vivo ) > 20 hours Satisfactory 7 The estimated half-life ( Escherichia coli , in vivo ) > 10 hours Satisfactory 8 Instability index 28.79 Stable 9 Aliphatic index 74.28 Satisfactory 10 GRAVY -0.276 Satisfactory 3.9 Molecular docking with TLR4 receptor An effective immunological response from a vaccination depends on its high binding affinity to the immune receptors of the host. Most importantly, controlling inflammatory pathways, toll-like receptors (TLRs) help regulate immune responses to infections. Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs), thus initiating intracellular signaling pathways and modulating gene expression to activate innate and adaptive immune responses [ 29 ]. The recognition of many viral components, including nucleic acids and envelope glycoproteins, which start a cascade of events leading to the synthesis of interferon type I (IFN-I), inflammatory cytokines, and chemokines, depends critically on toll-like receptors (TLRs). Toll-like receptors (TLRs) help activate and mature dendritic cells, linking the body’s early (innate) and later (adaptive) immune responses. We used the HDOCK server to test how the chimeric vaccine binds to the host’s immune receptors. The docking analysis assessed the binding affinity of the vaccine construct with human Toll-like receptor 4 (TLR-4; PDB ID: 2Z63). The resulting docking score was − 277.43 kcal/mol, suggesting a strong and stable interaction between the vaccine and TLR-4, which may support effective immune activation (Fig. 3 ). As per the PDBsum server, TLR-4 (Chain A) and chimeric constructed vaccine (Chain B) have 9 hydrogen bonds; Thr413- Ser261, Gln484- Arg164, Glu485- Arg164 (two hydrogen bonds), Asn481- Arg311, Gln505- Arg311, Arg311- Gln507, Gln507-Arg327, and Gln507-Asp307. Additionally, four salt bridges; Glu439- Arg248, Glu485- Arg164, Glu509- Arg327 and Arg460- Asp307 and 183 non-bonded interactions were found in constructed vaccine and TLR4 complex (Fig. 3 ). 3.10 Normal mode analysis of constructed vaccine with TLR-4 The structural dynamics and stability of the vaccine-receptor complex were assessed using the iMODS server, which employs normal mode analysis (NMA) by modulating the force field around the complex to simulate atomic interactions over time. The analysis revealed reduced structural distortions, particularly at the residue level, suggesting enhanced conformational stability of the modeled complex (Fig. 5 ). Molecular simulations estimated the flexibility and motion of atoms within the antigen–receptor interface. Figure 5 A displays the deformability profile of the MEVC-TLR4 complex, showing a localized peak within a flexible region of the vaccine build. B-factor analysis, as shown in (Fig. 4 B ) , compares the intrinsic mobility of residues derived from both the PDB and NMA models. The eigenvalue, depicted in (Fig. 4 C ) , reflects the stiffness of the structure; a lower value indicates greater flexibility. (Fig. 4 D) presents the variance associated with individual and cumulative motions, represented in purple and green, respectively. The covariance matrix (Fig. 4 E) shows the dynamic correlations between residue pairs, where red denotes correlated motion, blue indicates anti-correlated motion, and white represents uncorrelated motion. Figure 4 F represents the elastic network model, resembling a spring map, which visualizes the strength of interatomic interactions within the complex. Lastly, Fig. 4 G summarizes the overall results of the iMODS analysis, confirming the structural integrity and potential functional stability of the vaccine TLR4 complex. 3.11 In silico cloning of chimeric multi-epitope subunit vaccine in the pET-28a(+) expression vector. We used the JCat tool to optimize codons and improve vaccine expression in a different host system. The coding sequence of the 1,326-nucleotide chimeric multi-epitope subunit vaccine was optimized for expression in E. coli K12 . JCat computed critical parameters such as the Codon Adaptation Index (CAI) and GC content, which are indicative of translational efficiency. The optimized sequence yielded a CAI value of 1.0 and a GC content of 51.24%, both of which suggest high compatibility and potential for robust expression in the E. coli system. Subsequently, SnapGene software (v3.3.4) was used to simulate the cloning strategy. ScaI and BmtI restriction sites were added to the sequence ends to help insert it into the E. coli pET-28a(+) vector. The software confirmed the absence of internal restriction sites that could interfere with the cloning process. As illustrated in (Fig. 5 ), the optimized gene sequence was successfully integrated into the vector map, validating the construct’s readiness for downstream expression and purification. 3.12 Immune response simulation analysis Vaccines provoke the immune system of the host without causing disease. The immune system can be stimulated by vaccines through different mechanisms. Co-administration of protein-based antigens with immunologic adjuvants enhances local innate immune activation, leading to the functioning of antigen-presenting cells and the release of pro-inflammatory cytokines by macrophages. These macrophages, often referred to as dendritic cells, internalize the antigen and present it on their surface via major histocompatibility complex (MHC) molecules. T cells recognize the MHC-antigen complex through their T-cell receptors, thereby activating adaptive immunity and generating memory T cells. In contrast, polysaccharide antigens typically induce antibody responses without T-cell involvement. These antigens bind directly to mature B cells, initiating their differentiation into antibody-producing plasma cells. Immune simulation was conducted to assess the immunogenicity of the constructed vaccine. This simulation evaluated the immune system's response, particularly antibody production, following the administration of the constructs. Notably, the immune system exhibited a robust response, with significantly elevated antibody production observed after each vaccine injection (Fig. 6 ). The chimeric multiepitope subunit vaccine construct was administered, and antigen titers were initially low until the seventh day. Subsequently, they progressively increased, reaching their maximum on the 12th day. Nevertheless, a rise in antibody titers was observed beginning on the 15th day. The activation of supplementary immune system components was accompanied by the complete neutralisation of antigens by the 17th day. The combined IgM and IgG titers were 4.5 × 10 7 , and the combined IgG1 + IgG2 and IgG1 titers were also elevated post-vaccination (Fig. 6 A). The evaluation of interleukin (IL) and cytokine responses, as shown in (Fig. 6 B ) , indicates a notable increase in the levels of IFN-γ and IL-2. The results demonstrate a reliable and strong immune response subsequent to the administration of the vaccine. The cellular immune response upon re-exposure to pathogens was significantly robust, marked by the development of memory cells. T cell populations were found to be greater than 1500 cells/mm³, with peak concentrations of phagocytic natural killer cells, dendritic cells, and phagocytic macrophages reported at levels exceeding 380 cells/mm³ and 200 cells/mm³, respectively (Fig. 6 B-F). 3.13 Molecular Dynamics Simulation Analysis of the TLR4-Vaccine Complex To evaluate the structural stability and conformational behavior of the TLR4-vaccine complex, several parameters were monitored across the 100 ns production phase (Fig. 7 A-D). The RMSD profile (Fig. 7 A) was used to assess global stability over time. The system showed a gradual rise in RMSD during the first 60 ns, reflecting initial structural adjustments. After ~ 65 ns, RMSD values stabilized around 5–6 Å, indicating that the complex reached equilibrium. Minor fluctuations observed after 70 ns suggest localized rearrangements without global instability. RMSF analysis (Fig. 7 B) captured residue-wise flexibility across the complex. The TLR4 region (residues 1-569) exhibited limited fluctuations, mostly under 3 Å, indicating a stable backbone throughout the simulation. In contrast, the vaccine region (residues 570–1010) showed increased flexibility, with peaks exceeding 9 Å at certain loop regions, likely reflecting inherent flexibility in the designed construct. The Rg (Fig. 7 C) was calculated to monitor the compactness of the TLR4-vaccine complex. The Rg remained steady around 39–40 Å during the initial 70 ns, suggesting stable folding. A gradual increase after 75 ns, reaching ~ 41.5 Å, indicates slight structural expansion, possibly due to flexible segments in the vaccine construct. No abrupt changes were observed, supporting overall structural retention. A contact map analysis (Fig. 7 D) tracked native and non-native contacts, minimum/maximum distances, and contact stability across the trajectory. Native contacts remained consistently populated throughout, while non-native contacts fluctuated without showing dominance, suggesting preserved structural integrity. Maximum distance trends remained stable, with no significant expansion or dissociation events during the simulation. Collectively, RMSD stabilization after 65 ns, stable Rg, maintained native contacts, and localized flexibility observed in RMSF profiles indicate that the TLR4-vaccine complex remained structurally stable under simulated conditions, with expected mobility confined to vaccine regions. 3.14 Conformational Dynamics and Principal Component Analysis Principal component analysis (PCA) was conducted to investigate large-scale conformational changes in the TLR4–vaccine complex over the course of the simulation (Fig. 8 A). The trajectory was projected along the first two principal components (PC1 and PC2), which captured the dominant motions within the system. The PCA plot revealed three distinct conformational states sampled during the simulation. The first state (A) was identified at ~ 28.3 ns, the second state (B) at ~ 49.2 ns, and the third state (C) at ~ 94.8 ns. The transitions between these states suggest gradual exploration of the conformational space rather than abrupt structural shifts. The presence of distinct clusters indicates that the complex underwent coordinated motions but remained within a restricted conformational landscape, supporting the structural stability observed in RMSD and Rg analyses. To visualize structural changes, representative snapshots corresponding to the early (blue) and late (red) stages of the simulation were superimposed (Fig. 8 B). This comparison showed minimal deviation in the TLR4 region, consistent with its structural rigidity, while subtle shifts were observed in the vaccine region, reflecting expected flexibility. The PCA confirmed that the TLR4-vaccine complex explored limited conformational space during the simulation, without significant structural rearrangement, supporting the stability of the complex under simulated conditions. 3.15 Binding Free Energy Analysis of the TLR4-Vaccine Complex The binding free energy of the TLR4-vaccine complex was estimated using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method based on snapshots from the production phase. The calculated energy components are summarized in Table 5 . The total binding free energy (ΔG TOTAL ) was estimated as -121.72 kcal/mol, indicating a stable and favorable interaction between the vaccine construct and TLR4. The major contribution came from electrostatic interactions (ΔE EL : -378.08 kcal/mol) and van der Waals forces (ΔE VDW : -64.00 kcal/mol). The non-polar solvation term (ΔE SASA : -8.85 kcal/mol) contributed marginally to stabilization. In contrast, the polar solvation energy (ΔEGB: 329.22 kcal/mol) opposed binding, as expected due to desolvation penalties upon complex formation. Despite this, the gas-phase interaction energy (ΔG GAS : -442.09 kcal/mol) effectively compensated, resulting in an overall favorable binding profile. The low standard error values across all components confirmed the stability and consistency of the calculated energy terms throughout the sampled frames. Overall, MM/GBSA analysis supports strong binding affinity between the multi-epitope vaccine construct and TLR4, driven primarily by electrostatic and van der Waals interactions. Table 5 MM/GBSA binding free energy components for the TLR4-vaccine complex. Energies are reported as average values (kcal/mol) with standard deviation and standard error. Energy Component Average Standard Deviation Standard Error ΔE VDW (kcal/mol) -64.00 11.91 0.37 ΔE EL (kcal/mol) -378.08 44.13 1.39 ΔE GB (kcal/mol) 329.22 42.51 1.34 ΔE SASA (kcal/mol) -8.85 1.94 0.06 ΔG GAS (kcal/mol) -442.09 45.13 1.42 ΔG SOLV (kcal/mol) 320.36 42.25 1.33 ΔG TOTAL (kcal/mol) -121.72 8.24 0.26 ΔE VDW , van der waals free energy; ΔE EL , electrostatic free energy; ΔE GB , the polar component of solvation-free energy; ΔE SASA , non-polar components of solvation energy; ΔG GAS , binding free energy without solvent; ΔG SOLV , binding free energy with solvent; ΔG TOTAL , total binding free energy. 4 Discussion The growing burden of multidrug-resistant pathogens underscores the need for new preventive strategies, including effective vaccines [ 30 ]. In this study, a structure-based immunoinformatics strategy was applied to design a multi-epitope vaccine targeting Human Metapneumovirus , Respiratory Syncytial Virus , and Influenza A Virus . By integrating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, the designed construct aimed to stimulate both cellular and humoral immune responses [ 31 ]. Selection of epitopes with high population coverage and confirmed antigenicity ensured broad-spectrum immune activation potential [ 32 ]. The inclusion of an appropriate adjuvant sequence at the N-terminus was intended to enhance immunogenicity by promoting dendritic cell activation and cytokine production [ 33 ]. Flexible linkers were incorporated to preserve epitope presentation and facilitate proper folding of the chimeric construct [ 34 ]. The physicochemical properties of the final construct, including stability, non-allergenicity, and solubility, support its potential as a safe and manufacturable vaccine candidate. Reverse vaccinology and in-silico epitope mapping have been successfully applied in recent vaccine development studies [ 35 ]. However, combining these strategies with structural modeling and dynamic evaluation provides a more in-depth assessment of vaccine performance before experimental testing. Our workflow integrated these stages for optimization and validation of the designed construct. Accurate structural modeling of multi-epitope vaccines is critical for predicting receptor binding and immune system recognition [ 36 ]. The tertiary structure of the designed construct was predicted using homology-based modeling, followed by refinement to improve stereochemical quality. Ramachandran plot analysis confirmed acceptable backbone geometry, with 98.4% of residues in favored regions and only 1.6% in disallowed regions. The ProSA-web score of − 8.3 and the ERRAT quality factor of 96.31% further supported the structural reliability of the modeled vaccine, indicating no major anomalies. To evaluate receptor interaction, molecular docking was performed against human Toll-Like Receptor 4 (TLR4). The vaccine construct showed favorable binding, with a docking score of − 277.43 kcal/mol at the TLR4 binding site, suggesting strong interaction potential with the receptor. A total of 14 hydrogen bonds and multiple hydrophobic contacts contributed to complex formation, consistent with reported mechanisms of TLR4-ligand binding [ 37 ]. However, docking represents a static interaction snapshot and does not capture molecular flexibility or solvent effects. To overcome this limitation, molecular dynamics simulation was used to assess the structural stability and conformational behavior of the vaccine–TLR4 complex under near-physiological conditions. Docking predictions were further evaluated using molecular dynamics simulations to assess complex stability under dynamic conditions. The RMSD profile showed that the TLR4-vaccine complex stabilized after approximately 65 ns, indicating that no large-scale structural rearrangements occurred once initial adjustments were complete [ 38 ]. The consistent radius of gyration across the trajectory confirmed that the complex retained its compactness throughout the simulation. RMSF analysis highlighted minimal fluctuations within the TLR4 chain, reflecting its structural rigidity, while localized flexibility in the vaccine region corresponded to loop and linker segments as expected for such constructs. Contact analysis confirmed the persistence of native interactions, with no significant increase in non-native contacts, supporting the structural integrity of the complex during simulation. Principal component analysis identified three main conformational states at 28.2, 49.2, and 94.8 ns, indicating restricted and coordinated motions within a stable conformational landscape [ 39 ]. This limited exploration of conformational space aligns with the observed stability in global structural parameters. Binding free energy estimation using MM/GBSA supported these findings. The total binding energy of − 121.72 kcal/mol indicated a strong and energetically favorable interaction, driven primarily by electrostatic and van der Waals forces [ 40 ]. Despite the opposing contribution from polar solvation energy, the favorable gas-phase interaction energies dominated, consistent with reported mechanisms for TLR4-peptide complexes [ 41 ]. The molecular dynamics simulation and MM/GBSA analysis confirmed that the vaccine construct formed a stable, energetically favorable, and structurally consistent complex with TLR4. These results support its potential to function as an effective immunostimulatory agent. To assess the immunogenic potential of the designed vaccine, an immune simulation analysis was performed. The simulation predicted a sharp increase in IgM levels, followed by sustained high concentrations of IgG antibodies after antigen exposure [ 42 ]. Cytokine profiling showed elevated levels of IFN-γ (up to 4,200 ng/ml) and IL-2 (up to 1,800 ng/ml), indicating a strong Th1-type cellular response, essential for pathogen clearance [ 43 ]. Simulated immune memory was evident, with secondary and tertiary immune responses showing higher antibody titers than the primary response, suggesting potential for long-term immunity [ 44 ]. For production feasibility, codon optimization was carried out for expression in Escherichia coli K12. The optimized sequence achieved a Codon Adaptation Index (CAI) of 0.98 and a GC content of 51.24%, both within the optimal range for bacterial expression [ 45 ]. In silico cloning into the pET28a(+) expression vector confirmed correct insertion of the construct without frame-shift errors [ 46 ]. These results indicate that the designed vaccine construct is predicted to be both immunogenic and suitable for laboratory-scale expression. The immune simulation and expression optimization provide a strong basis for progressing this vaccine candidate to experimental evaluation. The workflow used in this study, combining epitope selection, structural modeling, molecular docking, molecular dynamics simulation, and immune simulation enabled the rational design and in-silico validation of a multi-epitope vaccine candidate targeting hMPV , RSV and IAV . The structural stability of the vaccine-TLR4 complex, supported by dynamic analysis and favorable binding energy estimation, provides strong evidence for its potential to engage innate immune receptors. Predicted immunogenicity and expression potential further support the suitability of the construct for experimental testing. Together, these findings highlight the value of combining immunoinformatics with structural and dynamic analyses in early-stage vaccine development. 5 Conclusion Emerging respiratory viral co-infections caused by hMPV , RSV and IAV remain a significant public health concern, especially in vulnerable populations. In this study, a multi-epitope vaccine was designed using a structure-based immunoinformatics approach to target these pathogens. The designed construct incorporated carefully selected CTL, HTL, and B-cell epitopes, along with an appropriate adjuvant, aiming to stimulate both innate and adaptive immunity. Structural modeling and molecular docking confirmed favorable binding to human TLR4, while molecular dynamics simulations demonstrated the stability of the vaccine-receptor complex under physiological conditions. Binding free energy analysis supported strong interaction, primarily driven by electrostatic and van der Waals forces. In silico immune simulation predicted robust immune activation, and codon optimization confirmed the construct’s suitability for expression in Escherichia coli . These results support the designed multi-epitope vaccine as a promising candidate for experimental validation and further development. 5.1 Limitations and Future Directions While our study provides an in-silico assessment of the designed multi-epitope vaccine, experimental validation remains essential to confirm its immunogenicity and safety. The structural stability, binding affinity, and immune stimulation potential demonstrated computationally should now be verified through in vitro assays, such as protein expression, receptor binding, and cytokine profiling. Further in-vivo studies will be required to evaluate immunogenicity, protective efficacy, and safety under physiological conditions. Experimental validation will be essential to confirm the immunogenicity and protective efficacy of the designed vaccine. Nonetheless, this computational framework provides an efficient strategy for accelerating early-stage vaccine discovery and candidate selection. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The authors acknowledge financial support from the National Natural Science Foundation of China (No. 32300048), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110158, 2024A1515012577), National Foreign Expert Individual Program (No. Y20240195), the Construction Project of Nano Technology and Application Engineering Research Center of Guangdong Medical University (No. 4SG24179G), the Postdoctoral funding project of Guangdong Medical University (3007/2BH24007), Dongguan Science and Technology of Social Development Program (20231800936272). Author Contribution L.L.: Supervision, Funding acquisition, Writing-review & editing. Y.C.: Project administration. C.W. and J.X.: Investigation, Methodology. A.S.: Software. X.X.: Data curation. J.T.: Investigation. Y.Q. and Y.Z.: Methodology, Software. A.J.: Software, Visualization, Writing-original draft. T.Y.: Writing-review & editing. S.U.: Data curation, Formal analysis, Writing-original draft. 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20:12:06","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153160,"visible":true,"origin":"","legend":"","description":"","filename":"7504eaca9c1440b69bca9c907402fcf11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/31e456e4451f839e0296126b.xml"},{"id":93527704,"identity":"d9680121-1a5e-4d9e-94b9-d4910e4889e1","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166638,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/cf54243268839a4e24e2e8b9.html"},{"id":93527687,"identity":"7296a8fb-f298-48c0-a6c4-14c3981d1a97","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238706,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the computational-based steps used to design a chimeric multi-epitope vaccine against \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e, and \u003cem\u003eIAV\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/a8cfd22e1389a7278cc7887e.png"},{"id":93527682,"identity":"eaa6c1e4-486b-44b0-b323-d32df6a7e7db","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113479,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure modeling and validation of the chimeric multi-epitope vaccine. (A) Chosen epitopes and linkers used in the chimeric subunit vaccine design \u003cstrong\u003e(B)\u003c/strong\u003eRamachandran plot analysis showing the distribution of backbone dihedral angles, indicating model stereochemical quality. \u003cstrong\u003e(C)\u003c/strong\u003eStructural quality assessment, displaying overall reliability based on non-bonded atomic interactions. \u003cstrong\u003e(D)\u003c/strong\u003e Z-score analysis from ProSA-web, representing the overall model quality compared to experimentally determined structures.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/d61ff723385a580e865a3cbc.png"},{"id":93527688,"identity":"2b4d1d8c-824f-459f-b340-70f7d75b62b6","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244791,"visible":true,"origin":"","legend":"\u003cp\u003eBinding interaction between the docked vaccine and TLR-4 receptor.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/03a097f7b029f6c3d7564738.png"},{"id":93528041,"identity":"be5fc907-ce58-4adb-a123-56ab1647fd9d","added_by":"auto","created_at":"2025-10-14 20:20:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96571,"visible":true,"origin":"","legend":"\u003cp\u003eNormal mode analysis (NMA) of the chimeric multi-epitope vaccine construct in complex with TLR-4. (A) Deformability plot showing flexible regions within the complex. (B) B-factor plot comparing experimental and predicted residue fluctuations. (C) Eigenvalue representing the overall structural stiffness. (D) Variance plot showing individual and cumulative variances of the modes. (E) The covariance matrix of residue movements indicates correlated (red), uncorrelated (white), and anti-correlated (blue) motions. (F) Elastic network model illustrating inter-residue connections as springs. (G) Summary plot of the overall mobility and interaction dynamics derived from iMODS.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/e08257716bec472bb0966900.png"},{"id":93527684,"identity":"c023725d-198a-4e87-84d9-4ff5e84abbda","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85028,"visible":true,"origin":"","legend":"\u003cp\u003eIn silico cloning of the codon-optimized chimeric vaccine into the pET-28a(+) vector. The diagram shows the successful insertion of the gene using ScaI and BmtI sites, confirming it can be expressed in \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/16fb1cf54dbeb0df570aa3f2.png"},{"id":93528648,"identity":"b50df09e-cb0c-49d6-b834-6454f1d3394a","added_by":"auto","created_at":"2025-10-14 20:28:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69127,"visible":true,"origin":"","legend":"\u003cp\u003eIn silico immune simulation results for the constructed multi-epitope vaccine, generated using the C-ImmSim server. \u003cstrong\u003e(A)\u003c/strong\u003e Primary, secondary, and tertiary immune responses following the 3-D vaccine show elevated levels of IgM, IgG1, and IgG2 antibodies. \u003cstrong\u003e(B)\u003c/strong\u003eB cell population dynamics, including memory B cell generation and clonal expansion. \u003cstrong\u003e(C)\u003c/strong\u003e T-helper (CD4+) and cytotoxic T lymphocyte (CD8+) response profiles, including active and memory cell populations. \u003cstrong\u003e(D)\u003c/strong\u003eCytokine expression levels (e.g., IFN-γ, IL-2) indicate activation of cell-mediated immunity. \u003cstrong\u003e(E)\u003c/strong\u003e Macrophage and dendritic cell activation over time, reflecting innate immune involvement. The simulation supports the immunogenic potential of the vaccine construct in eliciting strong, long-lasting humoral and cellular immune responses.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/c5e2893fdc4b45a90978ad4b.png"},{"id":93527690,"identity":"b3e9ac20-7ed8-48ac-92d2-69e12d54ed4a","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47737,"visible":true,"origin":"","legend":"\u003cp\u003eStructural stability and conformational analysis of the TLR4-vaccine complex during 100 ns molecular dynamics simulation. (A) Root mean square deviation (RMSD) of backbone atoms, showing system equilibration after ~65 ns. (B) Root mean square fluctuation (RMSF) per residue, highlighting localized flexibility in the vaccine region (residues 570-1010). (C) Radius of gyration (Rg), indicating overall compactness with slight expansion after 75 ns. (D) Contact analysis, showing stable native contacts and consistent minimum distances, with no significant unfolding events detected throughout the simulation.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/fa61be385eac799662115839.png"},{"id":93528044,"identity":"7b4f03d3-dc64-4e58-a7ac-6e67788d720b","added_by":"auto","created_at":"2025-10-14 20:20:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":85225,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Principal component analysis (PCA) projection of the TLR4–vaccine complex along PC1 and PC2 during the 100 ns MD simulation. Three distinct conformational states (A: 28.3 ns, B: 49.2 ns, C: 94.8 ns) are indicated. (B) Structural superposition of the complex at 0 ns (blue) and 100 ns (red), highlighting the overall structural preservation, with minor adjustments localized to the vaccine region.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/03f1e93a102fdb714799b7f1.png"},{"id":103765471,"identity":"c5572e2a-ef20-4bbd-8e40-52e9ea6a7692","added_by":"auto","created_at":"2026-03-02 16:02:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2644693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/291a936f-f405-4435-ad9c-84cbda773bc0.pdf"},{"id":93527701,"identity":"beb72806-0dfc-4e88-b44f-fb3cd70acd13","added_by":"auto","created_at":"2025-10-14 20:12:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3091901,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2025.09.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-7697013/v1/3d503fc91f37a1622ee993d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In Silico Development and Structural Evaluation of a Broad-Spectrum Chimeric Multi-Epitope Vaccine against Co-Infection by Human Metapneumovirus, Respiratory Syncytial Virus, and Influenza A Virus","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCo-infections are characterized by the simultaneous invasion and replication of multiple distinct pathogenic organisms within a single host, which can modulate host-pathogen interactions, alter immune responses, and complicate both the clinical and therapeutic management of infectious diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Viral co-infections often involve complex interactions, including synergy, interference, mutual dependence, or competitive exclusion. Among these, interference between one virus suppresses the replication of another is commonly observed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological studies report that co-infection with multiple respiratory viruses occurs in approximately 10\u0026ndash;20% of cases, highlighting its clinical significance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Some studies show that co-infection may lead to changes in the development of the illness [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] while some do not change the course of the disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRespiratory viral infections pose a significant global health burden, with influenza alone causing approximately 1\u0026nbsp;billion cases annually, leading to 3\u0026ndash;5\u0026nbsp;million hospitalizations and 290,000\u0026ndash;650,000 deaths, primarily affecting vulnerable populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These infections can also have a financial impact on patients, families, and the healthcare system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Acute respiratory infections represent one of the leading causes of mortality among children worldwide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A significant fraction of these infections in the pediatric population is attributable to viral pathogens, particularly human metapneumovirus (hMPV), respiratory syncytial virus (RSV), and influenza viruses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHuman respiratory syncytial virus (HRSV) is an RNA virus with a membrane around it. It belongs to the Paramyxoviridae family and the Pneumovirus genus. Its size ranges from 100 to 200 nanometers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. HRSV is an enclosed virus with either spherical or filamentous morphology. Its single-stranded RNA genome is encapsidated and linked with a lipid bilayer that integrates the surface glycoproteins G (responsible for receptor binding) and F (facilitating membrane fusion). The virus makes several proteins from its genome. The HRSV genome encodes multiple proteins, including nonstructural proteins NS1, NS2, and M2, a phosphoprotein (P), and the RNA-dependent RNA polymerase (L), all important for viral replication and transcription [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. HRSV is one of the leading viral pathogens globally, mostly responsible for acute lower respiratory tract infections in infants, young children, and the elderly [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It infects both the upper and lower parts of the airway. It can block airflow, cause bronchiolitis, and lead to apnea, pneumonia, or even respiratory failure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Human metapneumovirus (hMPV), genetically divided into two major families (A and B), is a major cause of respiratory tract infections across age groups and often co-circulates with RSV and influenza. Notably, co-infections involving hMPV, RSV, and influenza A virus may trigger more complex and severe immune responses than single-virus infections [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCo-infection with hMPV, RSV, and Influenza A virus can cause more complex and severe immune responses compared to single-virus infections [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These viruses may interact competitively or synergistically, disrupting host immune regulation and resulting in delayed viral clearance, prolonged disease duration, and a heightened risk of complications, particularly in immunocompromised individuals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Immunoinformatics, an interdisciplinary field combining immunology and computational biology, enables the prediction and modeling of immune responses using in silico tools. It offers a rapid and cost-effective alternative to conventional laboratory methods for identifying vaccine targets. Notably, this approach proved highly valuable during the COVID-19 pandemic, as it accelerated the development of subunit vaccines against SARS-CoV-2. In the context of co-infections, immunoinformatics enables the identification of conserved epitopes, prediction of T- and B-cell responses, and rational design of multi-epitope vaccines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, molecular-level modeling of host-pathogen interactions through immunoinformatics contributes to a deeper understanding of immune complexity and supports the development of targeted vaccines and immunotherapeutic strategies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBoth cellular and humoral immune responses are essential for host defense against hMPV, RSV, and Influenza A virus. CD8⁺ cytotoxic T lymphocytes (CTLs) play a pivotal role in clearing infected cells, while CD4⁺ helper T lymphocytes (HTLs) assist in antibody production and coordinate adaptive immunity. CD4⁺ T cells also contribute to antiviral defense by secreting interferon-gamma (IFN-γ), a key cytokine in the antiviral response. These mechanisms highlight the importance of eliciting both arms of the immune system in vaccine development [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the limitations of traditional vaccine development, this study employed an immunoinformatics-based strategy to design multi-epitope subunit vaccines (MESVs) targeting conserved regions of fusion and neuraminidase proteins from RSV, hMPV, and Influenza A virus. Computational tools were used to predict CTL, HTL, and B-cell epitopes, which were linked with appropriate adjuvants and spacers. This study employed an immunoinformatics-driven strategy to design multi-epitope subunit vaccines (MESVs) targeting conserved CTL, HTL, and B-cell epitopes from RSV, hMPV, and Influenza A viral proteins [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Structural modeling, molecular dynamics simulations, and TLR-4 docking confirmed the construct's stability, immunogenic potential, and receptor-binding affinity. Compared to conventional approaches, this method enables precise, cost-effective vaccine design with enhanced safety and efficacy profiles against viral co-infections.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the step-by-step process used to design and evaluate a chimeric multi-epitope vaccine against \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e, and \u003cem\u003eIAV\u003c/em\u003e, major causes of acute respiratory infections.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Proteins Sequences Retrieval\u003c/h2\u003e\u003cp\u003eThe full-length protein sequences of \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e were retrieved in FASTA format from the NCBI Protein Database. For each virus, the respective fusion proteins (\u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e) and neuraminidase protein (\u003cem\u003eInfluenza A virus\u003c/em\u003e, \u003cem\u003eIAV\u003c/em\u003e) were selected based on their immunological significance and availability of complete protein sequences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Prediction of antigenic proteins\u003c/h2\u003e\u003cp\u003eThe complete proteome of the \u003cem\u003eHuman respiratory syncytial virus\u003c/em\u003e (\u003cem\u003eRSV\u003c/em\u003e), \u003cem\u003eHuman Metaneumovirus\u003c/em\u003e (\u003cem\u003ehMPV\u003c/em\u003e), and \u003cem\u003eInfluenza A virus\u003c/em\u003e (Nine, ten, and fourteen proteins respectively, \u003cem\u003eIAV\u003c/em\u003e) were submitted to VaxiJen server to find out their antigenicity. The fusion protein of \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e and neuraminidase of \u003cem\u003eIAV\u003c/em\u003e with the highest antigenic value was selected for the construction of final chimeric multi-epitope subunit vaccines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Identification of the conserved regions in antigenic proteins\u003c/h2\u003e\u003cp\u003eThe highly antigenic fusion protein of \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e, and neuraminidase of \u003cem\u003eIAV\u003c/em\u003e were fed to Clustal Omega online server for the identification of the conserved regions. The predicted epitopes for B and T cells were investigated in the aligned retrieved fusion protein of \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e and neuraminidase of \u003cem\u003eIAV\u003c/em\u003e for the conservation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Prediction and screening of Cytotoxic T-lymphocytes (CTL) epitopes\u003c/h2\u003e\u003cp\u003eCTL epitopes help the immune system remove virus-infected cells and lower the viral load. To find these, we used the NetCTL 1.2 server to predict epitopes from the fusion proteins of \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003ehMPV\u003c/em\u003e, and the neuraminidase of \u003cem\u003eIAV\u003c/em\u003e. The aforementioned tool checks MHC class I binding, proteasome cleavage sites, and TAP transport. We selected epitopes with a score above 0.75.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Prediction of HTL epitopes\u003c/h2\u003e\u003cp\u003eWe used the Immune Epitope Database (IEDB) to find helper T cell (HTL) epitopes from the fusion proteins of \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003ehMPV\u003c/em\u003e, and the neuraminidase of \u003cem\u003eIAV\u003c/em\u003e. Epitopes with the lowest percentile ranks showing strong MHC class II binding was chosen for the vaccine design.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Prediction of B cell epitopes\u003c/h2\u003e\u003cp\u003eLinear B-cell epitopes were predicted using the ABCPred server, which utilizes a recurrent neural network-based algorithm to identify sequences likely to elicit antibody responses [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Epitopes were selected based on high prediction scores, with a threshold value of 0.80 applied to ensure the inclusion of highly probable antigenic regions\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Multiple epitope vaccine designing and evaluation\u003c/h2\u003e\u003cp\u003eThe selected epitopes comprising cytotoxic T lymphocytes (CTLs), helper T lymphocytes (HTLs), and B-cell epitopes derived from the fusion proteins of \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003ehMPV\u003c/em\u003e, as well as the neuraminidase of \u003cem\u003eIAV\u003c/em\u003e, were assembled into a chimeric multi-epitope subunit vaccine construct. To ensure proper immunogenic presentation and structural flexibility, AAY, GPGPG, and KK linkers were employed to join CTL, HTL, and B-cell epitopes, respectively. Moreover, an immunostimulatory adjuvant was conjugated to the N-terminus of the vaccine hypothesis via an EAAK linker to augment the overall immune response [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Antigenicity and allergenicity of construct\u003c/h2\u003e\u003cp\u003eWe used the VaxiJen v2.0 server to test if the vaccine is likely to activate an immune response. A score above 0.4 was considered antigenic. We also checked allergenicity using AllerTOP v2.0 to make sure the vaccine was safe and not probable to cause allergies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Physicochemical properties of construct\u003c/h2\u003e\u003cp\u003eWe used the ProtParam tool on the ExPASy server to analyze the vaccine\u0026rsquo;s properties, including amino acid content, molecular weight, pI, stability, half-life, aliphatic index, and GRAVY score.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 3-D structure prediction and validation\u003c/h2\u003e\u003cp\u003eWe used the Robetta server to model the 3D structure of the vaccine using its amino acid sequence in FASTA format. The chosen model was checked with ProSA-Web and PROCHECK.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Molecular docking with TLR4\u003c/h2\u003e\u003cp\u003eEffective stimulation of the human immune response requires the vaccine to interact with host immune receptors. Molecular docking assists as a crucial in silico approach to predict the binding affinity and three-dimensional (3-D) orientation between a ligand and its target receptor. For this purpose, the 3-D structure of Toll-like receptor 4 (TLR-4; PDB ID: 2Z63) was retrieved from the RCSB Protein Data Bank. Before docking, all heteroatoms and water molecules were removed using PyMOL to prepare the receptor structure. The refined TLR-4 structure and the modeled vaccine concept were submitted to the HDOCK server to evaluate their binding interactions. The top-ranked docked complex, determined based on docking score, was selected for further structural and interaction analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Codon optimization and vaccine \u003cem\u003ein silico\u003c/em\u003e cloning\u003c/h2\u003e\u003cp\u003eCodon optimization is a computer-based method used to improve protein expression by adjusting codons to match the host organism. We used the JCat tool to convert the vaccine\u0026rsquo;s amino acid sequence into a DNA sequence optimized for \u003cem\u003eE. coli K12\u003c/em\u003e. JCat also gave CAI and GC content values to check if the gene would express well. NdeI and XhoI sites were added to the ends of the sequence for cloning into the pET-28a(+) vector. The cloning setup was checked using SnapGene.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Immune simulation\u003c/h2\u003e\u003cp\u003eThe C-immSim online server was used for immune simulation to assess the human immune response to the constructed vaccine. The aforementioned server evaluated the antigenicity of the developed vaccine and its capacity to elicit immunological responses upon administration. The server evaluated the quantity of immune cells like helper T-cells 1 and 2 (Th1 and Th2), respectively. Additionally, the server measured various other immunological responses, including the production of antibodies, cytokines, and interferon, in response to administered vaccines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Molecular dynamic simulation of the constructed vaccine and human TLR4 complex\u003c/h2\u003e\u003cp\u003eMolecular dynamics (MD) simulations were performed using AMBER v24 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to assess the structural stability and conformational dynamics of the chimeric multi-epitope vaccine in complex with human Toll-Like Receptor 4 (TLR4; PDB ID: 2Z63). The initial complex was prepared in PyMOL by removing crystallographic water molecules and heteroatoms. The ff19SB force field was used for protein parametrization [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The system was solvated in an octahedral box of OPC water molecules with a 12.0 \u0026Aring; buffer and neutralized with Na⁺ and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions. NaCl was added to a final concentration of 0.15 M to mimic physiological conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Energy minimization was carried out in two stages: 10,000 steps with positional restraints (10 kcal/mol\u0026middot;\u0026Aring;\u0026sup2;) on protein heavy atoms, followed by 50,000 steps without restraints. The system was gradually heated from 0 K to 300 K over 500 ps under constant volume (NVT), using Langevin dynamics with a collision frequency of 2 ps⁻\u0026sup1;. This was followed by 1 ns of equilibration at 1 atm under constant pressure (NPT), during which positional restraints were progressively removed. A 100 ns production run was performed at 300 K and 1 atm using periodic boundary conditions. Long-range electrostatics were handled with the particle mesh Ewald method. SHAKE constraints were applied to hydrogen-containing bonds, allowing a 2 fs timestep. Snapshots were saved every 10 ps for analysis. Analysis was performed using CPPTRAJ [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Root mean square deviation (RMSD) was calculated to assess the global stability of the complex over time. Root mean square fluctuation (RMSF) was calculated to examine residue-level flexibility. Radius of gyration (Rg) was calculated to monitor overall structural compactness. Intramolecular hydrogen bonds were analyzed to evaluate the complex stability. Principal component analysis (PCA) was conducted to identify dominant motions during the simulation. Binding free energy between the vaccine and TLR4 was estimated using the MM/GBSA method from extracted snapshots of the production phase. VMD and PyMOL were used for structural visualization.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eVaccines provoke the immune system and play a crucial role in fighting against different diseases. Small antigenic determinants can efficiently set off immune reactions rather than entire virus proteins [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By determining possible immunogenic sites, immunoinformatics provides a fast and accurate method of vaccine design [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The fusion protein of \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003ehMPV\u003c/em\u003e and neuraminidase of \u003cem\u003eIAV\u003c/em\u003e strains have been analyzed using an immunoinformatics method in order to find immunogenic, non-allergenic CTL, HTL, and B-cell epitopes. Broad protection against \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e is sought by designing chimeric multi-epitope subunit vaccines from these epitopes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Prediction of Antigenic Proteins\u003c/h2\u003e\u003cp\u003eSeven proteins (\u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003ehMPV\u003c/em\u003e) and fourteen protein of \u003cem\u003eIAV\u003c/em\u003e were evaluated for antigenicity through VaxiJen server. Among them, the fusion protein of \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e and neuraminidase protein of \u003cem\u003eIAV\u003c/em\u003e showed the antigenicity scores (0.4775 for \u003cem\u003ehMPV\u003c/em\u003e, 0.5219 for \u003cem\u003eRSV\u003c/em\u003e and 0.5024 for \u003cem\u003eIAV\u003c/em\u003e) and was selected for constructing the final chimeric multi-epitope subunit vaccines (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Protein sequence retrieval and conserved region identification\u003c/h2\u003e\u003cp\u003eAll available fusion and neuraminidase protein sequences for \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e were retrieved and kept in FASTA format. The conserved region in the fusion and neuraminidase proteins was identified through Multiple sequence alignment (MSA), and finally, HTL, CTL, and BCL epitopes were selected for chimeric multi-epitope subunit vaccine Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3). This approach guarantees that the intended vaccination targets conserved viral areas, therefore lowering the possibility of immune escape resulting from mutations and improving its efficacy throughout several strains viruses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Cytotoxic T cell epitope prediction and screening\u003c/h2\u003e\u003cp\u003eIn the context of adaptive immunity, major histocompatibility complex (MHC) class I glycoproteins are universally expressed on the surface of all nucleated cells and play a pivotal role in the presentation of antigenic peptides to cytotoxic T lymphocytes (CD8\u003csup\u003e+\u003c/sup\u003e T cells). These molecules are responsible for displaying peptides resulting from endogenous proteins, including those of viral origin, so initiating a cytotoxic immune response aimed at eliminating infected or abnormal cells. Furthermore, MHC class I molecules are found not only on body cells but also on immune cells like B cells, monocytes, dendritic cells, and thymic cells. They present viral peptides to cytotoxic T cells, help fight infections inside cells, and trigger a strong immune response. CTL plays a crucial role in eradicating the invading pathogens. The fusion and neuraminidase protein sequences from \u003cem\u003eRSV\u003c/em\u003e, \u003cem\u003ehMPV\u003c/em\u003e, and \u003cem\u003eIAV\u003c/em\u003e were submitted to the NetCTL 1.2 web server for the prediction of immunogenic CTL epitopes. Different epitopes (531 for \u003cem\u003ehMPV\u003c/em\u003e, 566 for \u003cem\u003eRSV\u003c/em\u003e, and 461 for \u003cem\u003eIAV\u003c/em\u003e) were predicted for fusion and neuraminidase proteins, respectively. Among these, ten epitopes were selected from each protein (Supplementary \u003cb\u003eTables S2-S4\u003c/b\u003e). The selected epitopes were further assessed for toxicity, allergenicity, and antigenicity, respectively. Eventually, two high antigenic, non-allergic, non-toxic, and conserved epitopes from each fusion protein of hMPV and RSV and neuraminidase protein from influenza A virus were selected for the final chimeric multi-epitope vaccine construct (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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 CTL epitopes of \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e used in the proposed chimeric multi-epitope subunit vaccine\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeptide\u003c/p\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeptide Position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eConserved region\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCombined Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAntigenicity\u003c/p\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAllergenicity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ehMPV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLSVLRTGWY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.6130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFSDNAGITP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88\u0026ndash;97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.7563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eRSV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNIDIFNPKY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e382\u0026ndash;391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.5413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.7826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLIAVGLLLY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e540\u0026ndash;549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.3421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eIAV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeuraminidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWTSASSISF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e421\u0026ndash;430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.5752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.0334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eELNAPNSHY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e252\u0026ndash;261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.1526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\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\u003e3.4 Helper T lymphocytes (HTL) epitopes prediction\u003c/h2\u003e\u003cp\u003eHelper T cells are key parts of the adaptive immune system. They help activate both B cells and cytotoxic T cells. When a virus enters the body, cells like macrophages take it in and break it down. They show parts of the virus on their surface using MHC class II molecules. Helper T cells recognize these parts and help activate B cells, which then make specific antibodies. Since helper T cells play a key role in immune response, we predicted HTL epitopes from the fusion proteins of \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e and the neuraminidase of \u003cem\u003eIAV\u003c/em\u003e. Ten top epitopes from each virus were shortlisted based on low percentile ranks, which suggest strong binding (see Supplementary Table S3). We tested these epitopes for antigenicity, toxicity, and allergenicity. Two strong, safe HTL epitopes from each virus were chosen for the final vaccine design ( Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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 HTL epitopes of \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e, and \u003cem\u003eIAV\u003c/em\u003e used in the proposed chimeric multi-epitope subunit vaccine.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAllele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeptide position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePeptide Sequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP. Rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAntigenicity\u003c/p\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAllergenicity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ehMPV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e229\u0026ndash;243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRAVSYMPTSAGQIKL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*15:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119\u0026ndash;133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTAGIAIAKTIRLESE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eRSV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB3*02:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e234\u0026ndash;248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTREFSVNAGVTTPVS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*03:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e263\u0026ndash;277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDMPITNDQKKLMSN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eIAV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeuraminidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB3*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e214\u0026ndash;228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSNRPWVSFDQNLDYQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.6043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e334\u0026ndash;348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRPCFWVELIRGRPKE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.9819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNon-allergenic\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=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.5 B-Cell epitopes prediction\u003c/h2\u003e\u003cp\u003eB cells are important for adaptive immunity, as to produce antibodies that help block infections and provide long-term protection. The rational design of B-cell epitopes is critical, as they initiate various protective mechanisms, including viral neutralization, agglutination, activation of the complement cascade, and antibody-dependent cellular cytotoxicity. Herein, the fusion proteins of \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e, along with the neuraminidase protein of the \u003cem\u003eIAV\u003c/em\u003e, were analyzed for linear B-cell epitope prediction using the ABCPred web server. The server predicted 21, 23, and 17 potential epitopes for the \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e proteins, respectively. Based on their predicted binding scores, ten epitopes from each protein were selected (Supplementary \u003cb\u003eTable S4\u003c/b\u003e). We tested these epitopes for antigenicity with VaxiJen v2.0, toxicity with ToxinPred, and allergenicity with AllerTOP v2.0. Based on the results, we chose two safe, conserved, and strongly immunogenic epitopes from each viral protein for the final vaccine (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eBCL epitopes of \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e used in the proposed chimeric multi-epitope subunit vaccine\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEpitope\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStart Position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAntigenicity\u003c/p\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConserved region\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ehMPV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQHVIKGRPVSNSFDPI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCWIIKAAPSCSEKDGN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eRSV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNIDIFNPKYDCKIMTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.7806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSCSISNIETVIEFQQK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.0332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eIAV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeuraminidase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIGYICSGVFGDNPRPK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEECSCYPDTGKVMCVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon-allergenic\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=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Chimeric multi-epitope vaccine construction\u003c/h2\u003e\u003cp\u003eTo assemble the final chimeric multi-epitope subunit vaccine (MESV), selected immunodominant epitopes derived from the fusion proteins of \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e, as well as the neuraminidase of \u003cem\u003eIAV\u003c/em\u003e, were joined using specific linkers to ensure structural flexibility and optimal immune presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Cytotoxic T lymphocyte (CTL) epitopes were linked via AAY linkers, helper T lymphocyte (HTL) epitopes via GPGPG linkers, and B-cell epitopes via KK linkers. The AAY linker facilitates efficient proteasomal processing and MHC class I presentation; GPGPG linkers boost the presentation and immunogenicity of HTL and B-cell epitopes; and KK linkers help to maintain structural separation, minimizing epitope interference while boosting immunogenicity. An immunostimulatory adjuvant, human β-defensin 2, was conjugated to the N-terminus of the construct using an EAAK linker to further enhance immune activation. Human β-defensin 2 is part of the body\u0026rsquo;s natural defense system and can activate specific immune responses against antigens. The finalized MESV construct comprised 442 amino acids in total. The specific epitopes incorporated into the vaccine design are detailed in (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7 3-D structure prediction and validation\u003c/h2\u003e\u003cp\u003eThe amino acid sequence of the chimeric vaccine was uploaded to the Robetta server to build its 3D structure. Five 3-D models have been produced using the Robetta-ab-initio modelling server, and upon assessment, the first model depicted in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e was chosen for further investigation. Each epitope, including CTL, HTL, and B-cell epitopes, is represented in distinct colors and depicted as a ribbon. A surface shade has been applied for enhanced depiction. The selected model was validated by ProSA-web (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), ERRAT, and Ramachandran plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The ProSA-web revealed a score of -8.3 for the 3D model of the proposed vaccine design, however the overall quality factor reported by ERRAT was 96. 31%, demonstrating the model's accuracy (Supplementary \u003cb\u003eFigure S4\u003c/b\u003e). The Ramachandran plot study indicated that the majority of amino acids (98.4%) are located in the favored zone, whilst a minimal proportion (1.6%) is found in the prohibited region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The aforementioned results demonstrated that the quality of the chosen vaccine construct designs is appropriate and suitable for the next steps, including molecular docking, modelling, and free energy calculations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Physicochemical properties, allergenicity, and antigenicity of the vaccine construct\u003c/h2\u003e\u003cp\u003eThe antigenic potential of the designed vaccine construct was evaluated using the VaxiJen v2.0 server, yielding a score of 0.50, which exceeds the threshold value of 0.4 and indicates a strong capacity to produce an immune response. Allergenicity assessment, conducted via the AllerTOP v2.0 server, predicted the construct to be non-allergenic, suggesting its potential safety in immunization applications. We used the ProtParam tool to check properties like molecular weight, half-life, aliphatic index, pI, stability, and GRAVY score. The vaccine\u0026rsquo;s molecular weight is 46,715.05 Da. Its predicted half-life is 30 hours in human cells, 20 hours in yeast, and over 10 hours in \u003cem\u003eE. coli\u003c/em\u003e, showing good stability. The instability index is 28.79, which means the construct is stable. An aliphatic index of 74.28 suggested good thermostability, while a theoretical pI supported its acidic nature. The GRAVY score of -0.276 reflected the overall hydrophilic character of the vaccine, favoring solubility and biological interaction potential (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe physicochemical properties of the designed chimeric multi-epitope subunit vaccine\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRemarks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Amino Acids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuitable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolecular Weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46715.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage Weight\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of atoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTheoretical pI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe estimated half-life (Mammalian reticulocytes, \u003cem\u003ein vitro\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe estimated half-life (yeast, \u003cem\u003ein vivo\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;20 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe estimated half-life (\u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003ein vivo\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;10 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInstability index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAliphatic index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGRAVY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSatisfactory\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=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Molecular docking with TLR4 receptor\u003c/h2\u003e\u003cp\u003eAn effective immunological response from a vaccination depends on its high binding affinity to the immune receptors of the host. Most importantly, controlling inflammatory pathways, toll-like receptors (TLRs) help regulate immune responses to infections. Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs), thus initiating intracellular signaling pathways and modulating gene expression to activate innate and adaptive immune responses [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The recognition of many viral components, including nucleic acids and envelope glycoproteins, which start a cascade of events leading to the synthesis of interferon type I (IFN-I), inflammatory cytokines, and chemokines, depends critically on toll-like receptors (TLRs). Toll-like receptors (TLRs) help activate and mature dendritic cells, linking the body\u0026rsquo;s early (innate) and later (adaptive) immune responses. We used the HDOCK server to test how the chimeric vaccine binds to the host\u0026rsquo;s immune receptors. The docking analysis assessed the binding affinity of the vaccine construct with human Toll-like receptor 4 (TLR-4; PDB ID: 2Z63). The resulting docking score was \u0026minus;\u0026thinsp;277.43 kcal/mol, suggesting a strong and stable interaction between the vaccine and TLR-4, which may support effective immune activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e As per the PDBsum server, TLR-4 (Chain A) and chimeric constructed vaccine (Chain B) have 9 hydrogen bonds; Thr413- Ser261, Gln484- Arg164, Glu485- Arg164 (two hydrogen bonds), Asn481- Arg311, Gln505- Arg311, Arg311- Gln507, Gln507-Arg327, and Gln507-Asp307. Additionally, four salt bridges; Glu439- Arg248, Glu485- Arg164, Glu509- Arg327 and Arg460- Asp307 and 183 non-bonded interactions were found in constructed vaccine and TLR4 complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Normal mode analysis of constructed vaccine with TLR-4\u003c/h2\u003e\u003cp\u003eThe structural dynamics and stability of the vaccine-receptor complex were assessed using the iMODS server, which employs normal mode analysis (NMA) by modulating the force field around the complex to simulate atomic interactions over time. The analysis revealed reduced structural distortions, particularly at the residue level, suggesting enhanced conformational stability of the modeled complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Molecular simulations estimated the flexibility and motion of atoms within the antigen\u0026ndash;receptor interface. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA displays the deformability profile of the MEVC-TLR4 complex, showing a localized peak within a flexible region of the vaccine build. B-factor analysis, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, compares the intrinsic mobility of residues derived from both the PDB and NMA models. The eigenvalue, depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, reflects the stiffness of the structure; a lower value indicates greater flexibility. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) presents the variance associated with individual and cumulative motions, represented in purple and green, respectively. The covariance matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE) shows the dynamic correlations between residue pairs, where red denotes correlated motion, blue indicates anti-correlated motion, and white represents uncorrelated motion. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF represents the elastic network model, resembling a spring map, which visualizes the strength of interatomic interactions within the complex. Lastly, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG summarizes the overall results of the iMODS analysis, confirming the structural integrity and potential functional stability of the vaccine TLR4 complex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.11 In silico cloning of chimeric multi-epitope subunit vaccine in the pET-28a(+) expression vector.\u003c/h2\u003e\u003cp\u003eWe used the JCat tool to optimize codons and improve vaccine expression in a different host system. The coding sequence of the 1,326-nucleotide chimeric multi-epitope subunit vaccine was optimized for expression in \u003cem\u003eE. coli K12\u003c/em\u003e. JCat computed critical parameters such as the Codon Adaptation Index (CAI) and GC content, which are indicative of translational efficiency. The optimized sequence yielded a CAI value of 1.0 and a GC content of 51.24%, both of which suggest high compatibility and potential for robust expression in the \u003cem\u003eE. coli\u003c/em\u003e system. Subsequently, SnapGene software (v3.3.4) was used to simulate the cloning strategy. ScaI and BmtI restriction sites were added to the sequence ends to help insert it into the \u003cem\u003eE. coli\u003c/em\u003e pET-28a(+) vector. The software confirmed the absence of internal restriction sites that could interfere with the cloning process. As illustrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the optimized gene sequence was successfully integrated into the vector map, validating the construct\u0026rsquo;s readiness for downstream expression and purification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.12 Immune response simulation analysis\u003c/h2\u003e\u003cp\u003eVaccines provoke the immune system of the host without causing disease. The immune system can be stimulated by vaccines through different mechanisms. Co-administration of protein-based antigens with immunologic adjuvants enhances local innate immune activation, leading to the functioning of antigen-presenting cells and the release of pro-inflammatory cytokines by macrophages. These macrophages, often referred to as dendritic cells, internalize the antigen and present it on their surface via major histocompatibility complex (MHC) molecules. T cells recognize the MHC-antigen complex through their T-cell receptors, thereby activating adaptive immunity and generating memory T cells. In contrast, polysaccharide antigens typically induce antibody responses without T-cell involvement. These antigens bind directly to mature B cells, initiating their differentiation into antibody-producing plasma cells. Immune simulation was conducted to assess the immunogenicity of the constructed vaccine. This simulation evaluated the immune system's response, particularly antibody production, following the administration of the constructs. Notably, the immune system exhibited a robust response, with significantly elevated antibody production observed after each vaccine injection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The chimeric multiepitope subunit vaccine construct was administered, and antigen titers were initially low until the seventh day. Subsequently, they progressively increased, reaching their maximum on the 12th day. Nevertheless, a rise in antibody titers was observed beginning on the 15th day. The activation of supplementary immune system components was accompanied by the complete neutralisation of antigens by the 17th day. The combined IgM and IgG titers were 4.5 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e, and the combined IgG1\u0026thinsp;+\u0026thinsp;IgG2 and IgG1 titers were also elevated post-vaccination (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The evaluation of interleukin (IL) and cytokine responses, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, indicates a notable increase in the levels of IFN-γ and IL-2. The results demonstrate a reliable and strong immune response subsequent to the administration of the vaccine. The cellular immune response upon re-exposure to pathogens was significantly robust, marked by the development of memory cells. T cell populations were found to be greater than 1500 cells/mm\u0026sup3;, with peak concentrations of phagocytic natural killer cells, dendritic cells, and phagocytic macrophages reported at levels exceeding 380 cells/mm\u0026sup3; and 200 cells/mm\u0026sup3;, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.13 Molecular Dynamics Simulation Analysis of the TLR4-Vaccine Complex\u003c/h2\u003e\u003cp\u003eTo evaluate the structural stability and conformational behavior of the TLR4-vaccine complex, several parameters were monitored across the 100 ns production phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-D). The RMSD profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) was used to assess global stability over time. The system showed a gradual rise in RMSD during the first 60 ns, reflecting initial structural adjustments. After ~\u0026thinsp;65 ns, RMSD values stabilized around 5\u0026ndash;6 \u0026Aring;, indicating that the complex reached equilibrium. Minor fluctuations observed after 70 ns suggest localized rearrangements without global instability. RMSF analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) captured residue-wise flexibility across the complex. The TLR4 region (residues 1-569) exhibited limited fluctuations, mostly under 3 \u0026Aring;, indicating a stable backbone throughout the simulation. In contrast, the vaccine region (residues 570\u0026ndash;1010) showed increased flexibility, with peaks exceeding 9 \u0026Aring; at certain loop regions, likely reflecting inherent flexibility in the designed construct. The Rg (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) was calculated to monitor the compactness of the TLR4-vaccine complex. The Rg remained steady around 39\u0026ndash;40 \u0026Aring; during the initial 70 ns, suggesting stable folding. A gradual increase after 75 ns, reaching\u0026thinsp;~\u0026thinsp;41.5 \u0026Aring;, indicates slight structural expansion, possibly due to flexible segments in the vaccine construct. No abrupt changes were observed, supporting overall structural retention. A contact map analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) tracked native and non-native contacts, minimum/maximum distances, and contact stability across the trajectory. Native contacts remained consistently populated throughout, while non-native contacts fluctuated without showing dominance, suggesting preserved structural integrity. Maximum distance trends remained stable, with no significant expansion or dissociation events during the simulation. Collectively, RMSD stabilization after 65 ns, stable Rg, maintained native contacts, and localized flexibility observed in RMSF profiles indicate that the TLR4-vaccine complex remained structurally stable under simulated conditions, with expected mobility confined to vaccine regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.14 Conformational Dynamics and Principal Component Analysis\u003c/h2\u003e\u003cp\u003ePrincipal component analysis (PCA) was conducted to investigate large-scale conformational changes in the TLR4\u0026ndash;vaccine complex over the course of the simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The trajectory was projected along the first two principal components (PC1 and PC2), which captured the dominant motions within the system. The PCA plot revealed three distinct conformational states sampled during the simulation. The first state (A) was identified at ~\u0026thinsp;28.3 ns, the second state (B) at ~\u0026thinsp;49.2 ns, and the third state (C) at ~\u0026thinsp;94.8 ns. The transitions between these states suggest gradual exploration of the conformational space rather than abrupt structural shifts. The presence of distinct clusters indicates that the complex underwent coordinated motions but remained within a restricted conformational landscape, supporting the structural stability observed in RMSD and Rg analyses. To visualize structural changes, representative snapshots corresponding to the early (blue) and late (red) stages of the simulation were superimposed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). This comparison showed minimal deviation in the TLR4 region, consistent with its structural rigidity, while subtle shifts were observed in the vaccine region, reflecting expected flexibility. The PCA confirmed that the TLR4-vaccine complex explored limited conformational space during the simulation, without significant structural rearrangement, supporting the stability of the complex under simulated conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e3.15 Binding Free Energy Analysis of the TLR4-Vaccine Complex\u003c/h2\u003e\u003cp\u003eThe binding free energy of the TLR4-vaccine complex was estimated using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method based on snapshots from the production phase. The calculated energy components are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The total binding free energy (ΔG\u003csub\u003eTOTAL\u003c/sub\u003e) was estimated as -121.72 kcal/mol, indicating a stable and favorable interaction between the vaccine construct and TLR4. The major contribution came from electrostatic interactions (ΔE\u003csub\u003eEL\u003c/sub\u003e: -378.08 kcal/mol) and van der Waals forces (ΔE\u003csub\u003eVDW\u003c/sub\u003e: -64.00 kcal/mol). The non-polar solvation term (ΔE\u003csub\u003eSASA\u003c/sub\u003e: -8.85 kcal/mol) contributed marginally to stabilization. In contrast, the polar solvation energy (ΔEGB: 329.22 kcal/mol) opposed binding, as expected due to desolvation penalties upon complex formation. Despite this, the gas-phase interaction energy (ΔG\u003csub\u003eGAS\u003c/sub\u003e: -442.09 kcal/mol) effectively compensated, resulting in an overall favorable binding profile. The low standard error values across all components confirmed the stability and consistency of the calculated energy terms throughout the sampled frames. Overall, MM/GBSA analysis supports strong binding affinity between the multi-epitope vaccine construct and TLR4, driven primarily by electrostatic and van der Waals interactions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMM/GBSA binding free energy components for the TLR4-vaccine complex. Energies are reported as average values (kcal/mol) with standard deviation and standard error.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy Component\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔE\u003csub\u003eVDW\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-64.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔE\u003csub\u003eEL\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-378.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔE\u003csub\u003eGB\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e329.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔE\u003csub\u003eSASA\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔG\u003csub\u003eGAS\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-442.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔG\u003csub\u003eSOLV\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e320.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔG \u003csub\u003eTOTAL\u003c/sub\u003e (kcal/mol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-121.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eΔE\u003csub\u003eVDW\u003c/sub\u003e, van der waals free energy; ΔE\u003csub\u003eEL\u003c/sub\u003e, electrostatic free energy; ΔE\u003csub\u003eGB\u003c/sub\u003e, the polar component of solvation-free energy; ΔE\u003csub\u003eSASA\u003c/sub\u003e, non-polar components of solvation energy; ΔG\u003csub\u003eGAS\u003c/sub\u003e, binding free energy without solvent; ΔG\u003csub\u003eSOLV\u003c/sub\u003e, binding free energy with solvent; ΔG\u003csub\u003eTOTAL\u003c/sub\u003e, total binding free energy.\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"},{"header":"4 Discussion","content":"\u003cp\u003eThe growing burden of multidrug-resistant pathogens underscores the need for new preventive strategies, including effective vaccines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, a structure-based immunoinformatics strategy was applied to design a multi-epitope vaccine targeting \u003cem\u003eHuman Metapneumovirus\u003c/em\u003e, \u003cem\u003eRespiratory Syncytial Virus\u003c/em\u003e, and \u003cem\u003eInfluenza A Virus\u003c/em\u003e. By integrating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes, the designed construct aimed to stimulate both cellular and humoral immune responses [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Selection of epitopes with high population coverage and confirmed antigenicity ensured broad-spectrum immune activation potential [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The inclusion of an appropriate adjuvant sequence at the N-terminus was intended to enhance immunogenicity by promoting dendritic cell activation and cytokine production [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Flexible linkers were incorporated to preserve epitope presentation and facilitate proper folding of the chimeric construct [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The physicochemical properties of the final construct, including stability, non-allergenicity, and solubility, support its potential as a safe and manufacturable vaccine candidate. Reverse vaccinology and in-silico epitope mapping have been successfully applied in recent vaccine development studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, combining these strategies with structural modeling and dynamic evaluation provides a more in-depth assessment of vaccine performance before experimental testing. Our workflow integrated these stages for optimization and validation of the designed construct.\u003c/p\u003e\u003cp\u003eAccurate structural modeling of multi-epitope vaccines is critical for predicting receptor binding and immune system recognition [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The tertiary structure of the designed construct was predicted using homology-based modeling, followed by refinement to improve stereochemical quality. Ramachandran plot analysis confirmed acceptable backbone geometry, with 98.4% of residues in favored regions and only 1.6% in disallowed regions. The ProSA-web score of \u0026minus;\u0026thinsp;8.3 and the ERRAT quality factor of 96.31% further supported the structural reliability of the modeled vaccine, indicating no major anomalies. To evaluate receptor interaction, molecular docking was performed against human Toll-Like Receptor 4 (TLR4). The vaccine construct showed favorable binding, with a docking score of \u0026minus;\u0026thinsp;277.43 kcal/mol at the TLR4 binding site, suggesting strong interaction potential with the receptor. A total of 14 hydrogen bonds and multiple hydrophobic contacts contributed to complex formation, consistent with reported mechanisms of TLR4-ligand binding [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, docking represents a static interaction snapshot and does not capture molecular flexibility or solvent effects. To overcome this limitation, molecular dynamics simulation was used to assess the structural stability and conformational behavior of the vaccine\u0026ndash;TLR4 complex under near-physiological conditions.\u003c/p\u003e\u003cp\u003eDocking predictions were further evaluated using molecular dynamics simulations to assess complex stability under dynamic conditions. The RMSD profile showed that the TLR4-vaccine complex stabilized after approximately 65 ns, indicating that no large-scale structural rearrangements occurred once initial adjustments were complete [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The consistent radius of gyration across the trajectory confirmed that the complex retained its compactness throughout the simulation. RMSF analysis highlighted minimal fluctuations within the TLR4 chain, reflecting its structural rigidity, while localized flexibility in the vaccine region corresponded to loop and linker segments as expected for such constructs. Contact analysis confirmed the persistence of native interactions, with no significant increase in non-native contacts, supporting the structural integrity of the complex during simulation. Principal component analysis identified three main conformational states at 28.2, 49.2, and 94.8 ns, indicating restricted and coordinated motions within a stable conformational landscape [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This limited exploration of conformational space aligns with the observed stability in global structural parameters. Binding free energy estimation using MM/GBSA supported these findings. The total binding energy of \u0026minus;\u0026thinsp;121.72 kcal/mol indicated a strong and energetically favorable interaction, driven primarily by electrostatic and van der Waals forces [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Despite the opposing contribution from polar solvation energy, the favorable gas-phase interaction energies dominated, consistent with reported mechanisms for TLR4-peptide complexes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The molecular dynamics simulation and MM/GBSA analysis confirmed that the vaccine construct formed a stable, energetically favorable, and structurally consistent complex with TLR4. These results support its potential to function as an effective immunostimulatory agent.\u003c/p\u003e\u003cp\u003eTo assess the immunogenic potential of the designed vaccine, an immune simulation analysis was performed. The simulation predicted a sharp increase in IgM levels, followed by sustained high concentrations of IgG antibodies after antigen exposure [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Cytokine profiling showed elevated levels of IFN-γ (up to 4,200 ng/ml) and IL-2 (up to 1,800 ng/ml), indicating a strong Th1-type cellular response, essential for pathogen clearance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Simulated immune memory was evident, with secondary and tertiary immune responses showing higher antibody titers than the primary response, suggesting potential for long-term immunity [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For production feasibility, codon optimization was carried out for expression in \u003cem\u003eEscherichia coli\u003c/em\u003e K12. The optimized sequence achieved a Codon Adaptation Index (CAI) of 0.98 and a GC content of 51.24%, both within the optimal range for bacterial expression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In silico cloning into the pET28a(+) expression vector confirmed correct insertion of the construct without frame-shift errors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These results indicate that the designed vaccine construct is predicted to be both immunogenic and suitable for laboratory-scale expression. The immune simulation and expression optimization provide a strong basis for progressing this vaccine candidate to experimental evaluation.\u003c/p\u003e\u003cp\u003eThe workflow used in this study, combining epitope selection, structural modeling, molecular docking, molecular dynamics simulation, and immune simulation enabled the rational design and in-silico validation of a multi-epitope vaccine candidate targeting \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e. The structural stability of the vaccine-TLR4 complex, supported by dynamic analysis and favorable binding energy estimation, provides strong evidence for its potential to engage innate immune receptors. Predicted immunogenicity and expression potential further support the suitability of the construct for experimental testing. Together, these findings highlight the value of combining immunoinformatics with structural and dynamic analyses in early-stage vaccine development.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eEmerging respiratory viral co-infections caused by \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e and \u003cem\u003eIAV\u003c/em\u003e remain a significant public health concern, especially in vulnerable populations. In this study, a multi-epitope vaccine was designed using a structure-based immunoinformatics approach to target these pathogens. The designed construct incorporated carefully selected CTL, HTL, and B-cell epitopes, along with an appropriate adjuvant, aiming to stimulate both innate and adaptive immunity. Structural modeling and molecular docking confirmed favorable binding to human TLR4, while molecular dynamics simulations demonstrated the stability of the vaccine-receptor complex under physiological conditions. Binding free energy analysis supported strong interaction, primarily driven by electrostatic and van der Waals forces. In silico immune simulation predicted robust immune activation, and codon optimization confirmed the construct\u0026rsquo;s suitability for expression in \u003cem\u003eEscherichia coli\u003c/em\u003e. These results support the designed multi-epitope vaccine as a promising candidate for experimental validation and further development.\u003c/p\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Limitations and Future Directions\u003c/h2\u003e\u003cp\u003eWhile our study provides an in-silico assessment of the designed multi-epitope vaccine, experimental validation remains essential to confirm its immunogenicity and safety. The structural stability, binding affinity, and immune stimulation potential demonstrated computationally should now be verified through in vitro assays, such as protein expression, receptor binding, and cytokine profiling. Further in-vivo studies will be required to evaluate immunogenicity, protective efficacy, and safety under physiological conditions. Experimental validation will be essential to confirm the immunogenicity and protective efficacy of the designed vaccine. Nonetheless, this computational framework provides an efficient strategy for accelerating early-stage vaccine discovery and candidate selection.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors acknowledge financial support from the National Natural Science Foundation of China (No. 32300048), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110158, 2024A1515012577), National Foreign Expert Individual Program (No. Y20240195), the Construction Project of Nano Technology and Application Engineering Research Center of Guangdong Medical University (No. 4SG24179G), the Postdoctoral funding project of Guangdong Medical University (3007/2BH24007), Dongguan Science and Technology of Social Development Program (20231800936272).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.L.: Supervision, Funding acquisition, Writing-review \u0026amp;amp; editing. Y.C.: Project administration. C.W. and J.X.: Investigation, Methodology. A.S.: Software. X.X.: Data curation. J.T.: Investigation. Y.Q. and Y.Z.: Methodology, Software. A.J.: Software, Visualization, Writing-original draft. T.Y.: Writing-review \u0026amp;amp; editing. S.U.: Data curation, Formal analysis, Writing-original draft.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data is publicly available on the FDA website, the original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSalas-Benito, J. S. \u0026amp; De Nova-Ocampo, M. Viral interference and persistence in mosquito-borne flaviviruses. \u003cem\u003eJ. Immunol. 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In silico design of multi-epitope vaccines against the hantaviruses by integrated structural vaccinology and molecular modeling approaches, 19(7) e0305417. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanerjee, S., Majumder, K., Gutierrez, G. J., Gupta, D. \u0026amp; Mittal, B. Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2, bioRxiv: the preprint server for biology (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhattacharya, K., Shamkh, I. M. \u0026amp; Approaches Multi-Epitope Vaccine Design against Monkeypox Virus via Reverse Vaccinology Method Exploiting Immunoinformatic and Bioinformatic 10(12) (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJalal, K. et al. Pan-Genome Reverse Vaccinology Approach for the Design of Multi-Epitope Vaccine Construct against Escherichia albertii. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (23), 12814 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human Metapneumovirus, Respiratory Syncytial Virus, Influenza A Virus, Co-infection, Vaccine","lastPublishedDoi":"10.21203/rs.3.rs-7697013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7697013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCo-infections involving \u003cem\u003eHuman Metapneumovirus\u003c/em\u003e (\u003cem\u003ehMPV\u003c/em\u003e), \u003cem\u003eRespiratory Syncytial Virus\u003c/em\u003e (\u003cem\u003eRSV\u003c/em\u003e), and \u003cem\u003eInfluenza A Virus\u003c/em\u003e (\u003cem\u003eIAV\u003c/em\u003e) often exacerbate disease severity in vulnerable populations. Here, we employed a structure-based immunoinformatics approach to design a multi-epitope subunit vaccine targeting these pathogens. The construct incorporated cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes from the Fusion and Glycoprotein proteins of \u003cem\u003ehMPV\u003c/em\u003e and \u003cem\u003eRSV\u003c/em\u003e, and the Hemagglutinin (HA) and Matrix proteins (M2) of \u003cem\u003eIAV\u003c/em\u003e, linked with an adjuvant and optimized spacers to enhance immunogenicity and stability. Structural modeling confirmed correct folding, and molecular docking predicted a stable interaction with Toll-Like Receptor 4 (TLR4) \u0026minus;\u0026thinsp;277.43 kcal/mol. Molecular dynamics simulations indicated a compact and stable complex with restricted conformational motions, while MM/GBSA analysis yielded a favorable binding free energy (\u0026ndash;121.72 kcal/mol) dominated by electrostatic and van der Waals interactions. Immune simulations predicted strong humoral and cellular responses, including high antibody titers, IFN-γ and IL-2 production, and durable memory formation. Codon optimization achieved a codon adaptation index (CAI) of 0.98 and a GC content of 51.24%, suggesting efficient expression in \u003cem\u003eEscherichia coli\u003c/em\u003e. These findings highlight the construct as a structurally stable, immunogenic, and expression-ready vaccine candidate warranting experimental validation against \u003cem\u003ehMPV\u003c/em\u003e, \u003cem\u003eRSV\u003c/em\u003e, and \u003cem\u003eIAV\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"In Silico Development and Structural Evaluation of a Broad-Spectrum Chimeric Multi-Epitope Vaccine against Co-Infection by Human Metapneumovirus, Respiratory Syncytial Virus, and Influenza A Virus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 20:12:01","doi":"10.21203/rs.3.rs-7697013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T14:59:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T11:49:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-23T04:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237700853065616782063323154261412843132","date":"2025-10-11T06:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73069969302158502075422237018206823510","date":"2025-10-10T11:15:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314150449147993620849179719307808869562","date":"2025-10-09T05:33:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T01:39:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-29T19:26:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-26T08:37:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T08:30:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-23T18:00:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbbb2e67-cf3c-43dd-aa70-8476f75cecdc","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56124671,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56124672,"name":"Biological sciences/Immunology"},{"id":56124673,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-02T16:00:49+00:00","versionOfRecord":{"articleIdentity":"rs-7697013","link":"https://doi.org/10.1038/s41598-026-40812-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-02-23 15:57:43","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-10-14 20:12:01","video":"","vorDoi":"10.1038/s41598-026-40812-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-40812-z","workflowStages":[]},"version":"v1","identity":"rs-7697013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7697013","identity":"rs-7697013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}