Reverse vaccinology-based design of a universal multiepitope vaccine against chikungunya virus: phylogenetic and immunoinformatics approaches

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Hakim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7255061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chikungunya virus (CHIKV) infection is a re-emerging arboviral disease in tropical and subtropical regions. In addition to acute febrile syndrome, CHIKV infection may lead to chronic articular manifestations that significantly affect a long-term quality of life. This study aimed to design a universal vaccine candidate covering all circulating genotypes of CHIKV based on conserved multiepitope platform. We employed a large scale phylogenetic and immunoinformatic approach to identify conserved regions of the open reading frames (ORF2) region encoding viral structural proteins. This study ultimately identified 11 high-quality epitopes: 6 MHCI, 1 MHCII, and 3 B cell epitopes. The selected epitopes span multiple viral domains, including C, E1, E2, and E3, with high immunogenicity (VaxiJen ≥66%), non-toxic, and non-allergenic properties. These selected epitopes were utilized to design multiepitope vaccine constructs (MEV-CHIKV) linked with various linkers in combination with adjuvants (human β-defensin 3) to enhance the immune responses. Structural validation analysis showed high quality and stability of the vaccine construct. Based on molecular docking analysis, the designed vaccine has high binding affinities with the active site of TLR3. In silico immune simulation showed induction of robust adaptive immune responses, characterized by the activation and expansion of B and T cell populations. Codon optimization and rare codon analysis revealed a potentially high expression in bacterial system. Thus, the vaccine cadidate is anticipated to effectively and simultaneously induce robust cellular and humoral immune responses. In addition, it should retain its high protection upon emergence of novel mutations within the CHIKV genome. Since our study is merely in silico -based analysis, further in vitro and in vivo experimental validation to demonstrate the immunogenic properties of the vaccine candidate are still needed. chikungunya immunoinformatics multiepitope-based vaccine reverse vaccinology viral structural proteins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Chikungunya virus (CHIKV) is an enveloped and a positive-sense single-stranded RNA virus that belongs to the Alphavirus genus of the Togaviridae family. Its genome is approximately 11.8 kb in length and is composed of two open reading frames (ORFs), i.e. ORF1 and ORF2 that encode for four non-structural (nsP1, nsP2, nsP3, and nsP4) and five structural proteins [capsid (C), envelope E3, E2, 6K, and E1], respectively. CHIKV is phylogenetically classified into three main genotypes, namely Asian, West African and East/Central/South African (ECSA) genotypes [ 1 ]. In disease-endemic regions, CHIKV commonly cocirculates with other mosquito-transmitted virus, including dengue (DENV) and Zika viruses (ZIKV) [ 2 ]. Infection of CHIKV is a re-emerging arboviral disease in tropical and subtropical regions. In addition to acute febrile syndrome, CHIKV infection may lead to chronic articular manifestations that significantly affect a long-term quality of life (QOL) and disability-adjusted life years (DALY) [ 3 ]. In addition, a high rate of fatality (death) has been reported, particularly during an epidemic period [ 4 , 5 ]. The economic burden of CHIKV infection is associated with chronicity, severe clinical manifestations, increased risks of hospitalisation, and mortality. Thus, the impact of CHIKV infection to the health system and economy can not be underestimated [ 6 ]. Similar to other viral infections, effective clearance of CHIKV infection would highly depend on robust innate and adaptive (both B cell- and T cell-mediated) immune responses [ 1 ]. Several animal and human studies demonstrated thet the E2 protein is the primary target of CHIKV neutralising antibodies [ 7 – 10 ]. Interestingly, the E2 protein stimulated the production of IFNγ at the higehst level by T cells [ 11 ]. Understanding of these immune responses and identification of the immunodominant epitopes serve a basis for CHIKV vaccine development. Since structural proteins are essential for the fusion and entry process of CHIKV into the host cells, they are regarded as critical targets for the development of vaccines [ 1 ]. Recently, there are two CHIKV vaccine available for use to prevent CHIKV infection, i.e. live-attenuated vaccine VLA1553 (IXCHIQ, Valneva, Austria) and virus-like particle PXVX0317 (VIMKUNYA, initially developed by the US National Institutes of Health Vaccine Research Center) [ 12 ]. VLA1553 generated seroprotective CHIKV neutralising antibody levels in 98.9% subjects after a single immunisation shot [ 13 ]. IXCHIQ is administered as a single-dose vaccine and is approved for use in adults aged 18 years and older. However, its use is not recommended in individuals aged 60 years and above. Similarly, VIMKUNYA is administered as a single-dose vaccine and is approved for individuals aged 12 years and older [ 14 ]. A specific epitope may stimulate different arms of immune responses, either B cell- and T-cell mediated responses. A multiepitope vaccine, composed of a number of antigenic epitopes, is thus one of the promising platform to develop novel vaccine candidates [ 15 ]. To construct well-designed and potent multiepitope vaccines, it is essential to identify multiple epitopes that simultaneously elicit humoral (antibody) and cell-mediated CD8 + and CD4 + T cell responses [ 16 ]. Various bioinformatics and immunoinformatics tools have been employed to construct multiepitope-based vaccine against many viral diseases, including human immunodeficiency virus 1 (HIV-1) [ 15 ], severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [ 17 ], human papillomavirus (HPV) type 16 and 18 [ 18 ], and mpox virus [ 19 ]. Subsequent experimental validation will significantly improve the accuracy of the predicted epitopes [ 20 , 21 ]. The first vaccine developed using an immunoinformatics-based approach targeted Neisseria meningitidis , and its development was successfully achieved [ 22 ]. Effective B cell as well as CD4 + and CD8 + T cell responses are desirable outcomes of a promising vaccine candidate. Additionally, the vaccine candidate should retain its high protection against the emergence of new variants since CHIKV is constantly evolving [ 23 ]. Novel vaccine candidate designed using conserved multiepitope strategies are theoretically promising for enhancing vaccine coverage and immune specificity, while minimizing the risk of immunopathology and viral immune escape [ 15 ]. Thus, our study aimed to design a universal vaccine candidate covering all circulating genotypes of CHIKV based on conserved multiepitope platform. We employed a large scale phylogenetic and immunoinformatic approach to identify conserved regions of the ORF2 that overlap with B or T cell epitopes. The vaccine construct was designed by linking all selected epitopes with linkers and adjuvants. Finally, the vaccine construct was examined for its molecular interaction with Toll-like receptor 3 (TLR3), its potentiality to stimulate immune responses, and its in silico cloning and expression into a bacterial vector expression system Materials and Methods Schematic overview of the bioinformatics workflow used for designing a multiepitope vaccine against CHIKV (MEV-CHIKV) is presented in Fig. 1 . Data mining and phylogenetic tree construction A comprehensive collection of CHIKV genomic sequences was obtained from the NCBI database on May 27, 2025. The downloaded dataset comprised 2,798 entries annotated as coding sequences (CDS), including both nucleotide and translated amino acid sequences. From these, 2,557 entries were identified as encoding structural proteins. Genotypic classification based on NCBI metadata revealed that 1,652 sequences had known genotype annotations. To ensure adequate representation of the West African lineage, which is typically underrepresented in public databases, an additional seven sequences (HM045816.1, HM045785.1, HM045815.1, HM045818.1, AY726732.1, HM045820.1, and HM045817.1) were incorporated [ 24 ]. Quality control was performed by removing sequences containing ambiguous bases using the reformat.sh script from BBTools ( https://jgi.doe.gov/data-and-tools/bbtools/ ). To ensure data completeness, only sequences longer than 75% of the full CHIKV genome (≥ 2,811 bp out of 3,747 bp) were retained. The final curated dataset consisted of 1,429 high-quality sequences spanning three CHIKV lineages: 234 Asian; 1,158 ECSA; and 37 West African lineages. Data preprocessing was conducted using UNIX tools (e.g., grep, awk, sed, cat, and sort), SeqKit [ 25 ], and Samtools [ 26 , 27 ]. Multiple sequence alignment was performed using MAFFT with the FFT-NS-2 algorithm [ 28 ], followed by trimming poorly aligned regions using trimAl ( http://trimal.cgenomics.org/ ). ModelTest-NG [ 29 ] was used to select the optimal nucleotide substitution model (GTR + I + G4). RAxML-NG [ 30 ] generated a maximum likelihood (ML) tree with 1,000 bootstrap replicates, which was visualized using FigTree ( http://tree.bio.ed.ac.uk/software/figtree/ ). Protein alignment and consensus sequence generation A total of 1,420 translated amino acid sequences were extracted from the filtered dataset for further processing. Redundancy was eliminated using CD-HIT [ 31 ], resulting in 553 unique structural polyprotein sequences, including 85 from Asian, 457 from ECSA, and 11 from West African lineages. Each lineage’s dataset was aligned separately using MUSCLE [ 32 ], and consensus sequences were generated using EMBOSS [ 33 ]. These lineage-specific consensuses were then aligned to generate a global consensus. An ambiguous residue at position 643 (“X”) was resolved by substituting with “V” based on the ECSA consensus. The global consensus sequence was segmented into five major structural protein domains based on known amino acid positions: C (1–261), E3 (262–325), E2 (326–748), 6K (749–809), and E1 (810–1248). These segments were extracted using Samtools [ 26 , 27 ] for downstream immunoinformatic analysis. Epitope prediction and filtering Epitope prediction was performed for MHCI, MHCII, and B-cell responses using the IEDB Analysis Resource ( http://tools.iedb.org ). MHCI and MHCII predictions were carried out using NetMHCpan 4.1 EL [ 34 ] with a reference panel of 27 frequently occurring HLA alleles, via the IEDB MHCI ( http://tools.iedb.org/mhci/ ) and MHCII ( http://tools.iedb.org/mhcii/ ) web servers. B-cell epitopes were predicted using BepiPred Linear Epitope Prediction 2.0 [ 35 ]. Filtering criteria for MHCI epitopes included prediction scores ≥ 0.9 and percentile ranks ≤ 0.05, while MHCII filtering was based on scores ≥ 0.9 and ranks ≤ 0.05. Core peptides were selected when available. In cases lacking qualifying peptides, the highest scoring candidate was selected. B-cell filtering thresholds were region-specific, with values adjusted to ensure a minimum of one epitope per domain (e.g., threshold 0.625 for C, E1, and E2; threshold 0.5 for E3 and 6K; minimum length of 8 amino acids). Subsequently, overlapping epitopes among MHCI, MHCII, and B-cell predictions were identified. If the overlap was absent across all three, partial overlaps or single best-scoring peptides were retained. Selected epitopes were validated using VaxiJen v3.0 [ 36 ] for immunogenicity, AllerTOP v2.1 [ 37 ] for allergenicity, and ToxinPred3 [ 38 ] for toxicity. Filtering and integration were implemented using custom R scripts based on dplyr, plyr, stringr, data.table, readr, and fuzzyjoin packages. Vaccine construction The 11 validated epitopes were assembled into a multi-epitope vaccine (MEV-CHIKV), preserving the structural polyprotein order (C → E3 → E2 → 6K → E1). Linkers were selected for structural and functional optimization: AAY between MHCI and MHCII epitopes; GPGPG between MHCII and B-cell epitopes; and EAAAK between the adjuvant and MHCI block. A 6xHis tag was added at the N-terminus for purification. As an adjuvant, the antimicrobial human β-defensin 3 peptide was fused to the N-terminal end of the vaccine construct (UniProt Q5U7J2). Population coverage analysis To assess population-scale efficacy, predicted HLA-binding alleles were evaluated for population coverage using the IEDB Population Coverage tool ( http://tools.iedb.org/population/ ). Binding affinities for MHCI and MHCII alleles were estimated using NetMHCpan 4.1 BA and NetMHCIIpan 4.1, respectively [ 34 ]. The peptide length for MHCII prediction ranged from 12–15 amino acids. The resulting allele set was analyzed across a broad panel of global populations, including specific regions in Asia, such as Indonesia, Malaysia, Singapore, Thailand, Vietnam, and others. Structural modelling, 2D prediction, and 3D structure validation Secondary structure prediction was performed using PSIPRED [ 39 ], while tertiary structure modelling was executed using D-I-TASSER [ 40 ]. From five D-I-TASSER models, the one with the highest estimated TM-score was selected for refinement. This model was refined using GalaxyRefine [ 41 ]. The model with the lowest RMSD, highest GDT-HA, and most favorable Ramachandran statistics was selected for further validation. Validation included structural quality checks using ProSA [ 42 ], ProCheck and ERRAT modules from SAVES v6.1 ( https://saves.mbi.ucla.edu/ ) and geometric evaluation with MolProbity [ 43 ]. Hydrogen atoms were added to optimize Asn, Gln, and His flips during the MolProbity analysis. Flexibility analysis was carried out using CABS-Flex3.0 ( http://biocomp.chem.uw.edu.pl/CABSflex3 ) after PDB file cleanup using pdb-tools [ 44 ] and awk. Molecular docking Molecular docking simulations were carried out using HADDOCK 2.4 [ 45 ]. Eight target proteins were selected based on epitope-HLA allele matches and structural availability in the RCSB PDB database. Targets included TLR3 (PDB ID: 1ZIW), six MHCI alleles (HLA-B57:01, HLA-B58:01, HLA-B07:02, HLA-A68:01, HLA-B35:01, HLA-B15:01), and one MHCII allele (HLA-DRB1*01:01). Protein structures were preprocessed using PyMOL [ 46 ], including removal of water, ligands, and renumbering. Binding affinity of docked models was assessed using PRODIGY ( https://bianca.science.uu.nl/prodigy/ ). Selection of docked model for molecular dynamics Among the 36 generated docking poses of MEV-TLR3, the model with the lowest binding free energy and highest predicted affinity (as evaluated by PRODIGY at 37°C) was selected for subsequent molecular dynamics (MD) simulation. Molecular dynamics simulation MD simulations were conducted using iMODS ( https://imods.iqfr.csic.es/ ) which applies normal mode analysis (NMA) to examine the flexibility and stability of protein–protein complexes. The analysis yielded structural deformation maps, eigenvalues, RMSD distributions, and covariance matrices that confirmed the MEV-TLR3 complex's dynamic stability. Immune simulation To simulate host immune response post-vaccination, a 365-day immune response profile was generated using C-ImmSim [ 47 ]. The simulation modeled three doses administered at days 1, 30, and 60. Codon optimization Codon optimization was carried out using JCat [ 48 ] to adapt the MEV-CHIKV sequence for expression in Escherichia coli K12. Optimization settings avoided rho-independent terminators, prokaryotic ribosome binding sites, and restriction enzyme sites for EcoRI, BamHI, HindIII, PstI, SalI, XbaI, and SmaI. The resulting codon-adapted sequence is suitable for cloning and expression in bacterial vectors. Results Phylogenetic tree construction based on global CHIKV sequences and consensus sequence generation Summary of CHIKV genomic dataset used in phylogenetic analysis and summary of amino acid dataset and construction of consensus sequence was shown in Supplementary Table 1. It provides an overview of CHIKV sequences retrieved from the NCBI database, detailing the number of downloaded sequences, annotated structural proteins, genotyped entries, and curated sequences after quality control. Final lineage-specific distributions and preprocessing tools used for alignment and tree construction are also listed. Phylogenetic analysis of 1,429 high-quality CHIKV structural polyprotein sequences was performed using the ML method with the GTR + I + G4 substitution model and 1,000 bootstrap replicates ( Fig. 2 ) . The primary tree is midpoint-rooted (left) and illustrates the major phylogenetic divergence among the three known genotypes of CHIKV: West African (blue), Asian (red), and ECSA (green). Two additional views, an unrooted polar tree (top right) and a branch-length-transformed tree with equal distances (bottom right), provide alternative visualizations to highlight intra-lineage clustering and relationships. This lineage-resolved phylogeny offers robust evolutionary context for downstream epitope mapping and consensus sequence construction. Accurate lineage discrimination ensures that the derived global consensus sequence used in epitope prediction incorporates genetic variability across all three major CHIKV genotypes, enhancing vaccine design strategies with broader population coverage and immunogenic relevance. MHCI, MHCII, and B cell epitope prediction and epitope selection for multiepitope vaccine construct Table 1 lists the immunogenic epitopes identified from the CHIKV structural polyprotein and selected for inclusion in the multiepitope vaccine construct (MEV-CHIKV). This pipeline ultimately identified 11 high-quality epitopes: 6 MHCI, 1 MHCII, and 3 B cell epitopes. The epitopes were predicted based on their binding affinity to common HLA alleles and were further assessed for immunogenic potential (VaxiJen score), toxicity (ToxinPred), and allergenicity (AllerTOP). The selected epitopes span multiple viral domains, including C, E1, E2, and E3, and include B-cell, MHC-I, and MHC-II epitopes with high immunogenicity (VaxiJen ≥ 66%), non-toxic, and non-allergenic properties. The peptide start-end positions correspond to their location within the reference structural polyprotein. Graphical representation of the predicted and selected epitopes mapped along the 1248-amino acid CHIKV structural polyprotein is shown in Fig. 3 . Table 1 Predicted B cell, MHC class I, and MHC class II epitopes selected for MEV-CHIKV construction. Domain Target HLA Allele Peptide Sequence Start-End Position* Peptide Length Vaxijen (Immunogenicity, %) ToxinPred (Toxicity) Allertop (Allergenicity) C B-Cell NA PRPRPQRQA 30–38 9 100 non-toxin non-allergen C B-Cell NA KKQPPKKKPAQKKKKPGR 84–101 18 66 non-toxin non-allergen C B-Cell NA RRNRKNKKQKQKKQAP 62–77 16 100 non-toxin non-allergen C MHC-I HLA-B*57:01, HLA-B*58:01, HLA-A*32:01 RTALSVVTW 237–245 9 100 non-toxin non-allergen E1 MHC-I HLA-B*07:02 RPGYSPMVL 830–838 9 66 non-toxin non-allergen E1 MHC-I HLA-A*32:01 KVFTGVYPF 888–896 9 66 non-toxin non-allergen E1 MHC-I HLA-A*68:01 HTASASAKLR 934–943 10 66 non-toxin non-allergen E2 MHC-I HLA-B*35:01, HLA-B*53:01 YPDHPTLLSY 613–622 10 66 non-toxin non-allergen E2 MHC-I HLA-B*15:01, HLA-A*30:01 KARNPTVTY 595–603 9 66 non-toxin non-allergen E3 MHC-II HLA-DRB1*01:01 YQLLQASLT 309–317 8 66 non-toxin non-allergen * According to structural polyproteins Population coverage analysis Population coverage analysis of the selected epitope is shown in Table 2 . The table presents the estimated HLA population coverage (%) for the selected MHC-I and MHC-II epitopes across various global regions, including individual countries in Southeast Asia. Broad population coverage is observed for MHC-I epitopes globally, with the highest in East Asia (89.16%). In contrast, MHC-II coverage is relatively low, primarily due to the inclusion of only a single high-affinity MHC-II epitope in the construct, as a result of stringent threshold filtering during the epitope selection process. Overall, the MEV-CHIKV demonstrated high theoretical population coverage globally and regionally. Table 2 Estimated global and regional population coverage of selected MHC class I and class II epitopes. Calculations were performed using the IEDB population coverage tool. The "Coverage" column represents the estimated proportion of the population predicted to respond to at least one epitope, while "pc90" indicates the minimum number of epitope-HLA combinations recognized by 90% of the responding population. Population (area) Class I Class II Coverage pc90 Coverage pc90 World 81.14% 0.53 14.37% 0.12 Oceania 86.63% 0.75 11.87% 0.11 Europe 82.38% 0.57 17.07% 0.12 Central Africa 70.86% 0.34 3.77% 0.1 East Africa 78.16% 0.46 8.22% 0.11 North Africa 83.54% 0.61 8.00% 0.11 West Africa 85.79% 0.7 13.88% 0.12 Central America 7.76% 0.11 3.57% 0.1 North America 84.52% 0.65 14.70% 0.12 South America 72.70% 0.37 5.28% 0.11 West Indies 86.39% 0.73 12.10% 0.11 East Asia 89.16% 0.92 28.37% 0.14 Northeast Asia 82.82% 0.58 8.83% 0.11 South Asia 83.70% 0.61 10.21% 0.11 Southeast Asia 80.50% 0.51 9.34% 0.11 Southwest Asia 71.70% 0.35 4.46% 0.1 Indonesia 67.45% 0.31 5.46% 0.11 Malaysia 58.33% 0.24 14.64% 0.12 Singapore 77.10% 0.44 3.57% 0.1 Philippines 67.51% 0.31 10.43% 0.11 Thailand 76.95% 0.43 9.50% 0.11 Vietnam 80.24% 0.51 6.57% 0.11 Taiwan 86.81% 0.76 12.24% 0.11 Vaccine construction (MEV-CHIKV) Based on the previous epitope selection, a multiepitope vaccine against CHIKV (MEV-CHIKV) was designed. Schematic representation and tertiary structure of the MEV-CHIKV construct is shown in Fig. 4 . The construct comprises an adjuvant (human β-defensin 3) at the N-terminus (red), followed by a series of epitopes derived from the structural polyprotein of the CHIKV. These include six MHC class I epitopes (green), one MHC class II epitope (yellow), and three B-cell epitopes (orange). A 6×His tag (blue) was added at the C-terminus for purification purposes. The epitopes are arranged in gene order (C → E3 → E2 → 6K → E1) and are separated by appropriate linkers: EAAAK (grey) between the adjuvant and first epitope, AAY (purple) between MHC-I epitopes, and GPGPG (black) between MHC-II and B-cell epitopes. The design ensures proper epitope presentation, structural stability, and enhanced immunogenicity for in silico vaccine performance against diverse CHIKV lineages. The resulting 198-amino-acid construct was assessed for its safety and efficacy. It was predicted to be non-toxic (ToxinPred2), non-allergenic (AllerTOP), and immunogenic (VaxiJen score: 66%) Secondary and tertiary structure validation of the MEV-CHIKV construct Figure 5 and Supplementary Table 2 show the structural validation metrics obtained from several bioinformatics tools to evaluate the accuracy, quality, and stability of the modeled multiepitope vaccine (MEV-CHIKV). Secondary structure prediction by PSIPRED revealed that the vaccine construct is predominantly composed of coils (60.61%), followed by α-helices (26.26%) and β-strands (13.13%). Tertiary structure modeling and refinement by D-I-TASSER and GalaxyRefine showed a reliable model with a GDT-HA of 0.9255 and a MolProbity score of 1.392. Additional validation using ProSA (Z-score = -3.73) confirmed structural reliability within the range of experimentally determined protein structures. The SAVES suite validated the model with a high ERRAT score of 95.536 and a favorable Ramachandran plot profile (98.1% of residues in allowed and favored regions). MolProbity analysis further supported the model’s high geometric quality, and flexibility was assessed using CABS-Flex, which showed limited RMSF values, indicating a stable conformation. Next, the MEV-CHIKV construct was evaluated for its tertiary structure stability using CABS-Flex 3.0 ( Fig. 6 ). This analysis provides insight into the dynamic behavior of the vaccine construct under physiological conditions. The simulation revealed a median RMSF of 1.989 Å, indicating overall structural stability. High flexibility regions, with RMSF values peaking at 9.328 Å, corresponded to linker regions and terminal loops, which may enhance epitope accessibility. These results support the conformational robustness of the vaccine model while preserving dynamic features beneficial for immune recognition. Molecular docking and interaction analysis between MEV-CHIKV and human TLR3 receptor The MEV-CHIKV construct was then evaluated for its interaction with human TLR3 receptor and other immune receptors using molecular docking analysis ( Fig. 7 and Table 3 ). Table 3 Molecular docking and binding affinity evaluation of MEV-CHKV with immune receptors No Target Protein HLA Allele Type PDB ID HADDOCK Mean Binding Energy PRODIGY Analysis HADDOCK Selected Structure ΔG (kcal mol-1) Kd (M) at 37℃ Inter-molecular Contacts Notes for PRODIGY Analysis 1 TLR3 NA 1ZIW -273.572 ± 114.038 Cluster 9 − 3 -17.6 3.9E-13 128 Use all HADDOCK clusters then select one cluster based on the best binding free energy (ΔG, kcal/mol), dissociation constant (Kd, M) at 37°C, from PRODIGY (shown here) 2 MHC-I HLA-B*57:01 5VUE -227.961 ± 57.022 Cluster 5 − 1 -12.1 3.1E-09 90 Use only one representative cluster with the best HADDOCK score 3 MHC-I HLA-B*58:01 4LNR -227.444 ± 76.235 Cluster 7 − 1 -15.6 1E-11 122 4 MHC-I HLA-B*07:02 5VWH -249.286 ± 63.891 Cluster 15 − 2 -12.2 2.6E-09 95 5 MHC-I HLA-A*68:01 6PBH -231.812 ± 94.881 Cluster 3 − 1 -13.3 4.3E-10 118 6 MHC-I HLA-B*35:01 7LG0 -267.468 ± 50.333 Cluster 11 − 2 -12.6 1.3E-09 85 7 MHC-I HLA-B*15:01 8ELH -254.892 ± 77.034 Cluster 1–3 -13.9 1.6E-10 113 8 MHC-II HLA-DRB1*01:01 7YX9 -209.482 ± 44.894 Cluster 7 − 1 -13.5 3.2E-10 121 Table 3 summarizes the docking results of the MEV-CHIKV construct against innate and adaptive immune receptors using the HADDOCK web server and PRODIGY binding affinity analysis. For each receptor, the corresponding HLA allele (if applicable), PDB ID, and HADDOCK-derived mean binding energy (± standard deviation) of the selected docking cluster are shown. Binding free energy (ΔG, kcal/mol), dissociation constant (Kd, M) at 37°C, and the number of intermolecular contacts were predicted using PRODIGY. For TLR3, all HADDOCK clusters were analyzed in PRODIGY, and the best-scoring cluster was selected for downstream structural and molecular dynamics simulations. For MHC-I and MHC-II receptors, the PRODIGY analysis was conducted only on the representative cluster with the lowest HADDOCK binding energy. The predicted 3D complex of the multiepitope vaccine construct (MEV-CHIKV, green) with the TLR3 receptor (red) is shown in Fig. 7 , highlighting intermolecular contact regions. Interacting residues are indicated in blue (on TLR3) and magenta (on MEV-CHIKV). A total of 128 intermolecular contacts were identified, illustrating strong molecular interactions and stable binding. The lower panel presents a heatmap of residue-residue interactions between MEV-CHIKV (x-axis) and TLR3 (y-axis), where each magenta square represents contact. These interactions suggest a potential immune-activating interface, supporting the designed vaccine's immunogenic potential. Molecular dynamics (MD) simulation of MEV-CHIKV and TLR3 complex with iMODS MD simulation with iMODS Normal Mode Analysis was conducted to evaluate structural flexibility and stability of the MEV-TLR3 complex ( Fig. 8 ). The deformability plot and B-factor analysis revealed limited flexibility, suggesting a stable interaction. The calculated eigenvalue was 2.686883×10 − 6, indicating moderate energy requirements for deformation and supporting the rigidity of the complex. The covariance matrix revealed largely correlated motions at the interaction interface, further confirming the conformational stability of the complex. The elastic network model also demonstrated dense connections across the binding interface, indicating a robust structural network. Collectively, these results support the functional stability of the MEV–TLR3 complex for downstream immune activation. In silico immune simulation of MEV-CHIKV To simulate host immune response post-vaccination, a 365-day immune response profile was generated using C-ImmSim [ 47 ]. The simulation modeled three doses administered at days 1, 30, and 60 ( Fig. 9 ) . The simulation was performed over a period of 365 days with three vaccine doses administered at day 1, 30, and 60. The results show a strong adaptive immune response characterized by the induction of B and T cell populations, development of memory cells, and robust antibody production. Codon optimization and rare codon analysis of the MEV-CHIKV construct Codon optimization and rare codon analysis of the MEV-CHIKV construct is shown in Fig. 10 . The nucleotide sequence of the MEV-CHIKV was optimized for expression in Escherichia coli (strain K12) using JCat. The final construct has a total length of 594 base pairs, with a high Codon Adaptation Index (CAI) of 0.96 and a GC content of 55.05%, indicating suitability for heterologous expression. The schematic at the top represents the structural and functional domains of the construct, including epitope regions, linkers, a β-defensin adjuvant, and a 6×His tag. The coding sequence is shown below with annotated regions and highlighted rare codons. Three rare codons were identified: CAA at positions 19–21 and 388–390, and AGT at position 394–396. These rare codons, although infrequent, are not expected to significantly hinder expression due to their low occurrence and overall high CAI. Discussion CHIKV continuously poses serious threats to global health. As of early June 2025, around 220,000 chikungunya cases and 80 related deaths have been reported across 14 countries, primarily in the Americas, Africa, and Asia. While mainland Europe has reported no cases, outbreaks are ongoing in the EU outermost regions of Réunion and Mayotte. The Americas currently represent the most affected region globally [ 49 ]. The global burden of CHIKV infection has driven the development of CHIKV vaccines, leading to the approval of two: the live-attenuated vaccine VLA1553 (IXCHIQ) and the virus-like particle-based vaccine PXVX0317 (VIMKUNYA). However, post-licensure monitoring has revealed safety concerns. Recently, serious adverse events have been reported globally among vaccine recipients over the age of 60. As IXCHIQ is a live-attenuated vaccine, its use is also contraindicated in individuals with weakened immune systems, regardless of age [ 50 ]. Although a licensed vaccine for CHIKV has recently become available, the development of alternative vaccine candidates remains essential due to several limitations of the available vaccines. Current vaccines may offer suboptimal protection against the diverse genotypes of CHIKV circulating globally, and their safety and efficacy profiles in specific populations—such as the elderly or immunocompromised—may not yet be fully established [ 50 ]. Moreover, issues related to production cost, cold-chain requirements, and long-term immunogenicity highlight the need for improved vaccine platforms. Therefore, designing novel vaccine candidates with broader cross-protective potential, enhanced stability, and increased accessibility could play a critical role in complementing existing vaccination strategies and strengthening global preparedness against CHIKV outbreaks. Multiepitope-based vaccines offer more advantages than classical vaccine design, for example the presence of multiple MHC-restricted epitopes that can be recognized by T cell receptors (TCRs) from various clones [ 16 ]. Thus, the crucial step in the development of multiepitope-based vaccine design is identification and selection of potentially immunogenic epitopes. Various immunoinformatics tools are currently available to accelerate the vaccine design [ 51 ]. In this study, we selected epitopes from each structural protein of CHIKV to expand the spectra of the targeted antigens. We first generated a global consensus sequence from all available CHIKV genome sequences to be employed for epitope prediction. While our study extensively utilizes global CHIKV sequences, the other study was based on a limited number of sequences [ 52 , 53 ]. The conserved epitopes identified in this study are expected to confer broader and long-lasting protection across different CHIKV genotypes that are constantly mutating due to a lack of proof-reading activity of RNA-dependent RNA polymerase (RdRp). Therefore, the vaccine design aims to effectively target the extensive genetic diversity of CHIKV. The designed vaccine is expected to effectively stimulate both humoral and T cell-mediated immune responses. The multiepitope platform is inherently low in immunogenicity; therefore, human β-defensins 3 was added as adjuvants to enhance its immune response. A previous study compared SARS-CoV-2 epitopes predicted by various computational studies with experimentally validated T cell epitopes identified from the blood of convalescent COVID-19 patients [ 54 ]. The results showed a strong correlation between the predicted and experimentally determined epitopes, indicating the high accuracy of in silico epitope prediction [ 54 ]. Upon administration, multiepitope vaccines will be internalized by dendritic cells (DCs). Following DC differentiation into mature phenotypes, the multiepitope vaccines will subsequently be presented to naïve T cells in the context of MHC molecules. DCs are equipped with various pattern recognition receptors (PRR), including TLRs, to sense the presence of viral-derived antigens. TLR3 is intracellularly located in the endosome and can be stimulated by its natural ligands, including dsRNA and polyinosinic-polycytidylic acid [poly(I:C)] [ 55 ]. TLR recognition of viral-derived antigens and subsequent activation of downstream signaling pathways will induce DC maturation and the production of antiviral cytokines. Indeed, it has been shown that TLR3 stimulation can inhibit CHIKV infection in vitro [ 56 ]. TLR agonists have been extensively utilized as adjuvants for the development of arbovirus vaccines [ 55 ]. Based on molecular docking analysis, the designed vaccine has high binding affinities with the active site of TLR3. This indicates its capacity to efficiently stimulate the innate and adaptive immune responses Conclusions We have designed a conserved and universal CHIKV vaccine candidate against all circulating genotypes of CHIKV globally. The candidate is multiepitope-based vaccine platform by identifying and screening various T and B cell epitopes that are conserved in CHIKV genome sequences. Thus, the vaccine cadidate is anticipated to effectively and simultaneously induce robust cellular and humoral immune responses. In addition, it should retain its high protection upon emergence of novel mutations within the CHIKV genome. We also examined the predicted interaction between the vaccine construct and TLR3. In silico immune simulation showed induction of robust adaptive immune responses, characterized by the activation and expansion of B and T cell populations. Ultimately, codon optimization and rare codon analysis revealed a potentially high expression in bacterial system. However, since our study is merely in silico -based analysis, further in vitro and in vivo experimental validation to demonstrate the immunogenic properties of the vaccine candidate are still needed. Declarations Acknowledgments The authors would like to thank Aqsa Ikram (the University of Lahore, Pakistan) and Faris M. Gazali for their technical assistance during the early phase of this project. Author contributions M. S. H. was involved in conceptualization, data acquisition, data analysis, writing original draft, editing, and finalization. Statements and declarations The authors declare no conflict of interest. The authors declare that no funds, grants, or other support were received during the preparations of this manuscript. Data availability Datasets, output files, and other data generated during this study are available from the corresponding author upon request. 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Adv Drug Deliv Rev 171:29-47. https://doi.org/10.1016/j.addr.2021.01.007 Lani R, Thariq IM, Suhaimi NS, et al. (2024) From defense to offense: Modulating toll-like receptors to combat arbovirus infections. Hum Vaccin Immunother 20(1):2306675. https://doi.org/10.1080/21645515.2024.2306675 Garcia G, Jr., Irudayam JI, Jeyachandran AV, et al. (2023) Innate immune pathway modulator screen identifies STING pathway activation as a strategy to inhibit multiple families of arbo and respiratory viruses. Cell Rep Med 4(5):101024. https://doi.org/10.1016/j.xcrm.2023.101024 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Supplementary Table 1. Dataset summary for phylogenetic and epitope analysis of CHIKV. Supplementary Table 2. Structural validation scores and quality assessment of the final MEV-CHIKV tertiary model. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7255061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494035973,"identity":"e3325e21-380e-44a8-9237-375c4dbcdd24","order_by":0,"name":"Mohamad S. Hakim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFACHgbGBgMGOSibBC3GpGphYEhsIFqLbgPvwY8zCral991IYHzwto3Bnr+BgBazA3zJkhsMbufOvJHAbDi3jSFxxgGCWngMJB8AtWy4kcAmzdvGkMBAhBbjn0At6QY3Eth/A7XYyxOhxQzksASgFjZmoBbGDQS1HOYxs5xhcNtw5pmHzZJzzkkkbiSo5XiP8c2eP7fl+Y4nH/zwpszGXo6QFgZmGOMAKHoYJAipRwYEDR8Fo2AUjIIRCwCbMkKQpYDp5wAAAABJRU5ErkJggg==","orcid":"","institution":"Qassim University","correspondingAuthor":true,"prefix":"","firstName":"Mohamad","middleName":"S.","lastName":"Hakim","suffix":""}],"badges":[],"createdAt":"2025-07-30 16:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7255061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7255061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88412543,"identity":"f963adf6-c415-43d0-818a-91234eedfe02","added_by":"auto","created_at":"2025-08-06 08:38:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1737043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of the multiepitope vaccine design based on structural proteins of CHIKV.\u003c/strong\u003e The workflow includes data mining, sequence curation, multiple sequence alignment, phylogenetic analysis, consensus sequence construction, epitope prediction and filtering, vaccine construction, population coverage analysis, structural modeling, docking, molecular dynamics simulation, immune simulation, and codon optimization.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/6a4b70afdf7a42a35bc90172.jpg"},{"id":88414380,"identity":"3cdb40cd-5e94-4828-8784-d2516da9f29a","added_by":"auto","created_at":"2025-08-06 08:46:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2027279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic tree of CHIKV structural polyprotein sequences from global isolates\u003c/strong\u003e. Phylogenetic analysis of 1,429 high-quality CHIKV structural polyprotein sequences was performed using the maximum likelihood (ML) method with the GTR+I+G4 substitution model and 1,000 bootstrap replicates.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/d3b4f92dd0df9cd2fd1f0526.png"},{"id":88412544,"identity":"d4b5a81d-0cbd-45d0-8a8c-d094e5df181f","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1808927,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpitope mapping across the structural polyprotein of CHIKV and comparative conservation among genotypes.\u003c/strong\u003e Domain boundaries are color-coded: C (blue), E3 (green), E2 (orange), 6K (purple), and E1 (red). Predicted epitopes are overlaid as bars and color-coded based on epitope type: B-cell epitopes (dark grey), MHC class I epitopes (black), and MHC class II epitopes (light grey). Each epitope is labeled below the polyprotein with its corresponding amino acid sequence derived from the global consensus. To assess epitope conservation, lineage-specific consensus sequences (West African, Asian, and ECSA) are aligned with the global epitope sequence. Sequence variations are annotated with triangle markers at the positions of divergence, allowing visual identification of polymorphic residues across lineages. This mapping illustrates the distribution and conservation of selected epitopes and supports their broad applicability in a multivalent vaccine construct.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/8de324644c2ea4ad5cfec12e.png"},{"id":88414384,"identity":"dc4a06bc-63ee-4ab2-b83f-e717b4a88793","added_by":"auto","created_at":"2025-08-06 08:46:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2904753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation and tertiary structure of the MEV-CHIKV construct. \u003c/strong\u003e3D structure prediction and refinement of the MEV-CHIKV construct was performed using D-I-TASSER and GalaxyRefine. The model is visualized with domain-based color coding: β-defensin adjuvant (red), B-cell epitopes (orange), MHC-I (green), MHC-II (yellow), and His-tag (blue). The structure demonstrates proper folding and domain separation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/925b7a45428e55d453d4f0a2.png"},{"id":88412546,"identity":"d09ffa80-4934-437f-ac9e-954223e93eda","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7504906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSecondary and tertiary structure validation of the MEV-CHIKV construct.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eSecondary structure prediction of the multiepitope vaccine (MEV-CHIKV) using PSIPRED revealed a composition of 26.26% α-helices, 13.13% β-strands, and 60.61% coils, indicating a structurally flexible and antigenically favorable construct. \u003cstrong\u003e(b)\u003c/strong\u003e Tertiary structure quality assessment performed with MolProbity demonstrated excellent stereochemical properties, with 98.5% of residues in the favored regions and 99.5% in allowed regions of the Ramachandran plot, and only 1 residue as an outlier, confirming the structural reliability of the model.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/9a13834c532b8149794e17e3.png"},{"id":88412550,"identity":"a21895ca-ab79-4c05-9cd7-c743240ee2e3","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11063688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTertiary structure flexibility analysis of the MEV-CHIKV construct using CABS-Flex 3.0.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003eSuperimposition of the top 10 structural models generated by CABS-Flex reveals dynamic flexibility across the multiepitope vaccine, particularly in loop and coil regions. This structural ensemble highlights conformational variability, especially in surface-exposed domains. \u003cstrong\u003e(b)\u003c/strong\u003e Root Mean Square Fluctuation (RMSF) plot shows residue-level flexibility, with peaks corresponding to highly mobile regions and troughs indicating more stable secondary structures.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/9f70c3136e14c699855dce53.png"},{"id":88414382,"identity":"a7bb863d-c7f2-4401-ae57-2647be860c30","added_by":"auto","created_at":"2025-08-06 08:46:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11523114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking and interaction analysis between MEV-CHIKV and human TLR3 receptor.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/8d100abb5c7c6b49a4ad12d8.png"},{"id":88412554,"identity":"68c17f6e-daff-48fa-aae2-750d6ab0ea09","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16884031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamics (MD) simulation of MEV–TLR3 complex with iMODS\u003c/strong\u003e. Structural flexibility and stability analysis of the MEV–TLR3 complex using iMODS Normal Mode Analysis. \u003cstrong\u003e(a)\u003c/strong\u003e The eigenvalue of the first mode was 2.686883 × 10⁻⁶, indicating a stable but flexible conformation. \u003cstrong\u003e(b)\u003c/strong\u003e Deformability plot showed limited flexibility in most residues, with expected peaks at terminal and loop regions. \u003cstrong\u003e(c)\u003c/strong\u003e NMA-derived B-factors (red) were consistent with crystallographic B-factors (gray), confirming low fluctuation across the structure. \u003cstrong\u003e(d)\u003c/strong\u003e Variance plot indicated that the first 10 modes accounted for the majority of the motion, supporting coordinated global dynamics. \u003cstrong\u003e(e)\u003c/strong\u003eCovariance map of atomic fluctuations showed both correlated (red) and anti-correlated (blue) motions, particularly between domains. \u003cstrong\u003e(f)\u003c/strong\u003eElastic network model revealed a dense matrix of interatomic connections, especially in the core, reinforcing structural stability.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/d8f4888f18fbe4092798c7b2.png"},{"id":88412545,"identity":"3173280c-785e-46c4-925b-1386062a6d8f","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":6242878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e immune simulation of MEV-CHIKV using the C-ImmSim server.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Antigen concentration and immunoglobulin titers (IgM, IgG1, IgG2) over time. \u003cstrong\u003e(b)\u003c/strong\u003e B cell population dynamics by functional state (e.g., active, presenting, internalized). \u003cstrong\u003e(c)\u003c/strong\u003eTotal B cell population, memory B cell response, and isotype switching to IgG subclasses. \u003cstrong\u003e(d)\u003c/strong\u003e CD4⁺T-helper (Th) cell counts over time, including naïve and memory subsets. \u003cstrong\u003e(e)\u003c/strong\u003eCD4⁺ Th cells categorized by activation state (active, duplicating, resting, anergic). \u003cstrong\u003e(f)\u003c/strong\u003e CD4⁺ T-regulatory (Treg) cell population dynamics and activation profile. \u003cstrong\u003e(g)\u003c/strong\u003e CD8⁺ cytotoxic T-cell (Tc) population across the timeline. \u003cstrong\u003e(h)\u003c/strong\u003e CD8⁺ Tc cells broken down by activation states. \u003cstrong\u003e(i)\u003c/strong\u003eCytokine and interleukin secretion profile including IFN-γ and IL-2, indicating pro-inflammatory immune stimulation.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/c19b954dc101be03c6361038.png"},{"id":88412570,"identity":"da564eff-4cf4-4963-b56b-4e92e4e7b76b","added_by":"auto","created_at":"2025-08-06 08:38:11","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":11448276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCodon optimization and rare codon analysis of the MEV-CHIKV construct.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/37c7f149ed0d3d8929ac3d8e.png"},{"id":88412556,"identity":"a62a36b0-00be-4ade-a026-8000ea5e70c4","added_by":"auto","created_at":"2025-08-06 08:38:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. Dataset summary for phylogenetic and epitope analysis of CHIKV.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2. Structural validation scores and quality assessment of the final MEV-CHIKV tertiary model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7255061/v1/fcde0afd7c0e412e99c8ec9d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reverse vaccinology-based design of a universal multiepitope vaccine against chikungunya virus: phylogenetic and immunoinformatics approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChikungunya virus (CHIKV) is an enveloped and a positive-sense single-stranded RNA virus that belongs to the \u003cem\u003eAlphavirus\u003c/em\u003e genus of the \u003cem\u003eTogaviridae\u003c/em\u003e family. Its genome is approximately 11.8 kb in length and is composed of two open reading frames (ORFs), i.e. ORF1 and ORF2 that encode for four non-structural (nsP1, nsP2, nsP3, and nsP4) and five structural proteins [capsid (C), envelope E3, E2, 6K, and E1], respectively. CHIKV is phylogenetically classified into three main genotypes, namely Asian, West African and East/Central/South African (ECSA) genotypes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In disease-endemic regions, CHIKV commonly cocirculates with other mosquito-transmitted virus, including dengue (DENV) and Zika viruses (ZIKV) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInfection of CHIKV is a re-emerging arboviral disease in tropical and subtropical regions. In addition to acute febrile syndrome, CHIKV infection may lead to chronic articular manifestations that significantly affect a long-term quality of life (QOL) and disability-adjusted life years (DALY) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, a high rate of fatality (death) has been reported, particularly during an epidemic period [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The economic burden of CHIKV infection is associated with chronicity, severe clinical manifestations, increased risks of hospitalisation, and mortality. Thus, the impact of CHIKV infection to the health system and economy can not be underestimated [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilar to other viral infections, effective clearance of CHIKV infection would highly depend on robust innate and adaptive (both B cell- and T cell-mediated) immune responses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Several animal and human studies demonstrated thet the E2 protein is the primary target of CHIKV neutralising antibodies [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Interestingly, the E2 protein stimulated the production of IFNγ at the higehst level by T cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Understanding of these immune responses and identification of the immunodominant epitopes serve a basis for CHIKV vaccine development. Since structural proteins are essential for the fusion and entry process of CHIKV into the host cells, they are regarded as critical targets for the development of vaccines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecently, there are two CHIKV vaccine available for use to prevent CHIKV infection, i.e. live-attenuated vaccine VLA1553 (IXCHIQ, Valneva, Austria) and virus-like particle PXVX0317 (VIMKUNYA, initially developed by the US National Institutes of Health Vaccine Research Center) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. VLA1553 generated seroprotective CHIKV neutralising antibody levels in 98.9% subjects after a single immunisation shot [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. IXCHIQ is administered as a single-dose vaccine and is approved for use in adults aged 18 years and older. However, its use is not recommended in individuals aged 60 years and above. Similarly, VIMKUNYA is administered as a single-dose vaccine and is approved for individuals aged 12 years and older [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA specific epitope may stimulate different arms of immune responses, either B cell- and T-cell mediated responses. A multiepitope vaccine, composed of a number of antigenic epitopes, is thus one of the promising platform to develop novel vaccine candidates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To construct well-designed and potent multiepitope vaccines, it is essential to identify multiple epitopes that simultaneously elicit humoral (antibody) and cell-mediated CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T cell responses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Various bioinformatics and immunoinformatics tools have been employed to construct multiepitope-based vaccine against many viral diseases, including human immunodeficiency virus 1 (HIV-1) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], human papillomavirus (HPV) type 16 and 18 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and mpox virus [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequent experimental validation will significantly improve the accuracy of the predicted epitopes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The first vaccine developed using an immunoinformatics-based approach targeted \u003cem\u003eNeisseria meningitidis\u003c/em\u003e, and its development was successfully achieved [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEffective B cell as well as CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cell responses are desirable outcomes of a promising vaccine candidate. Additionally, the vaccine candidate should retain its high protection against the emergence of new variants since CHIKV is constantly evolving [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Novel vaccine candidate designed using conserved multiepitope strategies are theoretically promising for enhancing vaccine coverage and immune specificity, while minimizing the risk of immunopathology and viral immune escape [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus, our study aimed to design a universal vaccine candidate covering all circulating genotypes of CHIKV based on conserved multiepitope platform. We employed a large scale phylogenetic and immunoinformatic approach to identify conserved regions of the ORF2 that overlap with B or T cell epitopes. The vaccine construct was designed by linking all selected epitopes with linkers and adjuvants. Finally, the vaccine construct was examined for its molecular interaction with Toll-like receptor 3 (TLR3), its potentiality to stimulate immune responses, and its \u003cem\u003ein silico\u003c/em\u003e cloning and expression into a bacterial vector expression system\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSchematic overview of the bioinformatics workflow used for designing a multiepitope vaccine against CHIKV (MEV-CHIKV) is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData mining and phylogenetic tree construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA comprehensive collection of CHIKV genomic sequences was obtained from the NCBI database on May 27, 2025. The downloaded dataset comprised 2,798 entries annotated as coding sequences (CDS), including both nucleotide and translated amino acid sequences. From these, 2,557 entries were identified as encoding structural proteins. Genotypic classification based on NCBI metadata revealed that 1,652 sequences had known genotype annotations. To ensure adequate representation of the West African lineage, which is typically underrepresented in public databases, an additional seven sequences (HM045816.1, HM045785.1, HM045815.1, HM045818.1, AY726732.1, HM045820.1, and HM045817.1) were incorporated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eQuality control was performed by removing sequences containing ambiguous bases using the reformat.sh script from BBTools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jgi.doe.gov/data-and-tools/bbtools/\u003c/span\u003e\u003cspan address=\"https://jgi.doe.gov/data-and-tools/bbtools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To ensure data completeness, only sequences longer than 75% of the full CHIKV genome (\u0026ge;\u0026thinsp;2,811 bp out of 3,747 bp) were retained. The final curated dataset consisted of 1,429 high-quality sequences spanning three CHIKV lineages: 234 Asian; 1,158 ECSA; and 37 West African lineages. Data preprocessing was conducted using UNIX tools (e.g., grep, awk, sed, cat, and sort), SeqKit [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and Samtools [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultiple sequence alignment was performed using MAFFT with the FFT-NS-2 algorithm [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], followed by trimming poorly aligned regions using trimAl (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://trimal.cgenomics.org/\u003c/span\u003e\u003cspan address=\"http://trimal.cgenomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). ModelTest-NG [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used to select the optimal nucleotide substitution model (GTR\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G4). RAxML-NG [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] generated a maximum likelihood (ML) tree with 1,000 bootstrap replicates, which was visualized using FigTree (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tree.bio.ed.ac.uk/software/figtree/\u003c/span\u003e\u003cspan address=\"http://tree.bio.ed.ac.uk/software/figtree/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein alignment and consensus sequence generation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,420 translated amino acid sequences were extracted from the filtered dataset for further processing. Redundancy was eliminated using CD-HIT [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], resulting in 553 unique structural polyprotein sequences, including 85 from Asian, 457 from ECSA, and 11 from West African lineages. Each lineage\u0026rsquo;s dataset was aligned separately using MUSCLE [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and consensus sequences were generated using EMBOSS [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These lineage-specific consensuses were then aligned to generate a global consensus. An ambiguous residue at position 643 (\u0026ldquo;X\u0026rdquo;) was resolved by substituting with \u0026ldquo;V\u0026rdquo; based on the ECSA consensus.\u003c/p\u003e\u003cp\u003eThe global consensus sequence was segmented into five major structural protein domains based on known amino acid positions: C (1\u0026ndash;261), E3 (262\u0026ndash;325), E2 (326\u0026ndash;748), 6K (749\u0026ndash;809), and E1 (810\u0026ndash;1248). These segments were extracted using Samtools [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] for downstream immunoinformatic analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEpitope prediction and filtering\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEpitope prediction was performed for MHCI, MHCII, and B-cell responses using the IEDB Analysis Resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). MHCI and MHCII predictions were carried out using NetMHCpan 4.1 EL [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] with a reference panel of 27 frequently occurring HLA alleles, via the IEDB MHCI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhci/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhci/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MHCII (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhcii/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhcii/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) web servers. B-cell epitopes were predicted using BepiPred Linear Epitope Prediction 2.0 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFiltering criteria for MHCI epitopes included prediction scores\u0026thinsp;\u0026ge;\u0026thinsp;0.9 and percentile ranks\u0026thinsp;\u0026le;\u0026thinsp;0.05, while MHCII filtering was based on scores\u0026thinsp;\u0026ge;\u0026thinsp;0.9 and ranks\u0026thinsp;\u0026le;\u0026thinsp;0.05. Core peptides were selected when available. In cases lacking qualifying peptides, the highest scoring candidate was selected. B-cell filtering thresholds were region-specific, with values adjusted to ensure a minimum of one epitope per domain (e.g., threshold 0.625 for C, E1, and E2; threshold 0.5 for E3 and 6K; minimum length of 8 amino acids).\u003c/p\u003e\u003cp\u003eSubsequently, overlapping epitopes among MHCI, MHCII, and B-cell predictions were identified. If the overlap was absent across all three, partial overlaps or single best-scoring peptides were retained. Selected epitopes were validated using VaxiJen v3.0 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] for immunogenicity, AllerTOP v2.1 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] for allergenicity, and ToxinPred3 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] for toxicity. Filtering and integration were implemented using custom R scripts based on dplyr, plyr, stringr, data.table, readr, and fuzzyjoin packages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVaccine construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 11 validated epitopes were assembled into a multi-epitope vaccine (MEV-CHIKV), preserving the structural polyprotein order (C \u0026rarr; E3 \u0026rarr; E2 \u0026rarr; 6K \u0026rarr; E1). Linkers were selected for structural and functional optimization: AAY between MHCI and MHCII epitopes; GPGPG between MHCII and B-cell epitopes; and EAAAK between the adjuvant and MHCI block. A 6xHis tag was added at the N-terminus for purification. As an adjuvant, the antimicrobial human β-defensin 3 peptide was fused to the N-terminal end of the vaccine construct (UniProt Q5U7J2).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation coverage analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess population-scale efficacy, predicted HLA-binding alleles were evaluated for population coverage using the IEDB Population Coverage tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/population/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/population/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Binding affinities for MHCI and MHCII alleles were estimated using NetMHCpan 4.1 BA and NetMHCIIpan 4.1, respectively [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The peptide length for MHCII prediction ranged from 12\u0026ndash;15 amino acids. The resulting allele set was analyzed across a broad panel of global populations, including specific regions in Asia, such as Indonesia, Malaysia, Singapore, Thailand, Vietnam, and others.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStructural modelling, 2D prediction, and 3D structure validation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSecondary structure prediction was performed using PSIPRED [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], while tertiary structure modelling was executed using D-I-TASSER [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. From five D-I-TASSER models, the one with the highest estimated TM-score was selected for refinement. This model was refined using GalaxyRefine [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The model with the lowest RMSD, highest GDT-HA, and most favorable Ramachandran statistics was selected for further validation.\u003c/p\u003e\u003cp\u003eValidation included structural quality checks using ProSA [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], ProCheck and ERRAT modules from SAVES v6.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://saves.mbi.ucla.edu/\u003c/span\u003e\u003cspan address=\"https://saves.mbi.ucla.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and geometric evaluation with MolProbity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Hydrogen atoms were added to optimize Asn, Gln, and His flips during the MolProbity analysis. Flexibility analysis was carried out using CABS-Flex3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocomp.chem.uw.edu.pl/CABSflex3\u003c/span\u003e\u003cspan address=\"http://biocomp.chem.uw.edu.pl/CABSflex3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) after PDB file cleanup using pdb-tools [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and awk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular docking simulations were carried out using HADDOCK 2.4 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Eight target proteins were selected based on epitope-HLA allele matches and structural availability in the RCSB PDB database. Targets included TLR3 (PDB ID: 1ZIW), six MHCI alleles (HLA-B57:01, HLA-B58:01, HLA-B07:02, HLA-A68:01, HLA-B35:01, HLA-B15:01), and one MHCII allele (HLA-DRB1*01:01). Protein structures were preprocessed using PyMOL [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], including removal of water, ligands, and renumbering. Binding affinity of docked models was assessed using PRODIGY (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bianca.science.uu.nl/prodigy/\u003c/span\u003e\u003cspan address=\"https://bianca.science.uu.nl/prodigy/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelection of docked model for molecular dynamics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 36 generated docking poses of MEV-TLR3, the model with the lowest binding free energy and highest predicted affinity (as evaluated by PRODIGY at 37\u0026deg;C) was selected for subsequent molecular dynamics (MD) simulation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular dynamics simulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMD simulations were conducted using iMODS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imods.iqfr.csic.es/\u003c/span\u003e\u003cspan address=\"https://imods.iqfr.csic.es/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) which applies normal mode analysis (NMA) to examine the flexibility and stability of protein\u0026ndash;protein complexes. The analysis yielded structural deformation maps, eigenvalues, RMSD distributions, and covariance matrices that confirmed the MEV-TLR3 complex's dynamic stability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmune simulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo simulate host immune response post-vaccination, a 365-day immune response profile was generated using C-ImmSim [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The simulation modeled three doses administered at days 1, 30, and 60.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCodon optimization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCodon optimization was carried out using JCat [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] to adapt the MEV-CHIKV sequence for expression in \u003cem\u003eEscherichia coli\u003c/em\u003e K12. Optimization settings avoided rho-independent terminators, prokaryotic ribosome binding sites, and restriction enzyme sites for EcoRI, BamHI, HindIII, PstI, SalI, XbaI, and SmaI. The resulting codon-adapted sequence is suitable for cloning and expression in bacterial vectors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003ePhylogenetic tree construction based on global CHIKV sequences and consensus sequence generation\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSummary of CHIKV genomic dataset used in phylogenetic analysis and summary of amino acid dataset and construction of consensus sequence was shown in \u003cb\u003eSupplementary Table\u0026nbsp;1.\u003c/b\u003e It provides an overview of CHIKV sequences retrieved from the NCBI database, detailing the number of downloaded sequences, annotated structural proteins, genotyped entries, and curated sequences after quality control. Final lineage-specific distributions and preprocessing tools used for alignment and tree construction are also listed.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePhylogenetic analysis of 1,429 high-quality CHIKV structural polyprotein sequences was performed using the ML method with the GTR\u0026thinsp;+\u0026thinsp;I\u0026thinsp;+\u0026thinsp;G4 substitution model and 1,000 bootstrap replicates \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The primary tree is midpoint-rooted (left) and illustrates the major phylogenetic divergence among the three known genotypes of CHIKV: West African (blue), Asian (red), and ECSA (green). Two additional views, an unrooted polar tree (top right) and a branch-length-transformed tree with equal distances (bottom right), provide alternative visualizations to highlight intra-lineage clustering and relationships. This lineage-resolved phylogeny offers robust evolutionary context for downstream epitope mapping and consensus sequence construction. Accurate lineage discrimination ensures that the derived global consensus sequence used in epitope prediction incorporates genetic variability across all three major CHIKV genotypes, enhancing vaccine design strategies with broader population coverage and immunogenic relevance.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMHCI, MHCII, and B cell epitope prediction and epitope selection for multiepitope vaccine construct\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the immunogenic epitopes identified from the CHIKV structural polyprotein and selected for inclusion in the multiepitope vaccine construct (MEV-CHIKV). This pipeline ultimately identified 11 high-quality epitopes: 6 MHCI, 1 MHCII, and 3 B cell epitopes. The epitopes were predicted based on their binding affinity to common HLA alleles and were further assessed for immunogenic potential (VaxiJen score), toxicity (ToxinPred), and allergenicity (AllerTOP). The selected epitopes span multiple viral domains, including C, E1, E2, and E3, and include B-cell, MHC-I, and MHC-II epitopes with high immunogenicity (VaxiJen\u0026thinsp;\u0026ge;\u0026thinsp;66%), non-toxic, and non-allergenic properties. The peptide start-end positions correspond to their location within the reference structural polyprotein. Graphical representation of the predicted and selected epitopes mapped along the 1248-amino acid CHIKV structural polyprotein is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredicted B cell, MHC class I, and MHC class II epitopes selected for MEV-CHIKV construction.\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\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarget\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA Allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeptide Sequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStart-End Position*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePeptide Length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVaxijen\u003c/p\u003e\u003cp\u003e(Immunogenicity, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eToxinPred\u003c/p\u003e\u003cp\u003e(Toxicity)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAllertop\u003c/p\u003e\u003cp\u003e(Allergenicity)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB-Cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePRPRPQRQA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30\u0026ndash;38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB-Cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKKQPPKKKPAQKKKKPGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84\u0026ndash;101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB-Cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRRNRKNKKQKQKKQAP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62\u0026ndash;77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*57:01, HLA-B*58:01, HLA-A*32:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRTALSVVTW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e237\u0026ndash;245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*07:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRPGYSPMVL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e830\u0026ndash;838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-A*32:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKVFTGVYPF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e888\u0026ndash;896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-A*68:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHTASASAKLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e934\u0026ndash;943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*35:01, HLA-B*53:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYPDHPTLLSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e613\u0026ndash;622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*15:01, HLA-A*30:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKARNPTVTY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e595\u0026ndash;603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHC-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYQLLQASLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e309\u0026ndash;317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003enon-toxin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enon-allergen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e* According to structural polyproteins\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePopulation coverage analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePopulation coverage analysis of the selected epitope is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The table presents the estimated HLA population coverage (%) for the selected MHC-I and MHC-II epitopes across various global regions, including individual countries in Southeast Asia. Broad population coverage is observed for MHC-I epitopes globally, with the highest in East Asia (89.16%). In contrast, MHC-II coverage is relatively low, primarily due to the inclusion of only a single high-affinity MHC-II epitope in the construct, as a result of stringent threshold filtering during the epitope selection process. Overall, the MEV-CHIKV demonstrated high theoretical population coverage globally and regionally.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eEstimated global and regional population coverage of selected MHC class I and class II epitopes.\u003c/b\u003e Calculations were performed using the IEDB population coverage tool. The \"Coverage\" column represents the estimated proportion of the population predicted to respond to at least one epitope, while \"pc90\" indicates the minimum number of epitope-HLA combinations recognized by 90% of the responding population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePopulation (area)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eClass I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eClass II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003epc90\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003epc90\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorld\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81.14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOceania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEurope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.07%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Africa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.88%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentral America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.52%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth America\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Indies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNortheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.82%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.83%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoutheast Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest Asia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndonesia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.45%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalaysia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.64%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingapore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhilippines\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.51%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.43%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThailand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVietnam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaiwan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86.81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVaccine construction (MEV-CHIKV)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the previous epitope selection, a multiepitope vaccine against CHIKV (MEV-CHIKV) was designed. Schematic representation and tertiary structure of the MEV-CHIKV construct is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The construct comprises an adjuvant (human β-defensin 3) at the N-terminus (red), followed by a series of epitopes derived from the structural polyprotein of the CHIKV. These include six MHC class I epitopes (green), one MHC class II epitope (yellow), and three B-cell epitopes (orange). A 6\u0026times;His tag (blue) was added at the C-terminus for purification purposes. The epitopes are arranged in gene order (C \u0026rarr; E3 \u0026rarr; E2 \u0026rarr; 6K \u0026rarr; E1) and are separated by appropriate linkers: EAAAK (grey) between the adjuvant and first epitope, AAY (purple) between MHC-I epitopes, and GPGPG (black) between MHC-II and B-cell epitopes. The design ensures proper epitope presentation, structural stability, and enhanced immunogenicity for \u003cem\u003ein silico\u003c/em\u003e vaccine performance against diverse CHIKV lineages. The resulting 198-amino-acid construct was assessed for its safety and efficacy. It was predicted to be non-toxic (ToxinPred2), non-allergenic (AllerTOP), and immunogenic (VaxiJen score: 66%)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSecondary and tertiary structure validation of the MEV-CHIKV construct\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e show the structural validation metrics obtained from several bioinformatics tools to evaluate the accuracy, quality, and stability of the modeled multiepitope vaccine (MEV-CHIKV). Secondary structure prediction by PSIPRED revealed that the vaccine construct is predominantly composed of coils (60.61%), followed by α-helices (26.26%) and β-strands (13.13%). Tertiary structure modeling and refinement by D-I-TASSER and GalaxyRefine showed a reliable model with a GDT-HA of 0.9255 and a MolProbity score of 1.392. Additional validation using ProSA (Z-score = -3.73) confirmed structural reliability within the range of experimentally determined protein structures. The SAVES suite validated the model with a high ERRAT score of 95.536 and a favorable Ramachandran plot profile (98.1% of residues in allowed and favored regions). MolProbity analysis further supported the model\u0026rsquo;s high geometric quality, and flexibility was assessed using CABS-Flex, which showed limited RMSF values, indicating a stable conformation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, the MEV-CHIKV construct was evaluated for its tertiary structure stability using CABS-Flex 3.0 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e This analysis provides insight into the dynamic behavior of the vaccine construct under physiological conditions. The simulation revealed a median RMSF of 1.989 \u0026Aring;, indicating overall structural stability. High flexibility regions, with RMSF values peaking at 9.328 \u0026Aring;, corresponded to linker regions and terminal loops, which may enhance epitope accessibility. These results support the conformational robustness of the vaccine model while preserving dynamic features beneficial for immune recognition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular docking and interaction analysis between MEV-CHIKV and human TLR3 receptor\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe MEV-CHIKV construct was then evaluated for its interaction with human TLR3 receptor and other immune receptors using molecular docking analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\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\u003eMolecular docking and binding affinity evaluation of MEV-CHKV with immune receptors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTarget Protein\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHLA Allele Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePDB ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHADDOCK Mean Binding Energy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003ePRODIGY Analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHADDOCK Selected Structure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eΔG (kcal mol-1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKd (M) at 37℃\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eInter-molecular Contacts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNotes for PRODIGY Analysis\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\u003eTLR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1ZIW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-273.572\u0026thinsp;\u0026plusmn;\u0026thinsp;114.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 9\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.9E-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eUse all HADDOCK clusters then select one cluster based on the best binding free energy (ΔG, kcal/mol), dissociation constant (Kd, M) at 37\u0026deg;C, from PRODIGY (shown here)\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*57:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5VUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-227.961\u0026thinsp;\u0026plusmn;\u0026thinsp;57.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 5\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.1E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eUse only one representative cluster with the best HADDOCK score\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*58:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4LNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-227.444\u0026thinsp;\u0026plusmn;\u0026thinsp;76.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 7\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1E-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e122\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*07:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5VWH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-249.286\u0026thinsp;\u0026plusmn;\u0026thinsp;63.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 15\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.6E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e95\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-A*68:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6PBH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-231.812\u0026thinsp;\u0026plusmn;\u0026thinsp;94.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 3\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.3E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e118\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*35:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7LG0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-267.468\u0026thinsp;\u0026plusmn;\u0026thinsp;50.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 11\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.3E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e85\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\u003eMHC-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*15:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8ELH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-254.892\u0026thinsp;\u0026plusmn;\u0026thinsp;77.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.6E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e113\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\u003eMHC-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7YX9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e-209.482\u0026thinsp;\u0026plusmn;\u0026thinsp;44.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster 7\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.2E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the docking results of the MEV-CHIKV construct against innate and adaptive immune receptors using the HADDOCK web server and PRODIGY binding affinity analysis. For each receptor, the corresponding HLA allele (if applicable), PDB ID, and HADDOCK-derived mean binding energy (\u0026plusmn;\u0026thinsp;standard deviation) of the selected docking cluster are shown. Binding free energy (ΔG, kcal/mol), dissociation constant (Kd, M) at 37\u0026deg;C, and the number of intermolecular contacts were predicted using PRODIGY. For TLR3, all HADDOCK clusters were analyzed in PRODIGY, and the best-scoring cluster was selected for downstream structural and molecular dynamics simulations. For MHC-I and MHC-II receptors, the PRODIGY analysis was conducted only on the representative cluster with the lowest HADDOCK binding energy.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe predicted 3D complex of the multiepitope vaccine construct (MEV-CHIKV, green) with the TLR3 receptor (red) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, highlighting intermolecular contact regions. Interacting residues are indicated in blue (on TLR3) and magenta (on MEV-CHIKV). A total of 128 intermolecular contacts were identified, illustrating strong molecular interactions and stable binding. The lower panel presents a heatmap of residue-residue interactions between MEV-CHIKV (x-axis) and TLR3 (y-axis), where each magenta square represents contact. These interactions suggest a potential immune-activating interface, supporting the designed vaccine's immunogenic potential.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular dynamics (MD) simulation of MEV-CHIKV and TLR3 complex with iMODS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMD simulation with iMODS Normal Mode Analysis was conducted to evaluate structural flexibility and stability of the MEV-TLR3 complex \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The deformability plot and B-factor analysis revealed limited flexibility, suggesting a stable interaction. The calculated eigenvalue was 2.686883\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;6, indicating moderate energy requirements for deformation and supporting the rigidity of the complex. The covariance matrix revealed largely correlated motions at the interaction interface, further confirming the conformational stability of the complex. The elastic network model also demonstrated dense connections across the binding interface, indicating a robust structural network. Collectively, these results support the functional stability of the MEV\u0026ndash;TLR3 complex for downstream immune activation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico immune simulation of MEV-CHIKV\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo simulate host immune response post-vaccination, a 365-day immune response profile was generated using C-ImmSim [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The simulation modeled three doses administered at days 1, 30, and 60 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The simulation was performed over a period of 365 days with three vaccine doses administered at day 1, 30, and 60. The results show a strong adaptive immune response characterized by the induction of B and T cell populations, development of memory cells, and robust antibody production.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCodon optimization and rare codon analysis of the MEV-CHIKV construct\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCodon optimization and rare codon analysis of the MEV-CHIKV construct is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The nucleotide sequence of the MEV-CHIKV was optimized for expression in \u003cem\u003eEscherichia coli\u003c/em\u003e (strain K12) using JCat. The final construct has a total length of 594 base pairs, with a high Codon Adaptation Index (CAI) of 0.96 and a GC content of 55.05%, indicating suitability for heterologous expression. The schematic at the top represents the structural and functional domains of the construct, including epitope regions, linkers, a β-defensin adjuvant, and a 6\u0026times;His tag. The coding sequence is shown below with annotated regions and highlighted rare codons. Three rare codons were identified: CAA at positions 19\u0026ndash;21 and 388\u0026ndash;390, and AGT at position 394\u0026ndash;396. These rare codons, although infrequent, are not expected to significantly hinder expression due to their low occurrence and overall high CAI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCHIKV continuously poses serious threats to global health. As of early June 2025, around 220,000 chikungunya cases and 80 related deaths have been reported across 14 countries, primarily in the Americas, Africa, and Asia. While mainland Europe has reported no cases, outbreaks are ongoing in the EU outermost regions of R\u0026eacute;union and Mayotte. The Americas currently represent the most affected region globally [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The global burden of CHIKV infection has driven the development of CHIKV vaccines, leading to the approval of two: the live-attenuated vaccine VLA1553 (IXCHIQ) and the virus-like particle-based vaccine PXVX0317 (VIMKUNYA). However, post-licensure monitoring has revealed safety concerns. Recently, serious adverse events have been reported globally among vaccine recipients over the age of 60. As IXCHIQ is a live-attenuated vaccine, its use is also contraindicated in individuals with weakened immune systems, regardless of age [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough a licensed vaccine for CHIKV has recently become available, the development of alternative vaccine candidates remains essential due to several limitations of the available vaccines. Current vaccines may offer suboptimal protection against the diverse genotypes of CHIKV circulating globally, and their safety and efficacy profiles in specific populations\u0026mdash;such as the elderly or immunocompromised\u0026mdash;may not yet be fully established [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Moreover, issues related to production cost, cold-chain requirements, and long-term immunogenicity highlight the need for improved vaccine platforms. Therefore, designing novel vaccine candidates with broader cross-protective potential, enhanced stability, and increased accessibility could play a critical role in complementing existing vaccination strategies and strengthening global preparedness against CHIKV outbreaks.\u003c/p\u003e\u003cp\u003eMultiepitope-based vaccines offer more advantages than classical vaccine design, for example the presence of multiple MHC-restricted epitopes that can be recognized by T cell receptors (TCRs) from various clones [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, the crucial step in the development of multiepitope-based vaccine design is identification and selection of potentially immunogenic epitopes. Various immunoinformatics tools are currently available to accelerate the vaccine design [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In this study, we selected epitopes from each structural protein of CHIKV to expand the spectra of the targeted antigens. We first generated a global consensus sequence from all available CHIKV genome sequences to be employed for epitope prediction. While our study extensively utilizes global CHIKV sequences, the other study was based on a limited number of sequences [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The conserved epitopes identified in this study are expected to confer broader and long-lasting protection across different CHIKV genotypes that are constantly mutating due to a lack of proof-reading activity of RNA-dependent RNA polymerase (RdRp). Therefore, the vaccine design aims to effectively target the extensive genetic diversity of CHIKV.\u003c/p\u003e\u003cp\u003eThe designed vaccine is expected to effectively stimulate both humoral and T cell-mediated immune responses. The multiepitope platform is inherently low in immunogenicity; therefore, human β-defensins 3 was added as adjuvants to enhance its immune response. A previous study compared SARS-CoV-2 epitopes predicted by various computational studies with experimentally validated T cell epitopes identified from the blood of convalescent COVID-19 patients [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The results showed a strong correlation between the predicted and experimentally determined epitopes, indicating the high accuracy of \u003cem\u003ein silico\u003c/em\u003e epitope prediction [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUpon administration, multiepitope vaccines will be internalized by dendritic cells (DCs). Following DC differentiation into mature phenotypes, the multiepitope vaccines will subsequently be presented to na\u0026iuml;ve T cells in the context of MHC molecules. DCs are equipped with various pattern recognition receptors (PRR), including TLRs, to sense the presence of viral-derived antigens. TLR3 is intracellularly located in the endosome and can be stimulated by its natural ligands, including dsRNA and polyinosinic-polycytidylic acid [poly(I:C)] [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. TLR recognition of viral-derived antigens and subsequent activation of downstream signaling pathways will induce DC maturation and the production of antiviral cytokines. Indeed, it has been shown that TLR3 stimulation can inhibit CHIKV infection \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. TLR agonists have been extensively utilized as adjuvants for the development of arbovirus vaccines [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Based on molecular docking analysis, the designed vaccine has high binding affinities with the active site of TLR3. This indicates its capacity to efficiently stimulate the innate and adaptive immune responses\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe have designed a conserved and universal CHIKV vaccine candidate against all circulating genotypes of CHIKV globally. The candidate is multiepitope-based vaccine platform by identifying and screening various T and B cell epitopes that are conserved in CHIKV genome sequences. Thus, the vaccine cadidate is anticipated to effectively and simultaneously induce robust cellular and humoral immune responses. In addition, it should retain its high protection upon emergence of novel mutations within the CHIKV genome. We also examined the predicted interaction between the vaccine construct and TLR3. \u003cem\u003eIn silico\u003c/em\u003e immune simulation showed induction of robust adaptive immune responses, characterized by the activation and expansion of B and T cell populations. Ultimately, codon optimization and rare codon analysis revealed a potentially high expression in bacterial system. However, since our study is merely \u003cem\u003ein silico\u003c/em\u003e-based analysis, further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experimental validation to demonstrate the immunogenic properties of the vaccine candidate are still needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Aqsa Ikram (the University of Lahore, Pakistan) and Faris M. Gazali for their technical assistance during the early phase of this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM. S. H. was involved in conceptualization, data acquisition, data analysis, writing original draft, editing, and finalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. The authors declare that no funds, grants, or other support were received during the preparations of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets, output files, and other data generated during this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMohamad S. Hakim:\u0026nbsp;\u003c/em\u003ehttp://orcid.org/0000-0001-8341-461X\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHakim MS, Aman AT (2023) Understanding the biology and immune pathogenesis of chikungunya virus infection for diagnostic and vaccine development. Viruses 15(1):48. https://doi.org/10.3390/v15010048\u003c/li\u003e\n\u003cli\u003eSilva MMO, Tauro LB, Kikuti M, et al. (2019) Concomitant transmission of dengue, chikungunya, and zika viruses in Brazil: Clinical and epidemiological findings from surveillance for acute febrile illness. Clin Infect Dis 69(8):1353-1359. https://doi.org/10.1093/cid/ciy1083\u003c/li\u003e\n\u003cli\u003eVidal ERN, Frutuoso LCV, Duarte EC, et al. (2022) Epidemiological burden of Chikungunya fever in Brazil, 2016 and 2017. Trop Med Int Health 27(2):174-184. https://doi.org/10.1111/tmi.13711\u003c/li\u003e\n\u003cli\u003eMavalankar D, Shastri P, Bandyopadhyay T, et al. 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(2023) Innate immune pathway modulator screen identifies STING pathway activation as a strategy to inhibit multiple families of arbo and respiratory viruses. Cell Rep Med 4(5):101024. https://doi.org/10.1016/j.xcrm.2023.101024\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chikungunya, immunoinformatics, multiepitope-based vaccine, reverse vaccinology, viral structural proteins","lastPublishedDoi":"10.21203/rs.3.rs-7255061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7255061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChikungunya virus (CHIKV) infection is a re-emerging arboviral disease in tropical and subtropical regions. In addition to acute febrile syndrome, CHIKV infection may lead to chronic articular manifestations that significantly affect a long-term quality of life. This study aimed to design a universal vaccine candidate covering all circulating genotypes of CHIKV based on conserved multiepitope platform. We employed a large scale phylogenetic and immunoinformatic approach to identify conserved regions of the open reading frames (ORF2) region encoding viral structural proteins. This study ultimately identified 11 high-quality epitopes: 6 MHCI, 1 MHCII, and 3 B cell epitopes. The selected epitopes span multiple viral domains, including C, E1, E2, and E3, with high immunogenicity (VaxiJen ≥66%), non-toxic, and non-allergenic properties. These selected epitopes were utilized to design multiepitope vaccine constructs (MEV-CHIKV) linked with various linkers in combination with adjuvants (human β-defensin 3) to enhance the immune responses. Structural validation analysis showed high quality and stability of the vaccine construct. Based on molecular docking analysis, the designed vaccine has high binding affinities with the active site of TLR3. \u003cem\u003eIn silico \u003c/em\u003eimmune simulation showed induction of robust adaptive immune responses, characterized by the activation and expansion of B and T cell populations. Codon optimization and rare codon analysis revealed a potentially high expression in bacterial system. Thus, the vaccine cadidate is anticipated to effectively and simultaneously induce robust cellular and humoral immune responses. In addition, it should retain its high protection upon emergence of novel mutations within the CHIKV genome. Since our study is merely \u003cem\u003ein silico\u003c/em\u003e-based analysis, further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experimental validation to demonstrate the immunogenic properties of the vaccine candidate are still needed.\u003c/p\u003e","manuscriptTitle":"Reverse vaccinology-based design of a universal multiepitope vaccine against chikungunya virus: phylogenetic and immunoinformatics approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 08:38:01","doi":"10.21203/rs.3.rs-7255061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65d112ee-4f9b-4f26-b33f-03bf9a97d750","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T22:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 08:38:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7255061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7255061","identity":"rs-7255061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}