Artificial Intelligence-Guided Design of some Pan-H5N1-clade 2.3.4.4b Mosaic DNA-based vaccines to combat the circulating HPAI in birds

Artificial Intelligence-Guided Design of some Pan-H5N1-clade 2.3.4.4b Mosaic DNA-based vaccines to combat the circulating HPAI in birds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial Intelligence-Guided Design of some Pan-H5N1-clade 2.3.4.4b Mosaic DNA-based vaccines to combat the circulating HPAI in birds Nithyadevi Duraisamy, Abid Ullah Shah, Mohd Yasir Khan, Mohammed Cherkaoui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6711963/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 The presently used vaccines do not offer solid immunity/protection against the currently circulating strains of the H5N1 viruses. We aim to design a pan H5N1 vaccine that protects birds against the presently circulating clade 2.3.4.4b in chicken. We used AI tools, including epitope mapping, molecular docking, and immune simulation, to design a multiepitope DNA vaccine including the top-ranked B and T cell epitopes within four major proteins (HA, NA, NP, and M2) of the H%N1 clade 2.3.4.4b. We selected the top-ranked 12 epitopes and linked them together using linkers. The designed vaccine is linked to IL-18 as an adjuvant. The molecular docking results showed a high binding affinity of this vaccine construct with the chicken alleles. The immune simulation results showed that the designed vaccine has the potential to stimulate the host immune response, including antibody and cell-mediated immunity in chickens and other birds. We believe this vaccine is going to be a universal vaccine that offers good protection not only to chickens but also to different species of birds against the HPAI- H5N1 clade 2.3.4.4b. Further studies are required to validate this vaccine candidate in chickens. Biological sciences/Immunology Biological sciences/Microbiology Biological sciences/Molecular biology Health sciences/Diseases Highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b Epitope mapping DNA vaccine in silico prediction molecular docking IL8 TL3 TLR7 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1- Introduction The highly pathogenic avian influenza H5N1 (HPAIV) virus continues to pose a significant risk to the poultry industry. There is also a risk of spillover to humans, which results in the death of some affected patients ( 1 , 2 , 3 ). There is a continuous active dynamic change in the virus's genetic material for many reasons, including the poor proofreading capability of the viral polymerase enzyme, the antigenic shift/drift, the possibility of reassortment, and recombination ( 4 , 5 ). This pattern of frequent changes in the viral genomes resulted not only in the emergence of new viruses or clades of the same lineage of the virus but also could hamper the success of the currently used vaccine and diagnostic assays for the HPAIV. There is a mandate for active monitoring of these viruses at the genomic levels to monitor the emergence of new viruses that might have any abnormal genotypic/phenotypic patterns of the virus. There is also a high demand for the generation of novel diagnostic assays and vaccines that could detect/protect against the currently circulating strains of the virus in the field. Influenza viruses’ type-A (IAV) have segmented genomes consisting of 7–8 segments. Each segment of the viral genome encodes at least one important protein. Influenza viruses have several important proteins, including the hemagglutinin (HA), the neuraminidase (NA), the nucleoprotein (NP), and the matrix protein (M). This is in addition to some nonstructural proteins (NSP1 and NSP2, in addition to the viral polymerase, which consists of three subunits called PA, PB2, and PB2 proteins) ( 6 ). The AIV is classified based on their HA and NA proteins into 19 HA and 11 NA subtypes, respectively ( 6 ). The HA, NA, and M proteins are expressed on the surface of the viral particles. The NP wraps the viral genome to form the viral nucleoprotein ( 6 ). The HA epitopes proved to trigger high neutralizing antibodies in the infected/vaccinated host ( 7 ). The HA sequence is prone to frequent changes driven by antigenic shift and drift which enable the virus to evade the host immune response ( 7 ). The NA protein play several key roles in influenza virus replication, pathogenesis, and immune evasion as well ( 7 ). The AIV-NP also plays an important role in the suppression of the host immune response through the activation of the mitophagy pathways in the infected cells ( 8 ). The matrix protein of the IVA consists of M1 and M2 proteins. Both proteins play important roles in the immune response/evasion against the IVA in the host. The M2 protein plays an essential role in viral immune evasion by modulating the autophagy pathways in the infected cell through the prevention of the fusion between the autophagosome and the lysosome, which augments the viral immune evasion strategies ( 9 ). There are several approaches for the preparation of AIV vaccines, including live attenuated, inactivated, recombinant, and DNA vaccines. Each type of vaccine has advantages and disadvantages. DNA vaccines for the AIV hold great promises, especially these days, for several reasons ( 10 ). The cost of production of the DNA vaccine is very cheap compared to other types of vaccines, the possibility of upgrading the DNA vaccines to match any changes in the viral genetic materials and in case of the emergence compared to the other types of vaccines, the mass production of the DNA vaccine in a remarkable short time, and the stability of the DNA vaccines ( 10 ). However, one of the major concerns of the DNA vaccines is the delivery methods and their duration of actions in the vaccinated hosts. Several approaches have been recently adapted to prolong the actions of the DNA vaccines and to protect them from the actions of the host DNase enzymes. Several approaches have been developed to improve the quality of the DNA vaccines and to prolong their actions, including the encapsulation with various types of nanoparticles, particularly lipid nanoparticles and chitosan. The incorporation of the IVA DNA vaccine against the M protein with chitosan administered intranasally produced a prolonged immune response in mice ( 11 ). Encapsulation of the DNA vaccine with lipid nanoparticles enhanced the immune response of the vaccinated pigs against the H1N1 virus infection ( 12 ). In the present study, we designed a multiepitope DNA-based vaccine including the top-ranked B cell and T cell epitopes within the four major proteins (HA, NA, NP, and M2) of the H5N1 clade 2.3.4.4b. The in silico immune simulation of the designed vaccine showed promising results in the induction of robust immune response in the vaccinated birds against this clade of the AIV in birds. However, these studies require further experimental validation using these vaccines in chickens and other birds, such as turkeys. 2- Materials and Methods 2.1. Retrieval of the H5N1 clade 2.3.4.4b protein sequences A total of 279 isolate sequences belong to the H5N1 clade 2.3.4.4b, including four major viral proteins (hemagglutinin (HA), nucleoprotein (NP), neuraminidase (NA), and the matrix protein (M2)) were retrieved from the National Center for Biotechnology (NCBI) database ( https://www.ncbi.nlm.nih.gov/protein ). These sequences include (chickens = 115, ducks = 40, turkeys = 30, migratory birds = 43 (including Red-tailed hawk, Peregrine falcon, American wigeon, and Backyard bird), and Canadian geese = 51). The Supplementary Excel Files 1–4 presents information about these sequences. 2.2. The multiple sequence alignment (MSA) The MSA per each protein was conducted independently using Geneious software ( https://www.geneious.com/ ) and the crustal Omega server tool ( https://www.ebi.ac.uk/jdispatcher/msa/clustalo ). The highly conserved consensus sequences per each protein showing 100% identity were further considered for the epitope mapping. 2.3. Mapping B cell epitopes within the avian H5N1 clade 2.3.4.4b major proteins (HA, NA, NP, and M2). 2.3.1. Prediction of the linear B-cell epitopes To map the B cell epitopes from the generated consensus sequences of the four proteins (HA, NA, NP, and M2), we used the BCPREDS (BepiPred 2.0) ( http://services.healthtech.dtu.dk/services/BepiPred-2.0/ ) and the IEDB analysis resource server ( http://tools.iedb.org/bcell/ ) as described ( 13 ). We adjusted the length of the target epitopes to 20 mers. The identified epitopes were further filtered based on their (antigenicity, allergenicity, toxicity, and solubility) as previously described ( 14 ). 2.3.2. Prediction of the discontinuous/ conformational B-cell epitopes The Discontinuous/Conformational epitopes were predicted using the Ellipro server ( http://tools.iedb.org/ellipro/ ). The parameter was set at 0.5 for the minimum score and 6 Å for the maximum distance ( 15 ). This method is based on the protein antigen’s 3D structure, solvent accessibility, and flexibility. The Chimera software was used to display the position of predicted epitope clusters on 3D structures of all the structural proteins. 2.4. Mapping of the T-lymphocyte epitopes within the avian H5N1 clade 2.3.4.4b major proteins (HA, NA, NP, and M2). 2.4.1. Prediction of the Cytotoxic T-lymphocyte epitopes (MHC class I molecules) The IEDB server ( http://tools.iedb.org/main/tcell ) was used to predict the cytotoxic T-lymphocytes (CTL) and the Helper T-lymphocytes epitopes that bind to MHC-I and MHC-II, respectively. The epitope binding predictor NetMHCpan 4.1 BA (version 2023.09) was used to sort the peptides by IC50 value for the epitope prediction ( 16 ). The source species were entered as humans with peptide lengths of ( 9 – 10 ) and associated human alleles (HLA-A, HLB-B, and HLA-C). The mapped epitope list was filtered according to their percentile rank and IC50 value. It was then evaluated for their antigenic, non-allergic, non-toxic, and solubility characteristics with further refinement. 2.4.2. Prediction of the helper T-lymphocyte epitopes (MHC class II molecules) We used the IEDB analysis tool ( http://tools.iedb.org/mhcii/ ) to predict the MHC class II binding molecules using NetMHCIIpan 4.1 BA (recommended binding predictor: 2023.09), using the human allele (HLA -DP, HLA-DQ, and HLA-DR) as a selective species based on percentile rank and IC50 value ( 17 ). Following refinement of their antigenic, non-allergic, non-toxic, and solubility properties, the filtered epitopes were assessed and chosen following a percentile rank score of less than 10. It was then evaluated for antigenic, non-allergic, non-toxic, and solubility characteristics with further filtration to be utilized in the designed vaccine construct. The reference sequence of each of the four proteins (HA, NP, NA, and M2) of H5N1 clade 2.3.4.4b was analyzed against the human alleles (HLA-DR, DQ, DP) using IEDB MHC-II binding prediction tools with a percentile rank of (less than or equal to 10). A large number of epitopes were obtained as a result of the four proteins, and the epitopes were initially filtered based on half minimal inhibitory concentration (IC50) and percentile rank followed by filtering out on Allergenicity, antigenicity, non-toxic, and solubility. Here, to predict the MHC class II molecules for the chicken alleles (Gaga BLB1 & Gaga BLB2), we used mixmhc2pred.gfellerlab.org server tool, where the single fragment of amino acids with the length of 15mer was provided as input and predicted the results based on percentile rank. 2.5. Molecular docking and analysis of the binding interaction between the predicted T-cell epitopes with chicken MHC-I and MHC-II alleles The 3D structures of shortlisted CTL and HTL epitopes were modeled using the PEP-FOLD3 (De novo peptide structure prediction) server ( https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::PEP-FOLD3 ), using sOPEP energy function to cluster peptide conformation ( 18 ). The sequences of chicken MHC alleles (BL, BF) were retrieved either from the Protein Data Bank (PDB) or the Uniprot and are generated using the Biovia discovery studio ( 19 ). The molecular docking of the selected CTL and HTL epitopes with their respective MHC alleles was performed using the HADdock ( http://hdock.phys.hust.edu.cn/ ) ( 20 , 21 ). The binding interactions and key residue contact were visualized and further evaluated using the PDBsum server ( 22 ). The selected epitopes were further filtered based on their docking binding affinity and confidence score and processed to design the vaccine construct. 2.6. Assembly of the multi-epitope using the top-ranked epitopes The primary arrangements of the vaccine sequence were done by fusing the B cell and T cell predicted epitopes that were filtered out based on the predicted (antigenic, non-allergic, non-toxic, and good solubility) of the selected epitopes. The top-ranked B and T cell epitopes were linked using KK, GPGPG, and AAY as linkers. The C-terminal ends of the vaccine construct were linked with chicken IL-18 (Accession No. CAB96214) as an adjuvant after separation with the EAAAK linker. Moreover, the sequence was provided with 6×His-tag (H) attached to the C-terminus for purification and identification of the vaccine upon expression ( 23 , 24 ). 2.7. Codon Optimization and in-silico cloning of the multi-epitope vaccine construct. The multiepitope vaccine was optimized and cloned into the expression vector to ensure the potential effective cloning. Hence, the reverse translation of the vaccine protein sequences into a respective DNA sequence was performed using the Vector builder software ( https://en.vectorbuilder.com/tool/codon-optimization.html ). The codon adaptive index (CAI) value and the GC content of the multi-epitope construct were also calculated as described previously ( 25 ). The restriction enzyme sequences BamHI and EcoRI were added at the DNA's 3’ and 5’ ends, respectively. Along with this, the Kozak sequence was added to ensure efficient translational initiation in eukaryotic expression systems, which surround the starting codon. The restriction cloning module from Snapgene V.6.0.2 software was used to incorporate the multi-epitope construct into the pET28a(+) plasmids using the indicated restriction enzyme sites. 2.8. Assessment of the physiochemical properties of the designed multiepitope H5N1 clade 2.3.4.4b-DNA vaccine The physicochemical properties of the designed protein were assessed using the Protparam server ( https://web.expasy.org/protparam/ ). The potent antigenicity of selected proteins was predicted by using VaxiJen v2.0 server ( http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) ( 26 ) with a default threshold of 0.4. The allergenicity and toxicity of proteins were assessed by using AllerTOP v.2.1 server ( http://ddg- pharmfac.net/AllerTOP/) ( 27 ), and ToxinPred2 server (webs.iiitd.edu.in/raghava/toxinpred2/index.html) ( 28 ), respectively. The same server was later used to assess the physiochemical, antigenicity, allergenicity, and toxicity properties of the selection of epitopes as well as for the designed vaccine construct. The solubility nature of the proteins, epitopes, and final vaccine construct was analyzed using the Innovagen solubility check server ( https://innovagen.com/proteomics-tools ). 2.9. Prediction of the secondary and tertiary structures of the designed multi-epitope vaccine The secondary structure, topology, folds, and domain organization of the construct were predicted using the PDBsum server tool ( https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/ ) ( 29 ). The tertiary structure was predicted using vaccine sequence and modeled using Biovia Discovery Studio. Additionally, the ProSA server ( https://prosa.services.came.sbg.ac.at/prosa.php ) was used to determine the total number of residues in the multi-epitope vaccine construct. The stability was analyzed and compared through the Ramachandran plot from both Biovia Discovery Studio as well as the PDBSum server tool. 2.10. Molecular docking of the designed multi-epitope vaccine construct with the chicken Toll-like receptors (TLRs) We used TLR3 and TLR7 for the molecular docking analysis with the designed vaccine construct. Hence, the full-length protein sequence of chicken TLR3 (UniProt ID: 015455) and the chicken TLR7 (UniProt ID: Q9NYK1) were retrieved from Uniprot, and their respective structure was modeled using both AlphaFold collab ( https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb ) and Biovia discovery studio for better confirmation of the structure. The active binding sites were anticipated before the docking stage because it is crucial for greater binding affinity, and the docking study between the vaccine design and TLR3/TLR7 was performed through the Biovia discovery studio using ZDocker. Subsequently, a detailed analysis of the binding interfaces to characterize the molecular interfaces was performed using the PDBsum server tool. Analysis of protein-protein interaction was carried out through the Zdocker in the Biovia Discovery studio (v22.1.021297). For the best docking analysis, several modifications should be made, including removing the water molecules, and adding hydrogens, and minimizing the energy (CharmM). Out of the generated 10 poses per docking reaction, we selected the best pose having the higher binding energy between the target protein and its receptor as per the default of the molecular docking analysis as described in other studies ( 30 ). 2.11. In-silico immune simulation of the designed multiepitope H5N1 clade 2.3.4.4b-DNA vaccine To Predict the immune response activation in response to the designed multiepitope vaccine design, the in-silico immune simulation was performed using the C-ImmSim server ( https://150.146.2.1/C-IMMSIM/index.php ). The C-ImmSim server simulates the influence of vaccine construct on B and T lymphocytes by modeling the immune system of chicken's three major immune organs (bone marrow, thymus, and spleen). The parameters were set as a default with 50 and 1000 simulation steps. We are proposing the administration of the designed multiepitope vaccine construct three times using 4-week intervals. During simulation, each step indicates eight hours of real-time with periods of 1, 84, and 168 hours. Subsequently, this server also predicts the host cellular immune response and cytokine expression levels induced by the multi-epitope vaccine candidates in silico. 3- Results 3.1. Results of the prediction of the B cell epitopes (linear and discontinuous) within the major proteins of the H5N1 clade 2.3.4.4b The B-cell epitopes were analyzed from various structural proteins (HA, NA, NP, and M2) of the H5N1 clade 2.3.4.4b virus by utilizing the IEDB and BCpred server tool. We compared the results of those two servers in the prediction of the B cell epitopes using a threshold value of 0.75. Epitopes with a threshold value of 0.75 are more likely to have a higher peptide score. IEDB server tool results showing the number of epitopes were ((HA = 20), (NA = 14), (NP = 17, and M2 = 3). Results from the BCpred server tool showing the number of the predicted epitopes are as follows, (HA = 442), (NA = 449), (NP = 419) and M2 = 52). Among these peptides, the top-ranked B cell epitopes are selected based on overlapping results from those two methods, taking into consideration the epitopes showing high antigenic score values, as shown in (Table 1 ) . Table 1 List of the top-ranked antigenic B cell epitopes across the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their information Starting position Epitope prediction Score Antigen/non-antigen property IEDB BCpred HA protein 168 KKNDAYPTIKISYNNTNR KKNDAYPTIKISYNNTNRED 0.897 1.1073 221 STLNQRLAPKIATRSQVNGQRGINSSMPFHNI LNQRLAPKIATRSQVNGQRG 0.825 1.0247 270 RNSPLREKRRKR ATGLRNSPLREKRRKRGLFG 0.828 0.9293 NP protein 5 GTKRSYEQMETGGERQNATE GTKRSYEQMETGGERQNATE 0.985 0.5451 200 GINDRNFWRGENGRRTRIAY RNFWRGENGRRTRI 0.757 0.9417 345 SFIRGTRVVPRGQLSTERAT RGTRVVPRGQLS 0.743 0.4891 NA protein 33 WVSHSIQTGNQYQPEPCNQS QTGNQYQPEPCNQS 0.892 0.6502 209 NGIITDTIKSWRNNILRTQE TDTIKSWRNNILRT 0.836 0.5221 338 MSSNGAYGVKGFSFKYGNGV GNGV 0.77 0.9688 M2 protein 6 EVETPTKNEWECNCSDSSDP EVETPTKNEWE 0.976 0.7082 56 KYGLKGGPSTEGVPESMREE KYGLKGGPSTEGVPES MREEYRQEQQSAVDVDDGHFV 0.918 0.8569 72 MREEYRQEQQSAVDVDDGHF KYGLKGGPSTEGVPES MREEYRQEQQSAVDVDDGHFV 0.87 0.8804 Results from the Ellipro server to predict the discontinuous epitopes from the 3D structure of respective proteins we considered with minimum score of 0.5 and minimum distance of 6 Ǻ. The list of the predicted discontinuous B cell epitopes was recognized at different exposed surface areas are shown in (Table 2 ). The position of each predicted epitope on the surface of 3D structure of all the considered proteins of H5N1 clade 2.3.4.4b could be visualized using Chimera visualization tool. Table 2 List of the structure-based prediction of the discontinuous B cell epitopes across the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their information Predicted Discontinuous Epitope(s) No Protein Peptide No of residues Score 1 HA A:I505, A:C506, A:I507 3 0.993 2 A:E2, A:N3, A:I4, A:V5, A:L6, A:L7, A:L8, A:A9, A:I10, A:V11, A:S12, A:L13, A:V14, A:K15, A:S16, A:D17, A:D405, A:K406, A:V407, A:R408, A:L409, A:Q410, A:L411, A:R412, A:D413, A:N414, A:A415, A:E424, A:F425, A:Y426, A:H427, A:K428, A:C429, A:D430, A:N431, A:E432, A:C433, A:M434, A:E435, A:S436, A:V437, A:R438, A:N439, A:G440, A:T441, A:Y442, A:D443, A:Y444, A:P445, A:Q446, A:Y447, A:S448, A:E449, A:E450, A:A451, A:R452, A:L453, A:K454, A:R455, A:E456, A:E457, A:I458, A:S459, A:G460, A:V461, A:K462, A:L463, A:E464, A:S465, A:V466, A:G467, A:T468, A:Y469, A:Q470, A:I471, A:L472, A:S473, A:I474, A:S476, A:T477, A:A478, A:A479, A:S480, A:S481, A:L482, A:A483, A:L484, A:A485, A:I486, A:M487, A:M488, A:A489, A:G490, A:L491, A:S492, A:L493, A:W494, A:M495, A:C496, A:S497, A:N498, A:G499, A:S500, A:L501, A:Q502, A:C503 106 0.831 3 A:K177, A:I178, A:S179 3 0.721 A:L105, A:C106, A:Y107, A:P108, A:G109, A:F127, A:E128, A:K129, A:I130, A:L131, A:I132, A:I133, A:P134, A:K135, A:S136, A:S137, A:W138, A:P139, A:N140, A:H141, A:E142, A:T143, A:S144, A:L145, A:G146, A:V147, A:S148, A:A149, A:A150, A:C151, A:P152, A:G155, A:A156, A:P157, A:S158, A:F159, A:F160, A:V163, A:V164, A:W165, A:L166, A:I167, A:K168, A:K169, A:N170, A:D171, A:A172, A:Y173, A:P174, A:T175, A:I176, A:Y180, A:N181, A:N182, A:T183, A:N184, A:E186, A:D187, A:L188, A:L189, A:W192, A:G193, A:I194, A:H195, A:H196, A:S197, A:N198, A:N199, A:A200, A:E201, A:E202, A:Q203, A:T204, A:N205, A:L206, A:Y207, A:K208, A:N209, A:P210, A:T211, A:T212, A:Y213, A:I214, A:S215, A:V216, A:G217, A:T218, A:S219, A:T220, A:L221, A:N222, A:Q223, A:R224, A:L225, A:A226, A:P227, A:K228, A:I229, A:A230, A:T231, A:R232 101 0.676 4 A:N357, A:L358, A:I362, A:N364, A:L365, A:K368 5 A:N357, A:L358, A:I362, A:N364, A:L365, A:K368 6 0.582 6 A:N313, A:E314, A:Q315 3 0.579 7 A:G286, A:L287, A:F288, A:G289, A:A290, A:I291, A:A292, A:G293, A:F294, A:I295, A:E296, A:G297, A:G298, A:W299, A:M302 15 0.533 8 A:D70, A:G79, A:N80, A:P81, A:M82, A:D84, A:I87, A:N100, A:P101, A:A102, A:N103, A:Y153, A:Q154, A:R161, A:S233, A:Q234, A:V235, A:N236, A:G237 19 0.531 NA 1 A:R99, A:D101, A:G102, A:K103, A:W104 5 0.892 2 A:R8, A:S9, A:E11, A:Q12, A:E14, A:T15, A:G16, A:G17, A:E18 9 0.865 3 A:G200, A:I201, A:N202, A:D203, A:N205, A:F206, A:W207, A:R208, A:G209, A:E210, A:N211, A:G212, A:R213, A:R214, A:T215 15 0.856 4 A:D420, A:M421, A:S422, A:N423 4 0.85 5 A:M1, A:A2, A:S3, A:Q4, A:G5, A:T6, A:K7 7 0.739 6 A:G402, A:V403, A:F404, A:E405, A:L406, A:T407, A:D408, A:E409, A:K410, A:A411, A:T412, A:N413, A:P414, A:I415, A:V416, A:P417, A:S418, A:F419 18 0.729 7 A:R216, A:I217, A:E220, A:T232, A:A233, A:A234, A:A237, A:D240, A:Q241, A:R243, A:E244, A:S245, A:N247, A:P248, A:G249, A:N250, A:A251, A:E252, A:E254, A:I265, A:R348, A:G349, A:T350, A:V352, A:V353, A:P354, A:G356, A:Q357, A:L358, A:S359, A:T360, A:E361, A:A363, A:T364, A:I365, A:M366, A:A367, A:A368, A:F369, A:T370, A:G371, A:N372, A:T373, A:E374, A:G375, A:R376, A:T377, A:S378, A:D379, A:M380, A:R381, A:T382, A:E383, A:I384, A:I385, A:R386, A:M387, A:M388, A:E389, A:N390, A:A391, A:R392, A:P393, A:E394, A:D395 65 0.724 8 A:Q42, A:T45, A:E46, A:L47, A:K48, A:L49, A:S50, A:D51, A:Y52, A:E53, A:R55, A:F71, A:D72, A:N76, A:K77, A:Y78, A:L79, A:E80, A:E81, A:H82, A:P83, A:S84, A:A85, A:G86, A:K87, A:D88, A:P89, A:K90, A:K91, A:R98, A:R106, A:E107, A:L108, A:I109, A:L110, A:Y111, A:D112, A:K113, A:E114, A:E115, A:R117, A:R118, A:I119, A:Q122, A:S310, A:Q311 46 0.69 NP 1 A:Q45, A:P46, A:E47, A:P48, A:C49, A:N50 6 0.947 2 A:M1, A:N2, A:P3, A:N4, A:Q5, A:K6, A:I7, A:T8, A:T9, A:I10, A:G11, A:S12, A:I13, A:C14, A:M15, A:V16, A:I17, A:G18, A:I19, A:V20, A:S21, A:L22, A:M23, A:L24, A:Q25, A:I26, A:G27, A:N28, A:I29, A:I30, A:S31, A:I32, A:W33, A:V34, A:S35, A:H36, A:S37, A:I38, A:Q39, A:T40, A:G41, A:N42, A:Q43 43 0.93 3 A:E57, A:N58, A:N59, A:T60 4 0.894 4 A:Q51, A:S52, A:I53, A:I54, A:T55, A:Y56 6 0.878 5 A:V62, A:N63, A:Q64, A:T65, A:Y66, A:V67, A:N68, A:I69, A:S70, A:N71, A:T72, A:N73 12 0.764 6 A:L140, A:N141, A:D142, A:K143 4 0.723 7 A:I108, A:G109, A:S110, A:K111, A:G112 5 0.664 8 A:G105, A:H144, A:S145, A:N146, A:G147, A:T148, A:V149, A:K150, A:I427, A:G429, A:R430, A:P431, A:K432, A:E433, A:N434, A:T435, A:I436, A:T438, A:D459, A:G460, A:A461, A:L463, A:P464, A:F465, A:T466, A:I467, A:D468 27 0.624 M2 1 A:S2, A:L3, A:L4, A:T5, A:E6, A:V7, A:E8, A:T9, A:P10, A:T11, A:K12, A:N13, A:E14, A:E16, A:N18 15 0.804 2 A:A83, A:V84, A:D85, A:V86, A:D87, A:D88, A:G89, A:H90, A:F91, A:V92, A:N93, A:I94, A:E95 13 0.774 3 A:G61, A:G62, A:P63, A:S64, A:T65, A:E66 6 0.574 4 A:S20, A:D21, A:S22, A:S23, A:D24, A:P25, A:L26, A:A29, A:A30, A:I33 10 0.556 3.2. Results of the prediction of the cytotoxic T lymphocyte epitopes (MHC class I molecules) within the major proteins of the H5N1 clade 2.3.4.4b Table 3 shows the predicted MHC class I epitopes with the binding affinity (IC50; IC50 < 50 nM). Table 3 also shows the parameters of the top-ranked epitopes, taking into consideration the allergenicity, antigenicity, non-toxic, and solubility per each listed epitope. Table 3 List of the predicted MHC class I epitopes of the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their relevant information (IC50 value, Percentile Ranks, and Allele-specification). MHC class I molecules Protein Allele Chicken Allele Peptide IC50 < 50nM Per rank % Antigen/Non Antigen Allergic/Non allergic Toxin/non-toxin Solubility HA HLA-A*11:01 BF2*2101 STLNQRLAPK 7.41 0.02 1.1473 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-Toxin Good HLA-A*02:03 RLKREEISGV 7.72 0.09 0.9344 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*68:01 NTQFEAVGR 10.06 0.08 1.2894 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-B*40:01 REEISGVKL 14.22 0.04 0.6846 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*02:03 YIVERANPA 14.9 0.24 0.7800 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*68:01 MNTQFEAVGR 16.2 0.16 1.1615 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-B*15:01 GQRGINSSM 22.36 0.07 1.0202 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*03:01 TLNQRLAPK 30.75 0.08 1.1779 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*30:01 KVRLQLRDNA 36.27 0.17 1.5926 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*68:01 MNTQFEAVGR 16.2 0.16 1.1615 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-B*15:01 GQRGINSSM 22.36 0.07 1.0202 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-A*03:01 TLNQRLAPK 30.75 0.08 1.1779 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good NP HLA-C*16:01 ATYQRTRAL 14.58 0.04 0.5864 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*33:01 DLRVSSFIR 38.08 0.06 0.7704 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*02:06 FQGRGVFEL 8.03 0.06 1.2783 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*11:01 GVFELTDEK 36.27 0.17 1.1503 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-C*12:03 IAYERMCNI 9.03 0.03 0.9843 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-B*07:02 KDPKKTGGPI 21.15 0.07 0.6982 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*68:02 NATEIRASV 17.19 0.13 0.4532 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*68:01 NLNDATYQR 25.72 0.28 0.6676 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*30:01 RTRALVRTGM 14.07 0.05 0.5749 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*30:01 STERATIMAA 14.96 0.06 0.4494 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good HLA-A*68:01 VASGYDFER 32.46 0.35 0.8489 ( Probable ANTIGEN ) Probable NON-ALLERGEN Non toxin Good NA HLA-A*11:01 CYPDAGDIM 15.29 0.09 0.4201 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxic Good HLA-A*68:01 FISCSHLECR 30.11 0.4 1.0798 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxic Good M2 HLA-B*44:02 VETPTKNEW 108.42 0.1 0.6266 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-Toxin Good HLA-A*30:01 VYRRLKYGLK 77.63 0.39 1.2596 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-Toxin Good 3.4. Results of the prediction of the Helper T lymphocyte epitopes prediction within the major proteins of the H5N1 clade 2.3.4.4b Table 4 shows the list of the top-ranked epitopes that recognize the T helper lymphocytes and their parameters. Our results show the predicted epitopes per each protein (HA = 13, NP = 21, NA = 2, and M2 = 9), respectively (Table 4 ). Table 4 List of the predicted MHC class II of Binding Epitopes within different structural (HA, NP, NA & M2) proteins of H5N1 clade 2.3.4.4b showing their IC50 value, Percentile Ranks, and allele-specific interactions. MHC class II molecules Protein Allele Chicken Allele Peptide IC50 < 50nM Per rank % Antigen/Non Antigen Allergic/Non allergic Toxin/non-toxin Solubility HA HLA-DRB1*01:01 *Gaga_BLB1 *Gaga_BLB2 RVPEWSYIVERANPA 10.08 2.1 0.7022 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 WLIKKNDAYPTIKIS 13.85 0.46 0.9804 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB5*01:01 ATYQRTRALVRTGMD 10.99 0.15 0.4153 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*01:01 AELLVLMENERTLDF 15.51 4.2 1.0504 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*01:01 ELLVLMENERTLDFH 19.4 5.8 1.0452 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 LIKKNDAYPTIKISY 21.09 0.99 1.0760 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 RNVVWLIKKNDAYPT 25.07 1.3 1.2023 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 TIKISYNNTNREDLL 33.13 2.1 0.7852 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*04:01 PEWSYIVERANPAND 33.88 0.55 0.7539 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*11:01 FRNVVWLIKKNDAYP 37.72 2 1.1509 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 AYPTIKISYNNTNRE 38.33 2.5 0.8365 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good HLA-DRB1*13:02 PTIKISYNNTNREDL 41.05 2.8 0.7790 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-toxin Good NP HLA-DRB1*11:01 MELIRMIKRGINDRN 9.21 0.14 0.5862 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*07:01 AEIEDLIFLARSALI 10.77 0.29 0.8823 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 ATYQRTRALVRTGMD 10.99 0.15 0.4153 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*15:01 EDLIFLARSALILRG 14.06 0.17 0.7376 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*07:01 EIEDLIFLARSALIL 14.83 0.74 0.9266 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*01:01 PRMCSLMQGSTLPRR 15.32 4.1 0.4574 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 DATYQRTRALVRTGM 15.37 0.53 0.5614 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*01:01 RMCSLMQGSTLPRRS 16.83 4.8 0.5336 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 GRFYIQMCTELKLSD 17.36 0.64 0.4565 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB1*01:01 DPRMCSLMQGSTLPR 20.4 6.1 0.4614 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*05:01/DQB1*03:01 PRRSGAAGAAVKGVG 28.48 1.2 0.9345 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*05:01/DQB1*03:01 LPRRSGAAGAAVKGV 29.2 1.2 0.8733 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 SSFIRGTRVVPRGQL 30.02 1.8 0.5929 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*04:01/DQB1*04:02 ARSALILRGSVAHKS 41.48 0.49 0.6766 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 RSALILRGSVAHKSC 41.78 2.9 0.6269 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*05:01/DQB1*03:01 TLPRRSGAAGAAVKG 42.77 2.3 0.8370 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*04:01/DQB1*04:02 RSALILRGSVAHKSC 45.19 0.7 0.6269 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DPA1*03:01/DPB1*04:02 GRRTRIAYERMCNIL 46.18 0.71 0.6312 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 VGTMVMELIRMIKRG 48.56 3.6 0.4815 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DPA1*01:03/DPB1*02:01 FEDLRVSSFIRGTRV 49.13 1.4 0.8472 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DQA1*05:01/DQB1*03:01 LPRRSGAAGAAVKGV 29.2 1.2 0.8733 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good HLA-DRB5*01:01 SSFIRGTRVVPRGQL 30.02 1.8 0.5929 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non toxin Good NA HLA-DRB3*01:01 WAIYSKDNGIRIGSK 16.43 0.21 0.9819 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxic Good HLA-DRB1*01:01 SFKYGNGVWIGRTKS 25.69 7.9 1.2583 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxic Good M2 HLA-DRB1*11:01 VETPTKNEW 108.42 0.1 0.6266 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-Toxin Good HLA-DRB1*01:01 VYRRLKYGLK 77.63 0.39 1.2596 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non-Toxin Good HLA-DRB5*01:01 DRLFFKCVYRRLKYG 23.64 0.92 0.4858 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-DPA1*01:03/DPB1*02:01 SFKYGNGVWIGRTKS 25.69 7.9 1.2583 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxic Good HLA-DRB5*01:01 CVYRRLKYGLKGGPS 103.77 8.3 1.1811 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-DRB3*01:01 DRLFFKCVYRRLKYG 115.81 3.8 0.4858 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-DRB3*01:01 KCVYRRLKYGLKGGP 76.56 6.1 0.9916 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-DRB5*01:01 QQSAVDVDDGHFVNI 113.4 2.6 1.0804 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good HLA-DRB1*11:01 QSAVDVDDGHFVNIE 134.91 3 1.1815 ( Probable ANTIGEN ). Probable NON-ALLERGEN Non Toxin Good 3.5. Evaluation of the antigenicity, allergenicity, and toxicity of the predicted MHC I and MHC II epitopes within the major proteins of the H5N1 clade 2.3.4.4b (HA, NA, NP, M2) Our analysis shows a large number of epitopes were identified; we then filtered and ranked these epitopes based on (percentile rank < 4 and their IC50 value < 50nM) in the case of the MHC class I (Table 3 ). Our filtration criteria was mainly based on the percentile rank < 10 in the case of the MHC class II of molecules (Table 4 ) . The top-ranked antigenic epitopes shown in Table 4 per each class of the MHC molecules were further evaluated for their potential allergenicity and toxicity, as shown in Tables 3 and 4 . Finally, epitopes showing better solubility and stability were considered and ranked, as shown in Table 4 . 3.6. Results of the molecular docking of the selected MHC classes (I and II) epitopes with the chicken alleles The molecular docking analysis was performed by docking MHC class I and II class of molecules with chicken alleles (BF2*2101 – for MHC class I and Gaga_BLB1 & Gaga_BLB2 – MHC class II) using the HADdock server tool using peptide-binding groove affinity. We used the chicken alleles as receptors, and the MHC class I and MHC class II peptides listed in Tables 3 & 4 , were considered as ligands. Results show the binding affinity and confidence score as listed in Table 4 . The top-ranked epitopes showing the highest binding affinity score were chosen for the design of the final vaccine construct, as listed in Table 5 . Table 5 The sequences and the relevant information of the top-ranked selected epitopes used for the construction of the multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b S.No Protein Start Peptide Antigen Value Docking Score Confidence Score (> 0.8) MHC class I of molecules 1 HA 406 KVRLQLRDNA 1.5926 ( Probable ANTIGEN ) -188.17 0.6821 2 NP 398 FQGRGVFEL 1.2783 ( Probable ANTIGEN ) -214.75 0.7850 3 NA 121 FISCSHLECR 1.0798 ( Probable ANTIGEN ). -214.75 0.7850 4 M2 51 VYRRLKYGLK 1.2596 ( Probable ANTIGEN ). -178.50 0.6388 MHC class II of molecules 1 HA 41 RNVVWLIKKNDAYPT 1.2023 ( Probable ANTIGEN ). -263.89 0.9070 2 NP 252 EIEDLIFLARSALIL 0.9266 ( Probable ANTIGEN ). -214.79 0.7851 3 NA 350 SFKYGNGVWIGRTKS 1.2583 ( Probable ANTIGEN ). -255.61 0.8921 4 M2 51 VYRRLKYGLKGGPST 1.2088 ( Probable ANTIGEN ). -249.52 0.8798 B cell epitopes 1 HA 168 KKNDAYPTIKISYNNTNRED 1.1073( Probable ANTIGEN ). 2 NP 200 MSSNGAYGVKGFSFKYGNGV 0.9688( Probable ANTIGEN ). 3 NA 338 GINDRNFWRGENGRRTRIAY 0.9417( Probable ANTIGEN ). 4 M2 56 KYGLKGGPSTEGVPESMREE 0.8569( Probable ANTIGEN ). The interaction residues from those docking results of MHC class I and MHC class II molecules were analyzed using PDBsum server tool and are shown in Fig. 1 . 3.7. The structure and design of the multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b spanning top-ranked epitopes within the four viral major proteins (HA, NA, NP, and M2) We designed the final vaccine construct by combining the top-ranked B-cell epitopes T- cell epitopes of both MHC I and MHC II class of molecules (filtered from high antigenic, non-allergic, non-toxic and good solubility, and with the better binding affinity score of the structural proteins (HA, NP, NA, and M2) of H5N1 clade 2.3.4.4b as listed in the Table 5 . The top-ranked B cell and T cell epitopes were linked by using the (KK, GPGPG, and AAY) as linkers, respectively, whereas the C-terminal ends of the vaccine construct were linked to the full-length chicken IL-18 gene (Accession No. CAB96214) as an adjuvant after separation with the PEAK linker (Fig. 3). Additionally, we incorporated the 6×His-tag (HHHHHH) attached to the C-terminus for purification and identification of the vaccine upon expression. The final vaccine construct is designed as follows: (the B-cell epitopes are shown in purple, linked with KK; MHC-I T-cell epitopes are shown in green, linked with AAY; and MHC-II T-cell epitopes are shown in orange, linked with GPGPG. The linkers are shown in bold letters and underlined, and the adjuvant (IL18) is shown in red. MHCII is linked with Adjuvant using HEYGAEALERAG. The IL-18 adjuvant is linked with 6xHis tag using EAAAK). 3.8. Results of the physiochemical properties of the designed multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b The predicted vaccine weighed 49942.78 Dalton and possessed a theoretical isoelectric point of 9.04, indicating the alkaline nature of the constructed vaccine. The total number of negative and positively charged residues were 57 and 70, and the extinction coefficient measured at 280nm in water was shown to be 51395, assuming all pairs of Cys residues form cystines. The instability index (II) was about 36.74, showing the structure of the vaccine protein was stable. The aliphatic index was about 65.19, indicating the hydrophilic nature with a value of -0.596. 3.9. Results of the secondary and tertiary structures of the designed vaccine construct The secondary and tertiary structure of the multiepitope-based vaccine construct were analyzed and modeled through the PDBsum server tool and Biovia discovery studio. 3.10. Results of the molecular docking of the designed vaccine construct with the chickens Toll-like receptors (TLR3 and TLR7) To examine the potential immunogenic performance of a multiepitope-based vaccine construct combining four proteins (HA, NA, NP, and M2) of H5N1 clade 2.3.4.4b, the molecular docking studies were performed between the vaccine construct and Toll-like receptors (TLR3 and TLR7). As mentioned earlier, TLR3 and TLR7 were chosen among the ten toll-like receptors, as these intracellular receptors could trigger an innate immune response through several pathways. Initially, the sequence of TLR3 (Uniprot ID: QoPQ88) and TLR7 (Uniprot ID: C4PCM1) were retrieved from the database and modeled through Biovia discovery studio, followed by preparing the protein for the docking study by removing water molecules, adding hydrogen and performing energy minimization. Docking analysis was performed using the Zdocker, and the results obtained indicate the strong binding affinity between the vaccine construct and Toll-like receptors (TLR3 and TLR7). The best-ranked complexes with their respective ZDock score provide us with confirmation of the strong and stable interaction between them. The interaction residues, multiple hydrogen bonds, and hydrophobic bonds were analyzed through the PDBsum server tool. Figure 6 – 6 (a) shows the topology visualization of TLR3 and TLR7, Fig. 5 – 6 (b) shows the docking interaction analysis between the vaccine construct and Toll-like receptors, and finally, Fig. 5 – 6 (c) illustrates the interaction amino acid residues and formation of multiple hydrogen bonds, hydrophobic bonds, etc., through PDBsum server tool. 3.11. In silico cloning of the H5N1 clade 2.3.4.4b multiepitope-based vaccine spanning key epitopes within the major proteins (HA, NA, NP, and M2) proteins. Hence, the vaccine construct was cloned using the Vector builder from the decoded amino acid sequence of each epitope's respective DNA sequences to mimic the vaccine's expression in the E.coli K12 expression system. The GC content and codon adaptation index values generated by the vector builder server represent the level of expression in the E.coli system. Finally, Snap gene software was used to clone the constructed vaccines in the pET-28a (+) expression vector between the restriction enzyme cutting locations of BamHI and EcoRI , and the results obtained are shown in Fig. 8 . 3.12. In silico immune simulation of the designed H5N1 clade 2.3.4.4b multiepitope-based vaccine spanning key epitopes within the (HA, NA, NP, and M2) proteins. The predicted immune response of the constructed vaccine was analyzed through the interaction between the H5N1 clade 2.3.4.4b antigens and the B cell, T cell, and cytokines. 4. Discussion The HPAIV-H5N1 clade 2.3.4. 4b emerged in 2020 and continues to pose significant risks to the poultry industry and human health ( 31 , 32 ). This clade has also been reported in many mammalian species, including dairy cows, mink, cats, foxes, and sea lions ( 33 , 34 ). Several reports of the H5N1 clade 2.3.4.4b in birds have been recently reported in the USA ( 4 ). The viral infection with this clade in chicken resulted in high morbidity and mortality rates, which may reach up to 100%. ( 35 ). Chicken infection with this highly pathogenic AIV may require culling of the infected flock, which may have a devastating impact on the chicken meat and egg process. There is an urgent need to develop some effective vaccines that could protect chickens and other birds against this highly pathogenic emerging virus. The application of AI tools in vaccine design and development has grown in the past 5 years ( 36 , 37 ). The AI tools, including epitope prediction, molecular docking, and simulation, paved the way for a remarkable short-term vaccine pipeline development for many viral diseases of domestic animals and birds ( 30 , 38 ). Several traditional methods were used for epitope mapping throughout some viral genomes. The application of monoclonal antibodies (MAbs) was used in the past and may still be in use as a conventional method for epitope mapping for the H5N1 for a decade. The MAbs approach requires the use of animals and is time-consuming and labor-intense ( 39 ). This is in contrast to the application of AI in the prediction and simulation of protein/protein interactions, which are very efficient, fast, and have a high level of accuracy and precision. In silico cloning techniques and codon optimization were used to improve the expression and effectiveness of the candidate vaccines in the prokaryotic expression system. In the current study, we used several AI tools to design a multiepitope DNA-based vaccine against the currently circulating clade H5N1 2.3.4.4b in chickens. Further, the characteristic features such as antigenicity, allergenicity, and structural validation of the designed vaccine were analyzed, and in parallel, the molecular docking and in silico simulation provide the pathway for eliciting strong cellular and humoral immune responses. ( 40 ). Our approach for the design of the multiepitope DNA-based vaccine against the currently circulating H5N1 clade 2.3.4.4b includes several consecutive steps, including ( 1 ) retrieval of the sequences from the GenBank ( 2 ) multiple sequence alignment, ( 3 ) generation of the consensus sequences per each protein, ( 4 ) prediction of the B cell, T cell including MHC-Class (I and II), ( 5 ) selection of the top-ranked epitopes, ( 6 ) construct the multiepitope using the appropriate linkers, ( 7 ) incorporation of the IL18 to the vaccine construct, ( 8 ) In silico cloning of the designed vaccine, ( 9 ), prediction of the physicochemical properties of the designed vaccine, ( 10 ) prediction of the secondary and tertiary structures of the designed vaccine, ( 11 ) molecular docking of the designed vaccine with the chickens TL3/TLR7, and ( 12 ) immunosimulation of the final vaccine construct to assess its potential potency in the activation of the humoral and cell-mediated immunity of chickens. Our prediction shows many potential epitopes per protein. We established some filtration criteria to select the top-ranked epitope per each category of immunogens. First , we used the percentile score (< 4) for MHC class I molecules and (< 10) for MHC class II molecules with the IC50 value of (< 50nM). Second , the short-listed epitopes per each protein were examined for their allergenicity, antigenicity, non-toxic, and solubility profiles as previously described ( 41 , 42 ). Third , we used VaxiJen 2.0 and AllerTop to assess the antigenic properties and allergic nature of each candidate epitope. The acceptable antigenic score range was established to be (0.4–0.5). Fourth , we tested all the short-listed epitopes for potential toxicity using the ToxinPred server tools, as previously described ( 43 , 44 ). Sixth , the molecular docking analysis was performed between filtered epitopes and chicken alleles of MHC class I and MHC class II molecules through the HADdock docking tool. The top-ranked peptide was selected based on their binding score and high antigenic score for all structural genomes we considered for the study, for MHC class I of molecules – KVRLQLRDNA (1.5926 and − 188.17 docking score – HA), FQGRGVFEL (1.2783 and − 214.75 docking score – NP), FISCSHLECR (1.0798 and − 214.75 docking score – NA), VYRRLKYGLK (1.2596 and − 178.50 docking score – M2) and for MHC class II of molecules – RNVVWLIKKNDAYPT (1.2023 and docking score of -263.89- HA), EIEDLIFLARSALIL- (0.9266 and docking score of -214.79 - NP), SFKYGNGVWIGRTKS-(1.2583 and docking score of -255.61 – NA) and VYRRLKYGLKGGPST – (1.2088 and docking score of -249.52 – M2) ( 16 ). Finally , these epitopes were used in the vaccine construct and were designed using linkers and adjuvants. The interaction residues between them were identified through PDBsum and were displayed in the figure, which results in multiple hydrogen bonds and hydrophobic bonds, especially to capture their better binding interactions. One of the challenges in this study is the lack of data about the epitopes interacting with the chicken MHC-I, and MHC-II is not yet available on the IEDB server. To overcome this problem, we used alternative strategies to try to identify epitopes activating chicken CTL and HTL. We applied the surrogate model approach using the well-known human alleles because there aren't many computational tools available, specifically for MHC class I molecules of most avian species, particularly chickens. Both the human and chicken alleles are very similar in their structural and functional properties, including the peptide-binding grooves, which enable the peptide-MHC class molecules binding interactions. We selected the human alleles that match the chicken alleles' structural and functional properties through the IEDB.org server and performed the prediction. The default parameter setting was kept the same as the polymerase length of 12mer. Despite the species-specific diversity of the chicken MHC class-I molecules from the BF2 locus, the experimental data found in the IEDB MHC class I molecules server ( https://www.iedb.org/ ), have confirmed that specific BF2 alleles, like BF2 *2101 from previous studies, have similarities to human alleles (HLA – A02:01), especially in the motif binding and anchor residue preferences. Regarding the prediction of the helper T-lymphocyte epitopes (MHC class II molecules), we used the MixMHC2pred tools ( http://mixmhc2pred.gfellerlab.org/ ), to predict the corresponding chicken alleles (Gaga_BLB1_002_01, Gaga_BLB1_012_01, Gaga_BLB2_002_01, Gaga_BLB2_012_01, and Gaga_BLB2_012_02) for the selected list of epitopes. This approach successfully provided the best score data and matched the chicken alleles with the corresponding epitopes. Our docking simulation results showed the firm binding affinities between the designed vaccine epitopes and the conjugated TLRs, facilitating effective immune recognition and the initiation of the robust immune response ( 45 ). The Z-score identifies several high-affinity bindings poses in the molecular docking results of the designed multi-epitope-based vaccine construct of the H5N1 clade 2.3.4.4b. The high accuracy protein-protein docking resulted in the formation of multiple hydrogen bonds and hydrophobic interactions with Zdock score ( 18 ), Zrank score (-131.75) and E_Rdock score (-7.95511) for TLR3 and Zdock score (19.04), zrank score (-142.71) and E_Rdock score (-45.31) for TLR7. The PDBsum results showed the interaction residues were analyzed and the major interaction hydrogen bonds (Arg65 – Thr419), (His 109 – Cys261), (Ser133 – Asp263) and (Lys331 – Tyr310) for TLR3 with vaccine construct and the major interaction hydrogen bonds (SER550 – Arg398), (Arg 186 – Glu 408), (Arg 104 – Val 411) and (Tyr 190 – Glu 413) for TLR7 with the vaccine construct. In silico immune simulations using C-ImmSim provided critical insights about the potential immune responses elicited by the designed four structural proteins (HA, NP, NA, and M2) of the H5N1 clade 2.3.4.4b vaccine constructs. Our approach ensured that the vaccination candidates had reliable protein synthesis and effective translation using the optimized codons and computational tools such as vector builders and Snap gene ( 46 ). The simulation results revealed robust activation of T-cell populations, including cytotoxic T cells and helper T cells, crucial for cellular and humoral immunity ( 47 ). This comprehensive analysis demonstrated that the multi-epitope H5N1 vaccine constructs in this study would induce strong humoral and cell-mediated immunity that might play essential roles in protecting chickens and other species of birds against the currently circulating HPAI-H5N1 clade 2.3.4.4b. Based on the data provided above, a high level of humeral immune response (immunoglobulin antibodies) and the other immune cells are expected after the administration of the candidate vaccines ( 17 , 48 ). It also predicted the progression of the magnitude of the immune response with the progression of the time after administering these candidate vaccines (primary immune response). We think our designed multiepitope-based vaccine spanning the four major structural proteins (HA, NP, NA, and M2) of the H5N1 clade 2.3.4.4b will be effective in the protection of birds against the currently circulating clade of the H5N1 in chickens in the USA and other parts in the world. However, further studies are required to validate these vaccines in chickens. 5- Conclusions We successfully designed a multiepitope Pan-H5N1 clade 2.3.4.4b DNA-based vaccine spanning the top-ranked immunogenic, nonallergenic, and nontoxic epitopes. Twelve epitopes within the major proteins (HA, NA, NP, and M2), including (B cell, MHC-Class-I, and MHC-class-II). The T cell epitopes showed high binding affinities with the chicken alleles. We successfully made in silico cloning of these epitopes and linked them to the chicken IL-18. The designed vaccine construct showed high binding affinities to the chicken Toll-Like receptors 3 and 7. The designed vaccine construct showed high immunogenic potential in terms of the production of humoral and cell-mediated immunity in chickens using an immune simulation approach. We believe the designed vaccine in the current study will protect not only chickens but also other birds, such as turkeys, quails, pheasants, and wild birds, against the currently circulating HPAIV-H5N1 clade 2.3.4.4b. Declarations Conflict of interest : The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were editorial board members of Scientific report, at the time of submission. This had no impact on the peer review process and the final decision. Publisher Note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Funding: This study was funded by a seed grant (PI: MGH) from Long Island University (Grant no: 36524) and funds from the USDA-NIFA Animal Health and Disease Research grant (NI24AHDRXXXXG066). Author Contribution Author contributions: ND: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. MK: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. AS: Data curation, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing, Resources. MC: Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing, Conceptualization, Funding acquisition. MH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Acknowledgement We thank Dylan Feldman and Muddapuram Deeksha Goud for their technical assistance in retrieving the protein sequences from GenBank. Data availability statement: The raw data supporting the conclusions of this article will be made available by the authors upon request. References Burrough, E. R. et al. Highly Pathogenic Avian Influenza A(H5N1) Clade 2.3.4.4b Virus Infection in Domestic Dairy Cattle and Cats, United States, 2024. Emerg. Infect. Dis. 30 (7), 1335–1343 (2024). Cui, P. et al. Global dissemination of H5N1 influenza viruses bearing the clade 2.3.4.4b HA gene and biologic analysis of the ones detected in China. Emerg. Microbes Infect. 11 (1), 1693–1704 (2022). Pulit-Penaloza, J. A. et al. Highly pathogenic avian influenza A(H5N1) virus of clade 2.3.4.4b isolated from a human case in Chile causes fatal disease and transmits between co-housed ferrets. Emerg. Microbes Infect. 13 (1), 2332667 (2024). Meade, P. S. et al. Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City. J. Virol. 98 (6), e0062624 (2024). Raoufi, E. et al. Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review. Int. J. Pept. Res. Ther. 26 (2), 1155–1163 (2020). Dadonaite, B. et al. The structure of the influenza A virus genome. Nat. Microbiol. 4 (11), 1781–1789 (2019). Chen, X. et al. Host Immune Response to Influenza A Virus Infection. Front. Immunol. 9 , 320 (2018). Zhang, B. et al. The nucleoprotein of influenza A virus inhibits the innate immune response by inducing mitophagy. Autophagy 19 (7), 1916–1933 (2023). Wang, R. et al. Influenza M2 protein regulates MAVS-mediated signaling pathway through interacting with MAVS and increasing ROS production. Autophagy 15 (7), 1163–1181 (2019). Stachyra, A., Gora-Sochacka, A. & Sirko, A. DNA vaccines against influenza. Acta Biochim. Pol. 61 (3), 515–522 (2014). Sawaengsak, C. et al. Intranasal chitosan-DNA vaccines that protect across influenza virus subtypes. Int. J. Pharm. 473 (1–2), 113–125 (2014). Nguyen, T. N. et al. Lipid nanoparticle-encapsulated DNA vaccine confers protection against swine and human-origin H1N1 influenza viruses. mSphere 9 (8), e0028324 (2024). El-Manzalawy, Y., Dobbs, D. & Honavar, V. Predicting linear B-cell epitopes using string kernels. J. Mol. Recognit. 21 (4), 243–255 (2008). Clifford, J. N. et al. BepiPred-3.0: Improved B-cell epitope prediction using protein language models. Protein Sci. 31 (12), e4497 (2022). Elshafei, S. O., Mahmoud, N. A. & Almofti, Y. A. Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus. Sci. Rep. 14 (1), 2870 (2024). Awadelkareem, E. A. & Ali, S. A. Vaccine design of coronavirus spike (S) glycoprotein in chicken: immunoinformatics and computational approaches. Transl Med. Commun. 5 (1), 13 (2020). Mugunthan, S. P., Venkatesan, D., Govindasamy, C., Selvaraj, D. & Harish, M. C. Systems approach to design multi-epitopic peptide vaccine candidate against fowl adenovirus structural proteins for Gallus gallus domesticus. Front. Cell. Infect. Microbiol. 14 , 1351303 (2024). Maupetit, J., Tuffery, P. & Derreumaux, P. A coarse-grained protein force field for folding and structure prediction. Proteins 69 (2), 394–408 (2007). Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46 (W1), W296–W303 (2018). Kozakov, D. et al. How good is automated protein docking? Proteins 81 (12), 2159–2166 (2013). Kozakov, D. et al. The ClusPro web server for protein-protein docking. Nat. Protoc. 12 (2), 255–278 (2017). Laskowski, R. A., Jablonska, J., Pravda, L., Varekova, R. S. & Thornton, J. M. PDBsum: Structural summaries of PDB entries. Protein Sci. 27 (1), 129–134 (2018). Hung, L. H., Li, H. P., Lien, Y. Y., Wu, M. L. & Chaung, H. C. Adjuvant effects of chicken interleukin-18 in avian Newcastle disease vaccine. Vaccine 28 (5), 1148–1155 (2010). Li, K. et al. Adjuvant effects of interleukin-18 in DNA vaccination against infectious bursal disease virus in chickens. Vaccine 31 (14), 1799–1805 (2013). Grote, A. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. ; (2005). 33(Web Server issue):W526–W531 . Doytchinova, I. A. & Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8 , 4 (2007). Dimitrov, I., Flower, D. R. & Doytchinova, I. AllerTOP–a server for in silico prediction of allergens. BMC Bioinform. 14 (Suppl 6), S4 (2013). Sharma, N., Naorem, L. D., Jain, S. & Raghava, G. P. S. ToxinPred2: an improved method for predicting toxicity of proteins. Brief. Bioinform ; 23 (5). (2022). McGuffin, L. J., Bryson, K. & Jones, D. T. The PSIPRED protein structure prediction server. Bioinformatics 16 (4), 404–405 (2000). Duraisamy, N. et al. Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines. Front. Vet. Sci. 11 , 1468890 (2024). Tian, J. et al. Highly Pathogenic Avian Influenza Virus (H5N1) Clade 2.3.4.4b Introduced by Wild Birds, China, 2021. Emerg. Infect. Dis. 29 (7), 1367–1375 (2023). Xie, Z. et al. Clade 2.3.4.4b highly pathogenic avian influenza H5N1 viruses: knowns, unknowns, and challenges. J. Virol. :e0042425. (2025). Bordes, L. et al. Highly Pathogenic Avian Influenza H5N1 Virus Infections in Wild Red Foxes (Vulpes vulpes) Show Neurotropism and Adaptive Virus Mutations. Microbiol. Spectr. 11 (1), e0286722 (2023). Leguia, M. et al. Highly pathogenic avian influenza A (H5N1) in marine mammals and seabirds in Peru. Nat. Commun. 14 (1), 5489 (2023). Warren, C. J. et al. Assessment of Survival Kinetics for Emergent Highly Pathogenic Clade 2.3.4.4 H5Nx Avian Influenza Viruses. Viruses ; 16 (6). (2024). Ananya, Panchariya, D. C. et al. Vaccine design and development: Exploring the interface with computational biology and AI. Int. Rev. Immunol. 43 (6), 361–380 (2024). Testa, J. S. et al. MHC class I-presented T cell epitopes identified by immunoproteomics analysis are targets for a cross reactive influenza-specific T cell response. PLoS One . 7 (11), e48484 (2012). Hou, Y. et al. Prediction and identification of T cell epitopes in the H5N1 influenza virus nucleoprotein in chicken. PLoS One . 7 (6), e39344 (2012). Han, L. et al. Minor differences in peptide presentation between chicken MHC class I molecules can explain differences in disease susceptibility. bioRxiv 2022:2022.03.11.484051. Dashti, F. et al. A computational approach to design a multiepitope vaccine against H5N1 virus. Virol. J. 21 (1), 67 (2024). Kaur, B., Karnwal, A., Bansal, A. & Malik, T. An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus. Biomed. Res. Int. 2024 , 4066641 (2024). Fathollahi, M. et al. Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach. J. Biomol. Struct. Dyn. 42 (20), 10761–10784 (2024). Sami, S. A. et al. Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach. ACS Omega . 6 (47), 32043–32071 (2021). Mortazavi, B., Molaei, A. & Fard, N. A. Multi-epitopevaccines, from design to expression; an in silico approach. Hum. Immunol. 85 (3), 110804 (2024). Chen, Y. H., Wu, K. H. & Wu, H. P. Unraveling the Complexities of Toll-like Receptors: From Molecular Mechanisms to Clinical Applications. Int. J. Mol. Sci. ; 25 (9). (2024). Suleman, M. et al. Immunoinformatic-based design of immune-boosting multiepitope subunit vaccines against monkeypox virus and validation through molecular dynamics and immune simulation. Front. Immunol. 13 , 1042997 (2022). Bashir, S., Ali Abd-elrahman, K., Hassan, A. & Almofti, M. A. Multi Epitope Based Peptide Vaccine against Marekís Disease Virus Serotype 1 Glycoprotein H and B. Am. J. Microbiol. Res. 6 (4), 124–139 (2018). Mugunthan, S. P. & Mani Chandra, H. A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum. Front. Vet. Sci. 8 , 721061 (2021). Additional Declarations No competing interests reported. Supplementary Files SuppTable1.HAof279isolatesofH5N1clade2.3.4.4b.xlsx SuppTable2.NAof79isolatesofH5N12.3.4.4bChicken.xlsx SuppTable3.NPof79isolatesofH5N12.3.4.4b.xlsx SuppTable4.M2of79isolatesofH5N12.3.4.4b.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6711963","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":459834838,"identity":"fcbd4edb-47d8-4eb3-98e1-0a64e47b496a","order_by":0,"name":"Nithyadevi Duraisamy","email":"","orcid":"","institution":"Long Island University","correspondingAuthor":false,"prefix":"","firstName":"Nithyadevi","middleName":"","lastName":"Duraisamy","suffix":""},{"id":459834839,"identity":"34964dd9-8d0c-4001-91da-3876721b830d","order_by":1,"name":"Abid Ullah Shah","email":"","orcid":"","institution":"Long Island University","correspondingAuthor":false,"prefix":"","firstName":"Abid","middleName":"Ullah","lastName":"Shah","suffix":""},{"id":459834840,"identity":"2bd08c8a-7382-4933-a80d-d4158a51066f","order_by":2,"name":"Mohd Yasir Khan","email":"","orcid":"","institution":"Long Island University","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Yasir","lastName":"Khan","suffix":""},{"id":459834841,"identity":"fcbafb3c-95fb-4dc8-ae97-e0f54a95955a","order_by":3,"name":"Mohammed Cherkaoui","email":"","orcid":"","institution":"Long Island University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Cherkaoui","suffix":""},{"id":459834842,"identity":"84a7770b-1576-4f25-ac39-366db9b5c7c3","order_by":4,"name":"Maged Gomaa Hemida","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYLACHoYDDAzMDAYMHwokQHwD4rUwzjAgSQtQJTMPRDF+Lbrtxy8+eMNwR063nXnjZxsDi8QG9uZtEvi0mJ3JKTacw/DM2OwwW7F0joFEYgPPsTL8Wg7kpEnzMBxO3HaYxwCiRSLHDL+W82/SfwO11AO1GP+2AGmRf0NAy430Y8xALQlmh3nMpBnAtvAQ0vKGWXKOwWHDbYfZyix7DCSM23jSii3wOyz94Yc3FYflzc4f3nzjR0WdbD/74Y038GkBRooBakSw4VcOAuwPCKsZBaNgFIyCkQ0Av2BINpM4KL0AAAAASUVORK5CYII=","orcid":"","institution":"Long Island University","correspondingAuthor":true,"prefix":"","firstName":"Maged","middleName":"Gomaa","lastName":"Hemida","suffix":""}],"badges":[],"createdAt":"2025-05-21 03:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6711963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6711963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83325392,"identity":"82785f96-eac6-4d1a-9db4-880cec58f7c5","added_by":"auto","created_at":"2025-05-23 06:15:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238294,"visible":true,"origin":"","legend":"\u003cp\u003eThe\u003cstrong\u003e \u003c/strong\u003e3D structure representation (\u003cstrong\u003etop\u003c/strong\u003e) of molecular docking analysis of high-ranked MHC class I Epitopes from different structural proteins (HA, NP, NA \u0026amp; M2) of H5N1 clade 2.3.4.4b with the chicken alleles BF2*2101 using HADdock analysis server, \u003cstrong\u003e(A)\u003c/strong\u003e HA: KVRLQLRDNA, \u003cstrong\u003e(B)\u003c/strong\u003e NP: FQGRGVFEL, \u003cstrong\u003e(C)\u003c/strong\u003e NA: FISCSHLECR \u003cstrong\u003e(D)\u003c/strong\u003e M2: VYRRLKYGLK and their respective interaction residues (\u003cstrong\u003ebottom\u003c/strong\u003e) obtained from PDBsum database\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/8b95096e5f06fda1b25df3ed.jpg"},{"id":83325544,"identity":"9f889e3f-fa40-4e01-927d-787147cb7c1d","added_by":"auto","created_at":"2025-05-23 06:23:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198086,"visible":true,"origin":"","legend":"\u003cp\u003eThe 3D structure representation (\u003cstrong\u003etop\u003c/strong\u003e) of the molecular docking analysis of top-ranked MHC class II Epitopes from different structural proteins (HA, NP, NA \u0026amp; M2) of H5N1 clade 2.3.4.4b with chicken alleles BLB1 and BLB2 using HADdock analysis server, \u003cstrong\u003e(A)\u003c/strong\u003e HA: \u003cstrong\u003eRNVVWLIKKNDAYPT\u003c/strong\u003e, \u003cstrong\u003e(B)\u003c/strong\u003e NP: \u003cstrong\u003eEIEDLIFLARSALIL\u003c/strong\u003e, \u003cstrong\u003e(C)\u003c/strong\u003e NA: \u003cstrong\u003eFKYGNGVWIGRTKS\u003c/strong\u003e, \u003cstrong\u003e(D)\u003c/strong\u003e M2: \u003cstrong\u003eVYRRLKYGLKGGPST\u003c/strong\u003e and their respective interaction residues (\u003cstrong\u003ebottom\u003c/strong\u003e) obtained from PDBsum database.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/ecb37aea38f631f6b52208e4.jpg"},{"id":83325395,"identity":"77031946-8894-406b-8ec9-b0f095e53a99","added_by":"auto","created_at":"2025-05-23 06:15:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe sequences of the designed multiepitope DNA vaccine using the top-ranked epitopes within the H5N1 clade 2.3.4.4b major proteins (HA, NA, NP, and M2).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/b502d32c7a287ed0707b3c1a.png"},{"id":83325381,"identity":"1dee5b3e-f92a-4765-bfa9-f727a6943176","added_by":"auto","created_at":"2025-05-23 06:15:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the multiepitope DNA vaccine design prepared against the top-ranked epitopes of the major proteins (HA, NA, NP, and M2) of the currently circulating H5N1 clade 2.3.4.4b in chickens.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e \u0026nbsp;Mapping the B cell, MHC class I, and MHC class II epitopes within the H5N1 clade 2.3.4.4b. (B) Structure of the designed multiepitope-based vaccine showing twelve epitopes linked with the chicken IL-18 gene.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/dd1843644a2a9cf07982178c.jpg"},{"id":83325383,"identity":"18abfa02-14a9-47c4-a2ab-07d9f16157ba","added_by":"auto","created_at":"2025-05-23 06:15:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":263244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural analysis of the final multi-epitope vaccine construct against H5N1 clade 2.3.4.4b\u003c/strong\u003e: \u003cstrong\u003e(A)\u003c/strong\u003e shows the multi-epitope vaccine construct's topology diagram to visualize the secondary structure elements' arrangements. The PDB files of the vaccine construct were provided as input into the PDBsum server; the results showed the cylinders, arrows, and lines, which represent the alpha-helix and beta strands, and the lines explain the connection via loops and chains. \u003cstrong\u003e(B)\u003c/strong\u003e shows the 3D structure of the vaccine construct modeled through Biovia discovery studio using template sequence alignment method and its corresponding Ramachandran plot \u003cstrong\u003e(C) \u003c/strong\u003econfirms the stability, a greater number of blue dots on the respective region, confirms the quality of protein conformations, and ensures its accuracy. \u003cstrong\u003e(D)\u003c/strong\u003e, the secondary structure prediction exactly matches the topology diagram, allowing the identification of the flexible regions, surface exposure, and potential antigenic sites.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/977ee7c2336633564add6e43.jpg"},{"id":83325546,"identity":"91f7d56c-21f3-48b7-89f2-388a41c1b6f7","added_by":"auto","created_at":"2025-05-23 06:23:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":211424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis of multi-epitope vaccine construct of H5N1 clade 2.3.4.4b with the chicken Toll-like immune receptor (TLR3\u003c/strong\u003e) using Biovia discovery studio \u003cstrong\u003e(A)\u003c/strong\u003e Topology illustration of the chicken TLR3 protein structure analyzed by the PDBsum server, \u003cstrong\u003e(B)\u003c/strong\u003e The docking results of binding interaction between vaccine construct and TLR3, \u003cstrong\u003e(C) \u003c/strong\u003eMapping the interactive residues between the vaccine construct and the TLR-3 residues; \u003cstrong\u003e(D)\u003c/strong\u003ethe binding amino acids sequences between vaccine construct and TLR3 using PDBsum server.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/06ffb068493ce46572bc7fdc.jpg"},{"id":83325387,"identity":"2e468193-de02-4e99-b63f-55fb91be9bf6","added_by":"auto","created_at":"2025-05-23 06:15:05","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":224231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking analysis of multi-epitope vaccine construct of H5N1 clade 2.3.4.4b with the chicken Toll-like immune receptor (TLR7)\u003c/strong\u003e using Biovia discovery studio, \u003cstrong\u003e(A)\u003c/strong\u003eTopology illustration of the chicken TLR3 protein structure analyzed by the PDBsum server, \u003cstrong\u003e(B)\u003c/strong\u003e The docking results of the binding interaction between the designed vaccine construct and the TLR7, \u003cstrong\u003e(C)\u003c/strong\u003e the interaction residues; \u003cstrong\u003e(D)\u003c/strong\u003e and its binding amino acids between vaccine construct and TLR7 using PDBsum server.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/9d41650512f9aae66d8dbdb0.jpg"},{"id":83326155,"identity":"5c068368-1249-4425-a665-8670d0a5a698","added_by":"auto","created_at":"2025-05-23 06:31:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":220590,"visible":true,"origin":"","legend":"\u003cp\u003eThe vector map showing in silico cloning of the multiepitope H5N1 clade 2.3.4.4b (HA, NP, NA, and M2) into the pET-28(+) Expression Vector.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/081692ed3eeac33888d7d581.png"},{"id":83325390,"identity":"9ad67814-fa5b-498c-b26c-3e60f5e979dd","added_by":"auto","created_at":"2025-05-23 06:15:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":459687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIn silico immune simulation analysis of the H5N1 clade 2.3.4.4b multi-epitope vaccine construct\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The kinetics profile displaying antigen-immunoglobulin production (IgM, IgG1, IgG2, and IgM + IgG), \u003cstrong\u003e(B)\u003c/strong\u003eB lymphocytes population per entity-state (i.e., showing counts for active, presenting on class-II, internalized the Ag, duplicating and anergic, \u003cstrong\u003e(C)\u003c/strong\u003eB lymphocytes total count, memory cells, and sub-divided in isotypes IgM, IgG1 and IgG2, \u003cstrong\u003e(D)\u003c/strong\u003e CD4 T-helper lymphocytes count sub-divided per entity-state (i.e., active, resting, anergic and duplicating), \u003cstrong\u003e(E)\u003c/strong\u003e CD4 T-helper lymphocytes count. The plot shows total and memory counts, as well as \u003cstrong\u003e(F)\u003c/strong\u003eCD4 T-regulatory lymphocyte count. Both total memory and per entity-state counts are plotted: \u003cstrong\u003e(G)\u003c/strong\u003e The CD8 T-cytotoxic lymphocytes count per entity-state, \u003cstrong\u003e(H)\u003c/strong\u003e CD8 T-cytotoxic lymphocytes count. Total and memory shown, \u003cstrong\u003e(I)\u003c/strong\u003e Dendritic cells. The DC can present antigenic peptides on both MHC class-I and class-II molecules, \u003cstrong\u003e(J)\u003c/strong\u003e The epithelial cells. The total count is broken down into active, virus-infected, and presenting on class-I MHC molecule, \u003cstrong\u003e(K)\u003c/strong\u003e Macrophages. Total count, internalized, presenting on MHC class-II, active and resting macrophages, \u003cstrong\u003e(L)\u003c/strong\u003e Natural Killer cells (total count), \u003cstrong\u003e(M)\u003c/strong\u003e Plasma B lymphocyte count sub-divided per isotype (IgM, IgG1, and IgG2)\u003cstrong\u003e. \u003c/strong\u003eThe simulation was performed utilizing the antigen-combined sequence data from four proteins (HA, NA, NP, and M2) of the H5N1 clade 2.3.4.4b, and it was set to 100 with a volume of 10.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/f887e2460a93780d11a7b3e7.png"},{"id":83329106,"identity":"bb6155a4-2785-4022-9f2f-be503a4b06eb","added_by":"auto","created_at":"2025-05-23 07:17:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5251313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/dde4b4b2-eda9-41b4-bb96-b1a5cc30905c.pdf"},{"id":83325543,"identity":"000e5f38-b4c2-4e26-abd6-292fd7661cae","added_by":"auto","created_at":"2025-05-23 06:23:05","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26597,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable1.HAof279isolatesofH5N1clade2.3.4.4b.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/aae6d4002eef5bdb5c557676.xlsx"},{"id":83325386,"identity":"b7073709-a97d-4795-aea6-a1808e40f2a3","added_by":"auto","created_at":"2025-05-23 06:15:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34710,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable2.NAof79isolatesofH5N12.3.4.4bChicken.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/7bd151b7ad1f60c2cf909e16.xlsx"},{"id":83325393,"identity":"6a5ee671-5931-4ad1-8d49-45b1e0cf9ef9","added_by":"auto","created_at":"2025-05-23 06:15:08","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26232,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable3.NPof79isolatesofH5N12.3.4.4b.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/d7501c55032559cc4a634a14.xlsx"},{"id":83325545,"identity":"5e822d22-13a1-46f7-a7b3-7ce311c5d84c","added_by":"auto","created_at":"2025-05-23 06:23:05","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":32633,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable4.M2of79isolatesofH5N12.3.4.4b.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6711963/v1/e47c9bd6fc00c9c912d33af8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Artificial Intelligence-Guided Design of some Pan-H5N1-clade 2.3.4.4b Mosaic DNA-based vaccines to combat the circulating HPAI in birds","fulltext":[{"header":"1- Introduction","content":"\u003cp\u003eThe highly pathogenic avian influenza H5N1 (HPAIV) virus continues to pose a significant risk to the poultry industry. There is also a risk of spillover to humans, which results in the death of some affected patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). There is a continuous active dynamic change in the virus's genetic material for many reasons, including the poor proofreading capability of the viral polymerase enzyme, the antigenic shift/drift, the possibility of reassortment, and recombination (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This pattern of frequent changes in the viral genomes resulted not only in the emergence of new viruses or clades of the same lineage of the virus but also could hamper the success of the currently used vaccine and diagnostic assays for the HPAIV. There is a mandate for active monitoring of these viruses at the genomic levels to monitor the emergence of new viruses that might have any abnormal genotypic/phenotypic patterns of the virus. There is also a high demand for the generation of novel diagnostic assays and vaccines that could detect/protect against the currently circulating strains of the virus in the field. Influenza viruses\u0026rsquo; type-A (IAV) have segmented genomes consisting of 7\u0026ndash;8 segments. Each segment of the viral genome encodes at least one important protein. Influenza viruses have several important proteins, including the hemagglutinin (HA), the neuraminidase (NA), the nucleoprotein (NP), and the matrix protein (M). This is in addition to some nonstructural proteins (NSP1 and NSP2, in addition to the viral polymerase, which consists of three subunits called PA, PB2, and PB2 proteins) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The AIV is classified based on their HA and NA proteins into 19 HA and 11 NA subtypes, respectively (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The HA, NA, and M proteins are expressed on the surface of the viral particles. The NP wraps the viral genome to form the viral nucleoprotein (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The HA epitopes proved to trigger high neutralizing antibodies in the infected/vaccinated host (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The HA sequence is prone to frequent changes driven by antigenic shift and drift which enable the virus to evade the host immune response (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The NA protein play several key roles in influenza virus replication, pathogenesis, and immune evasion as well (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The AIV-NP also plays an important role in the suppression of the host immune response through the activation of the mitophagy pathways in the infected cells (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The matrix protein of the IVA consists of M1 and M2 proteins. Both proteins play important roles in the immune response/evasion against the IVA in the host. The M2 protein plays an essential role in viral immune evasion by modulating the autophagy pathways in the infected cell through the prevention of the fusion between the autophagosome and the lysosome, which augments the viral immune evasion strategies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). There are several approaches for the preparation of AIV vaccines, including live attenuated, inactivated, recombinant, and DNA vaccines. Each type of vaccine has advantages and disadvantages. DNA vaccines for the AIV hold great promises, especially these days, for several reasons (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The cost of production of the DNA vaccine is very cheap compared to other types of vaccines, the possibility of upgrading the DNA vaccines to match any changes in the viral genetic materials and in case of the emergence compared to the other types of vaccines, the mass production of the DNA vaccine in a remarkable short time, and the stability of the DNA vaccines (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, one of the major concerns of the DNA vaccines is the delivery methods and their duration of actions in the vaccinated hosts. Several approaches have been recently adapted to prolong the actions of the DNA vaccines and to protect them from the actions of the host DNase enzymes. Several approaches have been developed to improve the quality of the DNA vaccines and to prolong their actions, including the encapsulation with various types of nanoparticles, particularly lipid nanoparticles and chitosan. The incorporation of the IVA DNA vaccine against the M protein with chitosan administered intranasally produced a prolonged immune response in mice (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Encapsulation of the DNA vaccine with lipid nanoparticles enhanced the immune response of the vaccinated pigs against the H1N1 virus infection (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In the present study, we designed a multiepitope DNA-based vaccine including the top-ranked B cell and T cell epitopes within the four major proteins (HA, NA, NP, and M2) of the H5N1 clade 2.3.4.4b. The in silico immune simulation of the designed vaccine showed promising results in the induction of robust immune response in the vaccinated birds against this clade of the AIV in birds. However, these studies require further experimental validation using these vaccines in chickens and other birds, such as turkeys.\u003c/p\u003e"},{"header":"2- Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Retrieval of the H5N1 clade 2.3.4.4b protein sequences\u003c/h2\u003e \u003cp\u003eA total of 279 isolate sequences belong to the H5N1 clade 2.3.4.4b, including four major viral proteins (hemagglutinin (HA), nucleoprotein (NP), neuraminidase (NA), and the matrix protein (M2)) were retrieved from the National Center for Biotechnology (NCBI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/protein\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/protein\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These sequences include (chickens\u0026thinsp;=\u0026thinsp;115, ducks\u0026thinsp;=\u0026thinsp;40, turkeys\u0026thinsp;=\u0026thinsp;30, migratory birds\u0026thinsp;=\u0026thinsp;43 (including Red-tailed hawk, Peregrine falcon, American wigeon, and Backyard bird), and Canadian geese\u0026thinsp;=\u0026thinsp;51). The Supplementary Excel Files 1\u0026ndash;4 presents information about these sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2. The multiple sequence alignment (MSA)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe MSA per each protein was conducted independently using Geneious software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneious.com/\u003c/span\u003e\u003cspan address=\"https://www.geneious.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the crustal Omega server tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/jdispatcher/msa/clustalo\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/jdispatcher/msa/clustalo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The highly conserved consensus sequences per each protein showing 100% identity were further considered for the epitope mapping.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3. Mapping B cell epitopes within the avian H5N1 clade 2.3.4.4b major proteins (HA, NA, NP, and M2).\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Prediction of the linear B-cell epitopes\u003c/h2\u003e \u003cp\u003eTo map the B cell epitopes from the generated consensus sequences of the four proteins (HA, NA, NP, and M2), we used the BCPREDS (BepiPred 2.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://services.healthtech.dtu.dk/services/BepiPred-2.0/\u003c/span\u003e\u003cspan address=\"http://services.healthtech.dtu.dk/services/BepiPred-2.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the IEDB analysis resource server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/bcell/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/bcell/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as described (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We adjusted the length of the target epitopes to 20 mers. The identified epitopes were further filtered based on their (antigenicity, allergenicity, toxicity, and solubility) as previously described (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Prediction of the discontinuous/ conformational B-cell epitopes\u003c/h2\u003e \u003cp\u003eThe Discontinuous/Conformational epitopes were predicted using the Ellipro server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/ellipro/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/ellipro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The parameter was set at 0.5 for the minimum score and 6 \u0026Aring; for the maximum distance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This method is based on the protein antigen\u0026rsquo;s 3D structure, solvent accessibility, and flexibility. The Chimera software was used to display the position of predicted epitope clusters on 3D structures of all the structural proteins.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4. Mapping of the T-lymphocyte epitopes within the avian H5N1 clade 2.3.4.4b major proteins (HA, NA, NP, and M2).\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Prediction of the Cytotoxic T-lymphocyte epitopes (MHC class I molecules)\u003c/h2\u003e \u003cp\u003eThe IEDB server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/main/tcell\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/main/tcell\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the cytotoxic T-lymphocytes (CTL) and the Helper T-lymphocytes epitopes that bind to MHC-I and MHC-II, respectively. The epitope binding predictor NetMHCpan 4.1 BA (version 2023.09) was used to sort the peptides by IC50 value for the epitope prediction (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The source species were entered as humans with peptide lengths of (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and associated human alleles (HLA-A, HLB-B, and HLA-C). The mapped epitope list was filtered according to their percentile rank and IC50 value. It was then evaluated for their antigenic, non-allergic, non-toxic, and solubility characteristics with further refinement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Prediction of the helper T-lymphocyte epitopes (MHC class II molecules)\u003c/h2\u003e \u003cp\u003eWe used the IEDB analysis tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tools.iedb.org/mhcii/\u003c/span\u003e\u003cspan address=\"http://tools.iedb.org/mhcii/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict the MHC class II binding molecules using NetMHCIIpan 4.1 BA (recommended binding predictor: 2023.09), using the human allele (HLA -DP, HLA-DQ, and HLA-DR) as a selective species based on percentile rank and IC50 value (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Following refinement of their antigenic, non-allergic, non-toxic, and solubility properties, the filtered epitopes were assessed and chosen following a percentile rank score of less than 10. It was then evaluated for antigenic, non-allergic, non-toxic, and solubility characteristics with further filtration to be utilized in the designed vaccine construct. The reference sequence of each of the four proteins (HA, NP, NA, and M2) of H5N1 clade 2.3.4.4b was analyzed against the human alleles (HLA-DR, DQ, DP) using IEDB MHC-II binding prediction tools with a percentile rank of (less than or equal to 10). A large number of epitopes were obtained as a result of the four proteins, and the epitopes were initially filtered based on half minimal inhibitory concentration (IC50) and percentile rank followed by filtering out on Allergenicity, antigenicity, non-toxic, and solubility. Here, to predict the MHC class II molecules for the chicken alleles (Gaga BLB1 \u0026amp; Gaga BLB2), we used mixmhc2pred.gfellerlab.org server tool, where the single fragment of amino acids with the length of 15mer was provided as input and predicted the results based on percentile rank.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.5. Molecular docking and analysis of the binding interaction between the predicted T-cell epitopes with chicken MHC-I and MHC-II alleles\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 3D structures of shortlisted CTL and HTL epitopes were modeled using the PEP-FOLD3 (De novo peptide structure prediction) server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::PEP-FOLD3\u003c/span\u003e\u003cspan address=\"https://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::PEP-FOLD3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using sOPEP energy function to cluster peptide conformation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The sequences of chicken MHC alleles (BL, BF) were retrieved either from the Protein Data Bank (PDB) or the Uniprot and are generated using the Biovia discovery studio (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The molecular docking of the selected CTL and HTL epitopes with their respective MHC alleles was performed using the HADdock (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdock.phys.hust.edu.cn/\u003c/span\u003e\u003cspan address=\"http://hdock.phys.hust.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The binding interactions and key residue contact were visualized and further evaluated using the PDBsum server (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The selected epitopes were further filtered based on their docking binding affinity and confidence score and processed to design the vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Assembly of the multi-epitope using the top-ranked epitopes\u003c/h2\u003e \u003cp\u003eThe primary arrangements of the vaccine sequence were done by fusing the B cell and T cell predicted epitopes that were filtered out based on the predicted (antigenic, non-allergic, non-toxic, and good solubility) of the selected epitopes. The top-ranked B and T cell epitopes were linked using KK, GPGPG, and AAY as linkers. The C-terminal ends of the vaccine construct were linked with chicken IL-18 (Accession No. CAB96214) as an adjuvant after separation with the EAAAK linker. Moreover, the sequence was provided with 6\u0026times;His-tag (H) attached to the C-terminus for purification and identification of the vaccine upon expression (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Codon Optimization and in-silico cloning of the multi-epitope vaccine construct.\u003c/h2\u003e \u003cp\u003eThe multiepitope vaccine was optimized and cloned into the expression vector to ensure the potential effective cloning. Hence, the reverse translation of the vaccine protein sequences into a respective DNA sequence was performed using the Vector builder software ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en.vectorbuilder.com/tool/codon-optimization.html\u003c/span\u003e\u003cspan address=\"https://en.vectorbuilder.com/tool/codon-optimization.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The codon adaptive index (CAI) value and the GC content of the multi-epitope construct were also calculated as described previously (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The restriction enzyme sequences \u003cem\u003eBamHI\u003c/em\u003e and \u003cem\u003eEcoRI\u003c/em\u003e were added at the DNA's 3\u0026rsquo; and 5\u0026rsquo; ends, respectively. Along with this, the Kozak sequence was added to ensure efficient translational initiation in eukaryotic expression systems, which surround the starting codon. The restriction cloning module from Snapgene V.6.0.2 software was used to incorporate the multi-epitope construct into the pET28a(+) plasmids using the indicated restriction enzyme sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Assessment of the physiochemical properties of the designed multiepitope H5N1 clade 2.3.4.4b-DNA vaccine\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the designed protein were assessed using the Protparam server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The potent antigenicity of selected proteins was predicted by using VaxiJen v2.0 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\u003c/span\u003e\u003cspan address=\"http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) with a default threshold of 0.4. The allergenicity and toxicity of proteins were assessed by using AllerTOP v.2.1 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ddg-\u003c/span\u003e\u003cspan address=\"http://ddg-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e pharmfac.net/AllerTOP/) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and ToxinPred2 server (webs.iiitd.edu.in/raghava/toxinpred2/index.html) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), respectively. The same server was later used to assess the physiochemical, antigenicity, allergenicity, and toxicity properties of the selection of epitopes as well as for the designed vaccine construct. The solubility nature of the proteins, epitopes, and final vaccine construct was analyzed using the Innovagen solubility check server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://innovagen.com/proteomics-tools\u003c/span\u003e\u003cspan address=\"https://innovagen.com/proteomics-tools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Prediction of the secondary and tertiary structures of the designed multi-epitope vaccine\u003c/h2\u003e \u003cp\u003eThe secondary structure, topology, folds, and domain organization of the construct were predicted using the PDBsum server tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The tertiary structure was predicted using vaccine sequence and modeled using Biovia Discovery Studio. Additionally, the ProSA server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prosa.services.came.sbg.ac.at/prosa.php\u003c/span\u003e\u003cspan address=\"https://prosa.services.came.sbg.ac.at/prosa.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to determine the total number of residues in the multi-epitope vaccine construct. The stability was analyzed and compared through the Ramachandran plot from both Biovia Discovery Studio as well as the PDBSum server tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Molecular docking of the designed multi-epitope vaccine construct with the chicken Toll-like receptors (TLRs)\u003c/h2\u003e \u003cp\u003eWe used TLR3 and TLR7 for the molecular docking analysis with the designed vaccine construct. Hence, the full-length protein sequence of chicken TLR3 (UniProt ID: 015455) and the chicken TLR7 (UniProt ID: Q9NYK1) were retrieved from Uniprot, and their respective structure was modeled using both AlphaFold collab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb\u003c/span\u003e\u003cspan address=\"https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Biovia discovery studio for better confirmation of the structure. The active binding sites were anticipated before the docking stage because it is crucial for greater binding affinity, and the docking study between the vaccine design and TLR3/TLR7 was performed through the Biovia discovery studio using ZDocker. Subsequently, a detailed analysis of the binding interfaces to characterize the molecular interfaces was performed using the PDBsum server tool. Analysis of protein-protein interaction was carried out through the Zdocker in the Biovia Discovery studio (v22.1.021297). For the best docking analysis, several modifications should be made, including removing the water molecules, and adding hydrogens, and minimizing the energy (CharmM). Out of the generated 10 poses per docking reaction, we selected the best pose having the higher binding energy between the target protein and its receptor as per the default of the molecular docking analysis as described in other studies (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.11. In-silico immune simulation of the designed multiepitope H5N1 clade 2.3.4.4b-DNA vaccine\u003c/h2\u003e \u003cp\u003eTo Predict the immune response activation in response to the designed multiepitope vaccine design, the in-silico immune simulation was performed using the C-ImmSim server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://150.146.2.1/C-IMMSIM/index.php\u003c/span\u003e\u003cspan address=\"https://150.146.2.1/C-IMMSIM/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The C-ImmSim server simulates the influence of vaccine construct on B and T lymphocytes by modeling the immune system of chicken's three major immune organs (bone marrow, thymus, and spleen). The parameters were set as a default with 50 and 1000 simulation steps. We are proposing the administration of the designed multiepitope vaccine construct three times using 4-week intervals. During simulation, each step indicates eight hours of real-time with periods of 1, 84, and 168 hours. Subsequently, this server also predicts the host cellular immune response and cytokine expression levels induced by the multi-epitope vaccine candidates in silico.\u003c/p\u003e \u003c/div\u003e"},{"header":"3- Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Results of the prediction of the B cell epitopes (linear and discontinuous) within the major proteins of the H5N1 clade 2.3.4.4b\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe B-cell epitopes were analyzed from various structural proteins (HA, NA, NP, and M2) of the H5N1 clade 2.3.4.4b virus by utilizing the IEDB and BCpred server tool. We compared the results of those two servers in the prediction of the B cell epitopes using a threshold value of 0.75. Epitopes with a threshold value of 0.75 are more likely to have a higher peptide score. IEDB server tool results showing the number of epitopes were ((HA\u0026thinsp;=\u0026thinsp;20), (NA\u0026thinsp;=\u0026thinsp;14), (NP\u0026thinsp;=\u0026thinsp;17, and M2\u0026thinsp;=\u0026thinsp;3). Results from the BCpred server tool showing the number of the predicted epitopes are as follows, (HA\u0026thinsp;=\u0026thinsp;442), (NA\u0026thinsp;=\u0026thinsp;449), (NP\u0026thinsp;=\u0026thinsp;419) and M2\u0026thinsp;=\u0026thinsp;52). Among these peptides, the top-ranked B cell epitopes are selected based on overlapping results from those two methods, taking into consideration the epitopes showing high antigenic score values, as shown in (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of the top-ranked antigenic B cell epitopes across the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their information\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eStarting position\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEpitope prediction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAntigen/non-antigen property\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIEDB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBCpred\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eHA protein\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKKNDAYPTIKISYNNTNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKKNDAYPTIKISYNNTNRED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTLNQRLAPKIATRSQVNGQRGINSSMPFHNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNQRLAPKIATRSQVNGQRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNSPLREKRRKR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATGLRNSPLREKRRKRGLFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTKRSYEQMETGGERQNATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGTKRSYEQMETGGERQNATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGINDRNFWRGENGRRTRIAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNFWRGENGRRTRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSFIRGTRVVPRGQLSTERAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRGTRVVPRGQLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWVSHSIQTGNQYQPEPCNQS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQTGNQYQPEPCNQS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNGIITDTIKSWRNNILRTQE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDTIKSWRNNILRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSSNGAYGVKGFSFKYGNGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGNGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2 protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVETPTKNEWECNCSDSSDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEVETPTKNEWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKYGLKGGPSTEGVPESMREE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKYGLKGGPSTEGVPES\u003c/strong\u003eMREEYRQEQQSAVDVDDGHFV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMREEYRQEQQSAVDVDDGHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKYGLKGGPSTEGVPES\u003cstrong\u003eMREEYRQEQQSAVDVDDGHFV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eResults from the Ellipro server to predict the discontinuous epitopes from the 3D structure of respective proteins we considered with minimum score of 0.5 and minimum distance of 6 Ǻ. The list of the predicted discontinuous B cell epitopes was recognized at different exposed surface areas are shown in (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The position of each predicted epitope on the surface of 3D structure of all the considered proteins of H5N1 clade 2.3.4.4b could be visualized using Chimera visualization tool.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eList of the structure-based prediction of the discontinuous B cell epitopes across the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their information\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003ePredicted Discontinuous Epitope(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeptide\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo of residues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:I505, A:C506, A:I507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:E2, A:N3, A:I4, A:V5, A:L6, A:L7, A:L8, A:A9, A:I10, A:V11, A:S12, A:L13, A:V14, A:K15, A:S16, A:D17, A:D405, A:K406, A:V407, A:R408, A:L409, A:Q410, A:L411, A:R412, A:D413, A:N414, A:A415, A:E424, A:F425, A:Y426, A:H427, A:K428, A:C429, A:D430, A:N431, A:E432, A:C433, A:M434, A:E435, A:S436, A:V437, A:R438, A:N439, A:G440, A:T441, A:Y442, A:D443, A:Y444, A:P445, A:Q446, A:Y447, A:S448, A:E449, A:E450, A:A451, A:R452, A:L453, A:K454, A:R455, A:E456, A:E457, A:I458, A:S459, A:G460, A:V461, A:K462, A:L463, A:E464, A:S465, A:V466, A:G467, A:T468, A:Y469, A:Q470, A:I471, A:L472, A:S473, A:I474, A:S476, A:T477, A:A478, A:A479, A:S480, A:S481, A:L482, A:A483, A:L484, A:A485, A:I486, A:M487, A:M488, A:A489, A:G490, A:L491, A:S492, A:L493, A:W494, A:M495, A:C496, A:S497, A:N498, A:G499, A:S500, A:L501, A:Q502, A:C503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:K177, A:I178, A:S179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:L105, A:C106, A:Y107, A:P108, A:G109, A:F127, A:E128, A:K129, A:I130, A:L131, A:I132, A:I133, A:P134, A:K135, A:S136, A:S137, A:W138, A:P139, A:N140, A:H141, A:E142, A:T143, A:S144, A:L145, A:G146, A:V147, A:S148, A:A149, A:A150, A:C151, A:P152, A:G155, A:A156, A:P157, A:S158, A:F159, A:F160, A:V163, A:V164, A:W165, A:L166, A:I167, A:K168, A:K169, A:N170, A:D171, A:A172, A:Y173, A:P174, A:T175, A:I176, A:Y180, A:N181, A:N182, A:T183, A:N184, A:E186, A:D187, A:L188, A:L189, A:W192, A:G193, A:I194, A:H195, A:H196, A:S197, A:N198, A:N199, A:A200, A:E201, A:E202, A:Q203, A:T204, A:N205, A:L206, A:Y207, A:K208, A:N209, A:P210, A:T211, A:T212, A:Y213, A:I214, A:S215, A:V216, A:G217, A:T218, A:S219, A:T220, A:L221, A:N222, A:Q223, A:R224, A:L225, A:A226, A:P227, A:K228, A:I229, A:A230, A:T231, A:R232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:N357, A:L358, A:I362, A:N364, A:L365, A:K368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:N357, A:L358, A:I362, A:N364, A:L365, A:K368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:N313, A:E314, A:Q315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:G286, A:L287, A:F288, A:G289, A:A290, A:I291, A:A292, A:G293, A:F294, A:I295, A:E296, A:G297, A:G298, A:W299, A:M302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:D70, A:G79, A:N80, A:P81, A:M82, A:D84, A:I87, A:N100, A:P101, A:A102, A:N103, A:Y153, A:Q154, A:R161, A:S233, A:Q234, A:V235, A:N236, A:G237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:R99, A:D101, A:G102, A:K103, A:W104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:R8, A:S9, A:E11, A:Q12, A:E14, A:T15, A:G16, A:G17, A:E18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:G200, A:I201, A:N202, A:D203, A:N205, A:F206, A:W207, A:R208, A:G209, A:E210, A:N211, A:G212, A:R213, A:R214, A:T215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:D420, A:M421, A:S422, A:N423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:M1, A:A2, A:S3, A:Q4, A:G5, A:T6, A:K7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:G402, A:V403, A:F404, A:E405, A:L406, A:T407, A:D408, A:E409, A:K410, A:A411, A:T412, A:N413, A:P414, A:I415, A:V416, A:P417, A:S418, A:F419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:R216, A:I217, A:E220, A:T232, A:A233, A:A234, A:A237, A:D240, A:Q241, A:R243, A:E244, A:S245, A:N247, A:P248, A:G249, A:N250, A:A251, A:E252, A:E254, A:I265, A:R348, A:G349, A:T350, A:V352, A:V353, A:P354, A:G356, A:Q357, A:L358, A:S359, A:T360, A:E361, A:A363, A:T364, A:I365, A:M366, A:A367, A:A368, A:F369, A:T370, A:G371, A:N372, A:T373, A:E374, A:G375, A:R376, A:T377, A:S378, A:D379, A:M380, A:R381, A:T382, A:E383, A:I384, A:I385, A:R386, A:M387, A:M388, A:E389, A:N390, A:A391, A:R392, A:P393, A:E394, A:D395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:Q42, A:T45, A:E46, A:L47, A:K48, A:L49, A:S50, A:D51, A:Y52, A:E53, A:R55, A:F71, A:D72, A:N76, A:K77, A:Y78, A:L79, A:E80, A:E81, A:H82, A:P83, A:S84, A:A85, A:G86, A:K87, A:D88, A:P89, A:K90, A:K91, A:R98, A:R106, A:E107, A:L108, A:I109, A:L110, A:Y111, A:D112, A:K113, A:E114, A:E115, A:R117, A:R118, A:I119, A:Q122, A:S310, A:Q311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:Q45, A:P46, A:E47, A:P48, A:C49, A:N50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:M1, A:N2, A:P3, A:N4, A:Q5, A:K6, A:I7, A:T8, A:T9, A:I10, A:G11, A:S12, A:I13, A:C14, A:M15, A:V16, A:I17, A:G18, A:I19, A:V20, A:S21, A:L22, A:M23, A:L24, A:Q25, A:I26, A:G27, A:N28, A:I29, A:I30, A:S31, A:I32, A:W33, A:V34, A:S35, A:H36, A:S37, A:I38, A:Q39, A:T40, A:G41, A:N42, A:Q43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:E57, A:N58, A:N59, A:T60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:Q51, A:S52, A:I53, A:I54, A:T55, A:Y56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:V62, A:N63, A:Q64, A:T65, A:Y66, A:V67, A:N68, A:I69, A:S70, A:N71, A:T72, A:N73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:L140, A:N141, A:D142, A:K143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:I108, A:G109, A:S110, A:K111, A:G112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:G105, A:H144, A:S145, A:N146, A:G147, A:T148, A:V149, A:K150, A:I427, A:G429, A:R430, A:P431, A:K432, A:E433, A:N434, A:T435, A:I436, A:T438, A:D459, A:G460, A:A461, A:L463, A:P464, A:F465, A:T466, A:I467, A:D468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:S2, A:L3, A:L4, A:T5, A:E6, A:V7, A:E8, A:T9, A:P10, A:T11, A:K12, A:N13, A:E14, A:E16, A:N18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:A83, A:V84, A:D85, A:V86, A:D87, A:D88, A:G89, A:H90, A:F91, A:V92, A:N93, A:I94, A:E95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:G61, A:G62, A:P63, A:S64, A:T65, A:E66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA:S20, A:D21, A:S22, A:S23, A:D24, A:P25, A:L26, A:A29, A:A30, A:I33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Results of the prediction of the cytotoxic T lymphocyte epitopes (MHC class I molecules) within the major proteins of the H5N1 clade 2.3.4.4b\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the predicted MHC class I epitopes with the binding affinity (IC50; IC50\u0026thinsp;\u0026lt;\u0026thinsp;50 nM). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e also shows the parameters of the top-ranked epitopes, taking into consideration the allergenicity, antigenicity, non-toxic, and solubility per each listed epitope.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eList of the predicted MHC class I epitopes of the H5N1 clade 2.3.4.4b proteins (HA, NA, NP, and M2) and their relevant information (IC50 value, Percentile Ranks, and Allele-specification).\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eMHC class I molecules\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChicken Allele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeptide\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIC50\u0026thinsp;\u0026lt;\u0026thinsp;50nM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePer rank %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAntigen/Non Antigen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllergic/Non allergic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eToxin/non-toxin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSolubility\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"27\" align=\"left\"\u003e\n \u003cp\u003eBF2*2101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTLNQRLAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1473\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*02:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRLKREEISGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9344\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTQFEAVGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2894\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B*40:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREEISGVKL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6846\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*02:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYIVERANPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7800\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNTQFEAVGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1615\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGQRGINSSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0202\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLNQRLAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1779\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*30:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKVRLQLRDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5926\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMNTQFEAVGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1615\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGQRGINSSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0202\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLNQRLAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1779\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-C*16:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATYQRTRAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5864\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*33:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLRVSSFIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7704\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*02:06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFQGRGVFEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2783\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVFELTDEK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1503\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-C*12:03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIAYERMCNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9843\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B*07:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDPKKTGGPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6982\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNATEIRASV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4532\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLNDATYQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6676\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*30:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRTRALVRTGM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5749\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*30:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTERATIMAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4494\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVASGYDFER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8489\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYPDAGDIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4201\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*68:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFISCSHLECR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0798\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-B*44:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVETPTKNEW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6266\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-A*30:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVYRRLKYGLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2596\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Results of the prediction of the Helper T lymphocyte epitopes prediction within the major proteins of the H5N1 clade 2.3.4.4b\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the list of the top-ranked epitopes that recognize the T helper lymphocytes and their parameters. Our results show the predicted epitopes per each protein (HA\u0026thinsp;=\u0026thinsp;13, NP\u0026thinsp;=\u0026thinsp;21, NA\u0026thinsp;=\u0026thinsp;2, and M2\u0026thinsp;=\u0026thinsp;9), respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eList of the predicted MHC class II of Binding Epitopes within different structural (HA, NP, NA \u0026amp; M2) proteins of H5N1 clade 2.3.4.4b showing their IC50 value, Percentile Ranks, and allele-specific interactions.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"10\" align=\"left\"\u003e\n \u003cp\u003eMHC class II molecules\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChicken Allele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeptide\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIC50\u0026thinsp;\u0026lt;\u0026thinsp;50nM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePer rank %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAntigen/Non Antigen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAllergic/Non allergic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eToxin/non-toxin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSolubility\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"12\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"45\" align=\"left\"\u003e\n \u003cp\u003e*Gaga_BLB1\u003c/p\u003e\n \u003cp\u003e*Gaga_BLB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRVPEWSYIVERANPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7022\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLIKKNDAYPTIKIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9804\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATYQRTRALVRTGMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4153\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAELLVLMENERTLDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0504\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELLVLMENERTLDFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0452\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIKKNDAYPTIKISY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0760\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNVVWLIKKNDAYPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2023\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTIKISYNNTNREDLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7852\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEWSYIVERANPAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7539\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFRNVVWLIKKNDAYP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1509\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAYPTIKISYNNTNRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8365\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTIKISYNNTNREDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7790\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMELIRMIKRGINDRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5862\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAEIEDLIFLARSALI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8823\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATYQRTRALVRTGMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4153\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDLIFLARSALILRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7376\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEIEDLIFLARSALIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9266\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRMCSLMQGSTLPRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4574\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDATYQRTRALVRTGM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5614\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMCSLMQGSTLPRRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5336\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRFYIQMCTELKLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4565\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDPRMCSLMQGSTLPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4614\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*05:01/DQB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRRSGAAGAAVKGVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9345\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*05:01/DQB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPRRSGAAGAAVKGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8733\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSFIRGTRVVPRGQL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5929\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*04:01/DQB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARSALILRGSVAHKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6766\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSALILRGSVAHKSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6269\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*05:01/DQB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLPRRSGAAGAAVKG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8370\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*04:01/DQB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSALILRGSVAHKSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6269\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DPA1*03:01/DPB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRRTRIAYERMCNIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6312\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVGTMVMELIRMIKRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4815\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFEDLRVSSFIRGTRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8472\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DQA1*05:01/DQB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPRRSGAAGAAVKGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8733\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSFIRGTRVVPRGQL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5929\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB3*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWAIYSKDNGIRIGSK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9819\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSFKYGNGVWIGRTKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2583\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVETPTKNEW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6266\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVYRRLKYGLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2596\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRLFFKCVYRRLKYG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4858\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSFKYGNGVWIGRTKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2583\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCVYRRLKYGLKGGPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1811\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB3*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRLFFKCVYRRLKYG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4858\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB3*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKCVYRRLKYGLKGGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9916\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQQSAVDVDDGHFVNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0804\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQSAVDVDDGHFVNIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1815\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable NON-ALLERGEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon Toxin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Evaluation of the antigenicity, allergenicity, and toxicity of the predicted MHC I and MHC II epitopes within the major proteins of the H5N1 clade 2.3.4.4b (HA, NA, NP, M2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis shows a large number of epitopes were identified; we then filtered and ranked these epitopes based on (percentile rank\u0026thinsp;\u0026lt;\u0026thinsp;4 and their IC50 value\u0026thinsp;\u0026lt;\u0026thinsp;50nM) in the case of the MHC class I (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Our filtration criteria was mainly based on the percentile rank\u0026thinsp;\u0026lt;\u0026thinsp;10 in the case of the MHC class II of molecules (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. The top-ranked antigenic epitopes shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e per each class of the MHC molecules were further evaluated for their potential allergenicity and toxicity, as shown in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Finally, epitopes showing better solubility and stability were considered and ranked, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Results of the molecular docking of the selected MHC classes (I and II) epitopes with the chicken alleles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe molecular docking analysis was performed by docking MHC class I and II class of molecules with chicken alleles (BF2*2101 \u0026ndash; for MHC class I and Gaga_BLB1 \u0026amp; Gaga_BLB2 \u0026ndash; MHC class II) using the HADdock server tool using peptide-binding groove affinity. We used the chicken alleles as receptors, and the MHC class I and MHC class II peptides listed in Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, were considered as ligands. Results show the binding affinity and confidence score as listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The top-ranked epitopes showing the highest binding affinity score were chosen for the design of the final vaccine construct, as listed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe sequences and the relevant information of the top-ranked selected epitopes used for the construction of the multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStart\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeptide\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAntigen Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocking Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConfidence Score (\u0026gt;\u0026thinsp;0.8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eMHC class I of molecules\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKVRLQLRDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5926\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-188.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFQGRGVFEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2783\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-214.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFISCSHLECR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0798\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-214.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVYRRLKYGLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2596\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-178.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHC class II of molecules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRNVVWLIKKNDAYPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2023\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-263.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEIEDLIFLARSALIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9266\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-214.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSFKYGNGVWIGRTKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2583\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-255.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVYRRLKYGLKGGPST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2088\u0026nbsp;( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-249.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB cell epitopes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKKNDAYPTIKISYNNTNRED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1073( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"4\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSSNGAYGVKGFSFKYGNGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9688( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGINDRNFWRGENGRRTRIAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9417( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKYGLKGGPSTEGVPESMREE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8569( Probable\u0026nbsp;ANTIGEN\u0026nbsp;).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe interaction residues from those docking results of MHC class I and MHC class II molecules were analyzed using PDBsum server tool and are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7. The structure and design of the multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b spanning top-ranked epitopes within the four viral major proteins (HA, NA, NP, and M2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe designed the final vaccine construct by combining the top-ranked B-cell epitopes T- cell epitopes of both MHC I and MHC II class of molecules (filtered from high antigenic, non-allergic, non-toxic and good solubility, and with the better binding affinity score of the structural proteins (HA, NP, NA, and M2) of H5N1 clade 2.3.4.4b as listed in the Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The top-ranked B cell and T cell epitopes were linked by using the (KK, GPGPG, and AAY) as linkers, respectively, whereas the C-terminal ends of the vaccine construct were linked to the full-length chicken IL-18 gene (Accession No. CAB96214) as an adjuvant after separation with the PEAK linker (Fig.\u0026nbsp;3). Additionally, we incorporated the 6\u0026times;His-tag (HHHHHH) attached to the C-terminus for purification and identification of the vaccine upon expression.\u003c/p\u003e\n\u003cp\u003eThe final vaccine construct is designed as follows: (the B-cell epitopes are shown in purple, linked with KK; MHC-I T-cell epitopes are shown in green, linked with AAY; and MHC-II T-cell epitopes are shown in orange, linked with GPGPG. The linkers are shown in bold letters and underlined, and the adjuvant (IL18) is shown in red. MHCII is linked with Adjuvant using HEYGAEALERAG. The IL-18 adjuvant is linked with 6xHis tag using EAAAK).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8. Results of the physiochemical properties of the designed multiepitope DNA-based vaccine against the H5N1 clade 2.3.4.4b\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predicted vaccine weighed 49942.78 Dalton and possessed a theoretical isoelectric point of 9.04, indicating the alkaline nature of the constructed vaccine. The total number of negative and positively charged residues were 57 and 70, and the extinction coefficient measured at 280nm in water was shown to be 51395, assuming all pairs of Cys residues form cystines. The instability index (II) was about 36.74, showing the structure of the vaccine protein was stable. The aliphatic index was about 65.19, indicating the hydrophilic nature with a value of -0.596.\u003c/p\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9. Results of the secondary and tertiary structures of the designed vaccine construct\u003c/h2\u003e\n \u003cp\u003eThe secondary and tertiary structure of the multiepitope-based vaccine construct were analyzed and modeled through the PDBsum server tool and Biovia discovery studio.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.10. Results of the molecular docking of the designed vaccine construct with the chickens Toll-like receptors (TLR3 and TLR7)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo examine the potential immunogenic performance of a multiepitope-based vaccine construct combining four proteins (HA, NA, NP, and M2) of H5N1 clade 2.3.4.4b, the molecular docking studies were performed between the vaccine construct and Toll-like receptors (TLR3 and TLR7). As mentioned earlier, TLR3 and TLR7 were chosen among the ten toll-like receptors, as these intracellular receptors could trigger an innate immune response through several pathways. Initially, the sequence of TLR3 (Uniprot ID: QoPQ88) and TLR7 (Uniprot ID: C4PCM1) were retrieved from the database and modeled through Biovia discovery studio, followed by preparing the protein for the docking study by removing water molecules, adding hydrogen and performing energy minimization. Docking analysis was performed using the Zdocker, and the results obtained indicate the strong binding affinity between the vaccine construct and Toll-like receptors (TLR3 and TLR7). The best-ranked complexes with their respective ZDock score provide us with confirmation of the strong and stable interaction between them. The interaction residues, multiple hydrogen bonds, and hydrophobic bonds were analyzed through the PDBsum server tool. Figure\u0026nbsp;\u0026lt;link rid=\u0026quot;fig5\u0026quot;\u0026gt;\u003cspan class=\"InternalRef\"\u003e6\u0026lt;/link\u0026gt;\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cstrong\u003e(a)\u003c/strong\u003e shows the topology visualization of TLR3 and TLR7, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cstrong\u003e(b)\u003c/strong\u003e shows the docking interaction analysis between the vaccine construct and Toll-like receptors, and finally, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cstrong\u003e(c)\u003c/strong\u003e illustrates the interaction amino acid residues and formation of multiple hydrogen bonds, hydrophobic bonds, etc., through PDBsum server tool.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.11. In silico cloning of the H5N1 clade 2.3.4.4b multiepitope-based vaccine spanning key epitopes within the major proteins (HA, NA, NP, and M2) proteins.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHence, the vaccine construct was cloned using the Vector builder from the decoded amino acid sequence of each epitope\u0026apos;s respective DNA sequences to mimic the vaccine\u0026apos;s expression in the \u003cem\u003eE.coli\u003c/em\u003e K12 expression system. The GC content and codon adaptation index values generated by the vector builder server represent the level of expression in the \u003cem\u003eE.coli\u003c/em\u003e system. Finally, Snap gene software was used to clone the constructed vaccines in the pET-28a (+) expression vector between the restriction enzyme cutting locations of \u003cem\u003eBamHI\u003c/em\u003e and \u003cem\u003eEcoRI\u003c/em\u003e, and the results obtained are shown in \u003cstrong\u003eFig.\u0026nbsp;8\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.12. In silico immune simulation of the designed H5N1 clade 2.3.4.4b multiepitope-based vaccine spanning key epitopes within the (HA, NA, NP, and M2) proteins.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe predicted immune response of the constructed vaccine was analyzed through the interaction between the H5N1 clade 2.3.4.4b antigens and the B cell, T cell, and cytokines.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe HPAIV-H5N1 clade 2.3.4. 4b emerged in 2020 and continues to pose significant risks to the poultry industry and human health (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This clade has also been reported in many mammalian species, including dairy cows, mink, cats, foxes, and sea lions (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Several reports of the H5N1 clade 2.3.4.4b in birds have been recently reported in the USA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The viral infection with this clade in chicken resulted in high morbidity and mortality rates, which may reach up to 100%. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Chicken infection with this highly pathogenic AIV may require culling of the infected flock, which may have a devastating impact on the chicken meat and egg process. There is an urgent need to develop some effective vaccines that could protect chickens and other birds against this highly pathogenic emerging virus. The application of AI tools in vaccine design and development has grown in the past 5 years (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The AI tools, including epitope prediction, molecular docking, and simulation, paved the way for a remarkable short-term vaccine pipeline development for many viral diseases of domestic animals and birds (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral traditional methods were used for epitope mapping throughout some viral genomes. The application of monoclonal antibodies (MAbs) was used in the past and may still be in use as a conventional method for epitope mapping for the H5N1 for a decade. The MAbs approach requires the use of animals and is time-consuming and labor-intense (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This is in contrast to the application of AI in the prediction and simulation of protein/protein interactions, which are very efficient, fast, and have a high level of accuracy and precision. In silico cloning techniques and codon optimization were used to improve the expression and effectiveness of the candidate vaccines in the prokaryotic expression system.\u003c/p\u003e \u003cp\u003eIn the current study, we used several AI tools to design a multiepitope DNA-based vaccine against the currently circulating clade H5N1 2.3.4.4b in chickens. Further, the characteristic features such as antigenicity, allergenicity, and structural validation of the designed vaccine were analyzed, and in parallel, the molecular docking and in silico simulation provide the pathway for eliciting strong cellular and humoral immune responses. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Our approach for the design of the multiepitope DNA-based vaccine against the currently circulating H5N1 clade 2.3.4.4b includes several consecutive steps, including (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) retrieval of the sequences from the GenBank (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) multiple sequence alignment, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) generation of the consensus sequences per each protein, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) prediction of the B cell, T cell including MHC-Class (I and II), (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) selection of the top-ranked epitopes, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) construct the multiepitope using the appropriate linkers, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) incorporation of the IL18 to the vaccine construct, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) In silico cloning of the designed vaccine, (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), prediction of the physicochemical properties of the designed vaccine, (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) prediction of the secondary and tertiary structures of the designed vaccine, (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) molecular docking of the designed vaccine with the chickens TL3/TLR7, and (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) immunosimulation of the final vaccine construct to assess its potential potency in the activation of the humoral and cell-mediated immunity of chickens. Our prediction shows many potential epitopes per protein. We established some filtration criteria to select the top-ranked epitope per each category of immunogens. \u003cb\u003eFirst\u003c/b\u003e, we used the percentile score (\u0026lt;\u0026thinsp;4) for MHC class I molecules and (\u0026lt;\u0026thinsp;10) for MHC class II molecules with the IC50 value of (\u0026lt;\u0026thinsp;50nM). \u003cb\u003eSecond\u003c/b\u003e, the short-listed epitopes per each protein were examined for their allergenicity, antigenicity, non-toxic, and solubility profiles as previously described (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). \u003cb\u003eThird\u003c/b\u003e, we used VaxiJen 2.0 and AllerTop to assess the antigenic properties and allergic nature of each candidate epitope. The acceptable antigenic score range was established to be (0.4\u0026ndash;0.5). \u003cb\u003eFourth\u003c/b\u003e, we tested all the short-listed epitopes for potential toxicity using the ToxinPred server tools, as previously described (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). \u003cb\u003eSixth\u003c/b\u003e, the molecular docking analysis was performed between filtered epitopes and chicken alleles of MHC class I and MHC class II molecules through the HADdock docking tool. The top-ranked peptide was selected based on their binding score and high antigenic score for all structural genomes we considered for the study, for MHC class I of molecules \u0026ndash; KVRLQLRDNA (1.5926 and \u0026minus;\u0026thinsp;188.17 docking score \u0026ndash; HA), FQGRGVFEL (1.2783 and \u0026minus;\u0026thinsp;214.75 docking score \u0026ndash; NP), FISCSHLECR (1.0798 and \u0026minus;\u0026thinsp;214.75 docking score \u0026ndash; NA), VYRRLKYGLK (1.2596 and \u0026minus;\u0026thinsp;178.50 docking score \u0026ndash; M2) and for MHC class II of molecules \u0026ndash; RNVVWLIKKNDAYPT (1.2023 and docking score of -263.89- HA), EIEDLIFLARSALIL- (0.9266 and docking score of -214.79 - NP), SFKYGNGVWIGRTKS-(1.2583 and docking score of -255.61 \u0026ndash; NA) and VYRRLKYGLKGGPST \u0026ndash; (1.2088 and docking score of -249.52 \u0026ndash; M2) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). \u003cb\u003eFinally\u003c/b\u003e, these epitopes were used in the vaccine construct and were designed using linkers and adjuvants. The interaction residues between them were identified through PDBsum and were displayed in the figure, which results in multiple hydrogen bonds and hydrophobic bonds, especially to capture their better binding interactions.\u003c/p\u003e \u003cp\u003eOne of the challenges in this study is the lack of data about the epitopes interacting with the chicken MHC-I, and MHC-II is not yet available on the IEDB server. To overcome this problem, we used alternative strategies to try to identify epitopes activating chicken CTL and HTL. We applied the surrogate model approach using the well-known human alleles because there aren't many computational tools available, specifically for MHC class I molecules of most avian species, particularly chickens. Both the human and chicken alleles are very similar in their structural and functional properties, including the peptide-binding grooves, which enable the peptide-MHC class molecules binding interactions. We selected the human alleles that match the chicken alleles' structural and functional properties through the IEDB.org server and performed the prediction. The default parameter setting was kept the same as the polymerase length of 12mer. Despite the species-specific diversity of the chicken MHC class-I molecules from the BF2 locus, the experimental data found in the IEDB MHC class I molecules server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iedb.org/\u003c/span\u003e\u003cspan address=\"https://www.iedb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), have confirmed that specific BF2 alleles, like BF2 *2101 from previous studies, have similarities to human alleles (HLA \u0026ndash; A02:01), especially in the motif binding and anchor residue preferences.\u003c/p\u003e \u003cp\u003eRegarding the prediction of the helper T-lymphocyte epitopes (MHC class II molecules), we used the MixMHC2pred tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mixmhc2pred.gfellerlab.org/\u003c/span\u003e\u003cspan address=\"http://mixmhc2pred.gfellerlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to predict the corresponding chicken alleles (Gaga_BLB1_002_01, Gaga_BLB1_012_01, Gaga_BLB2_002_01, Gaga_BLB2_012_01, and Gaga_BLB2_012_02) for the selected list of epitopes. This approach successfully provided the best score data and matched the chicken alleles with the corresponding epitopes.\u003c/p\u003e \u003cp\u003eOur docking simulation results showed the firm binding affinities between the designed vaccine epitopes and the conjugated TLRs, facilitating effective immune recognition and the initiation of the robust immune response (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The Z-score identifies several high-affinity bindings poses in the molecular docking results of the designed multi-epitope-based vaccine construct of the H5N1 clade 2.3.4.4b. The high accuracy protein-protein docking resulted in the formation of multiple hydrogen bonds and hydrophobic interactions with Zdock score (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), Zrank score (-131.75) and E_Rdock score (-7.95511) for TLR3 and Zdock score (19.04), zrank score (-142.71) and E_Rdock score (-45.31) for TLR7. The PDBsum results showed the interaction residues were analyzed and the major interaction hydrogen bonds (Arg65 \u0026ndash; Thr419), (His 109 \u0026ndash; Cys261), (Ser133 \u0026ndash; Asp263) and (Lys331 \u0026ndash; Tyr310) for TLR3 with vaccine construct and the major interaction hydrogen bonds (SER550 \u0026ndash; Arg398), (Arg 186 \u0026ndash; Glu 408), (Arg 104 \u0026ndash; Val 411) and (Tyr 190 \u0026ndash; Glu 413) for TLR7 with the vaccine construct.\u003c/p\u003e \u003cp\u003eIn silico immune simulations using C-ImmSim provided critical insights about the potential immune responses elicited by the designed four structural proteins (HA, NP, NA, and M2) of the H5N1 clade 2.3.4.4b vaccine constructs. Our approach ensured that the vaccination candidates had reliable protein synthesis and effective translation using the optimized codons and computational tools such as vector builders and Snap gene (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe simulation results revealed robust activation of T-cell populations, including cytotoxic T cells and helper T cells, crucial for cellular and humoral immunity (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This comprehensive analysis demonstrated that the multi-epitope H5N1 vaccine constructs in this study would induce strong humoral and cell-mediated immunity that might play essential roles in protecting chickens and other species of birds against the currently circulating HPAI-H5N1 clade 2.3.4.4b.\u003c/p\u003e \u003cp\u003eBased on the data provided above, a high level of humeral immune response (immunoglobulin antibodies) and the other immune cells are expected after the administration of the candidate vaccines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). It also predicted the progression of the magnitude of the immune response with the progression of the time after administering these candidate vaccines (primary immune response).\u003c/p\u003e \u003cp\u003eWe think our designed multiepitope-based vaccine spanning the four major structural proteins (HA, NP, NA, and M2) of the H5N1 clade 2.3.4.4b will be effective in the protection of birds against the currently circulating clade of the H5N1 in chickens in the USA and other parts in the world. However, further studies are required to validate these vaccines in chickens.\u003c/p\u003e"},{"header":"5- Conclusions","content":"\u003cp\u003eWe successfully designed a multiepitope Pan-H5N1 clade 2.3.4.4b DNA-based vaccine spanning the top-ranked immunogenic, nonallergenic, and nontoxic epitopes. Twelve epitopes within the major proteins (HA, NA, NP, and M2), including (B cell, MHC-Class-I, and MHC-class-II). The T cell epitopes showed high binding affinities with the chicken alleles. We successfully made in silico cloning of these epitopes and linked them to the chicken IL-18. The designed vaccine construct showed high binding affinities to the chicken Toll-Like receptors 3 and 7. The designed vaccine construct showed high immunogenic potential in terms of the production of humoral and cell-mediated immunity in chickens using an immune simulation approach. We believe the designed vaccine in the current study will protect not only chickens but also other birds, such as turkeys, quails, pheasants, and wild birds, against the currently circulating HPAIV-H5N1 clade 2.3.4.4b.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were editorial board members of Scientific report, at the time of submission. This had no impact on the peer review process and the final decision.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePublisher Note\u003c/strong\u003e \u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was funded by a seed grant (PI: MGH) from Long Island University (Grant no: 36524) and funds from the USDA-NIFA Animal Health and Disease Research grant (NI24AHDRXXXXG066).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributions: ND: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. MK: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. AS: Data curation, Investigation, Methodology, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Resources. MC: Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Funding acquisition. MH: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Dylan Feldman and Muddapuram Deeksha Goud for their technical assistance in retrieving the protein sequences from GenBank.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBurrough, E. R. et al. Highly Pathogenic Avian Influenza A(H5N1) Clade 2.3.4.4b Virus Infection in Domestic Dairy Cattle and Cats, United States, 2024. \u003cem\u003eEmerg. Infect. Dis.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (7), 1335\u0026ndash;1343 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui, P. et al. Global dissemination of H5N1 influenza viruses bearing the clade 2.3.4.4b HA gene and biologic analysis of the ones detected in China. \u003cem\u003eEmerg. Microbes Infect.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), 1693\u0026ndash;1704 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePulit-Penaloza, J. A. et al. Highly pathogenic avian influenza A(H5N1) virus of clade 2.3.4.4b isolated from a human case in Chile causes fatal disease and transmits between co-housed ferrets. \u003cem\u003eEmerg. Microbes Infect.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 2332667 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeade, P. S. et al. Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City. \u003cem\u003eJ. Virol.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e (6), e0062624 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaoufi, E. et al. Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review. \u003cem\u003eInt. J. Pept. Res. Ther.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (2), 1155\u0026ndash;1163 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadonaite, B. et al. The structure of the influenza A virus genome. \u003cem\u003eNat. Microbiol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (11), 1781\u0026ndash;1789 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X. et al. Host Immune Response to Influenza A Virus Infection. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 320 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, B. et al. The nucleoprotein of influenza A virus inhibits the innate immune response by inducing mitophagy. \u003cem\u003eAutophagy\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (7), 1916\u0026ndash;1933 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R. et al. Influenza M2 protein regulates MAVS-mediated signaling pathway through interacting with MAVS and increasing ROS production. \u003cem\u003eAutophagy\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (7), 1163\u0026ndash;1181 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStachyra, A., Gora-Sochacka, A. \u0026amp; Sirko, A. DNA vaccines against influenza. \u003cem\u003eActa Biochim. Pol.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e (3), 515\u0026ndash;522 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawaengsak, C. et al. Intranasal chitosan-DNA vaccines that protect across influenza virus subtypes. \u003cem\u003eInt. J. Pharm.\u003c/em\u003e \u003cb\u003e473\u003c/b\u003e (1\u0026ndash;2), 113\u0026ndash;125 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, T. N. et al. Lipid nanoparticle-encapsulated DNA vaccine confers protection against swine and human-origin H1N1 influenza viruses. \u003cem\u003emSphere\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (8), e0028324 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Manzalawy, Y., Dobbs, D. \u0026amp; Honavar, V. Predicting linear B-cell epitopes using string kernels. \u003cem\u003eJ. Mol. Recognit.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (4), 243\u0026ndash;255 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClifford, J. N. et al. BepiPred-3.0: Improved B-cell epitope prediction using protein language models. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (12), e4497 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElshafei, S. O., Mahmoud, N. A. \u0026amp; Almofti, Y. A. Immunoinformatics, molecular docking and dynamics simulation approaches unveil a multi epitope-based potent peptide vaccine candidate against avian leukosis virus. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 2870 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwadelkareem, E. A. \u0026amp; Ali, S. A. Vaccine design of coronavirus spike (S) glycoprotein in chicken: immunoinformatics and computational approaches. \u003cem\u003eTransl Med. Commun.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (1), 13 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMugunthan, S. P., Venkatesan, D., Govindasamy, C., Selvaraj, D. \u0026amp; Harish, M. C. Systems approach to design multi-epitopic peptide vaccine candidate against fowl adenovirus structural proteins for Gallus gallus domesticus. \u003cem\u003eFront. Cell. Infect. Microbiol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1351303 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaupetit, J., Tuffery, P. \u0026amp; Derreumaux, P. A coarse-grained protein force field for folding and structure prediction. \u003cem\u003eProteins\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e (2), 394\u0026ndash;408 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e (W1), W296\u0026ndash;W303 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozakov, D. et al. How good is automated protein docking? \u003cem\u003eProteins\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (12), 2159\u0026ndash;2166 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozakov, D. et al. The ClusPro web server for protein-protein docking. \u003cem\u003eNat. Protoc.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (2), 255\u0026ndash;278 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaskowski, R. A., Jablonska, J., Pravda, L., Varekova, R. S. \u0026amp; Thornton, J. M. PDBsum: Structural summaries of PDB entries. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (1), 129\u0026ndash;134 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHung, L. H., Li, H. P., Lien, Y. Y., Wu, M. L. \u0026amp; Chaung, H. C. Adjuvant effects of chicken interleukin-18 in avian Newcastle disease vaccine. \u003cem\u003eVaccine\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (5), 1148\u0026ndash;1155 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, K. et al. Adjuvant effects of interleukin-18 in DNA vaccination against infectious bursal disease virus in chickens. \u003cem\u003eVaccine\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (14), 1799\u0026ndash;1805 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrote, A. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e ; (2005). 33(Web Server issue):W526\u0026ndash;W531 .\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoytchinova, I. A. \u0026amp; Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 4 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitrov, I., Flower, D. R. \u0026amp; Doytchinova, I. AllerTOP\u0026ndash;a server for in silico prediction of allergens. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (Suppl 6), S4 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma, N., Naorem, L. D., Jain, S. \u0026amp; Raghava, G. P. S. ToxinPred2: an improved method for predicting toxicity of proteins. \u003cem\u003eBrief. Bioinform\u003c/em\u003e ;\u003cb\u003e23\u003c/b\u003e(5). (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGuffin, L. J., Bryson, K. \u0026amp; Jones, D. T. The PSIPRED protein structure prediction server. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (4), 404\u0026ndash;405 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuraisamy, N. et al. Machine learning tools used for mapping some immunogenic epitopes within the major structural proteins of the bovine coronavirus (BCoV) and for the in silico design of the multiepitope-based vaccines. \u003cem\u003eFront. Vet. Sci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1468890 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, J. et al. Highly Pathogenic Avian Influenza Virus (H5N1) Clade 2.3.4.4b Introduced by Wild Birds, China, 2021. \u003cem\u003eEmerg. Infect. Dis.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (7), 1367\u0026ndash;1375 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, Z. et al. Clade 2.3.4.4b highly pathogenic avian influenza H5N1 viruses: knowns, unknowns, and challenges. \u003cem\u003eJ. Virol.\u003c/em\u003e :e0042425. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordes, L. et al. Highly Pathogenic Avian Influenza H5N1 Virus Infections in Wild Red Foxes (Vulpes vulpes) Show Neurotropism and Adaptive Virus Mutations. \u003cem\u003eMicrobiol. Spectr.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (1), e0286722 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeguia, M. et al. Highly pathogenic avian influenza A (H5N1) in marine mammals and seabirds in Peru. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (1), 5489 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren, C. J. et al. Assessment of Survival Kinetics for Emergent Highly Pathogenic Clade 2.3.4.4 H5Nx Avian Influenza Viruses. \u003cem\u003eViruses\u003c/em\u003e ;\u003cb\u003e16\u003c/b\u003e(6). (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnanya, Panchariya, D. C. et al. Vaccine design and development: Exploring the interface with computational biology and AI. \u003cem\u003eInt. Rev. Immunol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (6), 361\u0026ndash;380 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesta, J. S. et al. MHC class I-presented T cell epitopes identified by immunoproteomics analysis are targets for a cross reactive influenza-specific T cell response. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (11), e48484 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou, Y. et al. Prediction and identification of T cell epitopes in the H5N1 influenza virus nucleoprotein in chicken. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (6), e39344 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, L. et al. Minor differences in peptide presentation between chicken MHC class I molecules can explain differences in disease susceptibility. \u003cem\u003ebioRxiv\u003c/em\u003e 2022:2022.03.11.484051.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDashti, F. et al. A computational approach to design a multiepitope vaccine against H5N1 virus. \u003cem\u003eVirol. J.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 67 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur, B., Karnwal, A., Bansal, A. \u0026amp; Malik, T. An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus. \u003cem\u003eBiomed. Res. Int.\u003c/em\u003e \u003cb\u003e2024\u003c/b\u003e, 4066641 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFathollahi, M. et al. Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach. \u003cem\u003eJ. Biomol. Struct. Dyn.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (20), 10761\u0026ndash;10784 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSami, S. A. et al. Designing of a Multi-epitope Vaccine against the Structural Proteins of Marburg Virus Exploiting the Immunoinformatics Approach. \u003cem\u003eACS Omega\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e (47), 32043\u0026ndash;32071 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortazavi, B., Molaei, A. \u0026amp; Fard, N. A. Multi-epitopevaccines, from design to expression; an in silico approach. \u003cem\u003eHum. Immunol.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e (3), 110804 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. H., Wu, K. H. \u0026amp; Wu, H. P. Unraveling the Complexities of Toll-like Receptors: From Molecular Mechanisms to Clinical Applications. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e ;\u003cb\u003e25\u003c/b\u003e(9). (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuleman, M. et al. Immunoinformatic-based design of immune-boosting multiepitope subunit vaccines against monkeypox virus and validation through molecular dynamics and immune simulation. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1042997 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBashir, S., Ali Abd-elrahman, K., Hassan, A. \u0026amp; Almofti, M. A. Multi Epitope Based Peptide Vaccine against Marek\u0026iacute;s Disease Virus Serotype 1 Glycoprotein H and B. \u003cem\u003eAm. J. Microbiol. Res.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (4), 124\u0026ndash;139 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMugunthan, S. P. \u0026amp; Mani Chandra, H. A Computational Reverse Vaccinology Approach for the Design and Development of Multi-Epitopic Vaccine Against Avian Pathogen Mycoplasma gallisepticum. \u003cem\u003eFront. Vet. Sci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 721061 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Highly pathogenic avian influenza virus, H5N1, clade 2.3.4.4b, Epitope mapping, DNA vaccine, in silico prediction, molecular docking, IL8, TL3, TLR7","lastPublishedDoi":"10.21203/rs.3.rs-6711963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6711963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe presently used vaccines do not offer solid immunity/protection against the currently circulating strains of the H5N1 viruses. We aim to design a pan H5N1 vaccine that protects birds against the presently circulating clade 2.3.4.4b in chicken. We used AI tools, including epitope mapping, molecular docking, and immune simulation, to design a multiepitope DNA vaccine including the top-ranked B and T cell epitopes within four major proteins (HA, NA, NP, and M2) of the H%N1 clade 2.3.4.4b. We selected the top-ranked 12 epitopes and linked them together using linkers. The designed vaccine is linked to IL-18 as an adjuvant. The molecular docking results showed a high binding affinity of this vaccine construct with the chicken alleles. The immune simulation results showed that the designed vaccine has the potential to stimulate the host immune response, including antibody and cell-mediated immunity in chickens and other birds. We believe this vaccine is going to be a universal vaccine that offers good protection not only to chickens but also to different species of birds against the HPAI- H5N1 clade 2.3.4.4b. Further studies are required to validate this vaccine candidate in chickens.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence-Guided Design of some Pan-H5N1-clade 2.3.4.4b Mosaic DNA-based vaccines to combat the circulating HPAI in birds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 06:14:59","doi":"10.21203/rs.3.rs-6711963/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":"2260894c-6663-4032-af85-bde7ababb699","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48847019,"name":"Biological sciences/Immunology"},{"id":48847020,"name":"Biological sciences/Microbiology"},{"id":48847021,"name":"Biological sciences/Molecular biology"},{"id":48847022,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2025-05-23T07:08:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 06:14:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6711963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6711963","identity":"rs-6711963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}