Designing of peptide based multi-epitope vaccine construct against gallbladder cancer using immunoinformatics and computational approaches

Designing of peptide based multi-epitope vaccine construct against gallbladder cancer using immunoinformatics and computational approaches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Designing of peptide based multi-epitope vaccine construct against gallbladder cancer using immunoinformatics and computational approaches Mukhtar Ahmad Dar, Pawan Kumar, Prakash Kumar, Ashish Shrivastava, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1748441/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 Gallbladder cancer (GBC) is an aggressive and difficult to treat biliary tract carcinoma characterised by late presentation, poor prognosis, and a survival rate of <1 year. GBC is difficult to treat and the current treatments have yielded dismal outcomes. The aim of this study was to design a multi-epitope vaccine candidate against GBC using reverse vaccinology and immunoinformatics tools. Epitopes from three antigenic proteins (NT5E, ANPEP and MME) were used for designing the epitope vaccine. CTL, HTL and B-cell epitopes were predicted and screened on the basis of immunogenicity, antigenicity, allergenicity, toxicity and IFN-γ inducing properties. The selected epitopes were connected using linkers and suitable adjuvant for designing final vaccine construct. The physicochemical properties were analysed followed by 2D and 3D structure generation, refinement and validation. The binding affinity of vaccine construct with immune receptors (TLR2, 3 and 4) was assessed through molecular docking. The stability of the docked vaccine-receptor complexes were evaluated using 100ns MD simulations. The epitope vaccine showed an adequate antibody and cell mediated immune responses through in-silco immune simulation. Our vaccine construct demonstrated good solubility, stability, antigenicity, non-allergenicity and non-toxicity with potential to elicit strong immune responses. However, further experimental research is needed to validate the safety and efficacy of the designed vaccine. Immunoinformatics Vaccine Epitope Antigenicity TLR GBC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The global burden of cancer is increasing at a rapid pace with more than 19.3 million new incident cancers and 9.9 million deaths estimated in 2020. 1,2 Globally, Gallbladder cancer (GBC) is the 24 th most common cancer with incidence of 115949 new cases and 21 st leading cause of death with an estimated 84695 deaths in 2020. 1 GBC is the most frequently diagnosed biliary tract cancer with poor prognosis and high fatality rate. 3 The median survival of GBC is less than one year with overall five year survival rate ranging from 5-20%. 4-6 The poor survival of GBC is mostly associated with asymptomatic early stages consequently leading to late stage diagnosis on clinical presentation. More than 9% of GBC are diagnosed in advanced stages or metastatic phase. 7,8 Globally, the incidence of GBC varies with geographic locations and other concomitant risk factors. The world age standardized incidence and mortality rates (ASR) of GBC were 1.2 and 0.84 respectively. Data from Global Cancer Observatory-2020 suggests highest incidence associated with GBC in Asia (70.8%) followed by Europe (10.8%), South America (6.8%), Africa (4.7%) and Northern America (4.5%). 2,9 Among these GBC is highly prevalent in India, Pakistan, Japan, Korea, Chile, Ecuador, Bolivia, Czech Republic, Poland and Slovakia. 10 Studies have identified cholelithiasis and cholecystitis as major risk factors associated with development of GBC. 11-13 Additionally female gender and Salmonella infection are also recognized risk factors. GBC affects females 2-6 times more frequently than males. 5 The age standardized incidence rate of GBC is higher in females (1.8) as compared to male counterparts (0.97). 9 However the association of different risk factors with prognosis of GBC is poorly understood. Characterized by asymptomatic progression and lack of specific biomarkers for early detection, most of the GBC cancers present in advanced stages with aggressive tumour biology. Removal of gallbladder (surgical resection) is recognized as the best treatment plan for management of GBC. 14,15 However, following curative surgery the recurrence rate is high (35%) and prognosis remains relatively poor particularly in advanced disease. 16 Apart from surgery, chemotherapy with gemcitabine and oxaliplatin or gemcitabine and cisplatin are the mainstay of the chemotherapy. 17 The potential benefits of adjuvant radiation therapy in GBC is unclear due to lack of evidence regarding its impact on clinical outcomes and overall survival. 17 Despite the curative intent radical surgery and several combinations of cytotoxic drugs, the GBC has been difficult to treat and the results have been dismissal. As a result, the discovery of novel alternative therapeutic interventions for the treatment of GBC assumes critical importance. The overall survival, diagnosis, prophylactics, and prognosis in most common cancers have improved as a result of advances in drug discovery, diagnostics and screening technologies. However, because GBC is an uncommon malignancy, research into its diagnosis and therapy is very limited and there is currently no effective treatment available. Malignant cells evade immune recognition by escaping T lymphocytes, natural killer cells and exploiting the immunosuppressive tumour microenvironment. 18 Immunotherapies (immune check point inhibitors and monoclonal antibodies) have lately received a lot of traction as promising cancer therapeutic approaches. Vaccines with ability to stimulate the immune system to recognise and eliminate the cancer cells represent another novel and effective approach towards GBC treatment. 19-21 An effective vaccine can provide a long lasting immunity via immunological memory cells crucial for decreasing the incidence and mortality associated with GBC. Advanced ImmunoInformatics has revolutionized the field of immunological research and is becoming a powerful tool in designing and development of effective vaccines. 22 As immune response plays a crucial role in fighting cancer cells, ImmunoInformatics approaches allows for identification of the potential immunogenic and antigenic T and B cell epitopes that trigger a desired immune response. 23,24 Epitope based vaccines particularly peptide based multi-epitope vaccines provide uniquely designed cost and time effective therapeutic strategy capable of inducing simultaneous strong humoral and cellular immune response. 25 Recognizing the need for early diagnosis and treatment, researchers have identified several potential targets for detection and treatment of GBC. 19, 20, 26 Three proteins including 5ʹ-Nucleotidase isoform 2 (NT5E), Aminopeptidase N (ANPEP) and Membrane metallo-endopeptidase (MME) are reported to be over expressed in different stages of GBC. 12,27, 28 The over expression of these proteins in different cancers including GBC suppress T cells and promote cancer progression. 29 The aim of this study was to design a multi-epitope vaccine candidate against GBC based on NT5E, ANPEP and MME antigenic proteins using reverse vaccinology and immunoinformatics tools. To our knowledge, this is the first study using ImmunoInformatics/computational immunology approaches for designing peptide based multi-epitope vaccine candidate against GBC. Methods Identification of target proteins and sequence retrieval Based on the significance of NT5E, ANPEP and MME in cancer development, progression, metastasis and angiogenesis as well as reported over expression in different stages of GBC, these proteins were selected for epitope prediction and designing of potential vaccine candidate for controlling the progression of GBC. The sequences of selected proteins in FASTA format were retrieved from UniProtKB database for subsequent analysis. The physicochemical properties including stability and hydopathicity of the selected proteins were analysed in Protparam server (https://web.expasy.org/protparam/). 30 The flow diagram presented in supplementary figure 1 depicts the process of vaccine construct development. Prediction of epitopes from target proteins Prediction, screening and selection of Cytotoxic T- Lymphocyte (CTL) epitopes CTL epitopes were predicted using IEDB MHC-I binding server (http://tools.iedb.org/mhci/). 31 Artificial neural network method with HLA allele reference set was used for CTL prediction to cover most of the population. 32, 33 The predicted CTL epitopes are ranked based on IC50 value where lower IC50nM indicates higher MHCI-I binding affinity. An IC50 of <50nM indicate high affinity, <500nM moderate affinity and <5000nM are considered low affinity peptides. For this study IC50 of <500nM was used as cut-off for CTL epitope prediction and selection for vaccine construct. The predicted epitopes with IC50 of <500nM were checked for immunogenicity, antigenicity, allergenicity and toxicity. 31, 34 Prediction, screening and selection of Helper T- Lymphocyte (HTL) epitopes HTL epitopes were predicted using IEDB-MHCII server (http://tools.iedb.org/mhcii/). 35 The NN-align 2.3 technique was selected as prediction method and human HLA-DR was chosen as species/locus. 36,37 The HTL epitopes with 15-mer length were retrieved and ranked on the basis of IC50 value with lower IC50 indicating higher MHCI-II binding affinity. 38 The predicted HTL epitopes with IC50 <500nM were evaluated for interferon-gamma (IFN- γ) inducing activity using IFNepitope server (https://webs.iiitd.edu.in/raghava/ifnepitope/predict.php). This server uses IFN- γ inducing and non-inducing datasets of MHC-II binders for prediction and designing of HTL epitopes capable of generating an IFN- γ response. 39 The IFN- γ positive epitopes were further evaluated for antigenicity, immunogenicity, allergenicity and toxicity. Finally immunogenic, antigenic, non-allergic, non-toxic and IFN- γ positive HTL epitopes were selected. MHC-I and MHC-II population coverage The IEDB population coverage server (http://tools.iedb.org/population/) was used to examine the selected CTL and HTL epitopes corresponding to MHC I & II families and binding leukocyte antigens. 40 The server computes the distribution and proportion of population anticipated to respond to the selected epitopes. Moreover, the server also calculates the total numbers of epitope hits recognised by the total population. CTL and HTL epitopes searched depending on geographic locations and the corresponding HLA genotypic frequencies are derived. Population coverage was examined for world population, subcontinents as well as countries like India, Japan, South Korea and Chile among others. Prediction, screening and selection of linear B-cell epitopes B-cell epitopes were predicted using ABCpred (http://crdd.osdd.net/raghava/abcpred/) and BCPred (http://ailab-projects1.ist.psu.edu:8080/bcpred/) servers. The ABCpred web server used recurrent neural network for epitope prediction with an accuracy of 65.98%. 41 The BCPred server prediction is based on support vector machine algorithm. 42 The epitope length in BCPred was set at 16-mer in both servers using overlap filter. Epitopes with predicted score of ≤ 0.9 in BCPred and >.5 in ABCpred were further evaluated for antigenicity, allergenicity and toxicity properties. Prediction of antigenic, immunogenic, allergic and toxic properties Antigenicity of the predicted epitopes was examined using VaxiJen v.2.0 server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with target set to tumour and antigenicity threshold of 0.5. This server uses alignment independent technique for antigenicity prediction based on the physicochemical parameters. 43 The immunogenicity of the T cell epitopes was examined using IEDB Class-I (http://tools.iedb.org/immunogenicity/) and Class-II (http://tools.iedb.org/CD4episcore/) Immunogenicity servers. 44, 45 The allergenicity of the predicted epitopes was analysed using AllerTOP v. 2.0 server (https://www.ddg-pharmfac.net/AllerTOP/). AllerTOP uses auto cross covariance transformation algorithm and physicochemical characteristics of the proteins for allergenicity prediction. The server classifies epitopes as probable allergens or probable non-allergens on the basis of k-nearest neighbour algorithm. 46 Each predicted epitope was subjected to In-silco toxicity analysis using ToxinPred web server (http://crdd.osdd.net/raghava/toxinpred/). ToxinPred is used in prediction and designing of non-toxic proteins/peptides. 47 Designing of vaccine construct Top scoring epitopes were selected and linked together for designing of the final vaccine construct. The GPGPG linkers were added to link B-cell and HTL epitopes where as AAY linkers were used to connect CTL epitopes. To enhance the immunogenicity of the vaccine construct, human β-defensin 3 (hBD3) was used as an adjuvant. The adjuvant was connected to the construct using EAAAK linker. Evaluation of construct physicochemical properties The physicochemical parameters were examined using ProtParam tool (https://web.expasy.org/protparam/). 30 These parameters included molecular weight, isoelectric point, atomic composition, stability, and hydopathicity. The solubility was predicted using Protein-Sol web server (https://protein-sol.manchester.ac.uk/). The expected solubility value of >0.45 is estimated to have a higher solubility than the experimental dataset. 49 Prediction of Secondary structure PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/) was used to assess the secondary structure of the designed vaccine sequence. 50 PSIPRED uses two stage neural networks to predict the 2D structure including α-helix, β-pleated sheets and coils. 51 Prediction of tertiary structure The RaptorX (http://raptorx.uchicago.edu/) was used to perform the homology modelling for prediction of the tertiary structure. 52 The server is a template based modelling tool for predicting 3D model and assigns a rank to the predicted models on the basis of root mean square deviation (RMSD) score. The template based threading and alignment quality predictions are the key components of RaptorX. 53 Tertiary structure refinement and validation The template based 3D model generated in the RaptorX was further refined using Galaxy Refine web server (http://galaxy.seoklab.org/refine). 54 The refinement helps in improving the quality of the 3D model. Galaxy Refine uses molecular dynamics simulation to achieve recurrent structure perturbation and overall structural relaxation through side chain repacking. 55 The quality of the refined 3D model was validated using ERRAT and PROCHECK tools in SAVES v6.0 web server. 56-60 ERRAT analyzes the overall quality of the 3D model and PROCHECK verifies the stereo-chemical quality by generating a Ramachandran plot of protein residues. 56, 59 . The validation of the final 3D model was further verified through ProSA web server (https://prosa.services.came.sbg.ac.at/prosa.php) which compares the predicted model with known structures of proteins using NMR spectroscopy and X-ray analysis. 61 Prediction of discontinuous B-cell epitopes The discontinuous B-cell epitopes of validated 3D model were predicted using ElliPro server (http://tools.iedb.org/ellipro/). 62 The server predicts the surface accessible nearby cluster residues based on their protrusion index (PI) values. This score is calculated by taking the average PI value of each residue. ElliPro came out on top when compared to other structure-based techniques for predicting epitopes, with an AUC value of 0.732. 62, 63 Molecular docking with immune receptors The interaction between the designed construct and TLRs is crucial for immune response generation. TLR2, TLR3 and TLR4 were selected for docking purposes. The vaccine-receptor docking was performed using HDOCK server (http://hdock.phys.hust.edu.cn/) to examine the binding affinity/energy of the docked complex. 64 The server uses hybrid algorithm for protein-protein docking methodologies. 65 HDOCK provides a total of 100 docked complex predictions ranked on the basis of docked energy scores and ligand RMSD. The vaccine-receptor interactions were visualized in LigPlot+ v.2.2 server (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/). LigPlot+ generates schematic 2D vaccine-receptor interaction diagrams representing intermolecular hydrogen bonds and hydrophobic interactions. 66 Molecular dynamics simulation of vaccine-receptor complexes The stability and strength of the docked complexes were examined through molecular simulation. 67 The simulation was performed for each vaccine receptor docked complex for 100ns using Desmond’s System builder panel with OPLS_2005 force feild. 68 Before running MD simulation, the system was equilibrated using the relax model system protocol. Ligand-receptor complexes were prepared by solvating with TIP3P water molecules, periodic boundary conditions established as a cubic box using buffer technique and a distance threshold of 10. Dynamics panel was set on default parameters, trajectory was saved every 100ps and the Like Energy was captured at 1.2ps. At 300 K (temperature) and 1.01325 pressure, the volume of the box was equilibrated with the NPT ensemble (pressure). The Noose-Hoover chain temperature coupling with 1.0 ps relaxation time and Martyna-Tobias-Klein pressure coupling 2.0 ps relaxation time of Isotropic style were used during the simulation run at 2 (fs) time step. The short-range method's cut-off for columbic interaction was 9.0 radius. Immune simulation The generation of immunological response of the designed vaccine was demonstrated through immune simulation using C-ImmSim server (https://kraken.iac.rm.cnr.it/C-IMMSIM/). 69 The prediction of immune response is based on stimulation of three major anatomical systems including lymph node, thymus and bone marrow in mammals. Three injections were administered at 1, 84 and 168 time steps (each time step corresponds to 8 hours indicating that vaccine was administered at an interval of 0, 28 and 56 days). The simulation volume was set at 50 and total simulation time was 1050 time steps (350days) and other parameters were kept as default. 70 The output of C-ImmSim server provides the stimulation of different immunological cells and predicts the humoral immune response through immunoglobulins, cytokines and interleukin production during immune simulation. Results Identification of target proteins and sequence retrieval 5ʹ -Nucleotidase isoform 2 (NT5E) also called as ecto-5ʹ-Nucleotidase (CD73) is a surface enzyme present on B and T lymphocytes in humans. 27 CD73 encoded by NT5E gene converts adenosine monophosphate (AMP) into adenosine. Several studies have reported over expression of NT5E in GBC leading to accumulation of adenosine. 27, 70 The free adenosine produced by NT5E suppresses cellular immune responses through expansion of various immune suppressing cells like regulatory T cells and tumour associated macrophages while down regulating the natural killer T (NKT) cells and helper T (Th1) cells allowing malignant cells to evade detection. 71-73 Over expression of NT5E is negatively correlated with survival period in GBC. 27 In the tumour microenvironment, over expressed adenosine promotes tumour growth, invasion, angiogenesis, and metastasis while suppressing anticancer immune responses. 29, 70 As a result, it has been identified as a promising therapeutic target for controlling GBCprogression. 74 Aminopeptidase N (ANPEP) also referred to as CD13 is a membrane bound zinc metallo-enzyme expressed in macrophages and other human cells. 75 Over expression of ANPEP has been linked with tumour size, differentiation and metastasis in pancreatic, gastric, breast and gallbladder cancers. 28,29 Sanz et al. identified ANPEP activity in tumour tissue and plasma as an independent predictor and prognostic factor of 5-year survival in patients with colorectal cancer. 75 ANPEP is a ubiquitous enzyme with moonlighting functional roles and expressed in normal cells limiting its applicability as a potential therapeutic target. However, evidence suggests that the ANPEP expressed by cancer cells is different in function and activity as compared to normal tissues. 76 The activities of ANPEP have been linked to the progression of a number of malignancies, suggesting that it may have considerable potential in anticancer therapy. Membrane metallo-endopeptidase (MME) or Neprilysin is also membrane bound zinc metalloproteinase implicated in promoting cancer progression. 29 MME over expression has been reported in breast, lung, hepatocellular and gallbladder cancers and has been associated with poor prognosis. 77 Researchers have also identified MME as a potential target for GBC treatment. Sequences of selected proteins including NT5E (Uniprot ID- P21589), ANPEP (Uniprot ID- P15144) and MME (Uniprot ID- P08473) in FASTA format were retrieved from UniProtKB database. The stability of the proteins was verified by using ProtParam server. The instability index of NT5E, ANPEP and MME was found to be 32.59, 36.17 and 37.62 respectively indicating that selected proteins were stable. The physicochemical properties of the selected proteins were examined and are provided in Supplementary Table 1 . The FASTA sequences were subsequently used for B and T-cell epitope prediction for vaccine construction. Prediction of CTL and HTL epitopes The CTL and HTL epitopes were predicted and selected on the basis of IC50 (0.5), immunogenicity, non-allergenicity and non-toxicity from NT5E, ANPEP and MME proteins vaccine development. The selected CTL epitopes covering different Human Leucocyte Antigen (HLA) super types are depicted in Table 1 . The predicted HTL epitopes were also evaluated for IFN- γ inducing properties. The selected HTL epitopes and corresponding HLA-DR super types with IFN- γ score and antigenicity are shown in Table 2 . CTL and HTL Population Coverage Analysis In order to develop a feasible vaccine candidate relevant to the global population, it is important to take into account the population coverage of the selected T-cell epitopes. The world population coverage of selected CTL and HTL epitopes was 93.78% and 81.81% respectively. The coverage of the individual CTL and HTL epitopes and their respective HLA genotypic frequency is summarized in Table 3. The HTL epitopes had higher total HLA hits (48) as compared to CTL epitopes (22). The CTL and HTL population coverage was also assessed for different geographical regions such as South Asia (82.25%, 73.38%), South-East Asia (83.13%, 56.98), Europe (97.17%, 85.83%), South America (75.46, 58.59%), North America (94.35, 87.89%), East Africa (73.78%, 81.82%) and South Africa (78.07%, 32.10%). The coverage of CTL and HTL epitopes in different sub-continents and countries is shown in Figure 1. Prediction of B-cell epitopes The 16-mer B-cell epitopes were selected on the basis of binding score (>0.9). The epitopes with high binding score were further evaluated for antigenicity (>0.5), allergenic and toxic properties. The antigenic, non-allergic and non-toxic B-cell epitopes were selected from three proteins as shown in Table 4 . Designing of multi-epitope vaccine construct From the predicted epitopes, 7 CTL, 4 HTL and 6 B-cell epitopes were selected for designing of final vaccine construct. The GPGPG linkers were added to link B-cell and HTL epitopes where as AAY linkers were used to connect CTL epitopes. hBD3 (ID-Q5U7J2) a 45 amino acid long adjuvant was added to improve the immunogenicity of the vaccine construct. The adjuvant was linked using EAAAK linkers. The schematic diagram and amino acid sequence of designed vaccine construct is shown in Figure 2A. The overall antigenicity score was found to be 0.71 indicating that the vaccine construct is highly antigenic and classified as probable non-allergen. Evaluation of construct physicochemical properties The final construct consisted of 337 amino acids with molecular weight of 36.21 KDa (C 1644 H 2513 N 453 O 468 S 7 ) and an isoelectric point of 9.28. The predicted aliphatic index was 70.47 suggesting thermo-stability and instability index of 23.39 confirmed that the vaccine construct was stable. The GRAVY score was found to be -0.445 suggesting its hydrophilic nature. The estimated half-life was predicted as 30 hours in mammalian reticulocytes, >20 hours in Yeast and >10 hours in E. coli. Prediction of Secondary and tertiary structure The secondary structure consisted of 37% alpha-helix, 14% beta-strand and 49% coils. The 2D structure is shown in Figure 2B . RaptorX generated five 3D models of the final vaccine sequence with RMSD values ranging from 4.563 to 7.726. Each model was evaluated for overall quality using ERRAT and PROCHECK tools. Model 2 with RMSD-5.2374 was selected on the basis of the overall quality factor in ERRAT (93.7%) and PROCHECK (Ramachandran plot). The Ramachandran plot of Model 2 showed 87.2% of amino acid residues in most favoured, 12.5% in allowed and 0.4% in disallowed regions. The initial 3D model and associated Ramachandran plot are shown in Figure 3A, 3C respectively Tertiary structure refinement and validation Galaxy Refine generated five models of which Model-3 with RMSD of 0.359, Mol Probity of 1.965, GDT-HA of 0.9733, Clash score of 12.7and poor rotamers of 0.4 was selected for further analysis. Ramachandran plot of the refined 3D model showed 92.1% residues in most favoured, 6.8% in additional allowed regions, 0.8% in generously allowed regions and 0.4% in disallowed regions. Based on the results of Ramachandran plot and ERRAT score, that quality of Model-3 was found to be acceptable. The refined 3D model and its Ramachandran plot are shown in Figure 3B, 3D respectively. The 3D model was further examined and validated through ProSA Web server with predicted Z-score of -5.2 was in the range of experimentally validated protein structures obtained from X-ray and NMR spectroscopy analysis (Figure 3E) .The solubility score was found to be 0.523 which shows that construct is soluble upon expression (Supplementary Figure 2) . Prediction of discontinuous B-cell epitopes A total of six discontinuous B-cell epitopes were identified by ElliPro server. Almost 5 B-cell epitopes contained 167 amino acid residues present in the main region of the vaccine construct. A score value in the range of 0.67 – 0.76 was chosen for selection of discontinuous B-cell epitopes (Figure 4). 22 residues were predicted from 60-61, 63, 65-81 and 84-85. 24 residues were predicted from 300-303, 305-314 and 328-337. 60 residues were predicted from 97, 100-110, 121, 124, 125, 128-141, 143-145, 148-161, 182, 184, 185 and 187-197. 13 residues were predicted from 220-231 and 234. 48 residues were predicted from 1-7, 9-11, 13-23, 27, 44-47, 49, 50, 53 and 256-274 (Supplementary Table 2). Molecular docking with immune receptors The docking of the construct was performed with immune receptors including TLR2, TLR3 and TLR4. 3D docked scores for TLR-2, TLR-3 and TLR-4 were -344.38, -345.38and -324.47 respectively. The docking energy scores indicating the vaccine-receptor complex binding affinity and ligand RMSD are shown in Supplementary Table 3 and the vaccine - TLR2, TLR3 and TLR4 docked complexes are shown in Figures 5A, 5B and 5C respectively. The docked complexes were analysed in LigPlot+ for visualization of intermolecular hydrogen bonds representing vaccine-receptor interactions. For ligand-TLR2 complex, Lys8, Arg207, Ile30, Asn200, Tyr255, Asp259 and Glu333 residues were involved in intermolecular hydrogen bonding ( Figure 5D ). Similarly Asp268, Arg262, His336, Asp259, Asn85, Glu87, Asp322, Arg12, Arg14 residues in ligand-TLR3 ( Figure 5E ) and Ala299, Arg207, Ser199, Gln29, Lys32, Lys8 residues in ligand-TLR4 ( Figure 5F ) were identified in LigPlot. Molecular dynamics simulation of receptor-vaccine complex The Maestro's Schrodinger simulation event analysis module was used to analyse the trajectories. By superimposing the trajectories over the reference frame, RMSD and RMSF were calculated using trajectories from MD simulation data. This provides an estimate of conformational stability and volatility over the course of the simulation. The amount of hydrogen bonds established between the receptor and the docked ligand during the simulation also suggests the ligand's binding stability with the receptor. A binding arrangement with a higher number of hydrogen bonds is said to be more stable. For each TLR2, TLR3, and TLR4 receptor complex with ligand, the RMSD was computed. TLR2-complex RMSD demonstrated that after 60ns, the RMSD of TLR2 protein and ligand converged. Throughout the simulation, the protein-ligand complex remained stable. For TLR2 and TLR2 bound ligand, the standard deviations in RMSD were 0.932 and 1.4321, respectively, with average RMSD values of 2.75 and 5.59 ( Figure 6A ). For both TLR3 protein and ligand, the RMSD plot of the TLR3-complex revealed convergence after 5ns and remained stable throughout the simulation ( Figure 6B ). The RMSD of the ligand was raised around 20-60ns, but the binding site remained unchanged. TLR3 and TLR3 bound Ligand had standard deviations of 0.231 and 0.954, respectively, with average RMSD values of 3.07 and 5.75 ( Figure 6B ). The TLR4-complex RMSD data demonstrates convergence after 5ns for both the TLR4 protein and the ligand. Ligand RMSD increased up to 20ns before stabilising toward the end of the simulation. Throughout the simulation, the TLR4-ligand complex maintained its conformational stability ( Figure 6C ). This shows that the Protein-ligand complex was stable throughout the simulation. The standard deviation in RMSD was 0.314Å and 0.885Å, while Average RMSD values were 3.07Å and 6.03Å, for TLR4 and TLR4 bound Ligand, respectively. Throughout the simulation, the compactness of the proteins was examined, and the plot revealed that all three proteins were folded correctly. In comparison to TLR3 and TLR4, TLR2 demonstrated changes in compactness during the simulation. The contact between the two molecules is represented by intermolecular hydrogen bonds (H-Bonds), which are implicated in the intensity of binding through the number of H-Bonds. The more H-bonds there are, the more binding or interaction there is between two molecules. Throughout the simulation, the average number of Intermolecular H-bonds between complex TLR2-Ligand ( Figure 6D ), TLR3-Ligand ( Figure 6E ), and TLR4-Ligand ( Figure 6F ) were 15.36, 16.45, and 11.98 respectively. For complex TLR2-Ligand, TLR3-Ligand, and TLR4-Ligand, the range of hydrogen bonds during 100ns simulation over 1000 time frames was 7-24, 6-29, and 6-23, respectively. The number of hydrogen bond interactions at 10ns time intervals with RMSD values is shown in Table 5 . This MD simulation analysis indicates that all three immune receptors efficiently bound with the ligand and showed stable binding throughout the simulation ( Supplementary Videos ). The number of hydrogen bonds in 100ns simulation over 1000 time frames and corresponding receptor and ligand RMSD are provided in Supplementary material 2 . The average value of RMSF for TLR2, TLR3, and TLR4 were 1.23 Å, 2.71 Å, and 2.45Å, respectively. RMSF showed fewer fluctuations in TLR3 and TLR4 as compared to TLR2 Figure 6G. Moreover the ligand’s RMSF plot indicates less fluctuation in RMSF values of TLR2 as compared to TLR3 and TLR4 bound ligands. Figure 6H shows the radius of gyration of all three complexes. Immune simulations of final vaccine construct The output of C-ImmSim server provides the stimulation of different immunological cells including B-cell, helper-T cell (TH), cytotoxic-T cell (TC), natural killer cell (NK), macrophages (MA) and dendritic cell (DC) population. Moreover, the prediction of immunoglobulins, cytokines and interleukin production during immune simulation is also provided. The total B-cell population, memory B-cell and active B-cell population increased following each booster vaccine dose and remained stable with minimal decay over the period of 350 days Figures 7A, 7B . There was a considerable rise in antibody response with every exposure to the vaccine construct with corresponding decrease in the antigen levels. The humoral immune response was characterized by IgG and IgM antibodies and the IgM response was higher as compared to IgG after each vaccination ( Figure 7C ).The active TH cells and memory TH cells spiked after second and third vaccine doses which remained elevated up to 350 days ( Figures 7D, 7E ). Similarly the TC cell population per state showed steady increase in active TC population ( Figure 7F, 7G ) . The IFN- γ response was significantly higher after first and second dose and the concentrations of IL-10 and TGF-b also spiked following each vaccination ( Figure 7H ).Throughout the simulation, there was also a concurrent rise in the activity of macrophages, dendritic and natural killer cells ( Figure 7I ). Discussion GBC is a biliary tract carcinoma with poor prognosis, high death rate, and a survival rate of < 1 year. Gallbladder malignancies are highly aggressive and the current treatments have yielded dismal outcomes. 3 – 6 As a result, development of new and innovative therapies for the treatment and improvement of survival in this patient population are urgently needed. As there is no evidence of a vaccine for GBC, this motivated us to fashion a peptide based epitope vaccine to combat the development and progression of GBC. Priya et al. in a recent study reported significantly higher changes in NT5E, ANPEP and MME proteins in GBC patients as compared to the control groups and highlighted their potential as diagnostics and drug targets for GBC. 29 The authors reported that NT5E levels (expressed by cancer exosomes) were significantly elevated in advanced stages of GBC; ANPEP was increased in early as well as later GBC while s MME was significantly higher in early stages of GBC. These tumour associated extracellular vesicular proteins are implicated in tumour progression and immune suppression. Consequently, these proteins were selected as potential targets for antigenic epitope prediction to stimulate immune system and combat GBC progression. Reverse vaccinology and ImmunoInformatics approaches in vaccine development is a rapidly developing field. The epitope based vaccines designed using these approaches have demonstrated in vivo efficacy as well as protective immunity with several vaccines undergoing clinical trials. 25 Several studies have shown promising results on developing epitope vaccines against different cancers like breast cancer, Kaposi sarcoma, colon cancer and cervical cancer. 78 – 82 Lately peptide based vaccines have gained traction due to several advantages as compared to the conventional vaccines. Apart from the capability of inducing cancer specific immune response, the peptide vaccines are relatively safe and lower developmental costs. 82 Cell based immunity is generated upon binding of immunogenic peptides to MHC I & II. The selection of suitable epitopes for designing of a vaccine capable of eliciting a good immune response with maximum population coverage is critical. 83 In the study, the CTL and HTL epitopes were selected through rigorous screening, initially predicted epitopes were ranked on the basis of IC50 values and an IC50 value of < 500nM was used as threshold. Epitopes with IC50 < 50nM suggest high MHC I & II binding affinity while IC50 < 500nM indicate moderate affinity and IC50 < 5000nM is considered low affinity. The predicted epitopes with < 500nM IC50 were further examined for immunogenicity, population coverage, antigenicity, IFN- γ inducing capability and non-allergenicity. Designed with both CD4+, CD8 + immunogenic epitopes, this epitope vaccine could elicit a strong long-lasting cellular immunity. Finally for the designing of epitope vaccine construct, top 7 CTL, 4 HTL and 6 B-cell epitopes were linked using different linkers for designing of final vaccine construct. hBD3 was added as an adjuvant for improved immunogenicity. hBD3 acts as an immune regulator by stimulating monocytes and dendritic cells there by playing a crucial role in activating T cells and cytokine production. 84 The safety and efficacy of the vaccine is determined by the population in which it is administered. Representing both MHC I & II alleles, the maximum world population coverage was 93.78% for CTL epitopes and 81.81% for HTL epitopes, making it a promising vaccine candidate. GLOBOCAN 2020 reported highest incidence of GBC in Asia, Europe and South America. 2 , 9 The class I and class II epitopes showed an excellent population coverage in prevalent geographic regions like India (74.02% & 74.99%), Japan (94.26% & 74.83%), Korea (91.97% &85.32%), Chile (86.46% & 67.08%) among others. Finally, a 337 amino acids vaccine construct was designed and evaluated for stability, antigenicity, and physicochemical properties. The construct was stable as indicated by the predicted aliphatic index and instability index. Solubility is a vital physicochemical property and the construct demonstrated a higher solubility than the soluble E.coli proteins from the experimental data set. 48 Antigenicity and allergenicity are critical factors in multi-epitope vaccine development and because these properties were assessed before designing the construct, the final vaccine was found to be highly antigenic (0.71), non-allergic and non-toxic in nature. The secondary and tertiary models of the vaccine were successfully generated and satisfactorily validated. Ramachandran plot of the final 3D model showed more than 90% of the residues in allowed regions and only 0.4% in disallowed regions. For successful generation of immune response, the stimulation of immunological receptors such as TLRs is important. Activation of TLRs in immune and cancer cells is critical in triggering cancer associated immune response through multiple signalling pathways. 85 The binding affinity of the designed construct with TLR2, TLR3 and TLR4 was predicted using molecular docking and the stability of the vaccine-receptor docked complexes was examined through stability, hydrogen bonds and simulation trajectories using molecular dynamics simulation. According to molecular docking experiments, the vaccine has a strong affinity for TLR-2, TLR-3, and TLR-4 receptors. The average numbers of hydrogen bonds for vaccine-TLR 2, 3 and 4 complexes were 15.36, 16.45, and 11.98 respectively and remained consistent over a 100ns simulation period, which is critical for their function. The developed vaccine candidate demonstrated an acceptable cellular as well as humoral immune response in the immune simulation study. 69 Because the vaccine contained both CTL and HTL, it showed stimulation of respective immune cells, which may further lead to activation of other potential immune cells such as NK cells, macrophages and dendritic cells via complex signalling. Overall, the results of immune simulation showed that the immune response increased in tandem with each booster dose corresponding to activation of multiple immune cells. Moreover the vaccine construct contained several linear and discontinuous B-cell epitopes suggesting antibody mediated immune response properties which were clearly seen as increased levels of IgG and IgM. Conclusion Stimulation of immune system is critical in combating cancer and peptide based epitope vaccines have demonstrated the capability of generating a cancer specific immune response. This study reports the designing of a peptide based multi-epitope vaccine construct with a thorough analysis of its immunogenicity, antigenicity, allergenicity and stability using ImmunoInformatics approaches to trigger a robust immune response against GBC. The construct contains both CD4+, CD8 + and B-cell epitopes from three different antigenic proteins implicated in GBC progression. The interaction and binding strength of this vaccine construct with TLRs was excellent and the Insilco immune simulation has shown its ability to induce both cellular and antibody mediated immune responses. The promising results in the present study provide a strong basis for further evaluation through in-vitro/in-vivo experimental validation of safety and efficacy of the designed vaccine candidate. Declarations Author contributions MA Dar and P Kumar conceptualized, designed the study, contributed in vaccine designing and performed most in-silco analyses. P Kumar, A Shrivastava and A Singh contributed in computational analysis. MA Dar and S Dhingra drafted the initial manuscript. R Chauhan, V Ravichandiran contributed in manuscript writing, editing and revisions. The final version of the manuscript has been read and approved by all the authors for submission. Conflict of Interest The authors have no conflicts of interest to disclose for this study. Funding This research did not receive any specific grant from funding agencies in the public, commercial or not for profit sectors. Data Availability Statement The data presented in the study are included in this article and supplementary material. Further inquiries can be directed to the corresponding author. References Ferlay, J. et al . Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available from: https://gco.iarc.fr/today (2022). Sung, H. et al . Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin . 71, 209-249; https://doi.org/10.3322/caac.21660 (2021). Mahdavifar, N., Pakzad, R., Ghoncheh, M., Gandomani, H. S. & Salehiniya, H. Epidemiology, incidence, and mortality of gallbladder cancer and its relation with development in the world. Trop. Med. Public Health . 10 , 563–570 (2017). Mao, W., Deng, F., Wang, D., Gao, L. & Shi, X. Treatment of advanced gallbladder cancer: A SEER-based study. Cancer Med . 9 , 141-150; https://doi.org/10.1002/cam4.2679 (2020). Hundal, R. & Shaffer, E. A. Gallbladder cancer: epidemiology and outcome. Clin Epidemiol . 6 , 99‐109 (2014). Randi, G., Franceschi, S. & La Vecchi, C. Gallbladder cancer worldwide: Geographical distribution and risk factors. J. Cancer . 118 , 1591–1599 (2006). Zhu, A. X., Hong, T. S., Hezel, A. F. & Kooby, D. A. Current management of gallbladder carcinoma. Oncologist . 15 , 168–181; https://doi.org/10.1634/theoncologist.2009-0302 (2010). Kiran, R. P., Pokala, N. & Dudrick, S. J. Incidence pattern and survival for gallbladder cancer over three decades–an analysis of 10301 patients. Ann Surg Oncol . 14 , 827–832; https://doi.org/10.1245/s10434-006-9224-4 (2007). World Health Organization, International Agency for Research on Cancer. Globocan Gallbladder Cancer fact sheet. Available from: https://gco.iarc.fr/today (2020). Sharma, A., Sharma, K.L., Gupta, A., Yadav, A. & Kumar, A. Gallbladder cancer epidemiology, pathogenesis and molecular genetics: Recent update. World J Gastroenterol. 23 , 3978-3998; https://dx.doi.org/10.3748%2Fwjg.v23.i22.3978 (2017). Li, Y., Zhang, J. & Ma, H. Chronic inflammation and gallbladder cancer. Cancer Lett . 345 , 242–248; https://doi.org/10.1016/j.canlet.2013.08.034 (2014). Koshiol, J. et al . Association of inflammatory and other immune markers with gallbladder cancer: Results from two independent case-control studies. Cytokine . 83 , 217–225; https://doi.org/10.1016/j.cyto.2016.05.003 (2016). Wistuba, I. I. & Gazdar, A. F. Gallbladder cancer: lessons from a rare tumour. Rev . Cancer . 4 , 695–706 (2004). Gamboa, A.C. & Maithel, S.K. The Landmark Series: Gallbladder Cancer. Surg. Oncol . 27 , 2846–2858 (2020). Ilyas, F.Z., Beane, J.D. & Pawlik, T.M. The State of Immunotherapy in Hepatobiliary Cancers. Cells . 10 , 2096; https://doi.org/10.3390/cells10082096 (2021). Margonis, G. A. et al . Rates and patterns of recurrence after curative intent resection for gallbladder cancer: a multi-institution analysis from the US Extra-hepatic Biliary Malignancy Consortium. HPB ( Oxford ). 18 , 872-878; https://doi.org/10.1016/j.hpb.2016.05.016 (2016). Song, X. et al . Overview of current targeted therapy in gallbladder cancer. Signal Transduct Target Ther . 5 , 230; https://doi.org/10.1038/s41392-020-00324-2 (2020). Lang, K., Entschladen, F., Weidt, C. & Zaenker, K. S. Tumor immune escape mechanisms: Impact of the neuroendocrine system. Cancer Immunology, Immunotherapy: CII . 55 , 749–760; https://doi.org/10.1007/s00262-006-0126-x (2006). Cappello, P. & Novelli, F. Next generation of cancer immunotherapy calls for combination. Oncoscience , 4 , 19–20; https://doi.org/10.18632/oncoscience.343 (2017). Stanculeanu, D. L., Daniela, Z., Lazescu, A., Bunghez, R. & Anghel, R. Development of new immunotherapy treatments in different cancer types. Journal of Medicine and Life , 9 , 240–248 (2016). Ferro, S., Huber, V. & Rivoltini, L. Mechanisms of tumor immunotherapy, with a focus on thoracic cancers. Journal of Thoracic Disease , 10 , 4619–4631; https://doi.org/10.21037/jtd.2018.07.30 (2018). Bahrami, A.A., Payandeh, Z., Khalili, S., Zakeri, A. & Bandehpour, M. Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein. Int Rev Immunol . 38 , 307-322; https://doi.org/10.1080/08830185.2019.1657426 (2019). Nandy, A. & Basak, S. C. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. Int J Mol Sci . 17 , 666; https://doi.org/10.3390/ijms17050666 (2016). Zhang, L. Multi-epitope vaccines: a promising strategy against tumors and viral infections. Cell Mol Immunol . 15 , 182-184; https://doi.org/10.1038/cmi.2017.92 (2018). Parvizpour, S., Pourseif, M.M., Razmara, J., Rafi, M.A. & Omidi, Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov Today . 25 , 1034-1042; https://doi.org/10.1016/j.drudis.2020.03.006 (2020). Liu, Z. et al . Circulating Levels of Inflammatory Proteins and Survival in Patients with Gallbladder Cancer. Sci Rep . 8 , 5671; https://doi.org/10.1038/s41598-018-23848-8 (2018). Xiong, L., Wen, Y., Miao, X. & Yang, Z. NT5E and FcGBP as key regulators of TGF-1-induced epithelial-mesenchymal transition (EMT) are associated with tumor progression and survival of patients with gallbladder cancer. Cell Tissue Res . 355 , 365–374 (2014). Wickström, M., Larsson, R., Nygren, P. & Gullbo, J. Aminopeptidase N (CD13) as a target for cancer chemotherapy. Cancer Sci . 102 , 501–508 (2011). Priya, R. et al . Plasma-derived candidate biomarkers for detection of gallbladder carcinoma. Sci Rep . 11 , 23554; https://doi.org/10.1038/s41598-021-02923-7 (2021). Gasteiger E,Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook, 571-607 (2005). Kim, Y. et al . Immune epitope database analysis resource. Nucleic Acids Res . 40 , W525-30; https://doi.org/10.1093/nar/gks438 (2012). Nielsen, M. et al . Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci . 12 , 1007-17; https://doi.org/10.1110/ps.0239403 (2003). Lundegaard, C., Lund, O. & Nielsen, M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics . 24, 1397-8; https://doi.org/10.1093/bioinformatics/btn128 (2008). Lundegaard, C. et al . NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res . 36 , W509-12; https://doi.org/10.1093/nar/gkn202 (2008). Andreatta, M. et al . An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics. 34 , 1522-1528; https://doi.org/10.1093/bioinformatics/btx820 (2018). Nielsen, M. & Lund, O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics . 10 , 296; https://doi.org/10.1186/1471-2105-10-296 (2009). Wang, P. et al . Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics . 11 , 568; https://doi.org/10.1186/1471-2105-11-568 (2010). Jensen, K. K. et al . Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology . 154 , 394-406; https://doi.org/10.1111/imm.12889 (2018). Dhanda, S.K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct . 8 , 30; https://doi.org/10.1186/1745-6150-8-30 (2013). Bui, H. H. et al . Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics . 7 , 153; https://doi.org/10.1186/1471-2105-7-153 (2006). Saha, S. & Raghava, G.P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins . 65 , 40-8; https://doi.org/10.1002/prot.21078 (2006). El-Manzalawy, Y., Dobbs, D. & Honavar, V. Predicting linear B-cell epitopes using string kernels. J MolRecognit . 21 , 243-55; https://doi.org/10.1002/jmr.893 (2008). Doytchinova, I.A. & Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics . 8 , 4; https://doi.org/10.1186/1471-2105-8-4 (2007). Calis, J. J. et al . Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput Biol . 9 , 1003266; https://doi.org/10.1371/journal.pcbi.1003266 (2013). Dhanda, S. K. et al . Predicting HLA CD4 Immunogenicity in Human Populations. Front Immunol. 9 , 1369; https://doi.org/10.3389/fimmu.2018.01369 (2018). Dimitrov, I., Bangov, I., Flower, D.R. & Doytchinova, I. AllerTOP v.2--a server for in silico prediction of allergens. J Mol Model . 20 , 2278; https://doi.org/10.1007/s00894-014-2278-5 (2014). Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A. & Kumar, R. Open Source Drug Discovery Consortium, Raghava GP. In silico approach for predicting toxicity of peptides and proteins. PLoS One . 8 , 73957; https://doi.org/10.1371/journal.pone.0073957 (2013). Hebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R. & Warwicker, J. Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics . 33 , 3098-3100; https://doi.org/10.1093%2Fbioinformatics%2Fbtx345 (2017). Niwa, T. et al . Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins. Proc Natl Acad Sci U S A . 106 , 4201-6; https://doi.org/10.1073/pnas.0811922106 (2009). Buchan, D.W.A. & Jones, D.T. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res . 47 , W402-W407; https://doi.org/10.1093/nar/gkz297 (2019). Jones, D.T. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol . 292 , 195-202; https://doi.org/10.1006/jmbi.1999.3091 (1999). Källberg, M., Margaryan, G., Wang, S., Ma, J. & Xu, J. RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol . 1137 , 17-27; https://doi.org/10.1007/978-1-4939-0366-5_2 (2014). Peng, J. & Xu, J. RaptorX: exploiting structure information for protein alignment by statistical inference. Proteins . 79 , 161-71; https://doi.org/10.1002/prot.23175 (2011). Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic acids research . 41 , W384-8; https://doi.org/10.1093%2Fnar%2Fgkt458 (2013). Ko, J., Park, H., Heo, L. & Seok, C. GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res . 40 , W294-7; https://doi.org/10.1093/nar/gks493 (2012). Colovos, C. & Yeates, T.O. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci . 2 , 1511-9; https://doi.org/10.1002/pro.5560020916 (1993). Lüthy, R., Bowie, J.U. & Eisenberg, D. Assessment of protein models with three-dimensional profiles. Nature . 356 , 83-5; https://doi.org/10.1038/356083a0 (1992). Bowie, J.U., Lüthy, R. & Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science . 253 , 164-70; https://doi.org/10.1126/science.1853201 (1991). Laskowski, R. A., MacArthur, M. W., Moss, D. S. & Thornton, J. M. PROCHECK - a program to check the stereochemical quality of protein structures. App. Cryst. 26 , 283-291 (1993). Laskowski, R.A., Rullmannn, J.A., MacArthur, M.W., Kaptein, R. & Thornton, J. M. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR . 8 , 477-86; https://doi.org/10.1007/bf00228148 (1996). Wiederstein, M. & Sippl, M. J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res . 35 , W407-10; https://doi.org/10.1093%2Fnar%2Fgkm290 (2007). Ponomarenko, J. et al . ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics . 9 , 514; https://doi.org/10.1186/1471-2105-9-514 (2008). Ferdous S, Kelm S, Baker TS, Shi J, Martin ACR. B-cell epitopes: Discontinuity and conformational analysis. Mol Immunol . 114 , 643-650; https://doi.org/10.1186/1471-2105-9-514 (2019). Yan, Y., Tao, H., He, J. & Huang, S.Y. The HDOCK server for integrated protein-protein docking. Nat Protoc . 15 , 1829-1852; https://doi.org/10.1038/s41596-020-0312-x (2020). Yan, Y., Zhang, D., Zhou, P., Li, B. & Huang, S. Y. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res . 45 , W365-W373. https://doi.org/10.1093/nar/gkx407 (2017). Wallace, A.C., Laskowski, R. A. & Thornton, J. M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng . 8 , 127-34; https://doi.org/10.1093/protein/8.2.127 (1995). Hansson, T., Oostenbrink, C. & van Gunsteren, W. Molecular dynamics simulations. CurrOpinStruct Biol . 12 , 190-6; https://doi.org/10.1016/s0959-440x(02)00308-1 (2002). Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, (2021). Rapin, N., Lund, O., Bernaschi, M. & Castiglione, F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One . 5 , e9862; https://doi.org/10.1371/journal.pone.0009862 (2010). Snider, N.T. et al . Alternative splicing of human NT5E in cirrhosis and hepatocellular carcinoma produces a negative regulator of ecto-5'-nucleotidase (CD73). Mol Biol Cell . 25 , 4024-33; https://doi.org/10.1091/mbc.e14-06-1167 (2014). Huang, S. et al. Role of A2a extracellular adenosine receptor-mediated signaling in adenosine-mediated inhibition of T-cell activation and expansion. Blood . 90 , 1600–1610 (1997). Ohta, A. et al. A2A adenosine receptor may allow expansion of T cells lacking effector functions in extracellular adenosine-rich microenvironments. J Immunol . 183 , 5487–5493 (2009). Ohta, A. et al. A2A adenosine receptor protects tumors from antitumor T cells. Proc Nat Acad Sci . 103 , 13132–13137 (2006). Ghalamfarsa, G. et al . CD73 as a potential opportunity for cancer immunotherapy. Expert OpinTher Targets . 23 , 127-142; https://doi.org/10.1080/14728222.2019.1559829 (2019). Sanz, B. et al . Aminopeptidase N activity predicts 5-year survival in colorectal cancer patients. J Investig Med . 63 , 740-6; https://doi.org/10.1097/jim.0000000000000199 (2015). Barnieh, F.M., Loadman, P.M. & Falconer, R. A. Is tumour-expressed aminopeptidase N (APN/CD13) structurally and functionally unique? BiochimBiophys Acta Rev Cancer . 1876 , 188641; https://doi.org/10.1016/j.bbcan.2021.188641 (2021). Su, S. et al . CD10 + GPR77 + Cancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness. Cell . 172 , 841-856. https://doi.org/10.1016/j.cell.2018.01.009 (2018). Parvizpour, S., Razmara, J., Pourseif, M.M. & Omidi, Y. In silico design of a triple-negative breast cancer vaccine by targeting cancer testis antigens. Bioimpacts . 9 , 45-56; https://doi.org/10.15171%2Fbi.2019.06 (2019). Chauhan, V., Rungta, T., Goyal, K. & Singh, M.P. Designing a multi-epitope based vaccine to combat Kaposi Sarcoma utilizing immunoinformatics approach. Sci Rep . 9 , 2517; https://doi.org/10.1038/s41598-019-39299-8 (2019). Corulli, L. R. et al . Multi-Epitope-Based Vaccines for Colon Cancer Treatment and Prevention. Front Immunol . 12 , 729809; https://doi.org/10.3389/fimmu.2021.729809 (2021). Sanami, S. et al . Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches. Sci Rep . 11 , 12397; https://doi.org/10.1038/s41598-021-91997-4 (2021). Abd-Aziz, N. & Poh, C. L. Development of Peptide-Based Vaccines for Cancer. J Oncol. 2022, 9749363; https://doi.org/10.1155/2022/9749363 (2022). Testa, J.S. & Philip, R. Role of T-cell epitope-based vaccine in prophylactic and therapeutic applications. Future Virol . 7 , 1077-1088; https://doi.org/10.2217/fvl.12.108 (2012). Weinberg, A., Jin, G., Sieg, S. & McCormick, T.S. The yin and yang of human Beta-defensins in health and disease. Front Immunol . 8 , 294; https://doi.org/10.3389/fimmu.2012.00294 (2012). Javaid, N. & Choi, S. Toll-like Receptors from the Perspective of Cancer Treatment. Cancers (Basel). 12 , 297; https://doi.org/10.3390/cancers12020297 (2020) Tables Table 1: List of CTL epitopes selected for designing of vaccine construct CTL Epitope Sequence Start End Length Alleles IC-50 Rank Antigenicity/ Immunogenicity Allergenicity/ Toxicity MME Protein RYGNFDILR 2 43 51 9 HLA-A*31:01 8.38 0.06 1.49 and 0.24 NA/NT 2 43 51 9 HLA-A*33:01 454.92 1.4 TLDDLTWMDA 8 34 43 10 HLA-A*02:01 54.65 0.5 0.85 and 0.12 NA/NT 8 34 43 10 HLA-A*02:06 196.1 1.5 ANPEP Protein LASYLHTFAY 9 20 29 10 HLA-B*35:01 11.92 0.04 0.99 and 0.12 NA/NT 9 20 29 10 HLA-B*15:01 69.78 0.34 9 20 29 10 HLA-A*30:02 105.04 0.29 9 20 29 10 HLA-A*01:01 125.1 0.2 ASYLHTFAY 9 21 29 9 HLA-A*11:01 19.95 0.1 0.9536 and 0.17 NA/NT 9 21 29 9 HLA-A*30:02 31.44 0.06 9 21 29 9 HLA-B*35:01 32.57 0.1 9 21 29 9 HLA-A*03:01 63.6 0.25 9 21 29 9 HLA-A*01:01 81.21 0.15 9 21 29 9 HLA-A*32:01 108.79 0.1 9 21 29 9 HLA-B*15:01 129.05 0.55 NT5E Protein WPAAGAWEL 1 21 29 9 HLA-B*35:01 7.79 0.02 0.50 and 0.36 NA/NT 1 21 29 9 HLA-B*07:02 35.07 0.14 1 21 29 9 HLA-B*53:01 235.07 0.18 KVLPVGDEV 3 42 50 9 HLA-A*02:06 14.43 0.15 1.1955 and 0.13 NA/NT 3 42 50 9 HLA-A*02:01 286.68 2 VYKGAEVAHF 2 35 44 10 HLA-A*23:01 23.91 0.08 1.1216 and 0.19 NA/NT 2 35 44 10 HLA-A*24:02 65.7 0.12 Table 2: List of HTL epitopes selected for designing of vaccine construct HTL PEPTIDE Seq no Start End Length ALLELE IC50 IFN-γ score Antigenicity/ Immunogenicity Allergenicity/ Toxicity MME Protein IQNLKFSQSKQLKKL 9 32 46 15 HLA-DRB1*07:01 5.5 Positive 0.84/92.7 NA/NT 9 32 46 15 HLA-DRB5*01:01 20.7 9 32 46 15 HLA-DRB1*01:01 27.1 9 32 46 15 HLA-DRB1*09:01 41.3 9 32 46 15 HLA-DRB1*13:02 61.8 9 32 46 15 HLA-DRB1*15:01 152.7 9 32 46 15 HLA-DRB1*11:01 218.6 9 32 46 15 HLA-DRB1*08:02 379.8 9 32 46 15 HLA-DRB4*01:01 412.6 9 32 46 15 HLA-DRB1*03:01 458.6 9 32 46 15 HLA-DRB3*02:02 491.9 5 5 19 15 HLA-DPA1*03:01/DPB1*04:02 105.8 5 5 19 15 HLA-DPA1*01:03/DPB1*04:01 302.3 5 5 19 15 HLA-DQA1*04:01/DQB1*04:02 432.7 5 5 19 15 HLA-DPA1*01:03/DPB1*02:01 438 5 5 19 15 HLA-DPA1*02:01/DPB1*05:01 488.9 ANPEP Protein FSFSNLIQAVTRRFS 1 896 910 15 HLA-DRB1*01:01 13.5 Positive 0.63/83.3 NA/NT 1 896 910 15 HLA-DRB5*01:01 14.7 1 896 910 15 HLA-DRB1*11:01 16.6 1 896 910 15 HLA-DRB1*09:01 22.1 1 896 910 15 HLA-DRB1*07:01 34 1 896 910 15 HLA-DRB1*13:02 39.6 1 896 910 15 HLA-DRB1*15:01 74.6 1 896 910 15 HLA-DRB1*08:02 81.3 1 896 910 15 HLA-DRB1*04:01 101 1 896 910 15 HLA-DRB1*04:05 167 1 896 910 15 HLA-DRB3*02:02 175.3 1 896 910 15 HLA-DRB1*03:01 212.8 1 896 910 15 HLA-DRB3*01:01 360.9 1 896 910 15 HLA-DRB1*12:01 392.6 NT5E Protein KEAKFPILSANIKAK 3 13 27 15 HLA-DRB5*01:01 24.7 Positive 0.70/86.7 NA/NT 3 13 27 15 HLA-DRB1*01:01 39.6 3 13 27 15 HLA-DRB1*15:01 63.2 3 13 27 15 HLA-DRB4*01:01 73.8 3 13 27 15 HLA-DRB1*07:01 103.6 3 13 27 15 HLA-DRB1*13:02 144.2 3 13 27 15 HLA-DRB1*11:01 200.6 3 13 27 15 HLA-DRB1*09:01 270.7 3 13 27 15 HLA-DRB1*04:05 387.2 3 13 27 15 HLA-DRB1*08:02 407.4 3 13 27 15 HLA-DRB1*04:01 484.4 3 13 27 15 HLA-DPA1*02:01/DPB1*14:01 282.4 3 13 27 15 HLA-DPA1*02:01/DPB1*05:01 283.7 3 13 27 15 HLA-DPA1*03:01/DPB1*04:02 364.6 KLKTLNVNKIIALGH 4 26 40 15 HLA-DRB1*13:02 6.3 Positive 1.0/69.3 NA/NT 4 26 40 15 HLA-DRB3*02:02 21 4 26 40 15 HLA-DRB1*01:01 31.6 4 26 40 15 HLA-DRB1*07:01 71 4 26 40 15 HLA-DRB4*01:01 71.2 4 26 40 15 HLA-DRB1*11:01 75.6 4 26 40 15 HLA-DRB1*12:01 77.5 4 26 40 15 HLA-DRB5*01:01 80.6 4 26 40 15 HLA-DRB1*04:01 195.4 4 26 40 15 HLA-DRB1*08:02 318.5 4 26 40 15 HLA-DRB1*15:01 371.7 4 26 40 15 HLA-DRB1*03:01 478.1 4 26 40 15 HLA-DPA1*03:01/DPB1*04:02 121.1 Table 3: World population coverage of individual CTL and HTL epitopes (MHC class-I and MHC class-II) EPITOPES Coverage HLA Allele (genotypic frequency (%) Total HLA Hits Class I A*01:01 (10.09) A*02:01 (24.39) A*02:06 (1.09) A*03:01 (9.77) A*11:01 (8.99) A*23:01 (3.06) A*24:02 (12.59) A*30:02 (1.36) A*31:01 (3.02) A*32:01 (2.59) A*33:01 (0.99) B*07:02 (8.65) B*15:01 (5.65) B*35:01 (5.63) B*53:01 (1.67) RYGNFDILR 7.09% - - - - - - - - + - + - - - - 2 TLDDLTWMDA 40.60% - + + - - - - - - - - - - - - 2 LASYLHTFAY 32.81% + - - - - - - + - - - - + + - 4 ASYLHTFAY 58.51% + - - + + - - + - + - - + + - 4 WPAAGAWEL 22.28% - - - - - - - - - - - + - + + 7 KVLPVGDEV 40.60% - + + - - - - - - - - - - - - 2 VYKGAEVAHF 26.18% - - - - - + + - - - - - - - - 2 Epitope set 93.78% 2 2 2 1 1 1 1 2 1 1 1 1 2 3 1 22 EPITOPES Coverage HLA Allele (genotypic frequency (%)) Total HLA Hits Class II DRB1*01:01 (6.65) DRB1*03:01 (10.47) DRB1*04:01(6.46) DRB1*04:05 (1.70) DRB1*07:01 (10.71) DRB1*08:02(1.31) DRB1*09:01 (3.64) DRB1*11:01 (6.06) DRB1*12:01(2.53) DRB1*13:02 (3.81) DRB1*15:01 (10.82) DRB3*01:01 (0.0) DRB3*02:02 (0.0) DRB5*01:01 (0.0) DRB5*01:01 (0.0) IQNLKFSQSKQLKKL 72.74% + + - + + + + + + + - + + + 11 FSFSNLIQAVTRRFS 81.81% + + + + + + + + - + + + + - + 14 KEAKFPILSANIKAK 70.55% + - + + + + + + + + + - - + + 11 KLKTLNVNKIIALGH 77.51% + + + - + + - + - + + - + + + 12 Epitope Set 81.81% 4 3 3 2 4 4 3 4 2 4 4 1 3 3 4 48 + Restricted: - Nonrestricted Table 4: List of B-cell epitopes selected for designing of vaccine construct Protein B-cell epitope Start position Predicted Score Antigenicity Allergenicity/ Toxicity Server MME (P08473) QLKKLREKVDKDEWIS 522 0.93 1.20 NA/ NT BCPred GYPDDIVSNDNKLNNE 481 0.97 0.74 NA/ NT ANPEP (P15144) PLFIHFRNNTNNWREI 728 0.98 1.05 NA/ NT BCPred NAIAQGGEEEWDFAWE 799 0.99 1.14 NA/ NT NT5E (P21589) VVVGGHSNTFLYTGNP 238 0.88 1.36 NA/ NT ABCpred NSSIPEDPSIKADINK 311 0.88 1.13 NA/ NT B-cell epitopes were selected based on binding score (>0.9), high antigenicity, non-allergenicity and non-toxicity NA: Non-Allergenic, NT: Non-Toxic Table 5: Vaccine-Receptor complex hydrogen bonds with different frames in MD simulation TLR-2 TLR-3 TLR-4 Frames Number of Hydrogen bonds Receptor RMSD ( Å) Ligand RMSD ( Å) Number of Hydrogen bonds Receptor RMSD ( Å) Ligand RMSD ( Å) Number of Hydrogen bonds Receptor RMSD ( Å) Ligand RMSD ( Å) 0 ns 14 0 0 20 0 0 18 0 0 10 ns 14 3.017 2.625 17 3.586 5.026 12 2.974 5.484 20 ns 13 3.333 6.035 13 3.605 6.001 11 3.597 6.478 30 ns 12 2.995 6.458 12 3.315 6.561 11 3.763 6.870 40 ns 14 2.869 6.311 13 3.530 6.939 13 4.298 6.583 50 ns 19 3.089 6.444 14 3.171 6.383 11 3.086 6.702 60 ns 20 3.014 6.648 13 3.165 6.402 12 3.258 6.881 70 ns 14 2.927 6.680 19 3.293 6.671 10 3.223 6.552 80 ns 18 3.028 6.872 17 3.478 6.392 11 3.400 6.840 90 ns 16 2.969 6.720 17 3.319 6.397 12 3.163 7.007 100 ns 15 3.072 6.742 26 3.322 6.537 15 3.017 6.907 Average 15.36 2.75 5.59 16.45 3.07 5.75 12.36 3.07 6.03 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx SupplementaryMaterial2.xlsx VaccineTLR2SimulationVideo.mpeg VaccineTLR3SimulationVideo.mpeg VaccineTLR4SimulationVideo.mpeg 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-1748441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":114026732,"identity":"a79bde9a-6ea6-48e6-9a07-2d97f631e8be","order_by":0,"name":"Mukhtar Ahmad Dar","email":"","orcid":"","institution":"National Institute of Pharmaceutical Education and Research (NIPER)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mukhtar","middleName":"Ahmad","lastName":"Dar","suffix":""},{"id":114026733,"identity":"ebff7251-57cb-4fd0-a7c8-2612180a9a10","order_by":1,"name":"Pawan 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regions\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/57fab51befb37605537bedd7.png"},{"id":22953820,"identity":"c3d143a5-65fa-48f4-a2b1-4a77ef6d0bf9","added_by":"auto","created_at":"2022-06-22 17:34:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eDepicting designed multi-epitope vaccine construct and amino acid sequence. B cell and HTL epitopes were linked using GPGPG linkers, CTL epitopes were linked using AAY linkers and adjuvant was connected to N-terminal with EAAAK linker. \u003cstrong\u003e(B) \u003c/strong\u003ePredicted secondary structure of the epitope vaccine showing 37% alpha-helix, 14% beta-strand and 49% coils\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/2816b2d18760f1b34aac3636.jpg"},{"id":22953821,"identity":"5f532a23-c86b-4080-a3a5-223ddfd3f51c","added_by":"auto","created_at":"2022-06-22 17:34:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":194455,"visible":true,"origin":"","legend":"\u003cp\u003eVaccine construct 3D structure modelling, refinement and validation. \u003cstrong\u003e(A) \u003c/strong\u003eTertiary structure of the vaccine construct using RaptorX \u003cstrong\u003e(B)\u003c/strong\u003e Refined 3D model of the vaccine construct using Galaxy Refine (C) Initial Ramachandran plot showing 87.2% residues in most favoured regions, 12.5% residues in additional and generously allowed regions, and 0.4% residues in disallowed regions \u003cstrong\u003e(D)\u003c/strong\u003e Ramachandran plot after refinement showing 92.1% in favour, 6.8% is allowed\u0026nbsp;and 0.4% in disallowed regions of protein residues \u003cstrong\u003e(E)\u003c/strong\u003e Z\u0026nbsp;score of refined model using ProSA-web showing a score of -5.2\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/0bc6a5f3273553ebba02a7c5.jpg"},{"id":22954535,"identity":"f54d06c6-cfa3-4af9-b83d-9647f79e855e","added_by":"auto","created_at":"2022-06-22 17:39:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143250,"visible":true,"origin":"","legend":"\u003cp\u003eDiscontinuous B cell epitopes in the 3Dmodel of the designed construct. \u003cstrong\u003e(A-E)\u003c/strong\u003e The grey sticks represent the bulk of the peptide construct and the yellow surfaces depict the residues of discontinuous B cell epitopes\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/978dae0e11aaf65595c1d596.jpg"},{"id":22955314,"identity":"94ff1a74-b4b7-4ee4-8ee9-ee8ddf08a1ff","added_by":"auto","created_at":"2022-06-22 17:44:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":593075,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking and\u0026nbsp;intermolecular hydrogen bonds \u003cstrong\u003e(A)\u003c/strong\u003e Vaccine - TLR2 docked complex \u003cstrong\u003e(B) \u003c/strong\u003eVaccine - TLR3 docked complex\u003cstrong\u003e (C)\u003c/strong\u003e Vaccine - TLR-4 docked complex \u003cstrong\u003e(D, E, F )\u003c/strong\u003e Hydrogen bonds and hydrophobic interactions between vaccine and 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state (cells per mm\u003csup\u003e3\u003c/sup\u003e)\u0026nbsp;\u003cstrong\u003e(F)\u003c/strong\u003e TC cell population (cells per mm\u003csup\u003e3\u003c/sup\u003e) \u003cstrong\u003e(G) \u003c/strong\u003eTC cell population per state (cells per mm\u003csup\u003e3\u003c/sup\u003e)\u0026nbsp;\u003cstrong\u003e(H)\u003c/strong\u003e Concentrations of Cytokines and Interleukins production \u003cstrong\u003e(I)\u003c/strong\u003e Macrophage population per state (cells per mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/ad333d1a0ce5ae1277e3cbab.jpg"},{"id":24205516,"identity":"5bf720e8-5599-4b20-8897-a41e647843cf","added_by":"auto","created_at":"2022-07-22 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17:34:52","extension":"mpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18186332,"visible":true,"origin":"","legend":"","description":"","filename":"VaccineTLR3SimulationVideo.mpeg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/97eb3c544204bea71696f34f.mpeg"},{"id":22953825,"identity":"72f7676b-54e4-4dc0-ac38-de3a918b4d92","added_by":"auto","created_at":"2022-06-22 17:34:51","extension":"mpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12211545,"visible":true,"origin":"","legend":"","description":"","filename":"VaccineTLR4SimulationVideo.mpeg","url":"https://assets-eu.researchsquare.com/files/rs-1748441/v1/3661802337cd52e90bb82fd6.mpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Designing of peptide based multi-epitope vaccine construct against gallbladder cancer using immunoinformatics and computational approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global burden of cancer is increasing at a rapid pace with more than 19.3 million new incident cancers and 9.9 million deaths estimated in 2020.\u003csup\u003e\u0026nbsp;1,2\u0026nbsp;\u003c/sup\u003eGlobally, Gallbladder cancer (GBC) is the 24\u003csup\u003eth\u003c/sup\u003e most common cancer with incidence of 115949 new cases and 21\u003csup\u003est\u003c/sup\u003e leading cause of death with an estimated 84695 deaths in 2020.\u003csup\u003e1\u003c/sup\u003e GBC is the most frequently diagnosed biliary tract cancer with poor prognosis and high fatality rate.\u003csup\u003e3\u003c/sup\u003e The median survival of GBC is less than one year with overall five year survival rate ranging from 5-20%.\u003csup\u003e4-6\u003c/sup\u003eThe poor survival of GBC is mostly associated with asymptomatic early stages consequently leading to late stage diagnosis on clinical presentation. More than 9% of GBC are diagnosed in advanced stages or metastatic phase.\u003csup\u003e7,8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eGlobally, the incidence of GBC varies with geographic locations and other concomitant risk factors. The world age standardized incidence and mortality rates (ASR) of GBC were 1.2 and 0.84 respectively. Data from Global Cancer Observatory-2020 suggests highest incidence associated with GBC in Asia (70.8%) followed by Europe (10.8%), South America (6.8%), Africa (4.7%) and Northern America (4.5%).\u003csup\u003e2,9\u003c/sup\u003eAmong these GBC is highly prevalent in India, Pakistan, Japan, Korea, Chile, Ecuador, Bolivia, Czech Republic, Poland and Slovakia.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eStudies have identified cholelithiasis and cholecystitis as major risk factors associated with development of GBC.\u003csup\u003e11-13\u0026nbsp;\u003c/sup\u003eAdditionally female gender and \u003cem\u003eSalmonella\u003c/em\u003e infection are also recognized risk factors. GBC affects females 2-6 times more frequently than males.\u003csup\u003e5\u0026nbsp;\u003c/sup\u003eThe age standardized incidence rate of GBC is higher in females (1.8) as compared to male counterparts (0.97).\u003csup\u003e9\u0026nbsp;\u003c/sup\u003eHowever the association of different risk factors with prognosis of GBC is poorly understood.\u003c/p\u003e\n\u003cp\u003eCharacterized by asymptomatic progression and lack of specific biomarkers for early detection, most of the GBC cancers present in advanced stages with aggressive tumour biology. Removal of gallbladder (surgical resection) is recognized as the best treatment plan for management of GBC.\u003csup\u003e14,15\u003c/sup\u003e However, following curative surgery the recurrence rate is high (35%) and prognosis remains relatively poor particularly in advanced disease.\u003csup\u003e16\u003c/sup\u003eApart from surgery, chemotherapy with gemcitabine and oxaliplatin or gemcitabine and cisplatin are the mainstay of the chemotherapy.\u003csup\u003e17\u003c/sup\u003e The potential benefits of adjuvant radiation therapy in GBC is unclear due to lack of evidence regarding its impact on clinical outcomes and overall survival.\u003csup\u003e17\u003c/sup\u003eDespite the curative intent radical surgery and several combinations of cytotoxic drugs, the GBC has been difficult to treat and the results have been dismissal. As a result, the discovery of novel alternative therapeutic interventions for the treatment of GBC assumes critical importance.\u003c/p\u003e\n\u003cp\u003eThe overall survival, diagnosis, prophylactics, and prognosis in most common cancers have improved as a result of advances in drug discovery, diagnostics and screening technologies. However, because GBC is an uncommon malignancy, research into its diagnosis and therapy is very limited and there is currently no effective treatment available. Malignant cells evade immune recognition by escaping T lymphocytes, natural killer cells and exploiting the immunosuppressive tumour microenvironment.\u003csup\u003e18\u0026nbsp;\u003c/sup\u003eImmunotherapies (immune check point inhibitors and monoclonal antibodies) have lately received a lot of traction as promising\u0026nbsp;cancer therapeutic approaches. Vaccines with ability to stimulate the immune system to recognise and eliminate the cancer cells represent another novel and effective approach towards GBC treatment.\u003csup\u003e19-21\u0026nbsp;\u003c/sup\u003eAn effective vaccine can provide a long lasting immunity via immunological memory cells crucial for decreasing the incidence and mortality associated with GBC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdvanced ImmunoInformatics has revolutionized the field of immunological research and is becoming a powerful tool in designing and development of effective vaccines.\u003csup\u003e22\u003c/sup\u003e As immune response plays a crucial role in fighting cancer cells, ImmunoInformatics approaches allows for identification of the potential immunogenic and antigenic T and B cell epitopes that trigger a desired immune response.\u003csup\u003e23,24\u0026nbsp;\u003c/sup\u003eEpitope based vaccines particularly peptide based multi-epitope vaccines provide uniquely designed cost and time effective therapeutic strategy capable of inducing simultaneous strong humoral and cellular immune response.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eRecognizing the need for early diagnosis and treatment, researchers have identified several potential targets for detection and treatment of GBC.\u003csup\u003e19, 20, 26\u0026nbsp;\u003c/sup\u003eThree proteins including 5ʹ-Nucleotidase isoform 2 (NT5E), Aminopeptidase N (ANPEP) and Membrane metallo-endopeptidase (MME) are reported to be over expressed in different stages of GBC.\u003csup\u003e12,27, 28\u003c/sup\u003e The over expression of these proteins in different cancers including GBC suppress T cells and promote cancer progression.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to design a multi-epitope vaccine candidate against GBC based on NT5E, ANPEP and MME antigenic proteins using reverse vaccinology and immunoinformatics tools. To our knowledge, this is the first study using ImmunoInformatics/computational immunology approaches for designing peptide based multi-epitope vaccine candidate against GBC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eIdentification of target proteins and sequence retrieval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the significance of NT5E, ANPEP and MME in cancer development, progression, metastasis and angiogenesis as well as reported over expression in different stages of GBC, these proteins were selected for epitope prediction and designing of potential vaccine candidate for controlling the progression of GBC. The sequences of selected proteins in FASTA format were retrieved from UniProtKB database for subsequent analysis. The physicochemical properties including stability and hydopathicity of the selected proteins were analysed in Protparam server (https://web.expasy.org/protparam/).\u003csup\u003e30\u003c/sup\u003e The flow diagram presented in \u003cstrong\u003esupplementary figure 1 \u003c/strong\u003edepicts the process of vaccine construct development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of epitopes from target proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction, screening and selection of Cytotoxic T- Lymphocyte (CTL) epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTL epitopes were predicted using IEDB MHC-I binding server (http://tools.iedb.org/mhci/).\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eArtificial neural network method with HLA allele reference set was used for CTL prediction to cover most of the population.\u003csup\u003e32, 33 \u003c/sup\u003eThe predicted CTL epitopes are ranked based on IC50 value where lower IC50nM indicates higher MHCI-I binding affinity. An IC50 of \u0026lt;50nM indicate high affinity, \u0026lt;500nM moderate affinity and \u0026lt;5000nM are considered low affinity peptides. For this study IC50 of \u0026lt;500nM was used as cut-off for CTL epitope prediction and selection for vaccine construct. The predicted epitopes with IC50 of \u0026lt;500nM were checked for immunogenicity, antigenicity, allergenicity and toxicity.\u003csup\u003e31, 34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction, screening and selection of Helper T- Lymphocyte (HTL) epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHTL epitopes were predicted using IEDB-MHCII server (http://tools.iedb.org/mhcii/).\u003csup\u003e35\u003c/sup\u003e The NN-align 2.3 technique was selected as prediction method and human HLA-DR was chosen as species/locus.\u003csup\u003e36,37\u003c/sup\u003e The HTL epitopes with 15-mer length were retrieved and ranked on the basis of IC50 value with lower IC50 indicating higher MHCI-II binding affinity.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe predicted HTL epitopes with IC50 \u0026lt;500nM were evaluated for interferon-gamma (IFN- \u0026gamma;) inducing activity using IFNepitope server (https://webs.iiitd.edu.in/raghava/ifnepitope/predict.php). This server uses IFN- \u0026gamma; inducing and non-inducing datasets of MHC-II binders for prediction and designing of HTL epitopes capable of generating an IFN- \u0026gamma; response.\u003csup\u003e39 \u003c/sup\u003eThe IFN- \u0026gamma; positive epitopes were further evaluated for antigenicity, immunogenicity, allergenicity and toxicity. Finally immunogenic, antigenic, non-allergic, non-toxic and IFN- \u0026gamma; positive HTL epitopes were selected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMHC-I and MHC-II population coverage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IEDB population coverage server (http://tools.iedb.org/population/) was used to examine the selected CTL and HTL epitopes corresponding to MHC I \u0026amp; II families and binding leukocyte antigens.\u003csup\u003e40\u003c/sup\u003e The server computes the distribution and proportion of population anticipated to respond to the selected epitopes. Moreover, the server also calculates the total numbers of epitope hits recognised by the total population. CTL and HTL epitopes searched depending on geographic locations and the corresponding HLA genotypic frequencies are derived. Population coverage was examined for world population, subcontinents as well as countries like India, Japan, South Korea and Chile among others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction, screening and selection of linear B-cell epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB-cell epitopes were predicted using ABCpred (http://crdd.osdd.net/raghava/abcpred/) and BCPred (http://ailab-projects1.ist.psu.edu:8080/bcpred/) servers. The ABCpred web server used recurrent neural network for epitope prediction with an accuracy of 65.98%.\u003csup\u003e41 \u003c/sup\u003eThe BCPred server prediction is based on support vector machine algorithm.\u003csup\u003e42\u003c/sup\u003e The epitope length in BCPred was set at 16-mer in both servers using overlap filter. Epitopes with predicted score of \u0026le; 0.9 in BCPred and \u0026gt;.5 in ABCpred were further evaluated for antigenicity, allergenicity and toxicity properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of antigenic, immunogenic, allergic and toxic properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntigenicity of the predicted epitopes was examined using VaxiJen v.2.0 server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) with target set to tumour and antigenicity threshold of 0.5. This server uses alignment independent technique for antigenicity prediction based on the physicochemical parameters.\u003csup\u003e43\u003c/sup\u003e The immunogenicity of the T cell epitopes was examined using IEDB Class-I (http://tools.iedb.org/immunogenicity/) and Class-II (http://tools.iedb.org/CD4episcore/) Immunogenicity servers.\u003csup\u003e44, 45\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe allergenicity of the predicted epitopes was analysed using AllerTOP v. 2.0 server (https://www.ddg-pharmfac.net/AllerTOP/). AllerTOP uses auto cross covariance transformation algorithm and physicochemical characteristics of the proteins for allergenicity prediction. The server classifies epitopes as probable allergens or probable non-allergens on the basis of k-nearest neighbour algorithm.\u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eEach predicted epitope was subjected to \u003cem\u003eIn-silco\u003c/em\u003e toxicity analysis using ToxinPred web server (http://crdd.osdd.net/raghava/toxinpred/). ToxinPred is used in prediction and designing of non-toxic proteins/peptides.\u003csup\u003e47\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesigning of vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop scoring epitopes were selected and linked together for designing of the final vaccine construct. The GPGPG linkers were added to link B-cell and HTL epitopes where as AAY linkers were used to connect CTL epitopes. To enhance the immunogenicity of the vaccine construct, human \u0026beta;-defensin 3 (hBD3) was used as an adjuvant. The adjuvant was connected to the construct using EAAAK linker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of construct physicochemical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe physicochemical parameters were examined using ProtParam tool (https://web.expasy.org/protparam/).\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eThese parameters included molecular weight, isoelectric point, atomic composition, stability, and hydopathicity. The solubility was predicted using Protein-Sol web server (https://protein-sol.manchester.ac.uk/). The expected solubility value of \u0026gt;0.45 is estimated to have a higher solubility than the experimental dataset.\u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of Secondary structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/) was used to assess the secondary structure of the designed vaccine sequence.\u003csup\u003e50 \u003c/sup\u003ePSIPRED uses two stage neural networks to predict the 2D structure including \u0026alpha;-helix, \u0026beta;-pleated sheets and coils.\u003csup\u003e51\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of tertiary structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RaptorX (http://raptorx.uchicago.edu/) was used to perform the homology modelling for prediction of the tertiary structure.\u003csup\u003e52 \u003c/sup\u003eThe server is a template based modelling tool for predicting 3D model and assigns a rank to the predicted models on the basis of root mean square deviation (RMSD) score. The template based threading and alignment quality predictions are the key components of RaptorX.\u003csup\u003e53\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTertiary structure refinement and validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe template based 3D model generated in the RaptorX was further refined using Galaxy Refine web server (http://galaxy.seoklab.org/refine).\u003csup\u003e54\u003c/sup\u003e The refinement helps in improving the quality of the 3D model. Galaxy Refine uses molecular dynamics simulation to achieve recurrent structure perturbation and overall structural relaxation through side chain repacking.\u003csup\u003e55\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe quality of the refined 3D model was validated using ERRAT and PROCHECK tools in SAVES v6.0 web server.\u003csup\u003e56-60\u003c/sup\u003e ERRAT analyzes the overall quality of the 3D model and PROCHECK verifies the stereo-chemical quality by generating a Ramachandran plot of protein residues.\u003csup\u003e56, 59\u003c/sup\u003e. The validation of the final 3D model was further verified through ProSA web server (https://prosa.services.came.sbg.ac.at/prosa.php) which compares the predicted model with known structures of proteins using NMR spectroscopy and X-ray analysis.\u003csup\u003e61\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of discontinuous B-cell epitopes \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe discontinuous B-cell epitopes of validated 3D model were predicted using ElliPro server (http://tools.iedb.org/ellipro/).\u003csup\u003e62\u003c/sup\u003e The server predicts the surface accessible nearby cluster residues based on their protrusion index (PI) values. This score is calculated by taking the average PI value of each residue. ElliPro came out on top when compared to other structure-based techniques for predicting epitopes, with an AUC value of 0.732.\u003csup\u003e62, 63\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking with immune receptors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interaction between the designed construct and TLRs is crucial for immune response generation. TLR2, TLR3 and TLR4 were selected for docking purposes. The vaccine-receptor docking was performed using HDOCK server (http://hdock.phys.hust.edu.cn/) to examine the binding affinity/energy of the docked complex.\u003csup\u003e64 \u003c/sup\u003eThe server uses hybrid algorithm for protein-protein docking methodologies.\u003csup\u003e65 \u003c/sup\u003eHDOCK provides a total of 100 docked complex predictions ranked on the basis of docked energy scores and ligand RMSD.\u003c/p\u003e\n\u003cp\u003eThe vaccine-receptor interactions were visualized in LigPlot+ v.2.2 server (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/). LigPlot+ generates schematic 2D vaccine-receptor interaction diagrams representing intermolecular hydrogen bonds and hydrophobic interactions.\u003csup\u003e66\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation of vaccine-receptor complexes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stability and strength of the docked complexes were examined through molecular simulation.\u003csup\u003e67\u003c/sup\u003e The simulation was performed for each vaccine receptor docked complex for 100ns using Desmond\u0026rsquo;s System builder panel with OPLS_2005 force feild.\u003csup\u003e68\u003c/sup\u003e Before running MD simulation, the system was equilibrated using the relax model system protocol. Ligand-receptor complexes were prepared by solvating with TIP3P water molecules, periodic boundary conditions established as a cubic box using buffer technique and a distance threshold of 10. Dynamics panel was set on default parameters, trajectory was saved every 100ps and the Like Energy was captured at 1.2ps. At 300 K (temperature) and 1.01325 pressure, the volume of the box was equilibrated with the NPT ensemble (pressure).\u003c/p\u003e\n\u003cp\u003eThe Noose-Hoover chain temperature coupling with 1.0 ps relaxation time and Martyna-Tobias-Klein pressure coupling 2.0 ps relaxation time of Isotropic style were used during the simulation run at 2 (fs) time step. The short-range method\u0026apos;s cut-off for columbic interaction was 9.0 radius.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune simulation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generation of immunological response of the designed vaccine was demonstrated through immune simulation using C-ImmSim server (https://kraken.iac.rm.cnr.it/C-IMMSIM/).\u003csup\u003e69 \u003c/sup\u003eThe prediction of immune response is based on stimulation of three major anatomical systems including lymph node, thymus and bone marrow in mammals. Three injections were administered at 1, 84 and 168 time steps (each time step corresponds to 8 hours indicating that vaccine was administered at an interval of 0, 28 and 56 days). The simulation volume was set at 50 and total simulation time was 1050 time steps (350days) and other parameters were kept as default.\u003csup\u003e70\u003c/sup\u003eThe output of C-ImmSim server provides the stimulation of different immunological cells and predicts the humoral immune response through immunoglobulins, cytokines and interleukin production during immune simulation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of target proteins and sequence retrieval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e5ʹ -Nucleotidase isoform 2 (NT5E) also called as ecto-5ʹ-Nucleotidase (CD73) is a surface enzyme present on B and T lymphocytes in humans.\u003csup\u003e27 \u003c/sup\u003eCD73 encoded by NT5E gene converts adenosine monophosphate (AMP) into adenosine. Several studies have reported over expression of NT5E in GBC leading to accumulation of adenosine.\u003csup\u003e27, 70\u003c/sup\u003e The free adenosine produced by NT5E suppresses cellular immune responses through expansion of various immune suppressing cells like regulatory T cells and tumour associated macrophages while down regulating the natural killer T (NKT) cells and helper T (Th1) cells allowing malignant cells to evade detection.\u003csup\u003e71-73\u003c/sup\u003e Over expression of NT5E is negatively correlated with survival period in GBC.\u003csup\u003e27\u003c/sup\u003e In the tumour microenvironment, over expressed adenosine promotes tumour growth, invasion, angiogenesis, and metastasis while suppressing anticancer immune responses.\u003csup\u003e29, 70 \u003c/sup\u003eAs a result, it has been identified as a promising therapeutic target for controlling GBCprogression.\u003csup\u003e74\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAminopeptidase N (ANPEP) also referred to as CD13 is a membrane bound zinc metallo-enzyme expressed in macrophages and other human cells.\u003csup\u003e75\u003c/sup\u003e Over expression of ANPEP has been linked with tumour size, differentiation and metastasis in pancreatic, gastric, breast and gallbladder cancers.\u003csup\u003e28,29 \u003c/sup\u003eSanz et al. identified ANPEP activity in tumour tissue and plasma as an independent predictor and prognostic factor of 5-year survival in patients with colorectal cancer.\u003csup\u003e75 \u003c/sup\u003eANPEP is a ubiquitous enzyme with moonlighting functional roles and expressed in normal cells limiting its applicability as a potential therapeutic target. However, evidence suggests that the ANPEP expressed by cancer cells is different in function and activity as compared to normal tissues.\u003csup\u003e76 \u003c/sup\u003eThe activities of ANPEP have been linked to the progression of a number of malignancies, suggesting that it may have considerable potential in anticancer therapy.\u003c/p\u003e\n\u003cp\u003eMembrane metallo-endopeptidase (MME) or Neprilysin is also membrane bound zinc metalloproteinase implicated in promoting cancer progression.\u003csup\u003e29\u003c/sup\u003e MME over expression has been reported in breast, lung, hepatocellular and gallbladder cancers and has been associated with poor prognosis.\u003csup\u003e77\u003c/sup\u003e Researchers have also identified MME as a potential target for GBC treatment.\u003c/p\u003e\n\u003cp\u003eSequences of selected proteins including NT5E (Uniprot ID- P21589), ANPEP (Uniprot ID- P15144) and MME (Uniprot ID- P08473) in FASTA format were retrieved from UniProtKB database. The stability of the proteins was verified by using ProtParam server. The instability index of NT5E, ANPEP and MME was found to be 32.59, 36.17 and 37.62 respectively indicating that selected proteins were stable. The physicochemical properties of the selected proteins were examined and are provided in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. The FASTA sequences were subsequently used for B and T-cell epitope prediction for vaccine construction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of CTL and HTL epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CTL and HTL epitopes were predicted and selected on the basis of IC50 (\u0026lt;500nM), antigenicity (\u0026gt;0.5), immunogenicity, non-allergenicity and non-toxicity from NT5E, ANPEP and MME proteins vaccine development. The selected CTL epitopes covering different Human Leucocyte Antigen (HLA) super types are depicted in \u003cstrong\u003eTable 1\u003c/strong\u003e. The predicted HTL epitopes were also evaluated for IFN- \u0026gamma; inducing properties. The selected HTL epitopes and corresponding HLA-DR super types with IFN- \u0026gamma; score and antigenicity are shown in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCTL and HTL Population Coverage Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to develop a feasible vaccine candidate relevant to the global population, it is important to take into account the population coverage of the selected T-cell epitopes. The world population coverage of selected CTL and HTL epitopes was 93.78% and 81.81% respectively. The coverage of the individual CTL and HTL epitopes and their respective HLA genotypic frequency is summarized in \u003cstrong\u003eTable 3. \u003c/strong\u003eThe HTL epitopes had higher total HLA hits (48) as compared to CTL epitopes (22).\u003c/p\u003e\n\u003cp\u003eThe CTL and HTL population coverage was also assessed for different geographical regions such as South Asia (82.25%, 73.38%), South-East Asia (83.13%, 56.98), Europe (97.17%, 85.83%), South America (75.46, 58.59%), North America (94.35, 87.89%), East Africa (73.78%, 81.82%) and South Africa (78.07%, 32.10%). The coverage of CTL and HTL epitopes in different sub-continents and countries is shown in \u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of B-cell epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16-mer B-cell epitopes were selected on the basis of binding score (\u0026gt;0.9). The epitopes with high binding score were further evaluated for antigenicity (\u0026gt;0.5), allergenic and toxic properties. The antigenic, non-allergic and non-toxic B-cell epitopes were selected from three proteins as shown in \u003cstrong\u003eTable 4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesigning of multi-epitope vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the predicted epitopes, 7 CTL, 4 HTL and 6 B-cell epitopes were selected for designing of final vaccine construct. The GPGPG linkers were added to link B-cell and HTL epitopes where as AAY linkers were used to connect CTL epitopes. hBD3 (ID-Q5U7J2) a 45 amino acid long adjuvant was added to improve the immunogenicity of the vaccine construct. The adjuvant was linked using EAAAK linkers. The schematic diagram and amino acid sequence of designed vaccine construct is shown in \u003cstrong\u003eFigure 2A.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall antigenicity score was found to be 0.71 indicating that the vaccine construct is highly antigenic and classified as probable non-allergen. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of construct physicochemical properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final construct consisted of 337 amino acids with molecular weight of 36.21 KDa (C\u003csub\u003e1644\u003c/sub\u003eH\u003csub\u003e2513\u003c/sub\u003eN\u003csub\u003e453\u003c/sub\u003eO\u003csub\u003e468\u003c/sub\u003eS\u003csub\u003e7\u003c/sub\u003e) and an isoelectric point of 9.28. The predicted aliphatic index was 70.47 suggesting thermo-stability and instability index of 23.39 confirmed that the vaccine construct was stable. The GRAVY score was found to be -0.445 suggesting its hydrophilic nature. The estimated half-life was predicted as 30 hours in mammalian reticulocytes, \u0026gt;20 hours in Yeast and \u0026gt;10 hours in \u003cem\u003eE. coli.\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of Secondary and tertiary structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe secondary structure consisted of 37% alpha-helix, 14% beta-strand and 49% coils. The 2D structure is shown in \u003cstrong\u003eFigure 2B\u003c/strong\u003e. RaptorX generated five 3D models of the final vaccine sequence with RMSD values ranging from 4.563 to 7.726. Each model was evaluated for overall quality using ERRAT and PROCHECK tools. Model 2 with RMSD-5.2374 was selected on the basis of the overall quality factor in ERRAT (93.7%) and PROCHECK (Ramachandran plot). The Ramachandran plot of Model 2 showed 87.2% of amino acid residues in most favoured, 12.5% in allowed and 0.4% in disallowed regions. The initial 3D model and associated Ramachandran plot are shown in \u003cstrong\u003eFigure 3A, 3C \u003c/strong\u003erespectively\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTertiary structure refinement and validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGalaxy Refine generated five models of which Model-3 with RMSD of 0.359, Mol Probity of 1.965, GDT-HA of 0.9733, Clash score of 12.7and poor rotamers of 0.4 was selected for further analysis.\u003c/p\u003e\n\u003cp\u003eRamachandran plot of the refined 3D model showed 92.1% residues in most favoured, 6.8% in additional allowed regions, 0.8% in generously allowed regions and 0.4% in disallowed regions. Based on the results of Ramachandran plot and ERRAT score, that quality of Model-3 was found to be acceptable. The refined 3D model and its Ramachandran plot are shown in \u003cstrong\u003eFigure 3B, 3D\u003c/strong\u003e respectively. The 3D model was further examined and validated through ProSA Web server with predicted Z-score of -5.2 was in the range of experimentally validated protein structures obtained from X-ray and NMR spectroscopy analysis \u003cstrong\u003e(Figure 3E)\u003c/strong\u003e.The solubility score was found to be 0.523 which shows that construct is soluble upon expression \u003cstrong\u003e(Supplementary Figure 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of discontinuous B-cell epitopes \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of six discontinuous B-cell epitopes were identified by ElliPro server. Almost 5 B-cell epitopes contained 167 amino acid residues present in the main region of the vaccine construct. A score value in the range of 0.67 \u0026ndash; 0.76 was chosen for selection of discontinuous B-cell epitopes \u003cstrong\u003e(Figure 4). \u003c/strong\u003e22 residues were predicted from 60-61, 63, 65-81 and 84-85. 24 residues were predicted from 300-303, 305-314 and 328-337. 60 residues were predicted from 97, 100-110, 121, 124, 125, 128-141, 143-145, 148-161, 182, 184, 185 and 187-197. 13 residues were predicted from 220-231 and 234. 48 residues were predicted from 1-7, 9-11, 13-23, 27, 44-47, 49, 50, 53 and 256-274 \u003cstrong\u003e(Supplementary Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking with immune receptors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe docking of the construct was performed with immune receptors including TLR2, TLR3 and TLR4. 3D docked scores for TLR-2, TLR-3 and TLR-4 were -344.38, -345.38and -324.47 respectively. The docking energy scores indicating the vaccine-receptor complex binding affinity and ligand RMSD are shown in \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e and the vaccine - TLR2, TLR3 and TLR4 docked complexes are shown in \u003cstrong\u003eFigures 5A, 5B and 5C \u003c/strong\u003erespectively. The docked complexes were analysed in LigPlot+ for visualization of intermolecular hydrogen bonds representing vaccine-receptor interactions. For ligand-TLR2 complex, Lys8, Arg207, Ile30, Asn200, Tyr255, Asp259 and Glu333 residues were involved in intermolecular hydrogen bonding (\u003cstrong\u003eFigure 5D\u003c/strong\u003e). Similarly Asp268, Arg262, His336, Asp259, Asn85, Glu87, Asp322, Arg12, Arg14 residues in ligand-TLR3 (\u003cstrong\u003eFigure 5E\u003c/strong\u003e) and Ala299, Arg207, Ser199, Gln29, Lys32, Lys8 residues in ligand-TLR4 (\u003cstrong\u003eFigure 5F\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003ewere identified in LigPlot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular dynamics simulation of receptor-vaccine complex\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Maestro\u0026apos;s Schrodinger simulation event analysis module was used to analyse the trajectories. By superimposing the trajectories over the reference frame, RMSD and RMSF were calculated using trajectories from MD simulation data. This provides an estimate of conformational stability and volatility over the course of the simulation. The amount of hydrogen bonds established between the receptor and the docked ligand during the simulation also suggests the ligand\u0026apos;s binding stability with the receptor. A binding arrangement with a higher number of hydrogen bonds is said to be more stable. For each TLR2, TLR3, and TLR4 receptor complex with ligand, the RMSD was computed. TLR2-complex RMSD demonstrated that after 60ns, the RMSD of TLR2 protein and ligand converged. Throughout the simulation, the protein-ligand complex remained stable. For TLR2 and TLR2 bound ligand, the standard deviations in RMSD were 0.932 and 1.4321, respectively, with average RMSD values of 2.75 and 5.59 (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). For both TLR3 protein and ligand, the RMSD plot of the TLR3-complex revealed convergence after 5ns and remained stable throughout the simulation (\u003cstrong\u003eFigure 6B\u003c/strong\u003e). The RMSD of the ligand was raised around 20-60ns, but the binding site remained unchanged. TLR3 and TLR3 bound Ligand had standard deviations of 0.231 and 0.954, respectively, with average RMSD values of 3.07 and 5.75 (\u003cstrong\u003eFigure 6B\u003c/strong\u003e). The TLR4-complex RMSD data demonstrates convergence after 5ns for both the TLR4 protein and the ligand. Ligand RMSD increased up to 20ns before stabilising toward the end of the simulation. Throughout the simulation, the TLR4-ligand complex maintained its conformational stability (\u003cstrong\u003eFigure 6C\u003c/strong\u003e). This shows that the Protein-ligand complex was stable throughout the simulation. The standard deviation in RMSD was 0.314\u0026Aring; and 0.885\u0026Aring;, while Average RMSD values were 3.07\u0026Aring; and 6.03\u0026Aring;, for TLR4 and TLR4 bound Ligand, respectively.\u003c/p\u003e\n\u003cp\u003eThroughout the simulation, the compactness of the proteins was examined, and the plot revealed that all three proteins were folded correctly. In comparison to TLR3 and TLR4, TLR2 demonstrated changes in compactness during the simulation. The contact between the two molecules is represented by intermolecular hydrogen bonds (H-Bonds), which are implicated in the intensity of binding through the number of H-Bonds. The more H-bonds there are, the more binding or interaction there is between two molecules. Throughout the simulation, the average number of Intermolecular H-bonds between complex TLR2-Ligand (\u003cstrong\u003eFigure 6D\u003c/strong\u003e), TLR3-Ligand (\u003cstrong\u003eFigure 6E\u003c/strong\u003e), and TLR4-Ligand (\u003cstrong\u003eFigure 6F\u003c/strong\u003e) were 15.36, 16.45, and 11.98 respectively. For complex TLR2-Ligand, TLR3-Ligand, and TLR4-Ligand, the range of hydrogen bonds during 100ns simulation over 1000 time frames was 7-24, 6-29, and 6-23, respectively. The number of hydrogen bond interactions at 10ns time intervals with RMSD values is shown in \u003cstrong\u003eTable 5\u003c/strong\u003e. This MD simulation analysis indicates that all three immune receptors efficiently bound with the ligand and showed stable binding throughout the simulation (\u003cstrong\u003eSupplementary Videos\u003c/strong\u003e). The number of hydrogen bonds in 100ns simulation over 1000 time frames and corresponding receptor and ligand RMSD are provided in \u003cstrong\u003eSupplementary material 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe average value of RMSF for TLR2, TLR3, and TLR4 were 1.23 \u0026Aring;, 2.71 \u0026Aring;, and 2.45\u0026Aring;, respectively. RMSF showed fewer fluctuations in TLR3 and TLR4 as compared to TLR2 \u003cstrong\u003eFigure 6G.\u003c/strong\u003e Moreover the ligand\u0026rsquo;s RMSF plot indicates less fluctuation in RMSF values of TLR2 as compared to TLR3 and TLR4 bound ligands. \u003cstrong\u003eFigure 6H \u003c/strong\u003eshows the radius of gyration of all three complexes. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune simulations of final vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe output of C-ImmSim server provides the stimulation of different immunological cells including B-cell, helper-T cell (TH), cytotoxic-T cell (TC), natural killer cell (NK), macrophages (MA) and dendritic cell (DC) population. Moreover, the prediction of immunoglobulins, cytokines and interleukin production during immune simulation is also provided. \u003c/p\u003e\n\u003cp\u003eThe total B-cell population, memory B-cell and active B-cell population increased following each booster vaccine dose and remained stable with minimal decay over the period of 350 days \u003cstrong\u003eFigures 7A, 7B\u003c/strong\u003e. There was a considerable rise in antibody response with every exposure to the vaccine construct with corresponding decrease in the antigen levels. The humoral immune response was characterized by IgG and IgM antibodies and the IgM response was higher as compared to IgG after each vaccination (\u003cstrong\u003eFigure 7C\u003c/strong\u003e).The active TH cells and memory TH cells spiked after second and third vaccine doses which remained elevated up to 350 days (\u003cstrong\u003eFigures 7D, 7E\u003c/strong\u003e). Similarly the TC cell population per state showed steady increase in active TC population (\u003cstrong\u003eFigure 7F, 7G\u003c/strong\u003e)\u003cstrong\u003e. \u003c/strong\u003eThe IFN- \u0026gamma; response was significantly higher after first and second dose and the concentrations of IL-10 and TGF-b also spiked following each vaccination (\u003cstrong\u003eFigure 7H\u003c/strong\u003e).Throughout the simulation, there was also a concurrent rise in the activity of macrophages, dendritic and natural killer cells (\u003cstrong\u003eFigure 7I\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGBC is a biliary tract carcinoma with poor prognosis, high death rate, and a survival rate of \u0026lt;\u0026thinsp;1 year. Gallbladder malignancies are highly aggressive and the current treatments have yielded dismal outcomes.\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e As a result, development of new and innovative therapies for the treatment and improvement of survival in this patient population are urgently needed. As there is no evidence of a vaccine for GBC, this motivated us to fashion a peptide based epitope vaccine to combat the development and progression of GBC.\u003c/p\u003e \u003cp\u003ePriya et al. in a recent study reported significantly higher changes in NT5E, ANPEP and MME proteins in GBC patients as compared to the control groups and highlighted their potential as diagnostics and drug targets for GBC.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The authors reported that NT5E levels (expressed by cancer exosomes) were significantly elevated in advanced stages of GBC; ANPEP was increased in early as well as later GBC while s MME was significantly higher in early stages of GBC. These tumour associated extracellular vesicular proteins are implicated in tumour progression and immune suppression. Consequently, these proteins were selected as potential targets for antigenic epitope prediction to stimulate immune system and combat GBC progression.\u003c/p\u003e \u003cp\u003eReverse vaccinology and ImmunoInformatics approaches in vaccine development is a rapidly developing field. The epitope based vaccines designed using these approaches have demonstrated in vivo efficacy as well as protective immunity with several vaccines undergoing clinical trials.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Several studies have shown promising results on developing epitope vaccines against different cancers like breast cancer, Kaposi sarcoma, colon cancer and cervical cancer.\u003csup\u003e\u003cspan additionalcitationids=\"CR79 CR80 CR81\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e Lately peptide based vaccines have gained traction due to several advantages as compared to the conventional vaccines. Apart from the capability of inducing cancer specific immune response, the peptide vaccines are relatively safe and lower developmental costs.\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCell based immunity is generated upon binding of immunogenic peptides to MHC I \u0026amp; II. The selection of suitable epitopes for designing of a vaccine capable of eliciting a good immune response with maximum population coverage is critical.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e In the study, the CTL and HTL epitopes were selected through rigorous screening, initially predicted epitopes were ranked on the basis of IC50 values and an IC50 value of \u0026lt;\u0026thinsp;500nM was used as threshold. Epitopes with IC50\u0026thinsp;\u0026lt;\u0026thinsp;50nM suggest high MHC I \u0026amp; II binding affinity while IC50\u0026thinsp;\u0026lt;\u0026thinsp;500nM indicate moderate affinity and IC50\u0026thinsp;\u0026lt;\u0026thinsp;5000nM is considered low affinity. The predicted epitopes with \u0026lt;\u0026thinsp;500nM IC50 were further examined for immunogenicity, population coverage, antigenicity, IFN- γ inducing capability and non-allergenicity. Designed with both CD4+, CD8\u0026thinsp;+\u0026thinsp;immunogenic epitopes, this epitope vaccine could elicit a strong long-lasting cellular immunity.\u003c/p\u003e \u003cp\u003eFinally for the designing of epitope vaccine construct, top 7 CTL, 4 HTL and 6 B-cell epitopes were linked using different linkers for designing of final vaccine construct. hBD3 was added as an adjuvant for improved immunogenicity. hBD3 acts as an immune regulator by stimulating monocytes and dendritic cells there by playing a crucial role in activating T cells and cytokine production.\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe safety and efficacy of the vaccine is determined by the population in which it is administered. Representing both MHC I \u0026amp; II alleles, the maximum world population coverage was 93.78% for CTL epitopes and 81.81% for HTL epitopes, making it a promising vaccine candidate. GLOBOCAN 2020 reported highest incidence of GBC in Asia, Europe and South America.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The class I and class II epitopes showed an excellent population coverage in prevalent geographic regions like India (74.02% \u0026amp; 74.99%), Japan (94.26% \u0026amp; 74.83%), Korea (91.97% \u0026amp;85.32%), Chile (86.46% \u0026amp; 67.08%) among others.\u003c/p\u003e \u003cp\u003eFinally, a 337 amino acids vaccine construct was designed and evaluated for stability, antigenicity, and physicochemical properties. The construct was stable as indicated by the predicted aliphatic index and instability index. Solubility is a vital physicochemical property and the construct demonstrated a higher solubility than the soluble \u003cem\u003eE.coli\u003c/em\u003e proteins from the experimental data set.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e Antigenicity and allergenicity are critical factors in multi-epitope vaccine development and because these properties were assessed before designing the construct, the final vaccine was found to be highly antigenic (0.71), non-allergic and non-toxic in nature.\u003c/p\u003e \u003cp\u003eThe secondary and tertiary models of the vaccine were successfully generated and satisfactorily validated. Ramachandran plot of the final 3D model showed more than 90% of the residues in allowed regions and only 0.4% in disallowed regions. For successful generation of immune response, the stimulation of immunological receptors such as TLRs is important. Activation of TLRs in immune and cancer cells is critical in triggering cancer associated immune response through multiple signalling pathways.\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e The binding affinity of the designed construct with TLR2, TLR3 and TLR4 was predicted using molecular docking and the stability of the vaccine-receptor docked complexes was examined through stability, hydrogen bonds and simulation trajectories using molecular dynamics simulation. According to molecular docking experiments, the vaccine has a strong affinity for TLR-2, TLR-3, and TLR-4 receptors. The average numbers of hydrogen bonds for vaccine-TLR 2, 3 and 4 complexes were 15.36, 16.45, and 11.98 respectively and remained consistent over a 100ns simulation period, which is critical for their function.\u003c/p\u003e \u003cp\u003eThe developed vaccine candidate demonstrated an acceptable cellular as well as humoral immune response in the immune simulation study.\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e Because the vaccine contained both CTL and HTL, it showed stimulation of respective immune cells, which may further lead to activation of other potential immune cells such as NK cells, macrophages and dendritic cells via complex signalling. Overall, the results of immune simulation showed that the immune response increased in tandem with each booster dose corresponding to activation of multiple immune cells. Moreover the vaccine construct contained several linear and discontinuous B-cell epitopes suggesting antibody mediated immune response properties which were clearly seen as increased levels of IgG and IgM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eStimulation of immune system is critical in combating cancer and peptide based epitope vaccines have demonstrated the capability of generating a cancer specific immune response. This study reports the designing of a peptide based multi-epitope vaccine construct with a thorough analysis of its immunogenicity, antigenicity, allergenicity and stability using ImmunoInformatics approaches to trigger a robust immune response against GBC. The construct contains both CD4+, CD8\u0026thinsp;+\u0026thinsp;and B-cell epitopes from three different antigenic proteins implicated in GBC progression. The interaction and binding strength of this vaccine construct with TLRs was excellent and the Insilco immune simulation has shown its ability to induce both cellular and antibody mediated immune responses. The promising results in the present study provide a strong basis for further evaluation through in-vitro/in-vivo experimental validation of safety and efficacy of the designed vaccine candidate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMA Dar and P Kumar conceptualized, designed the study, contributed in vaccine designing and performed most \u003cem\u003ein-silco\u0026nbsp;\u003c/em\u003eanalyses. P Kumar, A Shrivastava and A Singh contributed in computational analysis. MA Dar and S Dhingra drafted the initial manuscript. R Chauhan, V Ravichandiran contributed in manuscript writing, editing and revisions. The final version of the manuscript has been read and approved by all the authors for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial or not for profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in the study are included in this article and supplementary material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay, J. \u003cem\u003eet al\u003c/em\u003e. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available from: https://gco.iarc.fr/today (2022).\u003c/li\u003e\n\u003cli\u003eSung, H. \u003cem\u003eet al\u003c/em\u003e. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. \u003cstrong\u003e71, \u003c/strong\u003e209-249; https://doi.org/10.3322/caac.21660 (2021).\u003c/li\u003e\n\u003cli\u003eMahdavifar, N., Pakzad, R., Ghoncheh, M., Gandomani, H. S. \u0026amp; Salehiniya, H. Epidemiology, incidence, and mortality of gallbladder cancer and its relation with development in the world. \u003cem\u003e Trop. Med. Public Health\u003c/em\u003e. \u003cstrong\u003e10\u003c/strong\u003e, 563\u0026ndash;570 (2017).\u003c/li\u003e\n\u003cli\u003eMao, W., Deng, F., Wang, D., Gao, L. \u0026amp; Shi, X. Treatment of advanced gallbladder cancer: A SEER-based study. \u003cem\u003eCancer Med\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 141-150; https://doi.org/10.1002/cam4.2679 (2020).\u003c/li\u003e\n\u003cli\u003eHundal, R. \u0026amp; Shaffer, E. A. Gallbladder cancer: epidemiology and outcome. \u003cem\u003eClin Epidemiol\u003c/em\u003e. \u003cstrong\u003e6\u003c/strong\u003e, 99‐109 (2014).\u003c/li\u003e\n\u003cli\u003eRandi, G., Franceschi, S. \u0026amp; La Vecchi, C. Gallbladder cancer worldwide: Geographical distribution and risk factors. \u003cem\u003e J. Cancer\u003c/em\u003e. \u003cstrong\u003e118\u003c/strong\u003e, 1591\u0026ndash;1599 (2006).\u003c/li\u003e\n\u003cli\u003eZhu, A. X., Hong, T. S., Hezel, A. F. \u0026amp; Kooby, D. A. Current management of gallbladder carcinoma. \u003cem\u003eOncologist\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e, 168\u0026ndash;181; https://doi.org/10.1634/theoncologist.2009-0302 (2010).\u003c/li\u003e\n\u003cli\u003eKiran, R. P., Pokala, N. \u0026amp; Dudrick, S. J. Incidence pattern and survival for gallbladder cancer over three decades\u0026ndash;an analysis of 10301 patients. \u003cem\u003eAnn Surg Oncol\u003c/em\u003e.\u003cstrong\u003e 14\u003c/strong\u003e, 827\u0026ndash;832; https://doi.org/10.1245/s10434-006-9224-4 (2007).\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, International Agency for Research on Cancer. Globocan Gallbladder Cancer fact sheet. Available from: https://gco.iarc.fr/today (2020).\u003c/li\u003e\n\u003cli\u003eSharma, A., Sharma, K.L., Gupta, A., Yadav, A. \u0026amp; Kumar, A. Gallbladder cancer epidemiology, pathogenesis and molecular genetics: Recent update. World J Gastroenterol. \u003cstrong\u003e23\u003c/strong\u003e, 3978-3998; https://dx.doi.org/10.3748%2Fwjg.v23.i22.3978 (2017).\u003c/li\u003e\n\u003cli\u003eLi, Y., Zhang, J. \u0026amp; Ma, H. Chronic inflammation and gallbladder cancer. \u003cem\u003eCancer Lett\u003c/em\u003e. \u003cstrong\u003e345\u003c/strong\u003e, 242\u0026ndash;248; https://doi.org/10.1016/j.canlet.2013.08.034 (2014).\u003c/li\u003e\n\u003cli\u003eKoshiol, J. \u003cem\u003eet al\u003c/em\u003e. Association of inflammatory and other immune markers with gallbladder cancer: Results from two independent case-control studies. \u003cem\u003eCytokine\u003c/em\u003e. \u003cstrong\u003e83\u003c/strong\u003e, 217\u0026ndash;225; https://doi.org/10.1016/j.cyto.2016.05.003 (2016).\u003c/li\u003e\n\u003cli\u003eWistuba, I. I. \u0026amp; Gazdar, A. F. Gallbladder cancer: lessons from a rare tumour. \u003cem\u003e Rev\u003c/em\u003e. \u003cem\u003eCancer\u003c/em\u003e. \u003cstrong\u003e4\u003c/strong\u003e, 695\u0026ndash;706 (2004).\u003c/li\u003e\n\u003cli\u003eGamboa, A.C. \u0026amp; Maithel, S.K. The Landmark Series: Gallbladder Cancer. \u003cem\u003e Surg. Oncol\u003c/em\u003e. \u003cstrong\u003e27\u003c/strong\u003e, 2846\u0026ndash;2858 (2020).\u003c/li\u003e\n\u003cli\u003eIlyas, F.Z., Beane, J.D. \u0026amp; Pawlik, T.M. The State of Immunotherapy in Hepatobiliary Cancers. \u003cem\u003eCells\u003c/em\u003e. \u003cstrong\u003e10\u003c/strong\u003e, 2096; https://doi.org/10.3390/cells10082096 (2021).\u003c/li\u003e\n\u003cli\u003eMargonis, G. A. \u003cem\u003eet al\u003c/em\u003e. Rates and patterns of recurrence after curative intent resection for gallbladder cancer: a multi-institution analysis from the US Extra-hepatic Biliary Malignancy Consortium. \u003cem\u003eHPB\u003c/em\u003e (\u003cem\u003eOxford\u003c/em\u003e). \u003cstrong\u003e18\u003c/strong\u003e, 872-878; https://doi.org/10.1016/j.hpb.2016.05.016 (2016).\u003c/li\u003e\n\u003cli\u003eSong, X. \u003cem\u003eet al\u003c/em\u003e. Overview of current targeted therapy in gallbladder cancer. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e, 230; https://doi.org/10.1038/s41392-020-00324-2 (2020).\u003c/li\u003e\n\u003cli\u003eLang, K., Entschladen, F., Weidt, C. \u0026amp; Zaenker, K. S. Tumor immune escape mechanisms: Impact of the neuroendocrine system. \u003cem\u003eCancer Immunology, Immunotherapy: CII\u003c/em\u003e. \u003cstrong\u003e55\u003c/strong\u003e, 749\u0026ndash;760; https://doi.org/10.1007/s00262-006-0126-x (2006).\u003c/li\u003e\n\u003cli\u003eCappello, P. \u0026amp; Novelli, F. Next generation of cancer immunotherapy calls for combination. \u003cem\u003eOncoscience\u003c/em\u003e, \u003cstrong\u003e4\u003c/strong\u003e, 19\u0026ndash;20; https://doi.org/10.18632/oncoscience.343 (2017).\u003c/li\u003e\n\u003cli\u003eStanculeanu, D. L., Daniela, Z., Lazescu, A., Bunghez, R. \u0026amp; Anghel, R. Development of new immunotherapy treatments in different cancer types. \u003cem\u003eJournal of Medicine and Life\u003c/em\u003e, \u003cstrong\u003e9\u003c/strong\u003e, 240\u0026ndash;248 (2016).\u003c/li\u003e\n\u003cli\u003eFerro, S., Huber, V. \u0026amp; Rivoltini, L. Mechanisms of tumor immunotherapy, with a focus on thoracic cancers. \u003cem\u003eJournal of Thoracic Disease\u003c/em\u003e, \u003cstrong\u003e10\u003c/strong\u003e, 4619\u0026ndash;4631; https://doi.org/10.21037/jtd.2018.07.30 (2018).\u003c/li\u003e\n\u003cli\u003eBahrami, A.A., Payandeh, Z., Khalili, S., Zakeri, A. \u0026amp; Bandehpour, M. Immunoinformatics: \u003cem\u003eIn Silico\u003c/em\u003eApproaches and Computational Design of a Multi-epitope, Immunogenic Protein. \u003cem\u003eInt Rev Immunol\u003c/em\u003e. \u003cstrong\u003e38\u003c/strong\u003e, 307-322; https://doi.org/10.1080/08830185.2019.1657426 (2019).\u003c/li\u003e\n\u003cli\u003eNandy, A. \u0026amp; Basak, S. C. A Brief Review of Computer-Assisted Approaches to Rational Design of Peptide Vaccines. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. \u003cstrong\u003e17\u003c/strong\u003e, 666; https://doi.org/10.3390/ijms17050666 (2016).\u003c/li\u003e\n\u003cli\u003eZhang, L. Multi-epitope vaccines: a promising strategy against tumors and viral infections. \u003cem\u003eCell Mol Immunol\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e, 182-184; https://doi.org/10.1038/cmi.2017.92 (2018).\u003c/li\u003e\n\u003cli\u003eParvizpour, S., Pourseif, M.M., Razmara, J., Rafi, M.A. \u0026amp; Omidi, Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. \u003cem\u003eDrug Discov Today\u003c/em\u003e. \u003cstrong\u003e25\u003c/strong\u003e, 1034-1042; https://doi.org/10.1016/j.drudis.2020.03.006 (2020).\u003c/li\u003e\n\u003cli\u003eLiu, Z. \u003cem\u003eet al\u003c/em\u003e. Circulating Levels of Inflammatory Proteins and Survival in Patients with Gallbladder Cancer. \u003cem\u003eSci Rep\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 5671; https://doi.org/10.1038/s41598-018-23848-8 (2018).\u003c/li\u003e\n\u003cli\u003eXiong, L., Wen, Y., Miao, X. \u0026amp; Yang, Z. NT5E and FcGBP as key regulators of TGF-1-induced epithelial-mesenchymal transition (EMT) are associated with tumor progression and survival of patients with gallbladder cancer. \u003cem\u003eCell Tissue Res\u003c/em\u003e. \u003cstrong\u003e355\u003c/strong\u003e, 365\u0026ndash;374 (2014).\u003c/li\u003e\n\u003cli\u003eWickstr\u0026ouml;m, M., Larsson, R., Nygren, P. \u0026amp; Gullbo, J. Aminopeptidase N (CD13) as a target for cancer chemotherapy. \u003cem\u003eCancer Sci\u003c/em\u003e. \u003cstrong\u003e102\u003c/strong\u003e, 501\u0026ndash;508 (2011).\u003c/li\u003e\n\u003cli\u003ePriya, R. \u003cem\u003eet al\u003c/em\u003e. Plasma-derived candidate biomarkers for detection of gallbladder carcinoma. \u003cem\u003eSci Rep\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 23554; https://doi.org/10.1038/s41598-021-02923-7 (2021).\u003c/li\u003e\n\u003cli\u003eGasteiger E,Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook, 571-607 (2005).\u003c/li\u003e\n\u003cli\u003eKim, Y. \u003cem\u003eet al\u003c/em\u003e. Immune epitope database analysis resource. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, W525-30; https://doi.org/10.1093/nar/gks438 (2012).\u003c/li\u003e\n\u003cli\u003eNielsen, M. \u003cem\u003eet al\u003c/em\u003e. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. \u003cem\u003eProtein Sci\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e, 1007-17; https://doi.org/10.1110/ps.0239403 (2003).\u003c/li\u003e\n\u003cli\u003eLundegaard, C., Lund, O. \u0026amp; Nielsen, M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. \u003cem\u003eBioinformatics\u003c/em\u003e. 24, 1397-8; https://doi.org/10.1093/bioinformatics/btn128 (2008).\u003c/li\u003e\n\u003cli\u003eLundegaard, C. \u003cem\u003eet al\u003c/em\u003e. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e36\u003c/strong\u003e, W509-12; https://doi.org/10.1093/nar/gkn202 (2008).\u003c/li\u003e\n\u003cli\u003eAndreatta, M. \u003cem\u003eet al\u003c/em\u003e. An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics. \u003cstrong\u003e34\u003c/strong\u003e, 1522-1528; https://doi.org/10.1093/bioinformatics/btx820 (2018).\u003c/li\u003e\n\u003cli\u003eNielsen, M. \u0026amp; Lund, O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. \u003cstrong\u003e10\u003c/strong\u003e, 296; https://doi.org/10.1186/1471-2105-10-296 (2009).\u003c/li\u003e\n\u003cli\u003eWang, P. \u003cem\u003eet al\u003c/em\u003e. Peptide binding predictions for HLA DR, DP and DQ molecules. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 568; https://doi.org/10.1186/1471-2105-11-568 (2010).\u003c/li\u003e\n\u003cli\u003eJensen, K. K. \u003cem\u003eet al\u003c/em\u003e. Improved methods for predicting peptide binding affinity to MHC class II molecules. \u003cem\u003eImmunology\u003c/em\u003e. \u003cstrong\u003e154\u003c/strong\u003e, 394-406; https://doi.org/10.1111/imm.12889 (2018).\u003c/li\u003e\n\u003cli\u003eDhanda, S.K., Vir, P. \u0026amp; Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. \u003cem\u003eBiol Direct\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 30; https://doi.org/10.1186/1745-6150-8-30 (2013).\u003c/li\u003e\n\u003cli\u003eBui, H. H. \u003cem\u003eet al\u003c/em\u003e. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. \u003cstrong\u003e7\u003c/strong\u003e, 153; https://doi.org/10.1186/1471-2105-7-153 (2006).\u003c/li\u003e\n\u003cli\u003eSaha, S. \u0026amp; Raghava, G.P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. \u003cem\u003eProteins\u003c/em\u003e. \u003cstrong\u003e65\u003c/strong\u003e, 40-8; https://doi.org/10.1002/prot.21078 (2006).\u003c/li\u003e\n\u003cli\u003eEl-Manzalawy, Y., Dobbs, D. \u0026amp; Honavar, V. Predicting linear B-cell epitopes using string kernels. \u003cem\u003eJ MolRecognit\u003c/em\u003e. \u003cstrong\u003e21\u003c/strong\u003e, 243-55; https://doi.org/10.1002/jmr.893 (2008).\u003c/li\u003e\n\u003cli\u003eDoytchinova, I.A. \u0026amp; Flower, D. R. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 4; https://doi.org/10.1186/1471-2105-8-4 (2007).\u003c/li\u003e\n\u003cli\u003eCalis, J. J. \u003cem\u003eet al\u003c/em\u003e. Properties of MHC class I presented peptides that enhance immunogenicity. \u003cem\u003ePLoS Comput Biol\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 1003266; https://doi.org/10.1371/journal.pcbi.1003266 (2013).\u003c/li\u003e\n\u003cli\u003eDhanda, S. K. \u003cem\u003eet al\u003c/em\u003e. Predicting HLA CD4 Immunogenicity in Human Populations. Front Immunol. \u003cstrong\u003e9\u003c/strong\u003e, 1369; https://doi.org/10.3389/fimmu.2018.01369 (2018).\u003c/li\u003e\n\u003cli\u003eDimitrov, I., Bangov, I., Flower, D.R. \u0026amp; Doytchinova, I. AllerTOP v.2--a server for in silico prediction of allergens. \u003cem\u003eJ Mol Model\u003c/em\u003e. \u003cstrong\u003e20\u003c/strong\u003e, 2278; https://doi.org/10.1007/s00894-014-2278-5 (2014).\u003c/li\u003e\n\u003cli\u003eGupta, S., Kapoor, P., Chaudhary, K., Gautam, A. \u0026amp; Kumar, R. Open Source Drug Discovery Consortium, Raghava GP. In silico approach for predicting toxicity of peptides and proteins. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 73957; https://doi.org/10.1371/journal.pone.0073957 (2013).\u003c/li\u003e\n\u003cli\u003eHebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R. \u0026amp; Warwicker, J. Protein-Sol: a web tool for predicting protein solubility from sequence. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cstrong\u003e33\u003c/strong\u003e, 3098-3100; https://doi.org/10.1093%2Fbioinformatics%2Fbtx345 (2017).\u003c/li\u003e\n\u003cli\u003eNiwa, T. \u003cem\u003eet al\u003c/em\u003e. Bimodal protein solubility distribution revealed by an aggregation analysis of the entire ensemble of Escherichia coli proteins. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. \u003cstrong\u003e106\u003c/strong\u003e, 4201-6; https://doi.org/10.1073/pnas.0811922106 (2009).\u003c/li\u003e\n\u003cli\u003eBuchan, D.W.A. \u0026amp; Jones, D.T. The PSIPRED Protein Analysis Workbench: 20 years on. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e47\u003c/strong\u003e, W402-W407; https://doi.org/10.1093/nar/gkz297 (2019).\u003c/li\u003e\n\u003cli\u003eJones, D.T. Protein secondary structure prediction based on position-specific scoring matrices. \u003cem\u003eJ Mol Biol\u003c/em\u003e. \u003cstrong\u003e292\u003c/strong\u003e, 195-202; https://doi.org/10.1006/jmbi.1999.3091 (1999).\u003c/li\u003e\n\u003cli\u003eK\u0026auml;llberg, M., Margaryan, G., Wang, S., Ma, J. \u0026amp; Xu, J. RaptorX server: a resource for template-based protein structure modeling. Methods \u003cem\u003eMol Biol\u003c/em\u003e. \u003cstrong\u003e1137\u003c/strong\u003e, 17-27; https://doi.org/10.1007/978-1-4939-0366-5_2 (2014).\u003c/li\u003e\n\u003cli\u003ePeng, J. \u0026amp; Xu, J. RaptorX: exploiting structure information for protein alignment by statistical inference. \u003cem\u003eProteins\u003c/em\u003e. \u003cstrong\u003e79\u003c/strong\u003e, 161-71; https://doi.org/10.1002/prot.23175 (2011).\u003c/li\u003e\n\u003cli\u003eHeo, L., Park, H. \u0026amp; Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. \u003cem\u003eNucleic acids research\u003c/em\u003e. \u003cstrong\u003e41\u003c/strong\u003e, W384-8; https://doi.org/10.1093%2Fnar%2Fgkt458 (2013).\u003c/li\u003e\n\u003cli\u003eKo, J., Park, H., Heo, L. \u0026amp; Seok, C. GalaxyWEB server for protein structure prediction and refinement. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, W294-7; https://doi.org/10.1093/nar/gks493 (2012).\u003c/li\u003e\n\u003cli\u003eColovos, C. \u0026amp; Yeates, T.O. Verification of protein structures: patterns of nonbonded atomic interactions. \u003cem\u003eProtein Sci\u003c/em\u003e. \u003cstrong\u003e2\u003c/strong\u003e, 1511-9; https://doi.org/10.1002/pro.5560020916 (1993).\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;thy, R., Bowie, J.U. \u0026amp; Eisenberg, D. Assessment of protein models with three-dimensional profiles. \u003cem\u003eNature\u003c/em\u003e. \u003cstrong\u003e356\u003c/strong\u003e, 83-5; https://doi.org/10.1038/356083a0 (1992).\u003c/li\u003e\n\u003cli\u003eBowie, J.U., L\u0026uuml;thy, R. \u0026amp; Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. \u003cem\u003eScience\u003c/em\u003e. \u003cstrong\u003e253\u003c/strong\u003e, 164-70; https://doi.org/10.1126/science.1853201 (1991).\u003c/li\u003e\n\u003cli\u003eLaskowski, R. A., MacArthur, M. W., Moss, D. S. \u0026amp; Thornton, J. M. PROCHECK - a program to check the stereochemical quality of protein structures. \u003cem\u003e App. Cryst.\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 283-291 (1993).\u003c/li\u003e\n\u003cli\u003eLaskowski, R.A., Rullmannn, J.A., MacArthur, M.W., Kaptein, R. \u0026amp; Thornton, J. M. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. \u003cem\u003eJ Biomol NMR\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 477-86; https://doi.org/10.1007/bf00228148 (1996).\u003c/li\u003e\n\u003cli\u003eWiederstein, M. \u0026amp; Sippl, M. J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e35\u003c/strong\u003e, W407-10; https://doi.org/10.1093%2Fnar%2Fgkm290 (2007).\u003c/li\u003e\n\u003cli\u003ePonomarenko, J. \u003cem\u003eet al\u003c/em\u003e. ElliPro: a new structure-based tool for the prediction of antibody epitopes. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 514; https://doi.org/10.1186/1471-2105-9-514 (2008).\u003c/li\u003e\n\u003cli\u003eFerdous S, Kelm S, Baker TS, Shi J, Martin ACR. B-cell epitopes: Discontinuity and conformational analysis. \u003cem\u003eMol Immunol\u003c/em\u003e. \u003cstrong\u003e114\u003c/strong\u003e, 643-650; https://doi.org/10.1186/1471-2105-9-514 (2019).\u003c/li\u003e\n\u003cli\u003eYan, Y., Tao, H., He, J. \u0026amp; Huang, S.Y. The HDOCK server for integrated protein-protein docking. \u003cem\u003eNat Protoc\u003c/em\u003e. \u003cstrong\u003e15\u003c/strong\u003e, 1829-1852; https://doi.org/10.1038/s41596-020-0312-x (2020).\u003c/li\u003e\n\u003cli\u003eYan, Y., Zhang, D., Zhou, P., Li, B. \u0026amp; Huang, S. Y. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. \u003cem\u003eNucleic Acids Res\u003c/em\u003e. \u003cstrong\u003e45\u003c/strong\u003e, W365-W373. https://doi.org/10.1093/nar/gkx407 (2017).\u003c/li\u003e\n\u003cli\u003eWallace, A.C., Laskowski, R. A. \u0026amp; Thornton, J. M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. \u003cem\u003eProtein Eng\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 127-34; https://doi.org/10.1093/protein/8.2.127 (1995).\u003c/li\u003e\n\u003cli\u003eHansson, T., Oostenbrink, C. \u0026amp; van Gunsteren, W. Molecular dynamics simulations. \u003cem\u003eCurrOpinStruct Biol\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e, 190-6; https://doi.org/10.1016/s0959-440x(02)00308-1 (2002).\u003c/li\u003e\n\u003cli\u003eDesmond Molecular Dynamics System, D. E. Shaw Research, New York, NY. Maestro-Desmond Interoperability Tools, Schr\u0026ouml;dinger, New York, NY, (2021).\u003c/li\u003e\n\u003cli\u003eRapin, N., Lund, O., Bernaschi, M. \u0026amp; Castiglione, F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e, e9862; https://doi.org/10.1371/journal.pone.0009862 (2010).\u003c/li\u003e\n\u003cli\u003eSnider, N.T. \u003cem\u003eet al\u003c/em\u003e. Alternative splicing of human NT5E in cirrhosis and hepatocellular carcinoma produces a negative regulator of ecto-5'-nucleotidase (CD73). \u003cem\u003eMol Biol Cell\u003c/em\u003e. \u003cstrong\u003e25\u003c/strong\u003e, 4024-33; https://doi.org/10.1091/mbc.e14-06-1167 (2014).\u003c/li\u003e\n\u003cli\u003eHuang, S. et al. Role of A2a extracellular adenosine receptor-mediated signaling in adenosine-mediated inhibition of T-cell activation and expansion. \u003cem\u003eBlood\u003c/em\u003e. \u003cstrong\u003e90\u003c/strong\u003e, 1600\u0026ndash;1610 (1997).\u003c/li\u003e\n\u003cli\u003eOhta, A. et al. A2A adenosine receptor may allow expansion of T cells lacking effector functions in extracellular adenosine-rich microenvironments. \u003cem\u003eJ Immunol\u003c/em\u003e. \u003cstrong\u003e183\u003c/strong\u003e, 5487\u0026ndash;5493 (2009).\u003c/li\u003e\n\u003cli\u003eOhta, A. et al. A2A adenosine receptor protects tumors from antitumor T cells. \u003cem\u003eProc Nat Acad Sci\u003c/em\u003e. \u003cstrong\u003e103\u003c/strong\u003e, 13132\u0026ndash;13137 (2006).\u003c/li\u003e\n\u003cli\u003eGhalamfarsa, G. \u003cem\u003eet al\u003c/em\u003e. CD73 as a potential opportunity for cancer immunotherapy. \u003cem\u003eExpert OpinTher Targets\u003c/em\u003e. \u003cstrong\u003e23\u003c/strong\u003e, 127-142; https://doi.org/10.1080/14728222.2019.1559829 (2019).\u003c/li\u003e\n\u003cli\u003eSanz, B. \u003cem\u003eet al\u003c/em\u003e. Aminopeptidase N activity predicts 5-year survival in colorectal cancer patients. \u003cem\u003eJ Investig Med\u003c/em\u003e. \u003cstrong\u003e63\u003c/strong\u003e, 740-6; https://doi.org/10.1097/jim.0000000000000199 (2015).\u003c/li\u003e\n\u003cli\u003eBarnieh, F.M., Loadman, P.M. \u0026amp; Falconer, R. A. Is tumour-expressed aminopeptidase N (APN/CD13) structurally and functionally unique? \u003cem\u003eBiochimBiophys Acta Rev Cancer\u003c/em\u003e. \u003cstrong\u003e1876\u003c/strong\u003e, 188641; https://doi.org/10.1016/j.bbcan.2021.188641 (2021).\u003c/li\u003e\n\u003cli\u003eSu, S. \u003cem\u003eet al\u003c/em\u003e. CD10\u003csup\u003e+\u003c/sup\u003eGPR77\u003csup\u003e+\u003c/sup\u003eCancer-Associated Fibroblasts Promote Cancer Formation and Chemoresistance by Sustaining Cancer Stemness. \u003cem\u003eCell\u003c/em\u003e. \u003cstrong\u003e172\u003c/strong\u003e, 841-856. https://doi.org/10.1016/j.cell.2018.01.009 (2018).\u003c/li\u003e\n\u003cli\u003eParvizpour, S., Razmara, J., Pourseif, M.M. \u0026amp; Omidi, Y. \u003cem\u003eIn silico\u003c/em\u003edesign of a triple-negative breast cancer vaccine by targeting cancer testis antigens. \u003cem\u003eBioimpacts\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 45-56; https://doi.org/10.15171%2Fbi.2019.06 (2019).\u003c/li\u003e\n\u003cli\u003eChauhan, V., Rungta, T., Goyal, K. \u0026amp; Singh, M.P. Designing a multi-epitope based vaccine to combat Kaposi Sarcoma utilizing immunoinformatics approach. \u003cem\u003eSci Rep\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 2517; https://doi.org/10.1038/s41598-019-39299-8 (2019).\u003c/li\u003e\n\u003cli\u003eCorulli, L. R. \u003cem\u003eet al\u003c/em\u003e. Multi-Epitope-Based Vaccines for Colon Cancer Treatment and Prevention. \u003cem\u003eFront Immunol\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e, 729809; https://doi.org/10.3389/fimmu.2021.729809 (2021).\u003c/li\u003e\n\u003cli\u003eSanami, S. \u003cem\u003eet al\u003c/em\u003e. Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches. \u003cem\u003eSci Rep\u003c/em\u003e. \u003cstrong\u003e11\u003c/strong\u003e, 12397; https://doi.org/10.1038/s41598-021-91997-4 (2021).\u003c/li\u003e\n\u003cli\u003eAbd-Aziz, N. \u0026amp; Poh, C. L. Development of Peptide-Based Vaccines for Cancer. \u003cem\u003eJ Oncol.\u003c/em\u003e 2022, 9749363; https://doi.org/10.1155/2022/9749363 (2022).\u003c/li\u003e\n\u003cli\u003eTesta, J.S. \u0026amp; Philip, R. Role of T-cell epitope-based vaccine in prophylactic and therapeutic applications. \u003cem\u003eFuture Virol\u003c/em\u003e. \u003cstrong\u003e7\u003c/strong\u003e, 1077-1088; https://doi.org/10.2217/fvl.12.108 (2012).\u003c/li\u003e\n\u003cli\u003eWeinberg, A., Jin, G., Sieg, S. \u0026amp; McCormick, T.S. The yin and yang of human Beta-defensins in health and disease. \u003cem\u003eFront Immunol\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 294; https://doi.org/10.3389/fimmu.2012.00294 (2012).\u003c/li\u003e\n\u003cli\u003eJavaid, N. \u0026amp; Choi, S. Toll-like Receptors from the Perspective of Cancer Treatment. \u003cem\u003eCancers (Basel).\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 297; https://doi.org/10.3390/cancers12020297 (2020)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: List of CTL epitopes selected for designing of vaccine construct\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.899224806201552%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTL Epitope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRank\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.7984496124031%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigenicity/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImmunogenicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.868217054263566%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllergenicity/ Toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" width=\"87.13178294573643%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMME Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.868217054263566%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eRYGNFDILR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-A*31:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e1.49 and 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*33:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e454.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eTLDDLTWMDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-A*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e54.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e0.85 and 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*02:06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e196.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" width=\"87.13178294573643%\"\u003e\n \u003cp\u003e\u003cstrong\u003eANPEP Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.868217054263566%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eLASYLHTFAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-B*35:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e0.99 and 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-B*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e69.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*30:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e105.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e125.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eASYLHTFAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-A*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e19.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e0.9536 and 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*30:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e31.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-B*35:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e32.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e81.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*32:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e108.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-B*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e129.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" width=\"87.13178294573643%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT5E Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.868217054263566%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eWPAAGAWEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-B*35:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e0.50 and 0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-B*07:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e35.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-B*53:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e235.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eKVLPVGDEV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-A*02:06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e1.1955 and 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e286.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"16.899224806201552%\"\u003e\n \u003cp\u003eVYKGAEVAHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.511627906976744%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.5813953488372094%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.093023255813954%\"\u003e\n \u003cp\u003eHLA-A*23:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.441860465116279%\"\u003e\n \u003cp\u003e23.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.527131782945736%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.7984496124031%\"\u003e\n \u003cp\u003e1.1216 and 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"12.868217054263566%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.659340659340659%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.89010989010989%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eHLA-A*24:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.186813186813186%\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: List of HTL epitopes selected for designing of vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.245989304812834%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHTL PEPTIDE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeq no\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.748663101604278%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.352941176470588%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.449197860962567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALLELE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.689839572192513%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.689839572192513%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIFN-\u0026gamma; score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.898395721925134%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigenicity/ Immunogenicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.16042780748663%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllergenicity/ Toxicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMME Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" width=\"17.245989304812834%\"\u003e\n \u003cp\u003eIQNLKFSQSKQLKKL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.748663101604278%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.352941176470588%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.449197860962567%\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.689839572192513%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"16\" width=\"8.689839572192513%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"16\" width=\"11.898395721925134%\"\u003e\n \u003cp\u003e0.84/92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"16\" width=\"10.16042780748663%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*09:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e152.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e218.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n 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width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e458.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n 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width=\"11.053984575835475%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DPA1*02:01/DPB1*05:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e488.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" width=\"77.97062750333778%\"\u003e\n \u003cp\u003e\u003cstrong\u003eANPEP Protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.882510013351135%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.146862483311082%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"14\" width=\"17.245989304812834%\"\u003e\n \u003cp\u003eFSFSNLIQAVTRRFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.748663101604278%\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.352941176470588%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.449197860962567%\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.689839572192513%\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"14\" width=\"8.689839572192513%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"14\" width=\"11.898395721925134%\"\u003e\n \u003cp\u003e0.63/83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"14\" width=\"10.16042780748663%\"\u003e\n 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width=\"11.311053984575835%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n 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width=\"11.311053984575835%\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*08:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n 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width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n 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width=\"7.1082390953150245%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.946688206785137%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.88529886914378%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.294022617124394%\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.500807754442649%\"\u003e\n \u003cp\u003e103.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" width=\"10.500807754442649%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" width=\"14.378029079159935%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" width=\"12.277867528271406%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n 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\u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*04:05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e387.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*08:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e407.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e484.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DPA1*02:01/DPB1*14:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e282.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DPA1*02:01/DPB1*05:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e283.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DPA1*03:01/DPB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e364.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"13\" width=\"17.245989304812834%\"\u003e\n \u003cp\u003eKLKTLNVNKIIALGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.882352941176471%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.748663101604278%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.352941176470588%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.449197860962567%\"\u003e\n \u003cp\u003eHLA-DRB1*13:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.689839572192513%\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"13\" width=\"8.689839572192513%\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"13\" width=\"11.898395721925134%\"\u003e\n \u003cp\u003e1.0/69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"13\" width=\"10.16042780748663%\"\u003e\n \u003cp\u003eNA/NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB3*02:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*07:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB4*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*11:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*12:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB5*01:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e80.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e195.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*08:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e318.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*15:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e371.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DRB1*03:01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e478.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.311053984575835%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.053984575835475%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.138817480719794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.47557840616967%\"\u003e\n \u003cp\u003eHLA-DPA1*03:01/DPB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.709511568123393%\"\u003e\n \u003cp\u003e121.1\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\u003eTable 3: World population coverage of individual CTL and HTL epitopes (MHC class-I and MHC class-II)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"7.841552142279709%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPITOPES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.688763136620857%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"11\" width=\"63.459983831851254%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHLA Allele (genotypic frequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.254648342764753%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.254648342764753%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.254648342764753%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.254648342764753%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"2.9911075181891675%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal HLA Hits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClass I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*01:01 (10.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*02:01 (24.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.985480943738657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*02:06 (1.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*03:01 (9.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*11:01 (8.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.985480943738657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*23:01 (3.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*24:02 (12.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*30:02 (1.36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.985480943738657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*31:01 (3.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*32:01 (2.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA*33:01 (0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB*07:02 (8.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB*15:01 (5.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB*35:01 (5.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eB*53:01 (1.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRYGNFDILR \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e7.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLDDLTWMDA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e40.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLASYLHTFAY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e32.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eASYLHTFAY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e58.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWPAAGAWEL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e22.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n 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width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRB1*11:01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(6.06)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.985480943738657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRB1*12:01(2.53)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRB1*13:02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(3.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRB1*15:01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(10.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.898366606170599%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDRB3*01:01\u003c/strong\u003e\u003c/p\u003e\n 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width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSFSNLIQAVTRRFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e81.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEAKFPILSANIKAK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e70.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKLKTLNVNKIIALGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e77.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.84789644012945%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpitope Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.692556634304207%\"\u003e\n \u003cp\u003e81.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.119741100323624%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.258899676375404%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.9935275080906147%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e+ Restricted: - Nonrestricted\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: List of B-cell epitopes selected for designing of vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.825581395348838%\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.25581395348837%\"\u003e\n \u003cp\u003eB-cell epitope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003eStart position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003ePredicted Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.662790697674419%\"\u003e\n \u003cp\u003eAntigenicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eAllergenicity/ Toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003eServer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"14.825581395348838%\"\u003e\n \u003cp\u003eMME\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(P08473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.25581395348837%\"\u003e\n \u003cp\u003eQLKKLREKVDKDEWIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.662790697674419%\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.046511627906977%\"\u003e\n \u003cp\u003eBCPred\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.372549019607842%\"\u003e\n \u003cp\u003eGYPDDIVSNDNKLNNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.431372549019606%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.392156862745097%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"14.825581395348838%\"\u003e\n \u003cp\u003eANPEP (P15144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.25581395348837%\"\u003e\n \u003cp\u003ePLFIHFRNNTNNWREI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.662790697674419%\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.046511627906977%\"\u003e\n \u003cp\u003eBCPred\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.372549019607842%\"\u003e\n \u003cp\u003eNAIAQGGEEEWDFAWE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.431372549019606%\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.392156862745097%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"14.825581395348838%\"\u003e\n \u003cp\u003eNT5E (P21589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.25581395348837%\"\u003e\n \u003cp\u003eVVVGGHSNTFLYTGNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.046511627906977%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.662790697674419%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.046511627906977%\"\u003e\n \u003cp\u003eABCpred\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.372549019607842%\"\u003e\n \u003cp\u003eNSSIPEDPSIKADINK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.431372549019606%\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.392156862745097%\"\u003e\n \u003cp\u003eNA/ NT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB-cell epitopes were selected based on binding score (\u0026gt;0.9), high antigenicity, non-allergenicity and non-toxicity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNA: Non-Allergenic, NT: Non-Toxic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVaccine-Receptor complex hydrogen bonds with different frames\u0026nbsp;in MD simulation\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.331259720062208%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"29.704510108864696%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLR-2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"30.48211508553655%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLR-3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"30.48211508553655%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLR-4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrames\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Hydrogen bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Hydrogen bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Hydrogen bonds\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceptor RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand RMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(\u003c/strong\u003e\u0026Aring;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e0 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.180124223602485%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e10 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e2.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e5.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e2.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e5.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e20 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e30 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e2.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e40 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e2.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e4.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e50 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e60 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e70 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.850931677018634%\"\u003e\n \u003cp\u003e6.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.180124223602485%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\"\u003e\n \u003cp\u003e6.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.316770186335404%\"\u003e\n \u003cp\u003e80 ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.540372670807454%\"\u003e\n \u003cp\u003e6.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.335403726708075%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e3.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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GBC","lastPublishedDoi":"10.21203/rs.3.rs-1748441/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1748441/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGallbladder cancer (GBC) is an aggressive and difficult to treat biliary tract carcinoma characterised by late presentation, poor prognosis, and a survival rate of \u0026lt;1 year. GBC is difficult to treat and the current treatments have yielded dismal outcomes. The aim of this study was to design a multi-epitope vaccine candidate against GBC using reverse vaccinology and immunoinformatics tools. \u003c/p\u003e\u003cp\u003eEpitopes from three antigenic proteins (NT5E, ANPEP and MME) were used for designing the epitope vaccine. CTL, HTL and B-cell epitopes were predicted and screened on the basis of immunogenicity, antigenicity, allergenicity, toxicity and IFN-γ inducing properties. The selected epitopes were connected using linkers and suitable adjuvant for designing final vaccine construct. The physicochemical properties were analysed followed by 2D and 3D structure generation, refinement and validation. The binding affinity of vaccine construct with immune receptors (TLR2, 3 and 4) was assessed through molecular docking. The stability of the docked vaccine-receptor complexes were evaluated using 100ns MD simulations. \u003c/p\u003e\u003cp\u003eThe epitope vaccine showed an adequate antibody and cell mediated immune responses through in-silco immune simulation. Our vaccine construct demonstrated good solubility, stability, antigenicity, non-allergenicity and non-toxicity\u0026nbsp;\u0026nbsp;\u0026nbsp;with potential to elicit strong immune responses. However, further experimental research is needed to validate the safety and efficacy of the designed vaccine.\u003c/p\u003e","manuscriptTitle":"Designing of peptide based multi-epitope vaccine construct against gallbladder cancer using immunoinformatics and computational approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-22 17:34:49","doi":"10.21203/rs.3.rs-1748441/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":"f4df8c29-200a-4623-8cc4-0a956cf16320","owner":[],"postedDate":"June 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-22T16:44:15+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-22 17:34:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1748441","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1748441","identity":"rs-1748441","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}