In-silico design of Multi-Epitope Vaccine Against Glioblastoma Using Tumor-Associated Antigens and TLR Agonist Adjuvants

In-silico design of Multi-Epitope Vaccine Against Glioblastoma Using Tumor-Associated Antigens and TLR Agonist Adjuvants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article In-silico design of Multi-Epitope Vaccine Against Glioblastoma Using Tumor-Associated Antigens and TLR Agonist Adjuvants Muhammad Jamil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7225965/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 Glioblastoma multiforme (GBM) is the most severe and chemo-resistant primary brain tumor in adult patients, which is accompanied by high molecular heterogeneity, immunosuppressive microenvironment of the tumor, and ultimately poor prognosis. Traditional treatment plans have shown to be only marginally effective, so we introduced this in-silico approach for developing highly efficient vaccine candidate. An HSPA1A that has high antigenic cross reactivity, extracellular localization, and have no reported allergic reactions was chosen as a chaperone protein. Powerful B-cell, MHC class I and MHC class II epitopes were identified and optimally combined into a single construct using proper linkers and adjuvant sequences thus ensuring high immunogenicity, stability and dual innate receptor stimulation. Physicochemical evaluations also indicated desirable solubility, thermal, and expression outlook aspects. A 3D conformation was obtained via structural modeling on AlphaFold 3 and rigorous validation to obtain a compact and stereochemically robust structure. Docking done with TLR-4 showed good docking activity, and after that, a molecular dynamics simulation was done for 500 ns, which further showed stable protein-ligand conjugates and negligible distortion. Simulated immune responses were predicted to be high at the humoral and T cell levels with the anticipation of memory T cell and B cell formation and high IFN- Y and IL 2. Taken together all these results, the multi-epitope vaccine proposed shows excellent potential to be a risk-free, targeted, and broadly effective immunotherapy tool against glioblastoma and, therefore, must be pursued into limiting preclinical and experimental testing. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Immunology Glioblastoma multiforme multi-epitope vaccine HSPA1A TLR agonist in silico design immunoinformatics molecular dynamics immune simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Introduction Glioblastoma multiforme (GBM) is one of the most aggressive and fatal primary intracranial neoplasms identified in adults taking a percentage of 15 percent of all people with intracranial tumors. With the current improvement in surgical resection, radiotherapy and chemotherapy, GBM still portrays a poor median survival of less than 15 months following diagnosis [1]. The tendency of its infiltrative growth and extensive molecular heterogeneity makes the tumor impervious to treatment and it leads to prompt recurrence. In addition, blood-brain barrier (BBB) limits the delivery of conventional therapeutic agents thus creating a limit to their effectiveness. The problem of such challenges underlines the desperate need in new treatment methods that would be able to overcome the shortcomings of the current standard-of-care treatment styles [2]. At the clinical level, GBM is characterized by rapid growth, a great amount of angiogenesis, necrosis, and a highly immunosuppressive microenvironment. Depending on the tumor location in the cerebral area, the patient will experience seizures, cognitive impairment and motor defit. GM molecularly show complicated genetic changes, particularly TP53 mutation, EGFR amplifications, and PTEN deletions. [3] Although there are several decades of exploration, no treatment intervention has shown sustainable and significant changes to the extent to which patients survive. Immunotherapeutic approaches directed at exploiting host immune system thus have been of high interest [4]. A promising way is vaccine-based immunotherapy. Compared to systemic chemotherapy, the features due to cancer vaccines are high target specificity, minimal toxicity and ability to induce immunological memory. Nevertheless, previous attempts have been mostly toward single-antigen or cell-based preparations, which are deficient in antigen homogeneity and recognition avoidance by the innate immune system [5]. Nor have GBM-specific vaccines received Food and Drug Administration (FDA) approval and the predominately cold immunological microenvironment of the tumor adds further to an ineffective passive modality disease. Rationally created multi-epitope vaccine against a number of tumor-related antigens (TAAs) which simultaneously activates innate immune system will therefore comprise a new powerful modality [6]. Using novel Toll-like receptors (TLR) Agonists as adjuvants can provide a strategic approach to the problem of providing greater immunogenicity in current vaccines. TLRs play vital connecting roles between the innate and adaptive immune systems and agonists of these receptors activate dendritic cells, enhance antigen presentation and guide T-cell responses to a shift toward a Th1-type, and the latter is essential in destruction of tumor cells [7]. Its disease etiology is an important consideration since selection of epitopes based on TAAs and use of TLR-suitable adjuvants presents a potential approach of achieving a wide and long-lasting immune response. The interaction would result in the stimulation of cytotoxic T lymphocytes and the helper T cells in addition to making the process binding between the two cells efficient in tumor recognition and destruction [8]. The aim of the current work is to develop a multi-epitope vaccine to attack glioblastoma by the inclusion of TAAs and TLR agonists as adjuvants. In silico pipeline was established to characterize highly immunogenic and non-allergenic and conserved epitopes that bind strongly with both MHC-I and MHC-II [9]. The proposed combination of structural modeling, molecular docking and immune simulation analyses guaranteed stability, antigenicity, and functional compatibility of the final vaccine construct. The computational method simplifies the process of screening and optimization of the candidates and less time is spent as well as the expenditure is also less as compared to the empirical correction method of trial and error [10]. The possible relevance of this study is that it represents a specific, safe, scalable, therapeutic agent against glioblastoma, a condition, which has little effective treatment. The speed of the translational pathway is increased by in silico methods of candidate evaluation and optimization. The aim of the proposed vaccine product, by combination of tumor-specific antigens and immune-stimulating adjuvants, is to induce a multidirectional and intense immune attack against the GBM cells. Finally, the study may support a preclinical validation and subsequent clinical translation of a next-generation, personalized cancer vaccine against a type of potentially fatal brain cancer [11]. Methodology This study employed a comprehensive in-silico immunoinformatic approach to develop a multi-epitope vaccine candidate targeting pathogenic viral strains. Through the application of computational methodologies and predictive modeling strategies, we systematically identified potential antigenic regions, constructed the vaccine sequence, and validated its structural and immunological properties. The ensuing sections provide a detailed account of the methodology employed. Retrieval and Selection of Target Proteins Protein sequences relevant to the viral strains were retrieved from the UniProt database (https://www.uniprot.org/) in FASTA format. UniProt is a comprehensive resource for protein sequence and function data. It makes it possible to retrieve the protein sequence, which is the initial stage in developing a vaccine [12]. Selection of targeted protein The selection criteria for determining suitable target proteins were based on their antigenicity, allergenicity, and toxicity profiles. Antigenicity was evaluated utilizing the VaxiJen server (http://www.ddg-pharmfac.net/vaxijen/), allergenicity was assessed through the AllerTOP v2.0 platform (https://www.ddg-pharmf ac.net/AllerTOP/) and toxicity was examined using the ToxinPred tool (https://webs.iiitd.edu.in/raghava/toxinpred/). Additionally, transmembrane topology and subcellular localization were determined through the TMHMM server to ensure that the selected proteins were accessible and appropriate for epitope mapping [13]. Epitope Prediction and Filtration The identification of B-cell, MHC class I, and MHC class II T-cell epitopes was performed utilizing the Immune Epitope Database (IEDB) analysis resource (https://www.iedb.org/). B-cell epitopes were predicted using the B-cell prediction server of IEDB, and the most promising sequences were evaluated for high antigenicity, absence of allergenicity, and non-toxicity. MHC class I and II epitopes were predicted employing IEDB tools utilizing ANN 4.0 and NN-align 2.3 methodologies, respectively [14]. Only epitopes characterized by low IC50 values and high predicted immunogenic potential were selected. In the context of glycan-focused vaccine design, as demonstrated in the virus study, epitopes containing N-linked glycosylation sequons were identified through the NetNGlyc 1.0 server . These glycosylation motifs facilitated the mimicry of native antigenic structures, thereby promoting optimal protein folding and immune recognition [15]. Vaccine Construct Assembly The selected epitopes were systematically concatenated utilizing appropriate linkers to ensure proper folding and minimize steric hindrance. GPGPG and AAY linkers were employed to connect MHC-II and MHC-I epitopes, respectively, while SSL, KK, or EAAAK linkers were utilized where flexibility or structural separation was necessary. An immunostimulatory adjuvant, specifically the β-defensin, was incorporated at the N-terminal and C-terminal end of the construct using an EAAAK linker. Moreover, a 6x-His tag was affixed to the C-terminal to facilitate expression and purification. The final vaccine construct underwent thorough evaluation for antigenicity, allergenicity, and toxicity to ascertain its safety and efficacy [13]. Vaccine allergenicity and antigenicity prediction: The allergenicity of the vaccine was evaluated through the utilization of the AllerTOP v.2.0 server (https://www.ddg-pharmfac.net/AllerTOP/). This methodology converts protein sequences into standardized vectors of uniform length by leveraging auto cross-covariance. The outcomes of this analysis indicate whether the protein exhibits non-allergenic or allergenic properties [16]. The antigenicity of the vaccine was assessed utilizing the VaxiJen web server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). This server employs a strategy that obviates the necessity for alignment, concentrating instead on the primary characteristics of amino acids, with a specified threshold [11]. Physiochemical properties A computational tool referred to as pepcal (https://pepcalc.com/) was employed to evaluate physicochemical properties. This tool provides comprehensive information regarding the dimensions of the vaccine’s intended structure, including metrics such as number of residues, solubility, molecular weight, extinction coefficient and isoelectric point [17]. Structural and Biophysical Evaluation The secondary structure of the vaccine construct was predicted using the PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/), which classifies sequences into alpha-helices, beta-strands, and coils [18]. Tertiary structure prediction In the proposed research, the structure of a target protein was predicted by using the AlphaFold 3 (https://alphafoldserver.com/), the latest deep-learning structure prediction framework by DeepMind. The method combines a sequence representation with structural and functional contextual information in an architecture that combines both self-attention operations, equivariant transformers, and spatial graph modeling in a homogeneous manner [19]. The FASTA sequences of the full-length protein were given and AlphaFold 3 used evolutionary experience contained in multiple sequence alignments (MSAs), structural alignments (where available) and high-confidence predicted residue-residue distance maps to point out using all-atom 3D models [20]. Protein structure validation Structural validation was conducted utilizing PROCHECK and the SAVES server (http://services.mbi.ucla.edu/SAVES/) to produce Ramachandran plots and ERRAT scores, thereby evaluating stereochemical quality and model accuracy [17]. Immune Interaction Analysis To evaluate the binding interactions between the vaccine candidate and human immune receptors, molecular docking studies were performed utilizing the ClusPro 2.0 (https://cluspro.org/tut_dock.php) computational server. The docked complexes underwent rigorous analysis concerning interaction energies, confidence scores, and the number of hydrogen bonds generated. The LigPlot+ software (https:/ /www.ebi.ac.uk/thornton-srv/software/LigPlus/ ) facilitated detailed visual representations of molecular interactions, illuminating hydrogen bond networks and binding residues that are critical to the interaction between the vaccine and its receptor [21]. Molecular Dynamics Simulations A structural dynamics study using a molecular dynamics (MD) simulation with a time of 500ns was conducted in the Desmond module, integrated in Schrodinger Maestro suite to investigate the dynamic characteristics and the stability of the protein-ligand complex. The most optimum binding conformation of the ligand was fitted on docking and the docking profile was refined and minimized using Protein Preparation wizard. Incomplete residues were created at this point as well. The resulting docked system was surrounded with an orthorhombic box filled with TIP3P water molecules and physiological conditions (300 K, 1 atm) were simulated via a coupling of the model to a stage-wise simulation with 0.15 M NaCl. The interactions were modeled through OPLS_2005 force field. The simulation was started upon relaxation of the system and was followed over 500 ns where snapshot archiving every 100 ps was done in order to perform simulation analysis [16]. Codon Optimization and In-Silico Cloning The terminal amino acid sequence of the vaccine construct was reverse-translated into a nucleotide sequence utilizing EMBOSS Backtranseq (https://www.ebi.ac.uk/jdispatcher/st/emboss_backtranseq). Subsequently, the codon usage was optimized for expression in Escherichia coli through either the JCat or ExpOptimizer tools (https://www.jcat.de/) (https://kraken.i ac.rm.cnr.it/C-IMMSIM/index.php) For the purpose of in-silico cloning, the optimized nucleotide sequence was incorporated into plasmid vectors such as PET28a(+) or PBR322 using SnapGene software, thereby facilitating prospective experimental expression [22]. Immune Simulation The C-ImmSim server (https://kraken.i ac.rm.cnr.it/C-IMMSIM/index.php) was utilized to simulate the immune response provoked by the vaccine construct. This computational tool models the activation of a variety of immune cells, including CD4+ helper T-cells, CD8+ cytotoxic T-cells, B-cells, as well as the secretion of immunoglobulins and cytokines such as IFN-γ and IL-2. The simulation contrasted responses with and without the adjuvant to evaluate its effect on immune activation and memory formation. These predictions provided valuable insights into the vaccine's potential to elicit a vigorous and lasting immune response [23]. Retrieval and Selection of Target Protein The desired protein, Heat Shock 70 kDa Protein 1A (HSPA1A) was effectively downloaded in the FASTA format in the UniProt database (UniProt ID: HS71A_HUMAN). This is a human protein and is a chaperon protein that is well conserved and had well characterized amino acid sequences. Fig 1 indicates the structure of the host cell protein modeling in 3D using SWISS- model. Selection of Targeted Protein The immunogenic potential of the analyzed protein was found to be remarkably high because its antigenicity score was high. The protein was analyzed as non-allergenic and toxicity profile indicated that it is non-toxic, proving its biosafety in future vaccine design. The TMHMM server (topology prediction) showed that there are no transmembrane helices, and the whole (1 641 amino acids) sequence was located on the extracellular side. This implies that the protein is completely open to recognize by the immune system and is also appropriate to predict an epitope. Fig 2 shows the location of proteins. Prediction of B-cell Epitope The IEDB resource was used to discover B- cell epitopes. Out of these, single linear B-cell epitope of high antigenicity and non-allergenic and non-toxic properties was identified to be included in the final vaccine construct. Table 1 provide lists of its characteristics. Table 1: Selected B-cell Epitope Protein No. Start End Epitope Length Antigenicity 11 351 364 QDFFNGRDLNKSIN 14 0.7316 Prediction of MHC Class I Epitopes Three epitopes derived by MHC class I were chosen depending on the binding affinity (IC50 value), percentile rank, and immunogenicity. All the epitopes were verified using VaxiJen, AllerTOP and ToxinPred and they all claimed antigenicity non-allergic, non-toxic. These are the epitopes that were expected to have a strong affinity to several HLA alleles, as illustrated in Table 2. Table 2: Selected MHC Class I Epitopes Start End Epitope Length IC50 Score Alleles Antigenicity 35 43 RQATKDAGV 9 80.67 HLA-A*02:06 1.0307 14 22 SQNKRAVRR 9 55.18 HLA-A*31:01 0.8747 45 54 VSYKGETKAF 10 96.14 HLA-B*15:01 0.9568 Prediction of MHC Class II Epitopes The selection of the three MHC class II epitopes was recognized to have borne sufficient binding recognition of wide variety of alleles, by the support of anti-genicity and low values of IC50. Epitopes went through the allergenicity and toxicity filters and were good candidates to use in vaccine design. Table 3 includes the list of the chosen epitopes. Table 3: Selected MHC Class II Epitopes Protein No. Start End Epitope Length IC50 Score Alleles Antigenicity score 10 2 16 AFNMKSAVE 15 5.6 HLA-DPA1*01:03/DPB1*04:01 HLA-DPA1*01:03/DPB1*02:01 0.7075 9 30 44 EEIERMVQE 15 24.9 HLA-DQA1*04:01/DQB1*04:02 HLA-DQA1*05:01/DQB1*02:01 0.9149 5 44 58 FEGIDFYTS 15 29.4 HLA-DPA1*01:03/DPB1*04:01 HLA-DPA1*01:03/DPB1*02:01 0.5463 Multi-Epitope Vaccine Construct Design The multi-epitope vaccine construct had succeeded in designing those particular elements of B-cell, MHC I, and MHC II epitopes and was linked together with suitable connectors in a manner that guarantees better folding, immunogenicity, as well as structural steadiness. The construct was started by adding the β-defensin-114 as an immunostimulatory adjuvant at the N-terminal followed by a fusion at an EAAAK linker, which aims at providing a better immunogenic response, and separate the spatially adjacent proteins. MHC class I epitopes were joined through AAY links so that they could stimulate the activation of cytotoxic T lymphocytes whereas MHC class I epitopes were linked with GPGPG links (to promote the activation of helper T cells). The B-cell epitopes have been combined with flexible linkers (GGGGS, EAAAK) to maintain the structural conformation and access of epitopes. C-terminal end contained β-defensin attached with KK linker to increase antimicrobial and immunomodulatory effects. It was followed by 6x-His tag within it to make purification and expression easy. The last construct was assessed and it was antigenic, non-allergenic and non-toxic which means it can be an effective and safe vaccine candidate. Fig 3 . The finalized vaccine construct sequence was: Physicochemical Characterization of the Vaccine Construct The resulting 241 amino acid residue, 27,656.83 g/mol multi-epitope vaccine construct had an effective molecular weight size. The theoretical isoelectric point (pI) was found to be 9.26 and therefore it is basic. The construct had a net positive charge of + 15.9 at physiological pH (7.0), indicating interaction with cell membranes which may be particularly negative. The coefficient of extinction was calculated at 16,640 M -1 cm -1 and this means that the protein could be detected at moderate level in terms of 280 nm during the process of purification. Prediction of solubility indicated the prediction of good water solubility, which is imperative in expression and formulation. Solvent accessibility of the Hopp & Woods Hydropathy analysis reveals that hydrophilic regions predominate over the construct, which corresponds to possible immunogenic accessibility and suitability to the aqueous environment. The set of parameters confirm that the vaccine construct is stable, expressible, and ready to go downstream. In Fig 4, it is calculated that the protein or peptide has an aggregate of both hydrophobic and hydrophilic areas and has a large convert in charge at about exceeding pH, which may have influence on its functionality or interrelationships. Structural and Biophysical Evaluation The multi-epitope vaccine construct tertiary structure was predicted on server PSIPRED. This tool is used to compare the primary amino acid sequence in predicting the probability of assembly of the most significant aspects of the secondary structure namely alpha-helices (pink bars), beta-strands (yellow bars), and random coils (black lines). The prediction also showed that the vaccine construct primarily consists of alpha-helical regions that constitute short b-strands and random coils. Specifically, there were a number of alpha-helices distributed throughout the structure, what makes it stable structurally speaking, and, possibly, could make it even more recognizable by immune cells. Beta-strands may also assist in compact folding and possible beta-sheet formation that has a bearing on protein protein interactions and epitope presentation itself. The black regions are the random coils regions and serve to be the flexible links between structured regions and can help increase the total flexibility and solubility of the structure as depicted in fig 5. Tertiary Structure Prediction and Validation The proposed 3D structure of multi-epitope vaccine construct was compact and stable with a proper spatial orientation of the epitopes and the linker regions in addition to the adjuvant elements. The reliability and stereochemical soundness of the model was ensured by structural validation with SAVES v6.0 suite. In Fig 6 the Ramachandran plot generated by the program PROCHECK it was possible to conclude that 91.2 percent of the residues were in the most favored and 8.3 percent in the additionally allowed and 0. 5 percent in the generously allowed. Importantly, no residues are present in prohibited petals, which shows a valid backbone structure and proper distribution of dihedrals. A overall quality factor of 93.63 percent was the outcome of the ERRAT analysis, which is much higher than a general threshold of 91 percent high-resolution models. This score underlines the portion of high accuracy of the non-bonded invasions of atoms and affirms goodness and stability of the assumed tentative design. Molecular docking Analysis Molecular docking by cluspro reveals that the vaccine candidate has high efficiency to get recognized by TLR-4 receptor. After docking the complex score shows 131 members in total, -1906.3 center energy and -1908.7 of lowest energy. These results clearly signify the efficiency of vaccine candidate with human receptor. In Fig 7 , chain A and chain C showed highest binding with the complex. Interaction Analysis Although the excellent results of docking were enough to validate the construct binding ability. But, we further validate this response with checking interaction analysis. This analysis, as shown in Fig 8 , revealed that 9 hydrogen bonds are present between the c chain of receptor and vaccine candidate. This encompasses strong binding ability of vaccine candidate with TLR-4. Molecular Dynamics Simulations The RMSD profile gauges the structural stability of the complex protein-ligand as it progressed to a full 500 ns. The first oscillation (1020 ns), is again explained by the equilibration leading to the averages to reach the range of 2.6-3.2 A, as shown in Fig 9 . The absence of sharp spikes and long-running drifts shows that the ligand and the protein domain were adequately equilibrated during the trajectory. The persistent backbone deviation highlights the ability of high ligand binding tightness and ligand maintenance of protein global conformation. RMSF plots the motility of individual residues throughout the 500-ns work. Most of the residues are in the range of below 2.0 A with sharp peaks at the N-terminus (residues ~10-30) as well as the C-terminus (~210-230), as represented in Fig 10 . The terminal flexibility is common. The residues at ligand-binding pocket and in the structural core however exhibit very little fluctuations and this is a testimony to their stability. This observation suggests that ligand binding fixed functional regions leaving peripheral loops and terminals free to make natural movements which did not interfere with strength. RMSD Histogram depicts a narrow range of dominant peak (2.8 to 3.0 ) over which most of the exploration of conformations was completed in the 500-ns trajectory. The low count of the distribution width and high frequency in this range confirm the RMSD trajectory, as presented in Fig 11 , which will prove that the majority of the conformations tend to be rather stable with regards to a minimal structural drift over time. SSE shows the backbone helical (red), edge (blue) and sheet (purple) structures shown throughout the 500-ns trajectory. There are no important unfolding to a coil or loop state; core secondary structural elements are maintained. Transient coil regions are only seen in the terminal ends and flexible loops such that the protein is dynamic. The nature of the structure shows that binding of the ligands did not alter the original fold. DCCM heatmap shows a summary of the correlations in the motions of the residues. The structured domains and residues near the active site are characterized by strong positive-correlation motions (blue) suggesting synchronized motions that are highly important to the maintenance of its function. On the other hand, moderate anti-correlated motions (red) are found between loops at great distances and there are flexible domains as a natural form of dynamic breathing. Such clear lines of correlation are a direct indicator of well-developed internal communication within 500 ns. PCA shows the movement of the complex on PC1 and PC2 in the 500-ns simulation. The high concentration of the conformations located in 2D space implies the shortage of the conformational dimorphism, which proves the fact that the protein-ligand complex was in a restricted and stable area of the conformational scenery. The fact that the points are not scattered or distributed in some way indicates that there are no sudden shifts that would occur in the simulation; structural and dynamical convergence is apparent throughout the long simulation. Codon Optimization and In-Silico Cloning Not only was the protein successfully reverse-translated and optimized to express in Escherichia coli, but also have the nucleotide sequence of the vaccine construct was also reverse-translated. Codon Adaptation Index (CAI) rose to 0.78 since it is better adapted to the host translation machinery than it was previously (by 0.64). A GC content of 62.66 percent has been lowered to 51.45 percent, which fits the scope of E. coli expression the best, and lowers the possibility of forming secondary mRNA structures that might slow down the translation process significantly. The refined gene sequence, which is representative of a 241 amino acids protein (~27.64 kDa) was effectively cloned into the pET28a(+) vector with SnapGene software. The construction contained suitable sites of restriction and controls in order to transcribe and translate efficiently. In silico cloning simulation showed that the cloning was successful and as shown in Fig 14 ; the construct was ready to be expressed in a prokaryotic system. Immune Interaction Analysis The immune simulation performed on the C-ImmSim webserver revealed the presence of a robust and dampened immune reaction caused by the multi-epitope vaccines construct. Specific CD4 + T helper cell activation and CD8 + cytotoxic T lymphocyte activation were clearly increased in the number of activated cells on first contact with antigen and more so with each subsequent dose, which is considered the development of effective T-cell memory. B cells were also strongly activated, and active and memory phenotypes were increased accordingly after booster exposures, as well as a sustained humoral immunity. Fig 15 shows graphical illustrations of immune simulation analysis. Class switching and long-term protection showed production of IgG1 and IgG2 finding confirmation in a linear pattern where production of IgM in the early stages of immunoglobulin production was followed by a high concentration of IgG1 and IgG2. There was subsequent enhancement in the formation of antigen antibodies complexes, indicating the successful neutralization of the antigens. A high level of IL-2 and IFN-g were found in the cytokine profile which distinguishes a prevalent Th1-type immune response and an active T-cell proliferation. Effective and controlled stimulation of the immune activation was indicated by the existence of a danger signal in initial stages of simulation, whose deterioration got progressively weaker. Besides, several epitopes in the construct were predicted to bind well to both MHC class I and II alleles and increase the chances of reaching a broad population and enable the presented antigens. All in all, the simulation in favor of the vaccine is its ability to construct cellular immunity, humoral immunity, and long-term immunological memory, which promotes the effectiveness of the said vaccine as a potential immunogen to research. Discussion Glioblastoma multiforme (GBM) is a clinically resistant tumour due to its mixed genomic signature, an immunosuppressive microenvironment, and unruffled reaction to regular treatment schemes. In this regard, this study is based on using an in silico process to develop a rational, multiepitope subunit vaccine that can provide a strong immune response against GBM by using tumour-associated antigens (TAAs) with TLR agonist immune stimulants. HSPA1A (profiled). It is a highly antigenic, non-allergenic, extracellularly localised chaperone is the target selection step in the strategy due to the presence of cavity that allows immune surveillance and the generation of epitopes that activate both B and T cells [24]. The screening strategy used to identify epitopes with forward- pipeline screen method provided powerful B-cell, MHC-I and MHC-II elements. Most of the epitopes also had favourable antigenicity scores, low toxicity and allergenicity and high affinities to widespread haplotypes of HLA further enhancing the translational aspects of the construct due to the broad coverage of the population [25]. The ultimate form of the vaccine incorporates conserved linkers (AAY, GPGPG, EAAAK, SSL) and an N-terminal adjuvant β-defensin enhance the solubility of the construct. Physicochemical characterisation shows that it would be a stable water-soluble construct (pI 9.26). The highly expressive characteristics are assigned by codon optimisation indicators (CAI 0.78; GC content 51.45 % and Hopp & Woods analysis confirms satisfactory hydropathic qualities [26]. Prediction of structural robustness via AlphaFold 3 was tested, and it was corroborated by the validation of PROCHECK module and ERRAT module of the PDB, which showed 91.2 % of the residues in the favoured zone of the Ramachandran plot and high-quality factor coming in at 93.63 % thus indicating a sound architecture. Molecular docking with TLR-4 revealed the positive application of binding energy, the number of hydrogen bonds and specific residues in stabilising binding pockets, which confirms molecular compatibility between innate immune receptors [16]. This reliability has been confirmed with large-scale molecular dynamics simulation of 500 ns and RMSD variation was negligible. The RMSF analysis showed conformational stability in the core and functional domains, with natural flexibility in the terminal regions maintaining local dynamics that did not hamper integrity. Maintenance of the native framework was also indicated with RMSD histograms and SSE plots. Principal component analysis (PCA) revealed synchronous movements in residues, whereas diffusion-coordinate Compression Metric (DCCM) analysis specified consistent residual-residual inter-actions across time [18]. C-ImmSim vaccine pointed to the ability to produce potent and sustained immune responses following vaccination. The main activation was being seen by the increase in the activity of CD4 and CD8 T cells, enrichment of B cells, breakthrough of IgG1 and IgG2, secretion of interleukin-2 and interferon-gamma. Immunological memory formation, which is the necessary factor in long-term protection, was established. In total, these data support the functionality of the construct to induce humoral and cellular immunity making up the innately immunologically cold character of GMB [27]. To sum up, this study demonstrates an approach to methodological interpenetration of immunoinformatics, structural modelling, and molecular dynamics of development of a multiepitope subunit vaccine against glioblastoma. The prudent choice of conserved TAAs and incorporation of immunostimulatory components give the resultant construct potential as an immunogenic agent. Such findings are a platform of rigorous preclinical characterisation and clinical development in the future of personalised vaccine-based immunotherapy against GBM [23]. Conclusion The current study presents a clear in silico-based platform applied to rational development of a multi-epitope subunit vaccine against glioblastoma multiforme (GBM) disease, which has a very poor prognosis and has a narrow therapeutic arsenal. In addition to tumor-associated antigens, especially the extracellular and highly antigenic HSPA1A protein, and immune-stimulating adjuvants, a well-defined vaccine construct could be engineered. This construct was highly antigenic, proved to be non allergic and non-toxic and induced desirable physicochemical characteristics and covered a broad global population spectrum. A structural assessment of the ligand-protein complex and analysis of the binding proved a robust interaction with TLR-4 and a subsequent 500 ns MD simulation demonstrated physiological stability. Immunological simulations also implied the strong activation of cellular and humoral immune pathways followed by the long-term memory induction which is absolutely necessary for long-term surveillance of the tumor. All these results affirmed the scenario that the suggested multi-epitope vaccine could be a safe and effective therapeutic intervention to GBM. Additional future experimental confirmation and preclinical comparisons are still critical in transforming this computationally manufactured construct to a clinically viable immunotherapeutic agent against glioblastoma. Declarations Funding None. Author Contribution M.J. conceptualized and designed the study, performed the immunoinformatics analyses, and wrote the main manuscript text. The author has reviewed and approved the final manuscript. Data Availability Heat Shock 70 kDa Protein 1A (HSPA1A) was downloaded in the FASTA format in the UniProt database (UniProt ID: HS71A_HUMAN). References S. S. K. Yalamarty et al. , “Mechanisms of Resistance and Current Treatment Options for Glioblastoma Multiforme (GBM),” Cancers , vol. 15, no. 7, p. 2116, Apr. 2023, doi: 10.3390/cancers15072116. W. Wu et al. , “Glioblastoma multiforme (GBM): An overview of current therapies and mechanisms of resistance,” Pharmacol. Res. , vol. 171, p. 105780, Sep. 2021, doi: 10.1016/j.phrs.2021.105780. S. Grochans et al. , “Epidemiology of Glioblastoma Multiforme–Literature Review,” Cancers , vol. 14, no. 10, p. 2412, May 2022, doi: 10.3390/cancers14102412. M. Makowska, B. Smolarz, and H. 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Immunol. , vol. 13, p. 934259, Jun. 2022, doi: 10.3389/fimmu.2022.934259. B. S. Gandhamaneni, H. R. Krishnamoorthy, S. Veerappapillai, S. R. Mohapatra, and R. Karuppasamy, “Envelope Glycoprotein based multi-epitope vaccine against a co-infection of Human Herpesvirus 5 and Human Herpesvirus 6 using in silico strategies,” Glycoconj. J. , vol. 39, no. 6, pp. 711–724, Dec. 2022, doi: 10.1007/s10719-022-10083-7. J. Jumper et al. , “Highly accurate protein structure prediction with AlphaFold.,” Nature , 2021, doi: 10.1038/s41586-021-03819-2. M. Gupta et al. , “Recent Advances in Cancer Vaccines: Challenges, Achievements, and Futuristic Prospects,” Vaccines , vol. 10, no. 12, p. 2011, Nov. 2022, doi: 10.3390/vaccines10122011. M. Enayatkhani et al. , “Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study,” J. Biomol. Struct. Dyn. , vol. 39, no. 8, pp. 2857–2872, May 2021, doi: 10.1080/07391102.2020.1756411. J. 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Ezzemani et al. , “Design of a multi-epitope Zika virus vaccine candidate – an in-silico study,” J. Biomol. Struct. Dyn. , vol. 41, no. 9, pp. 3762–3771, Jun. 2023, doi: 10.1080/07391102.2022.2055648. T. Fan, M. Zhang, J. Yang, Z. Zhu, W. Cao, and C. Dong, “Therapeutic cancer vaccines: advancements, challenges and prospects,” Signal Transduct. Target. Ther. , vol. 8, no. 1, p. 450, Dec. 2023, doi: 10.1038/s41392-023-01674-3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7225965","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502463393,"identity":"b9d3811e-ef1f-4886-95f4-0e8c2a7c24a6","order_by":0,"name":"Muhammad Jamil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACxgYGhgMPwEwexgdIEgn4tQClJYBamA2I0gKTBmlhkyBKC3P76cQDiTl2debtZ49VF9Tck2fgX3xMgnFHGm6H9eRuOJC4LVlC5kxe2u0Zx4oNGySepUkwnsnB4xewFmYJCYYcs9s8bAkJDBJnjA0Y2ypwa+l/C9JSLyHB/8asmOcfMVpmgG05LCEhkWPGzNsG1MLfY/iAsQ2Pw2aAbTkuOUPijbH0zL4EwzYJtsQHiWdwe9+wP3fzh4/bqvkl+HMMPxd8S5Dn5z984MDHHcm4tTQgcZhBBJtEAgNDYgM2xRAgj8wBa2HgPwBJFKNgFIyCUTAKoAAAtb1WX2YNRhIAAAAASUVORK5CYII=","orcid":"","institution":"PARC Arid Zone Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Jamil","suffix":""}],"badges":[],"createdAt":"2025-07-27 11:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7225965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7225965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89656547,"identity":"76853a64-d212-47f3-8296-6f0f23294217","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354150,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure of targeted protein.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/3f110963c213cbc6f83df568.png"},{"id":89658143,"identity":"3989021d-d1fe-4163-9a0a-2878f5946ed0","added_by":"auto","created_at":"2025-08-22 10:38:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74123,"visible":true,"origin":"","legend":"\u003cp\u003eSubcellular location analysis of targeted protein.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/e6dfabe5f4a22770530e41be.png"},{"id":89656544,"identity":"13b24e1c-4ebc-4b38-aac7-21bb1417e60a","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102921,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the multi-epitope vaccine construct. The \u003cstrong\u003eyellow region\u003c/strong\u003e denotes the 50S ribosomal protein used as an immunostimulatory adjuvant at N terminal and Beta defencin at C terminal. \u003cstrong\u003eBlue\u003c/strong\u003e represent B Cell epitope, \u003cstrong\u003eGreen segments\u003c/strong\u003e represent linker sequences. \u003cstrong\u003ePurple regions\u003c/strong\u003e indicate MHC class I epitopes, \u003cstrong\u003egray regions\u003c/strong\u003erepresent MHC class II epitopes, and the \u003cstrong\u003ered segment\u003c/strong\u003ecorresponds to the 6x-His tag\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/27e17f72603ea5dc5340649b.png"},{"id":89658784,"identity":"abe9745c-c833-405b-9d0e-d0f0597b6149","added_by":"auto","created_at":"2025-08-22 10:46:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90539,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiochemical properties analysis of vaccine construct.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/3fcc314fd096f26b32e4e2f2.png"},{"id":89658151,"identity":"1a163692-0454-4d96-8cb2-c8bf75349601","added_by":"auto","created_at":"2025-08-22 10:38:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":356753,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure prediction and its validation. (A) 3D structure of vaccine candidate. (B) Ramachandran plot analysis of predicted vaccine structure.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/0c96afb12d6c4e9bc545f433.png"},{"id":89656553,"identity":"2c6319b2-1f34-4f63-af50-083b62f20f80","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":314242,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure prediction and its validation. (A) 3D structure of vaccine candidate. (B) Ramachandran plot analysis of predicted vaccine structure.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/28b7fc74b1f97d4f50337d5d.png"},{"id":89658785,"identity":"2ace1a75-f2ea-4fe9-a9f8-a71ace4d0bb6","added_by":"auto","created_at":"2025-08-22 10:46:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":176142,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking analysis of vaccine construct and TLR-4.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/c8c7d195239e3e021a6b90d5.png"},{"id":89656561,"identity":"3c70b5ce-1d32-44c3-b4ba-18a97b5c20df","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":413381,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of docked complex. (A) 2D interaction analysis image. (B) 3D interaction analysis image.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/42bde9bf91a717231a875f5d.png"},{"id":89656568,"identity":"e5f48f2f-9e07-44ea-8b42-1e3ea0ff7a31","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":234714,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD analysis of docked complex.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/be342c2845064184c8851bcf.png"},{"id":89656555,"identity":"9517d9a3-152f-474f-948a-61cb9097c226","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":89792,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD analysis of docked complex.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/85ffb5776db94634e3f9420c.png"},{"id":89658158,"identity":"a576acd2-a0ca-4452-b6ba-f1bdd351d30e","added_by":"auto","created_at":"2025-08-22 10:38:34","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":169758,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram illustration via DESMOND.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/d3c53a7c3d47e9ba1eee9fb4.png"},{"id":89656570,"identity":"3edb9307-9306-402d-b778-77ea6d7ddaf7","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":361312,"visible":true,"origin":"","legend":"\u003cp\u003eSSE analysis of docked complex.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/7d4036e76b39b4c496a5888f.png"},{"id":89656571,"identity":"749cd388-2566-4fde-a6a9-b0e3cfb0449d","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":735202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 12.\u003c/strong\u003e DCCM analysis of docked complex.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/38f7ac97d73b50b0f3fcfded.png"},{"id":89658786,"identity":"44bec286-e7f5-4d39-b5e5-d742cf7a6149","added_by":"auto","created_at":"2025-08-22 10:46:34","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":141632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 13.\u003c/strong\u003e PCA analysis of docked complex.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/a6d7ed149221e127d17e7d3c.png"},{"id":89656564,"identity":"d97129fd-4f93-4067-aa2b-40d4264d252a","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":263298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 14.\u003c/strong\u003e Vector cloning of vaccine construct. (A) Cloned vector illustration. (B) History of cloned vector.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/540bd28c95c5c19cdef210c9.png"},{"id":89656567,"identity":"d84ff7d5-dd97-4f12-a22f-e1f6c1fba58d","added_by":"auto","created_at":"2025-08-22 10:30:34","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":318843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 15. \u003c/strong\u003eImmune simulation analysis of proposed vaccine candidate. \u003cstrong\u003e(A)\u003c/strong\u003e Antigen count within 35 days. \u003cstrong\u003e(B)\u003c/strong\u003e B-cell population count. \u003cstrong\u003e(C)\u003c/strong\u003e Cytotoxic T-cell population count. \u003cstrong\u003e(D) \u003c/strong\u003eCytokines count over the period. \u003cstrong\u003e(E)\u003c/strong\u003e Helper T-cell count. \u003cstrong\u003e(F)\u003c/strong\u003e DC population count per state.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/0ce0b5b13e943d2e1c4e523c.png"},{"id":91092113,"identity":"7afc589e-17e5-4e73-83b1-c1566a6eb162","added_by":"auto","created_at":"2025-09-11 13:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4868906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7225965/v1/a4b3670d-4099-4294-9639-95340539e0d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In-silico design of Multi-Epitope Vaccine Against Glioblastoma Using Tumor-Associated Antigens and TLR Agonist Adjuvants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma multiforme (GBM) is one of the most aggressive and fatal primary intracranial neoplasms identified in adults taking a percentage of 15 percent of all people with intracranial tumors. With the current improvement in surgical resection, radiotherapy and chemotherapy, GBM still portrays a poor median survival of less than 15 months following diagnosis [1]. The tendency of its infiltrative growth and extensive molecular heterogeneity makes the tumor impervious to treatment and it leads to prompt recurrence. In addition, blood-brain barrier (BBB) limits the delivery of conventional therapeutic agents thus creating a limit to their effectiveness. The problem of such challenges underlines the desperate need in new treatment methods that would be able to overcome the shortcomings of the current standard-of-care treatment styles [2].\u003c/p\u003e\u003cp\u003eAt the clinical level, GBM is characterized by rapid growth, a great amount of angiogenesis, necrosis, and a highly immunosuppressive microenvironment. Depending on the tumor location in the cerebral area, the patient will experience seizures, cognitive impairment and motor defit. GM molecularly show complicated genetic changes, particularly TP53 mutation, EGFR amplifications, and PTEN deletions. [3] Although there are several decades of exploration, no treatment intervention has shown sustainable and significant changes to the extent to which patients survive. Immunotherapeutic approaches directed at exploiting host immune system thus have been of high interest [4].\u003c/p\u003e\u003cp\u003eA promising way is vaccine-based immunotherapy. Compared to systemic chemotherapy, the features due to cancer vaccines are high target specificity, minimal toxicity and ability to induce immunological memory. Nevertheless, previous attempts have been mostly toward single-antigen or cell-based preparations, which are deficient in antigen homogeneity and recognition avoidance by the innate immune system [5]. Nor have GBM-specific vaccines received Food and Drug Administration (FDA) approval and the predominately cold immunological microenvironment of the tumor adds further to an ineffective passive modality disease. Rationally created multi-epitope vaccine against a number of tumor-related antigens (TAAs) which simultaneously activates innate immune system will therefore comprise a new powerful modality [6].\u003c/p\u003e\u003cp\u003eUsing novel Toll-like receptors (TLR) Agonists as adjuvants can provide a strategic approach to the problem of providing greater immunogenicity in current vaccines. TLRs play vital connecting roles between the innate and adaptive immune systems and agonists of these receptors activate dendritic cells, enhance antigen presentation and guide T-cell responses to a shift toward a Th1-type, and the latter is essential in destruction of tumor cells [7]. Its disease etiology is an important consideration since selection of epitopes based on TAAs and use of TLR-suitable adjuvants presents a potential approach of achieving a wide and long-lasting immune response. The interaction would result in the stimulation of cytotoxic T lymphocytes and the helper T cells in addition to making the process binding between the two cells efficient in tumor recognition and destruction [8].\u003c/p\u003e\u003cp\u003eThe aim of the current work is to develop a multi-epitope vaccine to attack glioblastoma by the inclusion of TAAs and TLR agonists as adjuvants. In silico pipeline was established to characterize highly immunogenic and non-allergenic and conserved epitopes that bind strongly with both MHC-I and MHC-II [9]. The proposed combination of structural modeling, molecular docking and immune simulation analyses guaranteed stability, antigenicity, and functional compatibility of the final vaccine construct. The computational method simplifies the process of screening and optimization of the candidates and less time is spent as well as the expenditure is also less as compared to the empirical correction method of trial and error [10].\u003c/p\u003e\u003cp\u003eThe possible relevance of this study is that it represents a specific, safe, scalable, therapeutic agent against glioblastoma, a condition, which has little effective treatment. The speed of the translational pathway is increased by in silico methods of candidate evaluation and optimization. The aim of the proposed vaccine product, by combination of tumor-specific antigens and immune-stimulating adjuvants, is to induce a multidirectional and intense immune attack against the GBM cells. Finally, the study may support a preclinical validation and subsequent clinical translation of a next-generation, personalized cancer vaccine against a type of potentially fatal brain cancer [11].\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employed a comprehensive in-silico immunoinformatic approach to develop a multi-epitope vaccine candidate targeting pathogenic viral strains. Through the application of computational methodologies and predictive modeling strategies, we systematically identified potential antigenic regions, constructed the vaccine sequence, and validated its structural and immunological properties. The ensuing sections provide a detailed account of the methodology employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetrieval and Selection of Target Proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein sequences relevant to the viral strains were retrieved from the UniProt database (https://www.uniprot.org/) in FASTA format. UniProt is a comprehensive resource for protein sequence and function data. It makes it possible to retrieve the protein sequence, which is the initial stage in developing a vaccine [12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of targeted protein\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selection criteria for determining suitable target proteins were based on their antigenicity, allergenicity, and toxicity profiles. Antigenicity was evaluated utilizing the VaxiJen server (http://www.ddg-pharmfac.net/vaxijen/), allergenicity was assessed through the AllerTOP v2.0 platform (https://www.ddg-pharmf ac.net/AllerTOP/) and toxicity was examined using the ToxinPred tool (https://webs.iiitd.edu.in/raghava/toxinpred/). Additionally, transmembrane topology and subcellular localization were determined through the TMHMM server to ensure that the selected proteins were accessible and appropriate for epitope mapping [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEpitope Prediction and Filtration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of B-cell, MHC class I, and MHC class II T-cell epitopes was performed utilizing the Immune Epitope Database (IEDB) analysis resource (https://www.iedb.org/). B-cell epitopes were predicted using the B-cell prediction server of IEDB, and the most promising sequences were evaluated for high antigenicity, absence of allergenicity, and non-toxicity. MHC class I and II epitopes were predicted employing IEDB tools utilizing ANN 4.0 and NN-align 2.3 methodologies, respectively [14]. Only epitopes characterized by low IC50 values and high predicted immunogenic potential were selected. In the context of glycan-focused vaccine design, as demonstrated in the virus study, epitopes containing N-linked glycosylation sequons were identified through the NetNGlyc 1.0 server . These glycosylation motifs facilitated the mimicry of native antigenic structures, thereby promoting optimal protein folding and immune recognition [15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVaccine Construct Assembly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selected epitopes were systematically concatenated utilizing appropriate linkers to ensure proper folding and minimize steric hindrance. GPGPG and AAY linkers were employed to connect MHC-II and MHC-I epitopes, respectively, while SSL, KK, or EAAAK linkers were utilized where flexibility or structural separation was necessary. An immunostimulatory adjuvant, specifically the\u0026nbsp;\u0026beta;-defensin, was incorporated at the N-terminal and C-terminal end of the construct using an EAAAK linker. Moreover, a 6x-His tag was affixed to the C-terminal to facilitate expression and purification. The final vaccine construct underwent thorough evaluation for antigenicity, allergenicity, and toxicity to ascertain its safety and efficacy [13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVaccine allergenicity and antigenicity prediction:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe allergenicity of the vaccine was evaluated through the utilization of the AllerTOP v.2.0 server (https://www.ddg-pharmfac.net/AllerTOP/). This methodology converts protein sequences into standardized vectors of uniform length by leveraging auto cross-covariance. The outcomes of this analysis indicate whether the protein exhibits non-allergenic or allergenic properties [16]. The antigenicity of the vaccine was assessed utilizing the VaxiJen web server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html). This server employs a strategy that obviates the necessity for alignment, concentrating instead on the primary characteristics of amino acids, with a specified threshold [11]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysiochemical properties\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA computational tool referred to as pepcal (https://pepcalc.com/) was employed to evaluate physicochemical properties. This tool provides comprehensive information regarding the dimensions of the vaccine\u0026rsquo;s intended structure, including metrics such as number of residues, solubility, molecular weight, extinction coefficient and isoelectric point [17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and Biophysical Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe secondary structure of the vaccine construct was predicted using the PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/), which classifies sequences into alpha-helices, beta-strands, and coils [18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTertiary structure prediction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the proposed research, the structure of a target protein was predicted by using the AlphaFold 3 (https://alphafoldserver.com/), the latest deep-learning structure prediction framework by DeepMind. The method combines a sequence representation with structural and functional contextual information in an architecture that combines both self-attention operations, equivariant transformers, and spatial graph modeling in a homogeneous manner [19]. The FASTA sequences of the full-length protein were given and AlphaFold 3 used evolutionary experience contained in multiple sequence alignments (MSAs), structural alignments (where available) and high-confidence predicted residue-residue distance maps to point out using all-atom 3D models [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein structure validation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructural validation was conducted utilizing PROCHECK and the SAVES server (http://services.mbi.ucla.edu/SAVES/) to produce Ramachandran plots and ERRAT scores, thereby evaluating stereochemical quality and model accuracy [17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Interaction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the binding interactions between the vaccine candidate and human immune receptors, molecular docking studies were performed utilizing the ClusPro 2.0 (https://cluspro.org/tut_dock.php) computational server. The docked complexes underwent rigorous analysis concerning interaction energies, confidence scores, and the number of hydrogen bonds generated. The LigPlot+ software (https:/ /www.ebi.ac.uk/thornton-srv/software/LigPlus/ ) facilitated detailed visual representations of molecular interactions, illuminating hydrogen bond networks and binding residues that are critical to the interaction between the vaccine and its receptor [21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structural dynamics study using a molecular dynamics (MD) simulation with a time of 500ns was conducted in the Desmond module, integrated in Schrodinger Maestro suite to investigate the dynamic characteristics and the stability of the protein-ligand complex. The most optimum binding conformation of the ligand was fitted on docking and the docking profile was refined and minimized using Protein Preparation wizard. Incomplete residues were created at this point as well. The resulting docked system was surrounded with an orthorhombic box filled with TIP3P water molecules and physiological conditions (300 K, 1 atm) were simulated via a coupling of the model to a stage-wise simulation with 0.15 M NaCl. The interactions were modeled through OPLS_2005 force field. The simulation was started upon relaxation of the system and was followed over 500 ns where snapshot archiving every 100 ps was done in order to perform simulation analysis [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCodon Optimization and In-Silico Cloning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe terminal amino acid sequence of the vaccine construct was reverse-translated into a nucleotide sequence utilizing EMBOSS Backtranseq (https://www.ebi.ac.uk/jdispatcher/st/emboss_backtranseq). Subsequently, the codon usage was optimized for expression in Escherichia coli through either the JCat or ExpOptimizer tools (https://www.jcat.de/) (https://kraken.i ac.rm.cnr.it/C-IMMSIM/index.php) For the purpose of in-silico cloning, the optimized nucleotide sequence was incorporated into plasmid vectors such as PET28a(+) or PBR322 using SnapGene software, thereby facilitating prospective experimental expression [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe C-ImmSim server (https://kraken.i ac.rm.cnr.it/C-IMMSIM/index.php) was utilized to simulate the immune response provoked by the vaccine construct. This computational tool models the activation of a variety of immune cells, including CD4+ helper T-cells, CD8+ cytotoxic T-cells, B-cells, as well as the secretion of immunoglobulins and cytokines such as IFN-\u0026gamma; and IL-2. The simulation contrasted responses with and without the adjuvant to evaluate its effect on immune activation and memory formation. These predictions provided valuable insights into the vaccine\u0026apos;s potential to elicit a vigorous and lasting immune response [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetrieval and Selection of Target Protein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe desired protein, Heat Shock 70 kDa Protein 1A (HSPA1A) was effectively downloaded in the FASTA format in the UniProt database (UniProt ID: HS71A_HUMAN). This is a human protein and is a chaperon protein that is well conserved and had well characterized amino acid sequences. Fig 1 indicates the structure of the host cell protein modeling in 3D using SWISS- model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of Targeted Protein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immunogenic potential of the analyzed protein was found to be remarkably high because its antigenicity score was high. The protein was analyzed as non-allergenic and toxicity profile indicated that it is non-toxic, proving its biosafety in future vaccine design. The TMHMM server (topology prediction) showed that there are no transmembrane helices, and the whole (1 641 amino acids) sequence was located on the extracellular side. This implies that the protein is completely open to recognize by the immune system and is also appropriate to predict an epitope. Fig 2 shows the location of proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of B-cell Epitope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IEDB resource was used to discover B- cell epitopes. Out of these, single linear B-cell epitope of high antigenicity and non-allergenic and non-toxic properties was identified to be included in the final vaccine construct. \u003cstrong\u003eTable 1\u003c/strong\u003e provide lists of its characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Selected B-cell Epitope\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpitope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigenicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQDFFNGRDLNKSIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e\u003cstrong\u003ePrediction of MHC Class I Epitopes\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThree epitopes derived by MHC class I were chosen depending on the binding affinity (IC50 value), percentile rank, and immunogenicity. All the epitopes were verified using VaxiJen, AllerTOP and ToxinPred and they all claimed antigenicity non-allergic, non-toxic. These are the epitopes that were expected to have a strong affinity to several HLA alleles, as illustrated in \u003cstrong\u003eTable 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Selected MHC Class I Epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpitope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC50 Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigenicity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRQATKDAGV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-A*02:06\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSQNKRAVRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-A*31:01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVSYKGETKAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e96.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-B*15:01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e\u003cstrong\u003ePrediction of MHC Class II Epitopes\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe selection of the three MHC class II epitopes was recognized to have borne sufficient binding recognition of wide variety of alleles, by the support of anti-genicity and low values of IC50. Epitopes went through the allergenicity and toxicity filters and were good candidates to use in vaccine design. \u003cstrong\u003eTable 3\u003c/strong\u003e includes the list of the chosen epitopes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSelected MHC Class II Epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpitope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLength\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC50 Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntigenicity score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAFNMKSAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"111\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEEIERMVQE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eHLA-DQA1*04:01/DQB1*04:02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eHLA-DQA1*05:01/DQB1*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFEGIDFYTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"111\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*04:01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eHLA-DPA1*01:03/DPB1*02:01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5463\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\u003eMulti-Epitope Vaccine Construct Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multi-epitope vaccine construct had succeeded in designing those particular elements of B-cell, MHC I, and MHC II epitopes and was linked together with suitable connectors in a manner that guarantees better folding, immunogenicity, as well as structural steadiness. The construct was started by adding the \u0026beta;-defensin-114 as an immunostimulatory adjuvant at the N-terminal followed by a fusion at an EAAAK linker, which aims at providing a better immunogenic response, and separate the spatially adjacent proteins. MHC class I epitopes were joined through AAY links so that they could stimulate the activation of cytotoxic T lymphocytes whereas MHC class I epitopes were linked with GPGPG links (to promote the activation of helper T cells). The B-cell epitopes have been combined with flexible linkers (GGGGS, EAAAK) to maintain the structural conformation and access of epitopes. C-terminal end contained \u0026beta;-defensin attached with KK linker to increase antimicrobial and immunomodulatory effects. It was followed by 6x-His tag within it to make purification and expression easy. The last construct was assessed and it was antigenic, non-allergenic and non-toxic which means it can be an effective and safe vaccine candidate. \u003cstrong\u003eFig 3\u003c/strong\u003e. The finalized vaccine construct sequence was:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysicochemical Characterization of the Vaccine Construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting 241 amino acid residue, 27,656.83 g/mol multi-epitope vaccine construct had an effective molecular weight size. The theoretical isoelectric point (pI) was found to be 9.26 and therefore it is basic. The construct had a net positive charge of + 15.9 at physiological pH (7.0), indicating interaction with cell membranes which may be particularly negative. The coefficient of extinction was calculated at 16,640 M -1 cm -1 and this means that the protein could be detected at moderate level in terms of 280 nm during the process of purification. Prediction of solubility indicated the prediction of good water solubility, which is imperative in expression and formulation.\u003c/p\u003e\n\u003cp\u003eSolvent accessibility of the Hopp \u0026amp; Woods Hydropathy analysis reveals that hydrophilic regions predominate over the construct, which corresponds to possible immunogenic accessibility and suitability to the aqueous environment. The set of parameters confirm that the vaccine construct is stable, expressible, and ready to go downstream. In \u003cstrong\u003eFig 4,\u003c/strong\u003e it is calculated that the protein or peptide has an aggregate of both hydrophobic and hydrophilic areas and has a large convert in charge at about exceeding pH, which may have influence on its functionality or interrelationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and Biophysical Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multi-epitope vaccine construct tertiary structure was predicted on server PSIPRED. This tool is used to compare the primary amino acid sequence in predicting the probability of assembly of the most significant aspects of the secondary structure namely alpha-helices (pink bars), beta-strands (yellow bars), and random coils (black lines). The prediction also showed that the vaccine construct primarily consists of alpha-helical regions that constitute short b-strands and random coils. Specifically, there were a number of alpha-helices distributed throughout the structure, what makes it stable structurally speaking, and, possibly, could make it even more recognizable by immune cells. Beta-strands may also assist in compact folding and possible beta-sheet formation that has a bearing on protein protein interactions and epitope presentation itself. The black regions are the random coils regions and serve to be the flexible links between structured regions and can help increase the total flexibility and solubility of the structure as depicted in fig 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTertiary Structure Prediction and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed 3D structure of multi-epitope vaccine construct was compact and stable with a proper spatial orientation of the epitopes and the linker regions in addition to the adjuvant elements. The reliability and stereochemical soundness of the model was ensured by structural validation with SAVES v6.0 suite. In Fig 6 the Ramachandran plot generated by the program PROCHECK it was possible to conclude that 91.2 percent of the residues were in the most favored and 8.3 percent in the additionally allowed and 0. 5 percent in the generously allowed. Importantly, no residues are present in prohibited petals, which shows a valid backbone structure and proper distribution of dihedrals. A overall quality factor of 93.63 percent was the outcome of the ERRAT analysis, which is much higher than a general threshold of 91 percent high-resolution models. This score underlines the portion of high accuracy of the non-bonded invasions of atoms and affirms goodness and stability of the assumed tentative design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking by cluspro reveals that the vaccine candidate has high efficiency to get recognized by TLR-4 receptor. After docking the complex score shows 131 members in total, -1906.3 center energy and -1908.7 of lowest energy. These results clearly signify the efficiency of vaccine candidate with human receptor. In \u003cstrong\u003eFig 7\u003c/strong\u003e, chain A and chain C showed highest binding with the complex.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the excellent results of docking were enough to validate the construct binding ability. But, we further validate this response with checking interaction analysis. This analysis, as shown in \u003cstrong\u003eFig 8\u003c/strong\u003e, revealed that 9 hydrogen bonds are present between the c chain of receptor and vaccine candidate. This encompasses strong binding ability of vaccine candidate with TLR-4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulations \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RMSD profile gauges the structural stability of the complex protein-ligand as it progressed to a full 500 ns. The first oscillation (1020 ns), is again explained by the equilibration leading to the averages to reach the range of 2.6-3.2 A, as shown in \u003cstrong\u003eFig 9\u003c/strong\u003e. The absence of sharp spikes and long-running drifts shows that the ligand and the protein domain were adequately equilibrated during the trajectory. The persistent backbone deviation highlights the ability of high ligand binding tightness and ligand maintenance of protein global conformation.\u003c/p\u003e\n\u003cp\u003eRMSF plots the motility of individual residues throughout the 500-ns work. Most of the residues are in the range of below 2.0 A with sharp peaks at the N-terminus (residues ~10-30) as well as the C-terminus (~210-230), as represented in \u003cstrong\u003eFig 10\u003c/strong\u003e. The terminal flexibility is common. The residues at ligand-binding pocket and in the structural core however exhibit very little fluctuations and this is a testimony to their stability. This observation suggests that ligand binding fixed functional regions leaving peripheral loops and terminals free to make natural movements which did not interfere with strength.\u003c/p\u003e\n\u003cp\u003eRMSD Histogram depicts a narrow range of dominant peak (2.8 to 3.0 ) over which most of the exploration of conformations was completed in the 500-ns trajectory. The low count of the distribution width and high frequency in this range confirm the RMSD trajectory, as presented in \u003cstrong\u003eFig 11\u003c/strong\u003e, which will prove that the majority of the conformations tend to be rather stable with regards to a minimal structural drift over time.\u003c/p\u003e\n\u003cp\u003eSSE shows the backbone helical (red), edge (blue) and sheet (purple) structures shown throughout the 500-ns trajectory. There are no important unfolding to a coil or loop state; core secondary structural elements are maintained. Transient coil regions are only seen in the terminal ends and flexible loops such that the protein is dynamic. The nature of the structure shows that binding of the ligands did not alter the original fold.\u003c/p\u003e\n\u003cp\u003eDCCM heatmap shows a summary of the correlations in the motions of the residues. The structured domains and residues near the active site are characterized by strong positive-correlation motions (blue) suggesting synchronized motions that are highly important to the maintenance of its function. On the other hand, moderate anti-correlated motions (red) are found between loops at great distances and there are flexible domains as a natural form of dynamic breathing. Such clear lines of correlation are a direct indicator of well-developed internal communication within 500 ns.\u003c/p\u003e\n\u003cp\u003ePCA shows the movement of the complex on PC1 and PC2 in the 500-ns simulation. The high concentration of the conformations located in 2D space implies the shortage of the conformational dimorphism, which proves the fact that the protein-ligand complex was in a restricted and stable area of the conformational scenery. The fact that the points are not scattered or distributed in some way indicates that there are no sudden shifts that would occur in the simulation; structural and dynamical convergence is apparent throughout the long simulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCodon Optimization and In-Silico Cloning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot only was the protein successfully reverse-translated and optimized to express in Escherichia coli, but also have the nucleotide sequence of the vaccine construct was also reverse-translated. Codon Adaptation Index (CAI) rose to 0.78 since it is better adapted to the host translation machinery than it was previously (by 0.64). A GC content of 62.66 percent has been lowered to 51.45 percent, which fits the scope of E. coli expression the best, and lowers the possibility of forming secondary mRNA structures that might slow down the translation process significantly. The refined gene sequence, which is representative of a 241 amino acids protein (~27.64 kDa) was effectively cloned into the pET28a(+) vector with SnapGene software. The construction contained suitable sites of restriction and controls in order to transcribe and translate efficiently. In silico cloning simulation showed that the cloning was successful and as shown in \u003cstrong\u003eFig 14\u003c/strong\u003e; the construct was ready to be expressed in a prokaryotic system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Interaction Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immune simulation performed on the C-ImmSim webserver revealed the presence of a robust and dampened immune reaction caused by the multi-epitope vaccines construct. Specific CD4 + T helper cell activation and CD8 + cytotoxic T lymphocyte activation were clearly increased in the number of activated cells on first contact with antigen and more so with each subsequent dose, which is considered the development of effective T-cell memory. B cells were also strongly activated, and active and memory phenotypes were increased accordingly after booster exposures, as well as a sustained humoral immunity. \u003cstrong\u003eFig 15\u003c/strong\u003e shows graphical illustrations of immune simulation analysis. Class switching and long-term protection showed production of IgG1 and IgG2 finding confirmation in a linear pattern where production of IgM in the early stages of immunoglobulin production was followed by a high concentration of IgG1 and IgG2. There was subsequent enhancement in the formation of antigen antibodies complexes, indicating the successful neutralization of the antigens.\u003c/p\u003e\n\u003cp\u003eA high level of IL-2 and IFN-g were found in the cytokine profile which distinguishes a prevalent Th1-type immune response and an active T-cell proliferation. Effective and controlled stimulation of the immune activation was indicated by the existence of a danger signal in initial stages of simulation, whose deterioration got progressively weaker. Besides, several epitopes in the construct were predicted to bind well to both MHC class I and II alleles and increase the chances of reaching a broad population and enable the presented antigens. All in all, the simulation in favor of the vaccine is its ability to construct cellular immunity, humoral immunity, and long-term immunological memory, which promotes the effectiveness of the said vaccine as a potential immunogen to research.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlioblastoma multiforme (GBM) is a clinically resistant tumour due to its mixed genomic signature, an immunosuppressive microenvironment, and unruffled reaction to regular treatment schemes. In this regard, this study is based on using an in silico process to develop a rational, multiepitope subunit vaccine that can provide a strong immune response against GBM by using tumour-associated antigens (TAAs) with TLR agonist immune stimulants. HSPA1A (profiled). It is \u0026nbsp;a highly antigenic, non-allergenic, extracellularly localised chaperone is the target selection step in the strategy due to the presence of cavity that allows immune surveillance and the generation of epitopes that activate both B and T cells [24]. The screening strategy used to identify epitopes with forward- pipeline screen method provided powerful B-cell, MHC-I and MHC-II elements. Most of the epitopes also had favourable antigenicity scores, low toxicity and allergenicity and high affinities to widespread haplotypes of HLA further enhancing the translational aspects of the construct due to the broad coverage of the population [25].\u003c/p\u003e\n\u003cp\u003eThe ultimate form of the vaccine incorporates conserved linkers (AAY, GPGPG, EAAAK, SSL) and an N-terminal adjuvant \u0026beta;-defensin enhance the solubility of the construct. Physicochemical characterisation shows that it would be a stable water-soluble construct (pI 9.26). The highly expressive characteristics are assigned by codon optimisation indicators (CAI 0.78; GC content 51.45 % and Hopp \u0026amp; Woods analysis confirms satisfactory hydropathic qualities [26].\u003c/p\u003e\n\u003cp\u003ePrediction of structural robustness via AlphaFold 3 was tested, and it was corroborated by the validation of PROCHECK module and ERRAT module of the PDB, which showed 91.2 % of the residues in the favoured zone of the Ramachandran plot and high-quality factor coming in at 93.63 % thus indicating a sound architecture. Molecular docking with TLR-4 revealed the positive application of binding energy, the number of hydrogen bonds and specific residues in stabilising binding pockets, which confirms molecular compatibility between innate immune receptors [16].\u003c/p\u003e\n\u003cp\u003eThis reliability has been confirmed with large-scale molecular dynamics simulation of 500 ns and RMSD variation was negligible. The RMSF analysis showed conformational stability in the core and functional domains, with natural flexibility in the terminal regions maintaining local dynamics that did not hamper integrity. Maintenance of the native framework was also indicated with RMSD histograms and SSE plots. Principal component analysis (PCA) revealed synchronous movements in residues, whereas diffusion-coordinate Compression Metric (DCCM) analysis specified consistent residual-residual inter-actions across time [18].\u003c/p\u003e\n\u003cp\u003eC-ImmSim vaccine pointed to the ability to produce potent and sustained immune responses following vaccination. The main activation was being seen by the increase in the activity of CD4 and CD8 T cells, enrichment of B cells, breakthrough of IgG1 and IgG2, secretion of interleukin-2 and interferon-gamma. Immunological memory formation, which is the necessary factor in long-term protection, was established. In total, these data support the functionality of the construct to induce humoral and cellular immunity making up the innately immunologically cold character of GMB [27].\u003c/p\u003e\n\u003cp\u003eTo sum up, this study demonstrates an approach to methodological interpenetration of immunoinformatics, structural modelling, and molecular dynamics of development of a multiepitope subunit vaccine against glioblastoma. The prudent choice of conserved TAAs and incorporation of immunostimulatory components give the resultant construct potential as an immunogenic agent. Such findings are a platform of rigorous preclinical characterisation and clinical development in the future of personalised vaccine-based immunotherapy against GBM [23].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study presents a clear in silico-based platform applied to rational development of a multi-epitope subunit vaccine against glioblastoma multiforme (GBM) disease, which has a very poor prognosis and has a narrow therapeutic arsenal. In addition to tumor-associated antigens, especially the extracellular and highly antigenic HSPA1A protein, and immune-stimulating adjuvants, a well-defined vaccine construct could be engineered. This construct was highly antigenic, proved to be non allergic and non-toxic and induced desirable physicochemical characteristics and covered a broad global population spectrum. A structural assessment of the ligand-protein complex and analysis of the binding proved a robust interaction with TLR-4 and a subsequent 500 ns MD simulation demonstrated physiological stability. Immunological simulations also implied the strong activation of cellular and humoral immune pathways followed by the long-term memory induction which is absolutely necessary for long-term surveillance of the tumor. All these results affirmed the scenario that the suggested multi-epitope vaccine could be a safe and effective therapeutic intervention to GBM. Additional future experimental confirmation and preclinical comparisons are still critical in transforming this computationally manufactured construct to a clinically viable immunotherapeutic agent against glioblastoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.J. conceptualized and designed the study, performed the immunoinformatics analyses, and wrote the main manuscript text. The author has reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eHeat Shock 70 kDa Protein 1A (HSPA1A) was downloaded in the FASTA format in the UniProt database (UniProt ID: HS71A_HUMAN).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. S. K. 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Dyn.\u003c/em\u003e, vol. 41, no. 9, pp. 3762\u0026ndash;3771, Jun. 2023, doi: 10.1080/07391102.2022.2055648.\u003c/li\u003e\n\u003cli\u003eT. Fan, M. Zhang, J. Yang, Z. Zhu, W. Cao, and C. Dong, \u0026ldquo;Therapeutic cancer vaccines: advancements, challenges and prospects,\u0026rdquo; \u003cem\u003eSignal Transduct. Target. Ther.\u003c/em\u003e, vol. 8, no. 1, p. 450, Dec. 2023, doi: 10.1038/s41392-023-01674-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma multiforme, multi-epitope vaccine, HSPA1A, TLR agonist, in silico design, immunoinformatics, molecular dynamics, immune simulation","lastPublishedDoi":"10.21203/rs.3.rs-7225965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7225965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlioblastoma multiforme (GBM) is the most severe and chemo-resistant primary brain tumor in adult patients, which is accompanied by high molecular heterogeneity, immunosuppressive microenvironment of the tumor, and ultimately poor prognosis. Traditional treatment plans have shown to be only marginally effective, so we introduced this \u003cem\u003ein-silico\u003c/em\u003e approach for developing highly efficient vaccine candidate. An HSPA1A that has high antigenic cross reactivity, extracellular localization, and have no reported allergic reactions was chosen as a chaperone protein. Powerful B-cell, MHC class I and MHC class II epitopes were identified and optimally combined into a single construct using proper linkers and adjuvant sequences thus ensuring high immunogenicity, stability and dual innate receptor stimulation. Physicochemical evaluations also indicated desirable solubility, thermal, and expression outlook aspects. A 3D conformation was obtained via structural modeling on AlphaFold 3 and rigorous validation to obtain a compact and stereochemically robust structure. Docking done with TLR-4 showed good docking activity, and after that, a molecular dynamics simulation was done for 500 ns, which further showed stable protein-ligand conjugates and negligible distortion. Simulated immune responses were predicted to be high at the humoral and T cell levels with the anticipation of memory T cell and B cell formation and high IFN- Y and IL 2. Taken together all these results, the multi-epitope vaccine proposed shows excellent potential to be a risk-free, targeted, and broadly effective immunotherapy tool against glioblastoma and, therefore, must be pursued into limiting preclinical and experimental testing.\u003c/p\u003e","manuscriptTitle":"In-silico design of Multi-Epitope Vaccine Against Glioblastoma Using Tumor-Associated Antigens and TLR Agonist Adjuvants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 10:30:29","doi":"10.21203/rs.3.rs-7225965/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":"2b81638b-9bd6-4d52-a7e5-a69882837d1e","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53373495,"name":"Biological sciences/Cancer"},{"id":53373496,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53373497,"name":"Biological sciences/Drug discovery"},{"id":53373498,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2025-09-11T13:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 10:30:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7225965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7225965","identity":"rs-7225965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}