IntroductionGlioblastoma multiforme (GBM) is the most common and the most aggressive primary brain tumor in adults1. As the most prevalent high-grade glioma, GBM occurs in 3.22 people per 100,000 population2. This incidence escalates notably beyond the age of 54, reaching a peak rate of 15.24 per 100,000 population in individuals aged 75–843. Despite its prevalence, the etiology of GBM remains largely elusive for the majority of affected patients. A minority, comprising less than 5% of cases, exhibits a genetic alteration rendering them more susceptible to various tumor types, GBM included. Furthermore, a mere fraction—less than 20% of GBM patients possess a significant familial history of cancer. Although exposure to ionizing radiation unequivocally stands as a confirmed cause of GBM, it accounts for only a minor fraction of cranial tumors ultimately diagnosed as GBMs. Other conceivable contributors, such as cell phone exposure, viral factors including cytomegalovirus, and genetic predispositions, are presently subjects of investigation, yet their roles as definitive causal elements remain unestablished. Early detection of GBM remains a challenge, with standard magnetic resonance imaging standing as the foremost method for initial identification. Unfortunately, by the time a GBM lesion becomes detectable on imaging, the tumor has already reached an advanced stage4.GBM exhibits distinctive attributes including extensive necrotic, hypoxic, and actively proliferating regions—characteristics commonly observed in high-grade tumors5. The prevailing standard of care entails tumor reduction pursued by chemoradiotherapy, yielding an average survival period of approximately 12–18 months for patients6. Nevertheless, novel clinical approaches have shown limited success in augmenting the survival rates of GBM patients so far7. Notably, immunotherapeutic modalities, including those involving dendritic cells (DCs) or targeting the Programmed Cell Death protein-1 (PD-1) immune checkpoint within GBM, have been introduced. Nonetheless, their effectiveness awaits confirmation through human clinical trials8,9. The development of a preventative or therapeutic vaccine offers a hope to combat GBM. Extensive research on both human and animal models has demonstrated that immunity against GBM is correlated with responses from CD4 + T helper cell (Th cell), CD8 + cytolytic T lymphocytes (CTLs), and mechanisms involving antibody-dependent cellular cytotoxicity10,11. The cytokines IFN-α, IFN-β, IFN-γ, IL-12, and IL-13 have been identified as essential factors for conferring protection against GBM or hindering the growth of human GBM cells12. Demonstrative evidence has established that IL-4 exerts an inhibitory effect on GBM xenograft growth, observable both in inducible IL-4 KO cell lines13 and through subcutaneous administration or retroviral delivery14. Conversely, the cytokines IL-8 and IL-10 are implicated in promoting tumor advancement, angiogenesis, and invasiveness15. Additionally, toll-like receptor 4 (TLR-4) has been implicated in orchestrating immune protective responses16. This study focuses on the in silico development of a potential peptide-based vaccine candidate for GBM, utilizing immuno-informatics analyses and molecular dynamics (MD) simulations. In the realm of GBM vaccines, two prominent targets have emerged: epidermal growth factor receptor variant III (EGFRvIII)17 and mutant isocitrate dehydrogenase 1 (IDH1)18. Owing to the recently found lack of survival benefit of a peptide vaccine targeting EGFRvIII (Rindopepimut) in the ACT IV randomized phase III trial19, the interests have shifted to IDH1 as the preferred focal point for GBM vaccine development. However, it is important to note that IDH1 functions as an intracellular enzyme, which could potentially influence the efficacy of the vaccine. Proteomics analysis revealed that among the 114 mutations unique to GBM, four are found in extracellular proteins: urokinase plasminogen activator surface receptor (PLAUR), Integrin beta-3 (ITGB3), and discrete subunits of the HLA class I histocompatibility antigens; the B-41 alpha chain (HLA-B) and A-24 alpha chain (HLA-A)20. Of these proteins, PLAUR is an attractive target for the treatment of cancer, as it is expressed at low levels in healthy tissues but at high levels in malignant tumours21. In addition, it is closely related to the invasion and metastasis of malignant tumours, plays important roles in the degradation of extracellular matrix (ECM), tumour angiogenesis, cell proliferation and apoptosis, and is related to the multidrug resistance (MDR) of tumour cells, which has vital guiding significance for the evaluation of tumor malignancy and prognosis21. On the other hand, ITGB3 expression correlates with high-grade GBM22. Similarly, it has been demonstrated that the HLA-A23 and HLA-B20 alleles are common among glioma patients. Taking these contexts into consideration, we have utilized the mutated segments of these four targets as the basis for constructing the vaccine.ResultsTarget selection and preliminary analysisAn ideal vaccine candidate should be prominently displayed on the surface, highly produced, and broadly distributed. In our research, we chose four cell surface proteins identified as GBM-associated antigens. These proteins should exhibit high expression in GBM, possess significant immunogenicity, and be surface-exposed. The gene expression profile of the target proteins was studied by using TCGA database which exhibited significant over expression in GBM samples in comparison to normal tissue (Figures S1a, S1c, S1e, and S1g). We further explored the association of the selected proteins with the survival in GBM patients. For this, the survival analysis of the GBM patient was accomplished using the clinical data present in the TCGA database and the results are shown in Figures S1b, S1d, S1f, and S1h. These figures demonstrate that as the gene expression of these proteins increases, there is a corresponding decrease in survival probability for patients. The amino acid sequences for four proteins were retrieved from the Uniprot database to design a construct of a multi-epitope vaccine candidate against GBM. Subsequently, the mutated segments of each sequence were used to formulate a total of 36 CTL epitopes, as depicted in Fig. 1. For the Th cell epitopes, those that could bind to the MHC class II supertypes were chosen for subsequent analysis, resulting in a total of 14 Th cell epitopes (Table 2). CTL epitopes were then evaluated for antigenicity, allergenicity, toxicity, IFN-γ –inducing, and IL-4- inducing using VaxiJen v2.0, AllerTOP v. 2.0, ToxinPred, IFNepitope, and IL4pred servers, respectively (Table 1). The same screening process was applied to Th cell epitopes, as summarized in Table 2. By evaluating the data presented in Tables 1 and 2, we identified four epitopes for inclusion in the final vaccine construct based on their antigenicity, non-allergenicity, cytokine induction potential, and structural stability. The top three CTL epitopes (C1: AQTTKRKWE, C2: QTTKRKWEA, and C3: TTKRKWEAA) exhibited the highest binding affinity to HLA class I molecules, while H1: DMAAQTTKRKWEAAH was selected as the only Th cell epitope that demonstrated both IFN-γ and IL-4 inducing activity. Given that Th cell epitopes play a critical role in long-term immune memory, selecting the most functionally relevant epitope was prioritized over including multiple less active ones. This strategic selection enhances the vaccine’s ability to elicit a strong and specific immune response against GBM. Furthermore, we conducted molecular docking studies to examine the interaction mode and affinity of each epitope with HLA-A1, HLA-A2, and HLA-A3 as illustrated in Figures S2 to S10. The molecular docking analysis demonstrated that the docking energies for various epitopes ranged from − 9.9 to -11.28 kcal/mol, indicating a high affinity between the epitopes and their respective binding sites in MHC molecules.Table 1 Predicted CTL epitopes of PLAUR, ITGB3, HLA-B and HLA-A proteins.Full size tableTable 2 Predicted Th cell epitopes of PLAUR, ITGB3, HLA-B and HLA-A proteins.Full size tableConstructing the vaccineTo construct the multi-epitope vaccine, we merged three CTL (C1, C2, and C3) epitopes and one Th cell (H1) epitope, connecting them using AAY linkers. This fusion process generated a sequence comprising 51 amino acids. To enhance the vaccine’s effectiveness, an adjuvant sequence, consisting of 130 amino acids (MAKLSTDELLDAF-KEMTLLELSDFVKKFEETFEVTAAAPVAVAAAGAAPAGAA-VE-AAEEQSEFDVILEAAGDKKIGVIKVVREIVSGLGLKEAKDLVDGAPKPLLEKVAKE-AADEAKAKLEAAGATVTVK), was appended to the N-terminal of the vaccine sequence, facilitated by an EAAAK linker. Consequently, the final vaccine construct, designed with improved efficacy, encompassed a total of 186 amino acids (Fig. 2a).Antigenicity, allergenicity, and physicochemical properties assessmentThe antigenicity of the RVC sequence was estimated using the VaxiJen 2.0 server and followed by ANTIGENpro. The overall prediction for the constructed vaccine, performed by using VaxiJen 2.0 server, was 0.6314 with a tumor model at a threshold of 0.5. Likewise, the overall prediction of antigenicity probability was 0.9399 performed by the ANTIGENpro server. The AllerTOP v. 2.0 server was utilized to forecast the allergic potential of the suggested vaccine, and the results suggested that it did not possess allergenic properties. The predicted molecular weight (MW) and theoretical isoelectric point value (pI) of the final vaccine were 19.88 kDa and 5.7, respectively. According to the PI parameter, the vaccine is predicted naturally acidic. The estimated half-life was 30 h in mammalian reticulocytes in vitro, more than 20 h in yeast, and over 10 h in E. coli in vivo. The vaccine was indicated to be considered thermally stable, as represented by instability index of 24.9624. The aliphatic index of the vaccine was 81.67, and its GRAVY score was reported to be − 0.242.Secondary structure prediction, tertiary structure modeling, refinement, and validationThe secondary structure composition of the multi-epitope vaccine was determined using the Prabi server. The analysis revealed the presence of alpha-helix (79.03%), extended strand (7.53%), and random coil (13.44%) components, as depicted in Fig. 2b. The five models of the 3D structure of the vaccine construct were generated by the I-TASSER server using the threading templates (PDB Hit: 1dd3A, 2ftcF, 1rqvA, 2ongA, 3n0fA, 7cjyA, 3m02A, 6o9pA, 3rkoM, and 1n1zA). The computed C-score values for models 1–5 were − -2.72, -2.74, -2.89, -3.35, and − 3.69, respectively. The C-score is usually within the range of − 5 to 2, where a higher C-score for the model demonstrates that it has a higher level of confidence25. In the next step, the quality of each generated model was tested by ProTSAV server (Fig. 3a–e). Based on ProTSAV overall score, model 2 was chosen for further refinement by GalaxyRefine2 web server and its ProTSAV overall score was assessed again (Fig. 3f).Prediction of the B-cell epitopeThree linear B-cell epitopes (7-mer) including AKAQTTK, AAYQTTK, and MAAQTTK were predicted by the BCPREDS with a score of 2.675. The ElliPro server also predicted five discontinuous B-cell epitopes in the tertiary structure of the vaccine (Fig. 4). The minimum and maximum scores for the predicted discontinuous B-cell epitopes were 0.539 and 0.826, respectively (Table 3).Table 3 A list of discontinuous B-cell epitopes predicted by the ellipro server.Full size tablePredicted interaction of the vaccine construct with monomeric TLR4The molecular docking between the vaccine construct and TLR4 was accomplished using the ClusPro 2.0 server. In this study, the server produced 26 clusters and subsequently organized them based on their energy levels. The cluster with the lowest energy, measuring at -918.2 kcal/mol, was selected as the optimal complex (Fig. 5). Detailed analysis of the binding interface (Fig. 5) revealed several stabilizing interactions between the vaccine construct and TLR4. Hydrogen bonds (shown in yellow) were observed between Arg152 and Asn205, and between Lys153 and Ser311, contributing to directional stability. Salt bridges (marked in magenta) formed between Lys153 and Glu287, reinforcing electrostatic interactions. A notable π-cation interaction (green) was detected between Trp154 of the vaccine and Lys230 of TLR4, further stabilizing the aromatic side chain within the binding groove. Additionally, π–π stacking interactions (cyan) between Trp154 and surrounding aromatic residues were identified. These interactions were concentrated in a surface-exposed cavity of the TLR4 receptor, depicted in the central panel. Collectively, this network of hydrogen bonding, salt bridges, and π-based interactions supports a strong and specific binding mode, potentially facilitating TLR4-mediated immune activation.MD simulation of vaccine construct with TLR4To assess the structural stability and interaction dynamics of the vaccine construct in a complex with TLR4, a 300-ns MD simulation was performed. Figure 6 presents key structural and energetic parameters obtained by a comprehensive analysis. Figure 6a displays the number of non-covalent interactions formed between the vaccine and TLR4 over time. Of all interactions, the number of hydrogen bonds (black line) was the highest, with a consistent average of ~ 13 bonds throughout the simulation, suggesting stable polar interactions. The number of salt bridges (red line) was the next with an average around 5, reinforcing electrostatic complementarity. π–π stacking (cyan) and π-cation (green) interactions were also observed, though in lower frequency, reflecting localized stabilization by aromatic side chains and charged residues.Figure 6b shows the root mean square deviation (RMSD) values, which represent the overall conformational stability. The vaccine-TLR4 complex (blue line) maintained a lower RMSD (~ 11 Å) compared to the vaccine alone in solution (~ 22 Å, black line), indicating that TLR4 binding significantly stabilizes the vaccine structure. The vaccine in the presence of TLR4 (red line) also displayed reduced fluctuation relative to the unbound state in the absence of TLR4. Figure 6c presents the root mean square fluctuation (RMSF) per residue. The vaccine construct in the absence of TLR4 showed higher flexibility (black line), particularly at the terminal and loop regions as expected. In contrast, the presence of TLR4 (red line) led to reduced fluctuation across most residues, suggesting constrained and stabilized dynamics upon binding. The radius of gyration (Rg), a measure of structural compactness is illustrated in Fig. 6d. The vaccine-TLR4 complex (red line) showed a stable Rg around 31 Å, while the vaccine alone showed higher and more fluctuating Rg values compared to the stable vaccine-TLR4 complex. Collectively, the MD simulation confirms that interaction with TLR4 enhances the structural rigidity, compactness, and overall stability of the vaccine, supporting its potential as an effective immunogen. Following the clustering of the MD trajectories, we conducted a comprehensive examination of the interactions between the vaccine and TLR4 residues, with the detailed findings presented in Table 4. In total, the analysis revealed the formation of fifteen hydrogen bonds and four salt-bridges between the vaccine and the receptor (Table 4).Table 4 The interaction between vaccine and TLR4 residues followed by cluster analysis during MD simulation.Full size tableImmune simulationThe in silico immune simulation using C-ImmSim for consecutive three injections given one month apart yielded outcomes congruent with real-world immune responses, as indicated by a significant augmentation in the production of secondary responses (Fig. 7a–i). The primary immune response was distinguished by its notable abundance of immunoglobulin M (IgM). For each successive immunization (secondary and tertiary responses), there were conspicuous rises in antibody levels (comprising IgG1 + IgG2, IgM, and IgG + IgM) with a corresponding decline in antigen concentration as shown in Fig. 7a and f. In our current investigation, we observed that the immune simulation for the vaccine demonstrated a substantial augmentation in the population of lymphocytes along with heightened production of IFN-γ and IL-2, as depicted in Fig. 7b. Furthermore, the vaccine construct initiated the proliferation of B-cell populations following each successive injection (Fig. 7c). Simulation results indicate the development of immune memory in the regions near the intermediate period (Fig. 7d). Moreover, there was a pronounced heightened response observed in both the helper and cytotoxic T-cell populations, concomitant with the development of memory cells, as depicted in Fig. 7d, e and g, and 7h. Following the administration of the vaccine, there was a notable increase in NK cell counts which was sustained throughout the simulation period (Fig. 7i).Vaccine cloningThe back-translation and codon optimization of the multi-epitope vaccine were carried out using the JCat server. The optimized nucleotide sequence exhibited a codon adaptation index (CAI) of 1 and a GC content of 48.92%. Subsequently, the vaccine construct was virtually cloned into the pET28a(+) vector using SnapGene software for visualization and analysis (Fig. 8).DiscussionGBM in adults stands out as one of the most lethal and challenging forms of malignant solid tumors. In the United States, approximately 12,120 patients were diagnosed with GBM in 2016, and they faced a daunting 5-year survival rate of only 5%. Despite extensive research endeavors, there has been minimal headway in extending the lifespan of GBM patients4. Hence, significant efforts are underway to explore novel approaches, including preventive and therapeutic GBM vaccines26. Various GBM vaccines, such as those based on heat shock proteins (HSPs) and dendritic cells (DCs), have demonstrated efficacy in animal models but have not yet successfully transitioned into human clinical trials 26,27,28. The emergence of advanced genomic sequencing technologies offers the potential for crafting individualized vaccines directed at specific neoantigens29. Neoantigens, arising from genetic mutations within cancer cells, can be identified as foreign antigens by the immune system30. Peptide-based cancer vaccines targeting neoantigens restrain the likelihood of tolerance as well as normal tissue toxicity and improve antitumor immune response compared with common cancer vaccines29.The current study focused on the development and in silico design of a potential peptide-based vaccine for GBM using four neoantigens (PLAUR, ITGB3, HLA-B, and HLA-A) that are overexpressed in GBM compared to normal brain tissue. These proteins hold great promise as targets for vaccine development, as any vaccine created could serve as potential preventive or therapeutic agents20. The surface proteins we chose showed promise as vaccine candidates in immunogenic investigations, as indicated by our bioinformatics analysis. In our study, we utilized the TCGA database to examine the gene expression profiles of our target proteins. Our analysis consistently revealed substantial overexpression of these proteins in GBM samples when compared to normal tissue. Additionally, we explored the correlation between the expression levels of these selected proteins and the survival of GBM patients using clinical data from the TCGA database. Our survival analysis unveiled a compelling trend: an increase in the gene expression of these proteins was associated with a notable decrease in the survival probability among GBM patients. These findings signify the potential clinical significance of these proteins in the context of GBM and provide a valuable foundation for vaccine design. In our epitope design process, we focused on the mutated segments within the neoantigens under investigation to craft both CTL and Th cell epitopes. This is essential to avoid targeting the wild-type proteins which could lead to further complications, as demonstrated by the involvement of platelets in immune responses generated against wild-type ITGB3, which can result in immune thrombocytopenia 31,32,33. Subsequently, we rigorously evaluated these epitopes for their antigenic, allergenic, and toxic properties, along with their ability to induce IFN-γ and IL-4 responses. The resultant vaccine construct was assembled with meticulous care, consisting of three CTL epitopes, one Th cell epitope, along with the inclusion of an adjuvant, EAAAK, and AAY linker sequences. The employed linker sequences promote epitope presentation, while they also decrease the possibility of the formation of junctional epitopes34. The presence of the EAAAK linker serves to diminish the interaction with adjacent protein regions, thereby enhancing overall stability3536. It has been demonstrated that the 50 S ribosomal protein L7/L12, which we employed as an adjuvant, possesses an affinity for TLR4 37,38,39.The suggested vaccine construct demonstrated antigenicity while remaining non-allergenic, signifying its ability to effectively trigger strong immune responses without posing the risk of provoking harmful allergic reactions. The theoretical pI of the vaccine was found to be 5.7, demonstrating that the vaccine is acidic in nature. The molecular weight of the vaccine was 19.88 kDa, which is appropriate since proteins with molecular weights less than 110 kDa are easier and quicker to purify40. The vaccine exhibited a substantial proportion of α-helical structure (79.03%), resulting in a calculated instability index of 24.96. This value falls below the threshold of 40, signifying that the vaccine can be classified as a stable protein41.Design of multi-epitope vaccine against glioblastoma using immunoinformatics approaches has been explored previously by several groups 42,43. Gharbavi et al. developed a GBM vaccine targeting IL-13Rα2, TNC, and PTPRZ-1, but their vaccine exhibited a predicted half-life of 1.1 h, suggesting limited stability42. In contrast, our designed vaccine demonstrated a substantially longer predicted half-life of 30 h, indicating enhanced stability and prolonged immune system exposure. Salahlou et al. designed a peptide-based multi-epitope vaccine for GBM and conducted docking studies with TLR2 and TLR4, reporting favorable binding energies based on short MD studies (50 ns)43. Whereas our study extended beyond docking by performing a 300-ns MD simulation, revealing consistent RMSD, stable Rg, and sustained hydrogen bonding throughout the trajectory, providing stronger evidence of complex stability. Furthermore, Gharbavi et al. focused on immunoinformatics-based vaccine construction but did not investigate the dynamic behavior of the vaccine-receptor complex through MD simulations42. Our comprehensive simulation analysis represents an advancement over prior static modeling approaches. Importantly, previous studies have often targeted intracellular antigens like mutant IDH1 or wild-type EGFRvIII, which present challenges for immune accessibility17,18,19. In contrast, our vaccine construct specifically targets mutated extracellular surface proteins (PLAUR, ITGB3, HLA-A, and HLA-B), enhancing its feasibility for recognition by immune cells. Taken together, compared to previously reported glioblastoma vaccine designs, our vaccine candidate demonstrated superior structural stability, stronger binding affinity toward TLR4, better immunogenic potential, and accessibility of extracellular neoantigens, making it a promising next-generation candidate for glioblastoma immunotherapy.After constructing the 3D structure of the vaccine, we refined it to enhance its quality and bring it closer to its native conformation. We conducted a thorough assessment of the model’s quality, confirming the reliability of our vaccine model. It has been found that TLR4 is expressed at least in 43% of GBM cells in the xenograft44. TLR4 exhibits anti-tumor effects that operate independently of the presence of active immune cells45. Hence, we conducted molecular docking analysis to examine the interaction between the vaccine and TLR4. The molecular docking analysis revealed a strong interaction between the vaccine and TLR4. Subsequently, we subjected the docked vaccine-TLR4 complex to MD simulation to assess the stability of the vaccine construct. Our MD data clearly demonstrated that hydrogen bonding stands out as the pivotal interaction between the vaccine and TLR4. The RMSD plot, which was generated for the proposed vaccine and TLR4, indicated the stability of both entities. Furthermore, the RMSF analysis unveiled that the vaccine construct exhibited minimal fluctuations, particularly in regions characterized by extensive interactions with TLR4.It has been well documented that immunity against GBM relies on the concerted action of both B and T lymphocytes4647. While the involvement of cytotoxic T cells in eliminating peripheral and brain tumors has been extensively studied and confirmed4849, research has also underscored the significant contribution of B cells in augmenting the costimulatory signaling between dendritic cells (DCs) and T cells50. According to our findings, the concentrations of IFN-γ and IL-2 exhibited an initial increase after the initial injection and consistently maintained their peak levels with subsequent exposures to the antigen. This observation suggests the presence of elevated T-helper cell (THC) activity, leading to an efficient production of immunoglobulins and endorsing a humoral immune response. The immune simulation yielded outcomes in alignment with conventional immune responses, demonstrating a general augmentation in immune responses with repeated exposure to the antigen. In the context of GBM, it is worth noting that IgG, IgM, and IgA responses to glioma antigens are implicated in disease protection 51,52,53.To maximize the expression efficiency of the vaccine candidate in E. coli (K12 strain), codon optimization was performed using the JCat server. The optimized vaccine sequence achieved a CAI of 1.0, indicating optimal codon usage for the target host. Additionally, the GC content of the optimized sequence was calculated to be 48.92%. A CAI value above 0.8 is generally considered optimal for efficient protein expression in a target host, as it reflects a high level of compatibility with the host’s preferred codon usage patterns54. Similarly, a GC content within the range of 30–70% is recommended for expression efficiency in E. coli55. The obtained GC content of 48.92% falls within this ideal range, suggesting that the optimized sequence is well-suited for stable and high-level expression in the selected host organism.Since in silico approaches have inherent limitations when it comes to predicting physicochemical properties, structural aspects, and immunogenicity, the effectiveness of our proposed vaccine must be substantiated through additional laboratory experiments. Furthermore, this study primarily relies on computational methods for epitope selection, molecular docking, and vaccine construct design. While these approaches provide valuable preliminary insights, experimental validation through in vitro and in vivo studies is essential to confirm immunogenicity, stability, and real-world efficacy. The selected epitopes were optimized based on specific HLA alleles, and while efforts were made to ensure broad population coverage, further studies should assess epitope presentation across diverse ethnic groups to enhance global applicability. Additionally, although molecular docking and MD simulations were conducted to predict vaccine stability and interactions, experimental validation techniques such as surface plasmon resonance (SPR), biolayer interferometry (BLI), or enzyme-linked immunosorbent assays (ELISA) should be performed to confirm predicted binding affinities and structural integrity. The immune system is highly dynamic, and while the designed vaccine construct aims to elicit a strong immune response, additional research should investigate potential immune evasion mechanisms, antigen processing efficiency, and T-cell activation pathways to ensure a robust and specific response. Another important consideration is that the current study does not address potential delivery mechanisms or formulation strategies, which are critical for real-world application. One promising delivery approach involves the use of our lab-developed terpolymer-lipid encapsulated nanoparticles, which have previously demonstrated the ability to cross the blood-brain barrier (BBB) and effectively target glioblastoma cells56. Incorporating this terpolymer-based delivery vehicle could enhance the bioavailability of the vaccine within the central nervous system and improve immune targeting of glioblastoma cells. Future research should explore vaccine stability, storage conditions, and optimal delivery platforms, such as nanoparticle-based systems, to enhance bioavailability and immune activation.ConclusionsFinding effective solutions for GBM treatment in a clinical setting remains a formidable challenge, necessitating the development of innovative approaches. No proper treatments are available for the patients yet. The emergence of immunotherapy presents a potential avenue for effective treatment, and the utilization of in silico methods could prove advantageous in designing a potent vaccine against this devastating disease. In this study, we harnessed immuno-informatic tools and an MD approach to formulate a multi-epitope peptide vaccine, consisting of overexpressed cell surface neoantigens, to target GBM. Our designed vaccine exhibited the capacity to elicit a robust immune response, as evidenced by computer-based predictions of its immunogenicity and antigenicity. As such, our proposed vaccine holds the potential to serve as a supplementary tool aimed at enhancing treatment outcomes for GBM. The proposed vaccine needs future both in vitro and in vivo studies.MethodsTarget selection and epitope screeningThis study is entirely based on computational modeling and simulations. No cell lines or experimental data were utilized. In the initial phase of this study, we extracted the wild-type amino acid sequences of four surface proteins—PLAUR (Q03405) (https://www.uniprot.org/uniprotkb/Q03405/entry ), ITGB3 (P05106) (https://www.uniprot.org/uniprotkb/P05106/entry ), HLA-B (P01889) (https://www.uniprot.org/uniprotkb/P01889/entry ), and HLA-A (P04439) (https://www.uniprot.org/uniprotkb/P04439/entry )—from the UniProt database in FASTA format57. For constructing the mutants, the residues Arg, Asn, Thr, and Ile were substituted by His, Asp, Lys, and Thr in PLAUR, ITGB3, HLA-B, and HLA-A, respectively20. All potential Cytotoxic T lymphocytes (CTL) and T helper cell (Th cell) epitopes capable of binding to MHC class II molecules were identified at their respective mutation sites (Fig. 1). The expression levels and survival probabilities of HLA-B, PLAUR, HLA-A, and ITGB3 in both healthy individuals and GBM patients were determined using the UALCAN database (https://ualcan.path.uab.edu/ )58. Given the extensive number of epitopes, they were screened for antigenicity, toxicity, and allergenicity to select the most suitable ones. The VaxiJen v2.0 server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) was employed to forecast the epitopes’ antigenicity59. The VaxiJen v2.0 server can compute the antigenicity of a wide range of microorganisms, including bacteria, viruses, tumors, parasites, and fungi. The prediction accuracy of the VaxiJen v2.0 server ranges from 70 to 89%. For this analysis, the tumor was chosen as the target organism, with the antigenicity threshold established at 0.5. The AllerTOP v. 2.0 server (https://www.ddg-pharmfac.net/AllerTOP/method.html ) was employed to evaluate the allergenicity of the epitopes60. In addition, The approach employed by this server hinges on the auto cross covariance (ACC) transformation of amino acid sequences into standard vectors of consistent length61. Furthermore, IL4pred (https://webs.iiitd.edu.in/raghava/il4pred/design.php ) was employed to predict IL-4 inducing epitopes, while IFNepitope (https://webs.iiitd.edu.in/raghava/ifnepitope/design.php ) was used for predicting IFN-γ inducing epitopes. For predicting IL-4 inducing epitopes, the SVM-based model with a threshold of 0.2 was chosen62. Meanwhile, for IFN-γ inducing epitopes, an SVM-based model combined with the IFN-gamma versus other cytokine models was selected63.Assembly of the multi-epitope vaccineFollowing multi-step evaluation mentioned above, we selected the three best CTL epitopes (C1: AQTTKRKWE, C2: QTTKRKWEA, C3: TTKRKWEAA) and the most immunogenic Th cell epitope (H1: DMAAQTTKRKWEAAH) to form the final vaccine construct. These epitopes demonstrated the highest antigenicity, non-allergenicity, non-toxicity, and cytokine-inducing potential, making them the most promising candidates for inclusion. For the CTL epitopes, AAY linkers were utilized. By inserting linkers, the representation and proper separation of the epitopes will be improved. Additionally, the 50 S ribosomal protein L7/L12 (Locus RL7_MYCTU) with the accession number P9WHE3 (https://www.uniprot.org/uniprotkb/P9WHE3/entry ) was chosen as an adjuvant to boost the immunogenicity of the vaccine candidate. Its amino acid sequence was connected to the N-terminus of the chimeric sequences using an EAAAK linker.Assessment of the vaccine’s antigenicity, allergenicity, and physicochemical characteristicsEvaluating antigenicity is crucial in the vaccine design process. We utilized two servers, VaxiJen v2.0 and ANTIGENpro, to forecast the antigenic tendencies of the final vaccine construct. ANTIGENpro (http://scratch.proteomics.ics.uci.edu/ ) predicts protein antigenicity by employing five machine learning methodologies and various representations of the primary sequence64. To confirm the non-allergenic nature of the vaccine, we utilized AllerTOP v. 2.0 for allergenicity predictions. In addition, the Expasy ProtParam server (https://web.expasy.org/protparam/ ) was employed to anticipate different physicochemical attributes of the multi-epitope vaccine, such as amino acid composition, theoretical pI, molecular weight, instability index, half-life, aliphatic index, and the grand average of hydropathicity (GRAVY)41.Secondary structure predictionThe Prabi server (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_gor4.html ) was employed to estimate the proportion of secondary structure elements in the vaccine construct. The server utilizes the GOR IV prediction method, which boasts an average accuracy rate of 64.4%65.3D structure modeling, refinement, and verification of the multi-epitope vaccineThe 3D structure of the multi-epitope vaccine was predicted using the I-TASSER server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/ ). This server predicts three-dimensional structures from the amino acid sequence by piecing together segments from threading templates and provides a C-score to gauge the precision of the generated models25. The quality of the models were assessed by using the ProTSAV server (https://scfbio-iitd.res.in/software/proteomics/protsav.jsp )66. ProTSAV is a validated server, tested on approximately 64,446 protein structures. These encompass experimental structures from RCSB, predicted model structures for CASP targets, and those from public decoy sets. For experimentally solved structures, ProTSAV boasts a specificity of 100% and a sensitivity of 98%. For predicted protein structures of CASP11 targets under 2 Å, it achieves a specificity of 88% and a sensitivity of 91%. By integrating multiple methodologies, the server addresses the constraints inherent to individual servers or methods, proving to be a robust tool for quality assessment66. The selected model from the previous step was refined by using GalaxyRefine2 web server67.B-cell epitopes predictionB lymphocytes are pivotal components of the immune system, responsible for antibody production, thereby fostering long-term immunity68.Linear B-cell epitopes were forecasted using the BCPREDS server (https://webs.iiitd.edu.in/raghava/bcepred/bcepred_submission.html)69. This server employs a subsequence kernel-based SVM classifier and boasts an accuracy of 74.57% in predicting linear B-cell epitopes70. Additionally, the ElliPro server (http://tools.iedb.org/ellipro/ ) was engaged for discontinuous B-cell epitope predictions. ElliPro implements residue clustering algorithms, combined with Tornton’s method, to predict these epitopes. Each predicted epitope is given a score, termed the PI (protrusion index) value71.Molecular docking and MD simulationsThe CTL epitopes that successfully passed the screening phase were subjected to molecular docking against MHC class I (HLA A1, HLA A2, and HLA A3). HyperChem 8 was used for constructing the epitopes structures72. Autodock vina73 with its default setting was engaged to dock the epitopes with the MHC molecules. After assembling the epitopes, incorporating the adjuvant, and constructing the vaccine, a 500 ns MD simulation was conducted to refine the structure and eliminate steric clashes. Subsequently, the final snapshot of the vaccine was retained for additional modeling analyses. To explore the potential interaction between the vaccine construct and chain A of TLR4 (PDB ID: 4G8A)74, the ClusPro 2.0 server (https://cluspro.org/login.php ) was utilized75. Subsequently, two independent 300 ns MD simulations were conducted for both the free vaccine and the vaccine–TLR4 complex.All MD simulations were done by Desmond simulation package from Schrödinger Inc76. During each MD run, parameters of 310 K temperature and 1 bar pressure were maintained. For the simulation, the OPLS3 (Optimized Potentials for Liquid Simulations version 3) force field parameters was used77. The long-range electrostatic interactions were computed using the particle mesh Ewald method78, with a cutoff radius of 9.0 Å for Coulomb interactions. The system was solvated using the explicit TIP3P (three-site transferrable intermolecular potential) water model within an cubic periodic box that maintained periodic boundary conditions79. Na+ and Cl− were added as the counter ions, ensuring system neutrality. A distance of 10.0 Å was maintained between the periodic boundary conditions and the nearest vaccine: TLR4 complex atoms. The Martyna–Tuckerman–Klein chain coupling scheme80 managed pressure, while the Nosé–Hoover chain coupling scheme81 oversaw temperature during MD runs. Trajectory data were recorded every 10 ps. Analysis of the MD outputs were conducted using Visual Molecular Dynamics (VMD)82, alongside the Simulation Quality Analysis and Simulation Interaction Diagram tools within the Desmond MD package.Analysis of the immune profile for the multi-epitope vaccine constructFor an effective immune response against cancer cells, it’s crucial to understand the immune characteristics of the constructed vaccine through an immune simulation method. Thus, we assessed the immune profile of the final multi-epitope vaccine by inputting the vaccine construct sequence into the C-ImmSim 10.1 web server (https://kraken.iac.rm.cnr.it/C-IMMSIM/index.php?page=1 )83. C-ImmSim operates on the principles encompassing several specific elements of the immune system. These include antigen processing and presentation to CTL and Th cell, intercellular cooperation, B-cell and T-cell maturation, memory cell formation, clonal selection based on antigen affinity, the theory of clonal deletion, antibody hypermutation, T-cell replicative senescence, and anergy in both B and T lymphocytes, among others.In Silico Cloning of the Vaccine Construct into pET28a(+).The back translation of the vaccine construct and its codon optimization was conducted using JAVA Codon Adaptation Tool (JCat) (https://www.jcat.de/Result.jsp )84. The vaccine protein sequence was submitted to the JCat server, with the E. coli K12 strain chosen as the host organism for expression. The server analyzes key parameters, including the CAI and GC content, both of which play crucial roles in assessing the potential efficiency of protein expression. Restriction sites for the XhoI and NdeI enzymes were added to the 5′ and 3′ ends of the vaccine construct, respectively. The modified sequence was subsequently inserted into the pET28a(+)expression vector using SnapGene software for in silico cloning and visualization (https://www.snapgene.com/free-trial ).Fig. 1The wild-type, mutant and CTL epitopes of PLAUR, ITGB3, HLA-B and HLA-A. The wild-type and point mutations are highlighted in cyan and red, respectively.Full size imageFig. 2(a) The configuration of the final multi-epitope vaccine design. (b) The graphical representation of the secondary structure configuration of the constructed vaccine.Full size imageFig. 3ProTSAV quality assessment of input vaccine models; (a) 1, (b) 2, (c) 3, (d) 4, (e) 5, and (f) further refined model. Green region indicates the input structure to be in 0–2 Å RMSD, yellow region 2–5 Å RMSD, orange region 5–8 Å RMSD and red region indicates structures beyond 8 Å RMSD. The blue colored asterisk symbol represents quality assessment score by individual module and blue colored round symbol represents overall score by ProTSAV.Full size imageFig. 4(a–e) 3D structure showing the discontinuous B-cell epitopes on the vaccine construct. The gray sticks and the yellow surface show the vaccine construct and discontinuous B-cell epitopes, respectively.Full size imageFig. 5Predicted molecular interactions between the vaccine construct and TLR4 receptor. The central panel shows the overall binding of the vaccine (cartoon representation, red helices) within the TLR4 receptor cavity (surface representation, grey). The surrounding close-up panels highlight key interactions at the interface: (top-left) hydrogen bonds (yellow) between Arg152 of the vaccine and Asn205/His229 of TLR4; (bottom-center) hydrogen bonds (yellow), salt bridges (magenta), and π–π stacking (cyan) between Lys153/Trp154 of the vaccine and Glu287/Ser311 of TLR4; (top-right) a π-cation interaction (green) between Trp154 and Lys230.Full size imageFig. 6Molecular dynamics (MD) simulation trajectory plot of final vaccine construct with TLR4. (a) Number of interactions between the vaccine and TLR4. (b) Root mean square deviation (RMSD) of vaccine (in the presence or absence of TLR4) and vaccine-TLR4 complex. (c) Root mean square fluctuation (RMSF) of vaccine (in the presence or absence of TLR4). (d) Radius of gyration (Rg) of vaccine (in the presence of TLR4) and vaccine-TLR4 complex.Full size imageFig. 7Predicted immune response following consecutive three injections of the final construct vaccine given one month apart. (a) The frequency of different Immunoglobulin and immuno-complexes production in response to antigen injections (black). Various subclasses are presented as colored peaks. (b) Various cytokines and interleukins. (c) The prediction of computed B-cell amounts. (d) The prediction of T-helper, (e) T-cytotoxic cell amounts per state, (f) various IgG subclasses, (g) CD4 T-regulatory lymphocytes count showing total/memory/per entity-state counts, (h) CD8 T-cytotoxic lymphocytes count showing total and memory populations, and (i) NK cell populations after three vaccine injections.Full size imageFig. 8The multi-epitope vaccine construct was inserted in silico into the pET28a(+) vector using the free trial version of SnapGene software (https://www.snapgene.com/free-trial ). In the resulting plasmid map, the vaccine construct is depicted in red, while the black segment represents the vector backbone, highlighting the structural integration of the construct within the expression system.Full size imageData availabilityThe datasets used in this study are publicly available. Amino acid sequences were obtained from the UniProt database under accession numbers Q03405 (PLAUR), P05106 (ITGB3), P01889 (HLA-B), and P04439 (HLA-A). The 3D structure of the TLR4 receptor used in molecular docking and simulations was retrieved from the Protein Data Bank (PDB) under the accession code 4G8A. The molecular dynamics trajectory data generated during this study have been deposited in the Zenodo repository and are available at www. zenodo.org.ReferencesRayati, M., Mansouri, V. & Ahmadbeigi, N. Gene therapy in glioblastoma multiforme: Can it be a role changer? Heliyon 10 (2024).Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro-oncology 21, v1–v100 (2019).Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-oncology 17, iv1–iv62 (2015).Alexander, B. M. & Cloughesy, T. F. Adult glioblastoma. J. Clin. Oncol. 35, 2402–2409 (2017).CAS PubMed Google Scholar Hanahan, D. Hallmarks of cancer: New dimensions. Cancer Discov. 12, 31–46 (2022).CAS PubMed Google Scholar Wang, Z. et al. Efficacy and safety of tumour-treating fields combined with concurrent chemoradiotherapy for newly diagnosed glioblastoma patients. (2024).Rong, L., Li, N. & Zhang, Z. Emerging therapies for glioblastoma: Current state and future directions. J. Exp. Clin. Cancer Res. 41, 142 (2022).CAS PubMed PubMed Central Google Scholar Cao, J. X. et al. Clinical efficacy of tumor antigen-pulsed DC treatment for high-grade glioma patients: Evidence from a meta-analysis. PloS One 9, e107173 (2014).ADS PubMed PubMed Central Google Scholar Maxwell, R., Jackson, C. M. & Lim, M. Clinical trials investigating immune checkpoint blockade in glioblastoma. Curr. Treat. Options Oncol. 18, 1–22 (2017).Google Scholar Kaneko, M. K. et al. Chimeric anti-podoplanin antibody suppresses tumor metastasis through neutralization and antibody‐dependent cellular cytotoxicity. Cancer Sci. 103, 1913–1919 (2012).CAS PubMed PubMed Central Google Scholar Kumai, T., Fan, A., Harabuchi, Y. & Celis, E. Cancer immunotherapy: Moving forward with peptide T cell vaccines. Curr. Opin. Immunol. 47, 57–63 (2017).CAS PubMed PubMed Central Google Scholar Okada, H. et al. Induction of CD8 + T-cell responses against novel glioma–associated antigen peptides and clinical activity by vaccinations with α-type 1 polarized dendritic cells and polyinosinic-polycytidylic acid stabilized by lysine and carboxymethylcellulose in patients with recurrent malignant glioma. J. Clin. Oncol. 29, 330 (2011).CAS PubMed Google Scholar Saleh, M., Davis, I. D. & Wilks, A. F. The paracrine role of tumour-derived mIL‐4 on tumour‐associated endothelium. Int. J. Cancer 72, 664–672 (1997).CAS PubMed Google Scholar Gadani, S. P., Cronk, J. C., Norris, G. T. & Kipnis, J. IL-4 in the brain: A cytokine to remember. J. Immunol. 189, 4213–4219 (2012).CAS PubMed Google Scholar Carlsson, A. et al. Plasma proteome profiling reveals biomarker patterns associated with prognosis and therapy selection in glioblastoma multiforme patients. PROTEOMICS–Clin. Appl. 4, 591–602 (2010).CAS PubMed Google Scholar Ferrandez, E., Gutierrez, O., Segundo, D. S. & Fernandez-Luna, J. L. NFκB activation in differentiating glioblastoma stem-like cells is promoted by hyaluronic acid signaling through TLR4. Sci. Rep. 8, 6341 (2018).ADS PubMed PubMed Central Google Scholar Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).CAS PubMed PubMed Central Google Scholar Schumacher, T. et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature 512, 324–327 (2014).ADS CAS PubMed Google Scholar Weller, M. et al. (Oxford University Press US, 2016).Rose, M. et al. Surfaceome proteomic of glioblastoma revealed potential targets for immunotherapy. Front. Immunol. 12, 746168 (2021).CAS PubMed PubMed Central Google Scholar Zhai, B. T. et al. Urokinase-type plasminogen activator receptor (uPAR) as a therapeutic target in cancer. J. Translational Med. 20, 135 (2022).CAS Google Scholar Echavidre, W., Picco, V., Faraggi, M. & Montemagno, C. Integrin-αvβ3 as a therapeutic target in glioblastoma: Back to the future? Pharmaceutics 14, 1053 (2022).Shraibman, B. et al. Identification of tumor antigens among the HLA peptidomes of glioblastoma tumors and plasma. Mol. Cell. Proteom. 17, 2132–2145 (2018).CAS Google Scholar Zaib, S. et al. Designing multi-epitope monkeypox virus-specific vaccine using immunoinformatics approach. J. Infect. Public Health 16, 107–116 (2023).PubMed Google Scholar Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–738 (2010).CAS PubMed PubMed Central Google Scholar Dey, M. et al. Dendritic cell–based vaccines that utilize myeloid rather than plasmacytoid cells offer a superior survival advantage in malignant glioma. J. Immunol. 195, 367–376 (2015).CAS PubMed Google Scholar Iglesia, R. P. et al. Heat shock proteins in glioblastoma biology: Where do we stand? Int. J. Mol. Sci. 20, 5794 (2019).CAS PubMed PubMed Central Google Scholar Yang, I., Han, S. & Parsa, A. T. Heat-shock protein vaccines as active immunotherapy against human gliomas. Expert Rev. Anticancer Ther. 9, 1577–1582 (2009).CAS PubMed Google Scholar Liu, J., Miao, L., Sui, J., Hao, Y. & Huang, G. Nanoparticle cancer vaccines: Design considerations and recent advances. Asian J. Pharm. Sci. 15, 576–590 (2020).PubMed Google Scholar Kiyotani, K., Chan, H. T. & Nakamura, Y. Immunopharmacogenomics towards personalized cancer immunotherapy targeting neoantigens. Cancer Sci. 109, 542–549 (2018).CAS PubMed PubMed Central Google Scholar Yougbare, I. et al. Maternal anti-platelet Β3 integrins impair angiogenesis and cause intracranial hemorrhage. J. Clin. Investig. 125, 1545–1556 (2015).PubMed PubMed Central Google Scholar Li, J., Sullivan, J. A. & Ni, H. Pathophysiology of immune thrombocytopenia. Curr. Opin. Hematol. 25, 373–381 (2018).CAS PubMed Google Scholar Ni, H. & Freedman, J. Platelets in hemostasis and thrombosis: Role of integrins and their ligands. Transfus. Apheres. Sci. 28, 257–264 (2003).Google Scholar Nezafat, N. et al. Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches. Comput. Biol. Chem. 62, 82–95 (2016).CAS PubMed Google Scholar Arai, R., Ueda, H., Kitayama, A., Kamiya, N. & Nagamune, T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. 14, 529–532 (2001).CAS PubMed Google Scholar Pandey, R. K. et al. Exploring dual inhibitory role of febrifugine analogues against plasmodium utilizing structure-based virtual screening and molecular dynamic simulation. J. Biomol. Struct. Dyn. 35, 791–804 (2017).CAS Google Scholar Deng, H. et al. Development of a multivalent enterovirus subunit vaccine based on immunoinformatic design principles for the prevention of HFMD. Vaccine 38, 3671–3681 (2020).CAS PubMed Google Scholar Pandey, R. K. & Prajapati, V. K. Exploring sand fly salivary proteins to design multiepitope subunit vaccine to fight against visceral leishmaniasis. J. Cell. Biochem. 120, 1141–1155 (2019).CAS PubMed Google Scholar Chatterjee, N., Ojha, R., Khatoon, N. & Prajapati, V. K. Scrutinizing Mycobacterium tuberculosis membrane and secretory proteins to formulate multiepitope subunit vaccine against pulmonary tuberculosis by utilizing immunoinformatic approaches. Int. J. Biol. Macromol. 118, 180–188 (2018).CAS PubMed Google Scholar Barh, D. et al. Exoproteome and secretome derived broad spectrum novel drug and vaccine candidates inVibrio cholerae targeted by Piper betel derived compounds. PloS One 8, e52773 (2013).ADS CAS PubMed PubMed Central Google Scholar Gasteiger, E. et al. Protein Identification and Analysis Tools on the ExPASy Server (Springer, 2005).Gharbavi, M., Danafar, H., Amani, J. & Sharafi, A. Immuno-informatics analysis and expression of a novel multi-domain antigen as a vaccine candidate against glioblastoma. Int. Immunopharmacol. 91, 107265 (2021).CAS PubMed Google Scholar Salahlou, R. et al. Design and in Silico analysis of a novel peptide-based multiepitope vaccine against glioblastoma multiforme by targeting tumor-associated macrophage. Heliyon 10 (2024).Alvarado, A. G. et al. Glioblastoma cancer stem cells evade innate immune suppression of self-renewal through reduced TLR4 expression. Cell. Stem Cell. 20, 450–461 (2017).CAS PubMed PubMed Central Google Scholar Finocchiaro, G. TLRgeting evasion of immune pathways in glioblastoma. Cell. Stem Cell. 20, 422–424 (2017).CAS PubMed Google Scholar Candolfi, M. et al. B cells are critical to T-cell—mediated antitumor immunity induced by a combined immune-stimulatory/conditionally cytotoxic therapy for glioblastoma. Neoplasia 13, 947–IN923 (2011).CAS PubMed PubMed Central Google Scholar Pucci, F. Location-dependent B-cell function in glioblastoma. Cancer Immunol. Res. 7, 1902–1902 (2019).CAS PubMed Google Scholar Wiencke, J. K. et al. Epigenetic biomarkers of T-cells in human glioma. Epigenetics 7, 1391–1402 (2012).CAS PubMed PubMed Central Google Scholar Woroniecka, K. & Fecci, P. T-cell exhaustion in glioblastoma. Oncotarget 9, 35287 (2018).PubMed PubMed Central Google Scholar Crawford, A., MacLeod, M., Schumacher, T., Corlett, L. & Gray, D. Primary T cell expansion and differentiation in vivo requires antigen presentation by B cells. J. Immunol. 176, 3498–3506 (2006).CAS PubMed Google Scholar Oji, Y. et al. Association of WT1 IgG antibody against WT1 peptide with prolonged survival in glioblastoma multiforme patients vaccinated with WT1 peptide. Int. J. Cancer 139, 1391–1401 (2016).CAS PubMed PubMed Central Google Scholar Busch, S. et al. Circulating monocytes and tumor-associated macrophages express recombined immunoglobulins in glioblastoma patients. Clin. Translational Med. 8, 1–14 (2019).Google Scholar Turki, S. G. Immunoglobulins levels and complements in patients with brain tumour (Meningioma and Glioma). Iraqi Natl. J. Nurs. Specialties 30 (2017).Morla, S., Makhija, A. & Kumar, S. Synonymous codon usage pattern in glycoprotein gene of rabies virus. Gene 584, 1–6 (2016).CAS PubMed Google Scholar Ali, M. et al. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Sci. Rep. 7, 9232 (2017).ADS PubMed PubMed Central Google Scholar Alves, S. R., Colquhoun, A., Wu, X. Y. & de Silva, O. Synthesis of terpolymer-lipid encapsulated diruthenium (II, III)-anti-inflammatory metallodrug nanoparticles to enhance activity against glioblastoma cancer cells. J. Inorg. Biochem. 205, 110984 (2020).CAS PubMed Google Scholar Consortium, U. Reorganizing the protein space at the universal protein resource (UniProt). Nucleic Acids Res. 40, D71–D75 (2012).Google Scholar Chandrashekar, D. S. et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25, 18–27 (2022).Google Scholar Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 1–7 (2007).Google Scholar Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP V. 2—a server for in silico prediction of allergens. J. Mol. Model. 20, 1–6 (2014).CAS Google Scholar Wold, S., Jonsson, J., Sjörström, M., Sandberg, M. & Rännar, S. DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal. Chim. Acta. 277, 239–253 (1993).CAS Google Scholar Dhanda, S. K., Gupta, S., Vir, P. & Raghava, G. Prediction of IL4 inducing peptides. Clin. Dev. Immunol. (2013).Dhanda, S. K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct 8, 1–15 (2013).Google Scholar Magnan, C. N. et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 26, 2936–2943 (2010).CAS PubMed PubMed Central Google Scholar Garnier, J. GOR secondary structure prediction method version IV. Meth Enzym RF Doolittle Ed. 266, 540–553 (1998).Google Scholar Singh, A., Kaushik, R., Mishra, A., Shanker, A. & Jayaram, B. ProTSAV: A protein tertiary structure analysis and validation server. Biochim. Et Biophys. Acta (BBA)-Proteins Proteom. 1864, 11–19 (2016).CAS Google Scholar Lee, G. R., Won, J., Heo, L. & Seok, C. GalaxyRefine2: simultaneous refinement of inaccurate local regions and overall protein structure. Nucleic Acids Res. 47, W451–W455 (2019).CAS PubMed PubMed Central Google Scholar Khan, M. et al. Immunoinformatics approaches to explore Helicobacter pylori proteome (virulence factors) to design B and T cell multi-epitope subunit vaccine. Sci. Rep. 9, 13321 (2019).ADS PubMed PubMed Central Google Scholar Saha, S. & Raghava, G. P. S. in International conference on artificial immune systems. 197–204 (Springer).Chen, J., Liu, H., Yang, J. & Chou, K. C. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33, 423–428 (2007).CAS PubMed Google Scholar Ponomarenko, J. et al. ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinform. 9, 1–8 (2008).Google Scholar Laxmi, D. & Priyadarshy, S. HyperChem 6.03. Biotech. Softw. Internet Report: Comput. Softw. J. Scientists 3, 5–9 (2002).Google Scholar Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).CAS PubMed PubMed Central Google Scholar Ohto, U., Yamakawa, N., Akashi-Takamura, S., Miyake, K. & Shimizu, T. Structural analyses of human toll-like receptor 4 polymorphisms D299G and T399I. J. Biol. Chem. 287, 40611–40617 (2012).CAS PubMed PubMed Central Google Scholar Desta, I. T., Porter, K. A., Xia, B., Kozakov, D. & Vajda, S. Performance and its limits in rigid body protein-protein docking. Structure 28, 1071–1081 (2020). e1073.CAS PubMed PubMed Central Google Scholar Release, S. 3: Desmond molecular dynamics system (DE Shaw Research, New York, NY, 2017).Banks, J. L. et al. Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem. 26, 1752–1780 (2005).CAS PubMed PubMed Central Google Scholar Toukmaji, A. Y. & Board, J. A. Jr Ewald summation techniques in perspective: A survey. Comput. Phys. Commun. 95, 73–92 (1996).ADS CAS Google Scholar Mark, P. & Nilsson, L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 105, 9954–9960 (2001).CAS Google Scholar GhavamiNejad, A. et al. Transdermal delivery of a somatostatin receptor type 2 antagonist using microneedle patch technology for hypoglycemia prevention. Drug Deliv. Transl. Res., 1–13 (2022).Lu, B. et al. Smart composite microneedle patch stabilizes glucagon and prevents nocturnal hypoglycemia: Experimental studies and molecular dynamics simulation. ACS Appl. Mater. Interfaces. 14, 20576–20590 (2022).CAS PubMed Google Scholar Humphrey, W., Dalke, A. & Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).CAS PubMed Google Scholar Rapin, N., Lund, O., Bernaschi, M. & Castiglione, F. Computational immunology meets bioinformatics: The use of prediction tools for molecular binding in the simulation of the immune system. PloS One 5, e9862 (2010).ADS PubMed PubMed Central Google Scholar Grote, A. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 33, W526–W531 (2005).ADS CAS PubMed PubMed Central Google Scholar Download referencesAcknowledgementsThe authors would like to acknowledge the Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery for providing a Grant to XYW, which supported this research.Author informationAuthors and AffiliationsAdvanced Pharmaceutics and Drug Delivery Laboratory, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, CanadaSako Mirzaie, Kevin Da Yuan & Xiao Yu WuDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, CanadaHeyu NiKeenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, ON, CanadaHeyu NiCanadian Blood Services Centre for Innovation, Toronto, ON, CanadaHeyu NiAuthorsSako MirzaieView author publicationsSearch author on:PubMed Google ScholarKevin Da YuanView author publicationsSearch author on:PubMed Google ScholarHeyu NiView author publicationsSearch author on:PubMed Google ScholarXiao Yu WuView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization, S.M. and X.Y.W.; methodology, S.M.; writing—original draft preparation, S.M., K.D.Y, X.Y.W. and H.N.; writing—review and editing, S.M., K.D.Y, X.Y.W. and H.N; supervision, X.Y.W.; All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to Sako Mirzaie or Xiao Yu Wu.Ethics declarationsCompeting interestsThe authors declare no competing interests.Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Electronic supplementary materialBelow is the link to the electronic supplementary material.Supplementary Material 1.Rights and permissionsOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.Reprints and permissionsAbout this article