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Original Article

Immunoinformatics study of CD40 ligand-targeting vaccine constructs: a novel immunotherapeutic approach

Seyed Amir Sadeghi1orcid, Mahroo Mohamadi2orcid, Hadi Bamehr3orcid, Fatemeh Heidarnejad1orcid, Azam Bolhassani1orcid
Osong Public Health and Research Perspectives 2025;16(4):311-332.
Published online: August 11, 2025

1Department of Hepatitis, AIDS and Blood-borne Diseases, Pasteur Institute of Iran, Tehran, Iran

2Department of Clinical Biochemistry, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

Corresponding author: Fatemeh Heidarnejad Department of Hepatitis, AIDS and Blood-borne Diseases, Pasteur Institute of Iran, NO. 69, Pasteur Ave., Tehran, Iran E-mail: heidarnejadfatemeh@gmail.com
Co-Corresponding author: Azam Bolhassani Department of Hepatitis, AIDS and Blood-borne Diseases, Pasteur Institute of Iran, NO. 69, Pasteur Ave., Tehran, Iran E-mail: A_bolhasani@pasteur.ac.ir
• Received: March 9, 2025   • Revised: May 29, 2025   • Accepted: June 15, 2025

© 2025 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • Objectives
    Incorporating CD40 ligand (CD40L) into vaccine strategies has shown considerable potential for enhancing immune responses. In this study, we designed and formulated a CD40L-based multi-epitope vaccine construct using immunoinformatics approaches, and compared it to a full-length CD40L-based vaccine construct.
  • Methods
    The study commenced with the identification and screening of potential T-cell and B-cell epitopes derived from the CD40L protein, followed by the construction of a multi-epitope vaccine from these selected epitopes. We analyzed and validated the physicochemical and structural properties of the vaccine constructs. Further, we predicted disulfide bonds, performed protein-protein docking, and conducted molecular dynamics simulations to evaluate the constructs. Comparative analyses of the ligand-binding site localization were conducted using LigPlot. Additionally, simulation trajectories were analyzed using multiple descriptors, including root mean square deviations, radius of gyration, and root mean square fluctuations.
  • Results
    Our findings indicated that the CD40L multi-epitope vaccine construct possessed favorable physicochemical properties and a validated structural profile. Immune simulation studies showed a stronger affinity of the multi-epitope construct for the CD40 receptor compared to the full-length CD40L construct.
  • Conclusion
    Overall, the CD40L multi-epitope vaccine construct demonstrated greater potency in eliciting an effective immune response than the full-length CD40L construct. These results highlight a promising approach to vaccine design for the prevention or treatment of infections and cancers.
  • Keywords: CD40 ligand; CD40 receptor; Molecular docking simulation; Molecular dynamics simulation; Therapeutic vaccine
CD40 ligand (CD40L), also known as CD154, is a key member of the tumor necrosis factor superfamily, playing a central role in immune regulation. CD40L is essential for coordinating immune cell interactions and serves as a primary mediator in diverse immune processes [1]. Predominantly expressed on activated T cells, CD40L binds to its receptor CD40 on antigen-presenting cells (APCs), initiating signaling cascades that influence both innate and adaptive immunity [2]. Its functions include B cell activation, class-switching, and the formation of memory responses [3]. Beyond these roles, CD40L is now extensively studied for its therapeutic potential in immunotherapy and vaccine development [4–6]. Epitope vaccines, also known as subunit vaccines, represent a precise approach to vaccination [7]. Compared to whole-sequence vaccines, multi-epitope vaccines provide greater precision and safety by targeting specific antigenic regions, reducing adverse reactions and focusing the immune response [8]. They also allow formulation customization to include multiple epitopes, broadening effectiveness, and their synthetic production simplifies manufacturing [8]. Furthermore, multi-epitope vaccines can target conserved regions shared across pathogen strains, addressing the challenge of pathogen variability [9]. These benefits—improved safety, tailored responses, and broader protection—make epitope vaccines an attractive option [10,11].
Bioinformatics has revolutionized vaccine design by accelerating candidate identification through computational tools [12,13]. Several multi-epitope vaccines developed using immunoinformatics have reached clinical trials or commercialization, such as Russia’s EpiVacCorona (using synthetic SARS-CoV-2 peptides) and the R21/Matrix-M malaria vaccine, which targets conserved Plasmodium falciparum epitopes [14,15]. Similarly, multi-epitope vaccines against rotavirus have been developed by coupling selected epitopes with adjuvants and linkers, which showed favorable physicochemical properties and strong receptor binding in computational analyses [16]. In veterinary medicine, multi-epitope vaccines targeting Newcastle disease virus were designed by combining conserved epitopes from viral glycoproteins, demonstrating significant immune receptor interactions and the potential to induce protective immunity [17]. These cases highlight the increasing success of immunoinformatics-driven multi-epitope vaccine design, paving the way for innovative immunotherapeutic strategies in both human and animal health. Hence, this article presents a comprehensive comparative analysis of CD40L whole sequence and CD40L multi-epitope constructs, evaluating them through molecular docking, immune simulation, molecular dynamics (MD), and in silico cloning. By comparing these constructs, we aim to provide new insights into CD40L’s potential as a vaccine candidate and inform future therapeutic development.
Retrieval of Protein Sequence
The CD40L protein sequence in FASTA format was retrieved from the National Center for Biotechnology Information (NCBI) database (accession NP_035746.2).
Epitope Prediction Analyses
We utilized the NetMHCpan 4.1 server (https://www.cbs.dtu.dk/services/NetMHCpan/) to predict cytotoxic T lymphocyte (CTL) epitopes from selected linear peptides, applying a threshold of 0.5 and default parameters. Peptide binding affinity was assessed against human leukocyte antigen (HLA)-I supertypes and common global HLA-I alleles [18]. For major histocompatibility complex (MHC)-II-restricted CD4+ helper T lymphocyte (HTL) epitopes, NetMHCIIpan (http://www.cbs.dtu.dk/services/NetMHCpan/) was used, setting epitope length to 15, with thresholds of 1 and 5 for high- and low-affinity binders, respectively [19]. MHC-I antigen processing—including proteasomal cleavage, TAP transport, and MHC-I binding—was analyzed using NetCTLpan 1.1 (https://services.healthtech.dtu.dk/services/NetCTLpan-1.1/), which calculates weighted prediction scores based on C-terminal cleavage, binding affinities, and TAP transport efficiency [20]. Linear B-cell epitopes (LBL) were identified using ElliPro (http://tools.iedb.org/ellipro/) with default settings. Molecular docking between selected CD40L epitopes and MHC alleles was performed using GalaxyPepDock (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=PEPDOCK), with relevant HLA structures sourced from RCSB Protein Data Bank (PDB, https://www.rcsb.org), as listed in Table 1. Antigenicity was determined with VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), allergenicity with AllerTOP 2.0 (https://www.ddg-pharmfac.net/AllerTOP/), and toxicity with ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) [21]. The HTL epitopes’ ability to induce interferon (IFN)-γ, interleukin (IL)-4, and IL-10 was predicted using IFNepitope (http://crdd.osdd.net/raghava/ifnepitope/), IL4pred (http://crdd.osdd.net/raghava/il4pred/), and IL10pred (http://crdd.osdd.net/raghava/IL-10pred/) [22,23]. Finally, population coverage for selected CTL and HTL epitopes and their associated HLA alleles (MHC-I and MHC-II) was analyzed using the IEDB population coverage tool (http://tools.iedb.org/population/) [24].
Design and Physicochemical Features of Vaccine Constructs
Dominant T and B cell epitopes were linked with AYY linkers to design multi-epitope vaccines using SnapGene 3.2.1 (SnapGene). Antigenicity and allergenicity of both multi-epitope and whole-sequence CD40L constructs were evaluated using VaxiJen 2.0 (https://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) and Aller-TOP 2.0 [25]. Construct solubility was predicted using the Protein-Sol (https://protein-sol.manchester.ac.uk/) server [26]. The physicochemical characteristics of the vaccine constructs were evaluated using ExPASy ProtParam (https://web.expasy.org/protparam/) server [27].
Secondary Structure Prediction
Secondary structural elements (α-helices, β-sheets, β-turns, coils) were predicted using the self-optimized prediction method with alignment (SOPMA) method (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) [28].
Tertiary Structure Prediction, Refinement, and Validation
Three-dimensional (3D) models of constructs were generated with Robetta (https://robetta.bakerlab.org/) [29], refined, and validated using GalaxyRefine (http://galaxy.seoklab.org/refine) [30] and the PROCHECK server (https://saves.mbi.ucla.edu/) for Ramachandran plots, Verify3D modules and ERRAT score plots [31,32].
Discontinuous B-Cell Epitope Prediction
Discontinuous B-cell epitopes were predicted with ElliPro (http://tools.iedb.org/ellipro/), which uses protein 3D structure to identify accessible residues and cluster them as conformational epitopes. This computational approach helps identify B cell epitopes for further experimental validation or vaccine development [33].
Disulfide Bond Prediction of the Protein Structures
We used the DIpro Scratch server (http://scratch.proteomics.ics.uci.edu/) to predict disulfide bonds, including their number, cysteine bonding state, and paired connections, with 85% accuracy and 90% recall [34].
Molecular Docking Analysis of the CD40 Receptor and Vaccine Constructs
CD40 receptor structures were obtained from RCSB PDB. Refined 3D vaccine constructs (multi-epitope and whole sequence) were docked using ClusPro 2.0 (https://cluspro.bu.edu/login.php) and HDOCK (http://hdock.phys.hust.edu.cn). The best complex was selected based on lowest energy-weighted score and docking efficiency. Both servers use FFT-based global docking, widely applied in protein-protein docking studies [35,36].
Protein-Ligand Interactions
LigPlot was used to visualize protein–ligand interactions, producing 2-dimensional diagrams showing hydrogen bonds (dashed lines) and hydrophobic contacts (arcs), with participating protein residues indicated by spokes [37].
In Silico Immune Simulation
An immune simulation study was performed to investigate the immunogenicity and immune response profile using the C-ImmSim web tool (http://150.146.2.1/CIMMSIM/index.php), which incorporates machine learning and real-life-like immune interactions [38]. Default parameters were used, with time steps at 1, 84, and 170 (representing 3 injections at 4-week intervals, as in commercial vaccine regimens) [39,40].
Molecular Dynamics Simulation
Vaccine constructs docked with the CD40 receptor were subjected to 50 ns MD simulations using Gromacs v2021.5 (GROMACS Development Team) [41]. Gromacs is designed for efficient, parallel biomolecular simulations [42]. The CHARMM-36 force field was used for topology generation [43]. Each complex was placed in a simulation box with 1 nm distance from the box edges and solvated using the TIP3P water model. Sodium and chloride ions were added for charge neutralization. Energy minimization was performed via the steep descent algorithm. Equilibration was run for 1 ns at 298 K and 1 bar in the normal pressure and temperature (NPT) ensemble, using a Berendsen thermostat and barostat. For the production run, each system was simulated under constant normal volume and temperature at 300 K, using a modified Berendsen thermostat [43]. Afterwards, systems were further simulated under constant pressure (NPT) using a Berendsen barostat at 1 atm [44]. Long-range electrostatic interactions were calculated with the Particle Mesh Ewald method, applying a cutoff distance of 1.0 nm [45]. Periodic boundary conditions were removed before analysis. Simulation trajectories were analyzed for root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg) to assess conformational stability of the complexes.
In Silico Cloning
Codon optimization for Escherichia coli expression was performed with JCat (http://www.jcat.de/CAICalculation.jsp), using the codon adaptation index (CAI) to assess synonymous codon bias [46]. SnapGene 3.2.1 was used to identify restriction sites, simulate cloning, validate sequences, and insert optimized constructs into pET24a(+) for expression.
Epitope Prediction Analyses
The CD40L reference sequence was obtained from NCBI in FASTA format for all subsequent analyses. Using NetMHCpan 4.1, we predicted CTL epitopes of CD40L, identifying 6 overlapping sequences with high binding affinities for multiple HLA-I alleles as top CTL epitopes. For HTL epitopes, 5 high-scoring candidates for MHC-II were selected based on strict criteria, all demonstrating strong proteasomal cleavage and TAP transport efficiency (Table 2).
B-cell linear epitopes were predicted using the ElliPro server to improve accuracy. The resulting CD40L epitopes were non-allergenic, non-toxic, and antigenic (Table 3).
CD40L protein was screened for 2 linear B cell epitopes, 5 T cell epitopes and 6 CTL epitopes. Notably, amino acids 1 to 33 overlapped in both B-cell and T-cell epitopes and were the most dominant. The MHC alleles for peptide-protein docking are listed in Table 1. Top models with the highest interaction similarity between CTL/HTL epitopes and their respective HLA class I and II alleles are presented in Tables 4 and 5. Epitopes with support vector machine (SVM) scores above threshold were classified as IL-10 and IL-4 inducers, while positive SVM scores indicated IFN-γ induction. All selected CD40L epitopes induced IFN-γ. Specifically, CD40L200–219 induced both IL-4 and IL-10; CD40L135–154 and CD40L141–160 induced IL-10; and CD40L90–108 and CD40L228–249 induced IL-4 (Table 6).
Tables 7 and 8 show the highest-scoring epitopes and their corresponding population coverage percentages. For CTL epitopes, the highest population coverage of the world’s population was calculated for CD40L1-13 with 95.11%. For helper T-cell epitopes, most of the CD40L epitopes exhibit population coverage exceeding 90%. Overall, the IEDB server indicated that most of the identified epitopes had coverage of over 80%. Finally, the epitopes that met all the specified criteria were chosen for the design of the final multi-epitope construct.
Physicochemical Features of Vaccine Construct
The SnapGene 3.2.1 tool was used to design the vaccine construct by joining LBL, CTL, and HTL epitopes with an AAY linker (Figure 1). Properties of epitope-based and whole-protein constructs were compared. ProtParam analysis showed the multi-epitope construct had a molecular weight (MW) of 28.70 kDa (<100 kDa), pI of 9.56, mammalian half-life >20 hours, and E. coli half-life >10 hours. The instability index was 35.73 (<40), indicating stability. Protein-Sol analysis gave a solubility score of 0.713 (<45), indicating high hydrophilicity. VaxiJen 2.0 confirmed antigenicity (0.567 >0.4), and Aller-TOP 2.0 predicted non-allergenicity. Compared to the whole sequence, the multi-epitope construct showed superior solubility (0.713 vs. 0.361), stability (46.29 vs. 35.73), and antigenicity (0.567 vs. 0.504). Full comparisons are in Table 9.
Secondary Structure Prediction
SOPMA analysis of both constructs (Table 10) revealed the multi-epitope construct contained significantly more alpha helices, beta sheets, and beta turns than the whole sequence (Figure 2).
Tertiary Structure Prediction, Refinement, and Validation
3D models were generated using Robetta and refined with Galaxy Refine 2. Validation by ERRAT, PROCHECK, and Verify3D showed ERRAT scores of 91.25 for the multi-epitope and 87.288 for the whole sequence. Ramachandran plots showed 92% (multi-epitope) and 90.7% (whole sequence) of residues in favored regions. Verify3D analysis confirmed >80% of residues scored above 0.1 for both constructs. Figures 3 and 4 present the 3D models and validation results.
Discontinuous B-Cell Epitope Prediction
ElliPro analysis on the refined 3D CD40L models identified 5 potential discontinuous B-cell epitopes in the multi-epitope construct. The number and scores of these predicted epitopes suggest strong potential to trigger a humoral immune response (Table 11).
Disulfide Bond Prediction
The DIpro Scratch server predicted disulfide bond connectivity in both the CD40L whole sequence and multi-epitope constructs. The whole sequence had 4 cysteines (positions 72, 84, 177, and 217), forming 2 disulfide bonds. The multi-epitope construct also contained 4 cysteines (positions 6, 20, 155, and 213), predicted to form 2 disulfide bonds.
Molecular Docking Analysis of the CD40 Receptor and Vaccine Constructs
Molecular docking was performed using ClusPro 2.0 and HDOCK web servers. There is an inverse correlation between energy values and binding affinity. ClusPro showed highly negative energies for CD40L multi-epitope and whole sequence constructs docked to CD40 receptor (–1,330.3 kcal/mol and –1,117.3 kcal/mol, respectively). HDOCK docking scores were –226.16 for the multi-epitope and –197.79 for the whole sequence construct. Both tools produced consistent results, indicating that the multi-epitope construct had lower binding energy and a stronger docking score with CD40. Protein-protein docking results are illustrated in Figure 5.
Protein–Ligand Interactions
LigPlot analysis revealed that both CD40L-based constructs interact with the CD40 receptor through hydrophobic and hydrogen bonds, with hydrophobic interactions predominating. The multi-epitope construct had more amino acids involved in hydrogen bonding than the whole sequence. In the multi-epitope structure, aspartic acid 69 bonded with threonine, whereas in the full-length sequence, it bonded with tyrosine. Additionally, glutamic acid 107 in the multi-epitope construct formed a hydrogen bond with tyrosine, while in the whole sequence, it bonded with arginine (Table 12, Figure 6).
In Silico Immune Simulation
Immune simulation assessed adaptive responses for both vaccine constructs. After each injection, both primary and secondary immune responses increased, as shown by rising levels of active B-cells, immunoglobulin (Ig) G1+IgG2, IgM, and IgG+IgM (Figures 7, 8A, B, H), as well as helper and cytotoxic T cells (Figures 7, 8C–F). These results suggest strong secondary responses, improved antigen clearance, and robust immune memory generation. Both constructs also stimulated notable IFN-γ and IL-2 cytokine secretion (Figures 7, 8G).
Molecular Dynamics Simulation
Complex stability was analyzed in Gromacs 2021.5. The CD40L multi-epitope construct reached equilibrium after approximately 5 ns, while the whole sequence required approximately 15 ns, indicating consistent stability for both. Average RMSD values were 1.11 nm (range, 0.5–1.7 nm) for the multi-epitope and 1.75 nm (range, 1.3–2.2 nm) for the whole sequence, remaining below 2 nm. RMSD graphs showed similar stability, with minimal variation across replicates (Figure 9A). RMSF analysis indicated higher local flexibility in amino acids 20 to 60 of the multi-epitope construct, likely due to proline content. Other regions with notable fluctuations in the whole sequence included residues 8, 10, 18, 119, and 180. Average RMSF was 1.4 (0.5–2.3) for the multi-epitope and 1.65 (0.6–2.7) for the whole sequence (Figure 9B). The Rg showed the multi-epitope construct was more stable (mean Rg 1.76 vs. 1.50), with Rg fluctuations less than 2 Å for both systems (Figure 9C).
In Silico Cloning
CAI and GC content were determined using the JCat server. The CD40L whole sequence construct had a CAI of 0.97 and GC content of 47.31%. The multi-epitope construct had a CAI of 1.0 and GC content of 46.60%, suggesting higher expression potential. Adapted sequences were inserted into the pET24a(+) vector using SnapGene 3.2.1 (Figure 10).
Conventional vaccine development often relies on whole organisms, which can lead to unwanted antigen exposure and potential allergic reactions. In contrast, truncated multi-epitope vaccines have shown promise in generating targeted and robust immune responses while reducing the risk of allergic events [9]. Developing vaccines traditionally involves complex and costly in vivo and in vitro procedures to ensure efficacy [9]. However, advances in computational biology and immunoinformatics have lessened the dependence on in vitro experiments and have enabled the design of effective in silico vaccines. For instance, vaccinomics has facilitated the creation of multi-epitope-based vaccines against a range of infections, multiple viruses, and cancers, with efficacy validated in vitro [47–50]. Epitope-based vaccine design allows for more precise and efficient induction of either humoral or cellular immune responses [51]. Despite the proliferation of web servers for predicting peptide immunogenicity, accurately forecasting immune responses to antigens in living organisms remains a significant challenge [52,53].
CD40 stimulation via CD40L has been explored experimentally as a molecular adjuvant in vaccine research, where it enhances the activation of both CD4+ and CD8+ T cells. The use of CD40L in epitope vaccines has shown potential to advance vaccine technology [53]. The interaction between CD40L on T cells and CD40 on APCs upregulates co-stimulatory molecules, promotes cytokine production, and boosts antigen presentation, thereby amplifying the immune response [54]. CD40L has been widely studied for its effectiveness in activating both CD8+ and CD4+ T cell responses, making it a promising vaccine component for diseases such as human immunodeficiency virus and cancer [4,55–57]. Utilizing CD40L in cancer immunotherapy has shown considerable potential to enhance antitumor immune responses and improve therapeutic outcomes by activating dendritic cells [58,59]. Using recombinant modified vaccinia virus Ankara encoding CD40L as a vaccine against solid tumors has demonstrated therapeutic benefits by inducing cytotoxic CD8+ T cells and activating natural killer cells [60]. CD40L also serves as an effective adjuvant to enhance the immune response generated by epitope-based vaccines [61,62]. Thus, in this study, we assessed and compared the ability of the CD40L whole sequence construct and CD40L multi-epitope construct to enhance immune responses against infections and cancers. Both constructs were analyzed using a range of in silico tools. Recent research has underscored the value of immunoinformatics pipelines for designing multi-epitope vaccines, highlighting the crucial roles of epitope prediction, structural validation, molecular docking, and immune simulation in predicting vaccine efficacy. These approaches support our methodology and reinforce the rationale for utilizing a multi-epitope strategy in CD40L-targeted vaccine development [63–67].
Initially, we selected the CD40L protein sequence from NCBI to identify its HTL, CTL, and B-cell epitopes, given their roles in host protection against infections [68,69]. Both B-cell and T-cell epitopes were employed to design peptides capable of stimulating humoral and cellular immunity. We conducted a comprehensive screening to identify potential T-cell and B-cell epitopes from CD40L, evaluating them primarily for antigenicity, allergenicity, and toxicity. Subsequent analysis examined T-cell antigen processing for these CD40L epitopes. Higher processing scores indicated more efficient antigen processing [70]. All predicted CD40L epitopes achieved top-tier identification scores, signifying superior proteasomal cleavage and efficient TAP transport. Molecular docking of protein-peptide interactions demonstrated high interaction similarity scores for all predicted CTL and HTL epitopes. Among the CTL epitopes, CD40L residues 4–17 and 84–95 displayed particularly strong binding affinity to the HLA-B5101 allele, as shown by high average docking scores. For HTL epitopes, CD40L segments 135–154 and 139–157 exhibited robust interactions with the HLA-DRB10301 allele, also achieving the highest docking scores. These findings highlight the strong HLA binding potential of these epitopes, indicating promise for effective immune recognition [71].
Since IFN-γ, IL-10, and IL-4 are pivotal cytokines in both innate and adaptive immunity, particularly for reducing viral load [72], we assessed the ability of MHC-II epitopes to induce these cytokines. Notably, most predicted CD40L HTL epitopes were found to induce IL-4. Published data indicate that IL-4 promotes viral replication and progression, as well as T cell expansion and antibody production [73]. In contrast, most CD40L epitopes did not induce IL-10. IL-10 can both inhibit and stimulate IFN-γ and IL-4 production, respectively; elevated IL-10 levels may suggest immune dysfunction [74–77]. Thus, these constructs may mitigate the deleterious effects of IL-10 during infection. Crucially, all predicted CD40L epitopes were found to induce IFN-γ, which is associated with reduced viral load in infected hosts [78]. Collectively, these results suggest that the selected HTL epitopes can induce both T helper (Th) 1 and Th2 immune responses in vivo. Our vaccine construct demonstrated a population coverage of 87.92%, supporting its global applicability. We further identified 6 candidate CD40L epitopes for MHC-I, 5 immunodominant epitopes for MHC-II, and 2 B-cell epitopes. Among the predicted CTL epitopes, CD40L4–17 and CD40L1–13, as well as HTL epitopes CD40L135–154 and CD40L139–157, showed superior MHC binding ranks.
The selected epitopes were used to construct a multi-epitope vaccine, incorporating AAY linkers to preserve the functional integrity of each epitope and regulate flexibility and rigidity [79]. Linkers offer important advantages, such as reducing junctional antigen formation and improving antigen processing and presentation [80]. AAY linkers in particular act as proteasome cleavage sites and help minimize junctional immunogenicity [81–83]. When attached to epitopes, these linkers facilitate the recognition and separation of each epitope [39,84,85]. Consequently, the designed vaccine was found to be highly antigenic and non-allergenic, based on evaluations by multiple prediction servers. In comparison to the CD40L whole sequence construct, this finding highlights the CD40L multi-epitope construct’s ability to provoke robust immune responses without causing undesired allergic reactions. Additionally, the average MW of the CD40L multi-epitope and whole sequence constructs was 28.70 kDa and 29.37 kDa, respectively. This difference supports the enhanced antigenicity observed in the multi-epitope construct [86]. Proteins with MW below 110 kDa are considered good vaccine candidates [87]. The theoretical isoelectric point (pI) for the multi-epitope and whole sequence constructs was 9.56 and 8.26, respectively, suggesting stable interactions within the human body. The short half-life of peptides is a notable limitation in therapeutic protein development [88]. Nevertheless, both vaccine constructs demonstrated half-lives of over 10 and 30 hours in E. coli and mammalian cells, respectively, which is considered satisfactory [89]. The CD40L multi-epitope construct exhibited an instability index of 35.73, whereas the whole sequence construct’s predicted index was 46.29, indicating greater stability for the multi-epitope construct in biological environments. Compounds with instability indices below 40 are classified as stable [90]. The solubility score indicated that the vaccine is hydrophilic, facilitating formulation and purification [91,92]. Protein solubility in E. coli is also crucial for functional and biochemical analyses [93]. The CD40L multi-epitope construct was found to be soluble (0.713), while the whole sequence construct’s solubility score was 0.361. This further demonstrates that the multi-epitope construct is more amenable to post-production processing, as highly soluble proteins are easier to purify during downstream applications [94].
In this study, the SOPMA technique was employed to analyze protein secondary structure. Secondary structure analysis determines whether amino acids are located in alpha helices or beta sheets, both of which are essential for protein structure and function. The alpha helix is especially beneficial for proteins requiring strength and stability [95]. The results for the CD40L multi-epitope construct indicated higher proportions of amino acids in alpha helices, beta sheets, and beta turns compared to the CD40L whole sequence construct. Notably, the multi-epitope protein exhibited a pronounced helical structure, suggesting a more condensed and tightly bound configuration, including its transmembrane segment [96].
After predicting and refining the 3D structure of the vaccine model using Robetta and Galaxy Refine, the resulting models were validated by SAVES v6.0 ERRAT, PROCHECK, and Verify3D. Evaluating the tertiary structure quality is crucial, as it influences peptide presentation for immune activation [97]. The refined CD40L multi-epitope model had 92% of residues in the most favored Ramachandran plot zones, compared to 90.7% for the whole sequence construct, indicating the high quality of the multi-epitope model. Modeling quality metrics and Verify3D results further showed that the multi-epitope vaccine model was at least as acceptable as the whole sequence construct. Using ElliPro, we identified numerous linear and discontinuous B-cell epitopes within the multi-epitope construct, underscoring its strong potential to stimulate a robust humoral immune response [98].
Two disulfide bonds were predicted in both structures, which are critical for protein folding and stability. Disulfide bond formation limits conformational diversity, enhancing thermal stability and reducing entropy [93]. To assess the interaction between vaccine constructs and the CD40 receptor, molecular docking was performed with ClusPro and HDOCK. Previous studies showed that CD40 receptor engagement with CD40L increases cytokine production and co-stimulatory molecule expression, linking adaptive and innate immunity and contributing to protection against infection and cancer [99–102]. Docking revealed negative binding energy values, indicating strong affinity between the vaccine constructs and the CD40 receptor. The multi-epitope constructs showed stronger docking with CD40 compared to the whole sequence, suggesting enhanced potential to elicit a protective immune response.
LigPlot analysis revealed that more amino acids participate in the interaction between the CD40 receptor and the CD40L multi-epitope construct than with the whole sequence, indicating a stronger interaction. An increased number of hydrogen bonds between 2 proteins correlates with greater interaction strength [103]. In the CD40L multi-epitope structure, a hydrogen bond formed between glutamic acid 107 and tyrosine, whereas in the whole sequence, glutamic acid 107 bonded to arginine. Studies suggest the Tyr-Glu bond is stronger than the Arg-Glu bond, with machine learning analyses indicating that the Tyr/Glu pair most often forms strong hydrogen bonds in proteins [104]. At position 69, the CD40 receptor forms a hydrogen bond with threonine in the multi-epitope construct, whereas tyrosine replaces threonine in the whole sequence. Threonine is crucial for maintaining protein stability [105]. Thus, LigPlot results confirmed the higher affinity between the CD40 receptor and the multi-epitope structure, supporting the docking findings.
To be effective, a vaccine construct must induce a robust immune response [106]. Immune simulation analysis showed that administration of both vaccine constructs activated both primary and secondary immune responses. Elevated concentrations of memory B-cells, helper T-cells, and cytotoxic T-cells were observed after vaccination, indicating enhanced immune activation. High levels of IgM, IgG2, and IgG1 antibodies were produced during both responses. Additionally, IFN-γ and IL-2 levels increased with repeated exposures, while IL-10 remained low, a pattern linked to higher Th1 cell frequency and improved viral immunity [107].
Increased IgM and IgG reflect adaptive immune system activation. IgM is produced first and rapidly neutralizes pathogens while activating complement. IgG antibodies arise later, providing lasting protection through neutralization, opsonization, and complement activation. Together, these antibodies establish protective immunity, reducing disease severity upon future exposures. Cytokines coordinate the immune response: IFN-γ promotes pathogen elimination and immune cell activation; IL-2 supports T cell proliferation and memory; IL-10 regulates and restrains the immune response, preventing excessive inflammation. This interplay ensures a strong, lasting immunity [108].
In this study, MD simulations indicated improved stability and favorable binding of the CD40L multi-epitope/CD40 complex compared to the whole sequence/CD40 complex. The RMSD, Rg, and RMSF values for the multi-epitope complex showed less fluctuation and more consistent patterns. In this study, cloning the vaccine construct into the pET24a(+) vector using SnapGene 3.2.1 was expected to increase codon expression. CAI values confirmed that the multi-epitope construct had higher compatibility and expression levels than the whole sequence construct.
Immunoinformatics approaches, such as those used here to design a CD40L-based multi-epitope vaccine, provide valuable insights into epitope prediction, structural stability, and receptor binding affinity. However, these findings must be interpreted cautiously, as computational models cannot fully replicate the complexity of biological systems. Experimental validation is essential to confirm immunogenicity, safety, and efficacy. Only through in vitro and in vivo testing can the true potential of the multi-epitope vaccine be determined, and discrepancies between predictions and actual responses identified. Computational methods often simplify immune complexity, overlooking diverse cell interactions and microenvironmental factors. Epitope prediction tools rely on existing datasets, which may not capture full antigenic diversity or population-specific HLA variation, potentially causing false positives or negatives. Molecular docking and dynamics simulations, while informative, may not precisely mirror physiological conditions. For example, greater in silico affinity does not guarantee superior immunogenicity in vivo, where factors like tolerance, adjuvant effects, and genetic variation matter. Likewise, immune simulations are based on theoretical frameworks that cannot fully recapitulate complex in vivo antigen processing, presentation, and regulation.
Using an immunoinformatics approach, we constructed a safe and effective multi-epitope vaccine candidate. The amino acid sequence of CD40L was analyzed, and B-cell, T-cell, and CTL epitopes were predicted and screened. These epitopes were combined to create the multi-epitope vaccine, using AAY linkers for optimal assembly. The safety and stability of the vaccine were assessed by comparing its biochemical properties and structure to the CD40L whole sequence. Results showed that the CD40L multi-epitope vaccine had high antigenicity, safety, and stability. Molecular docking and dynamics studies revealed strong binding affinity and stability between the multi-epitope construct and the CD40 receptor. Normal mode analysis and immune simulations provided satisfactory results. In silico cloning in the pET24a(+) vector indicated greater expression potential for the multi-epitope construct compared to the whole sequence vaccine. Overall, these findings highlight promising strategies for vaccine development against infections and cancers. However, further experimental studies are needed to verify the efficacy, effectiveness, and safety of this vaccine candidate.
• Our study demonstrates that a novel multi-epitope vaccine based on CD40 ligand (CD40L) significantly boosts immune responses compared to traditional full-length CD40L vaccines.
• We utilized advanced in silico methods to design the multi-epitope construct, illustrating the potential of this approach to improve vaccine efficacy.
• This study included comprehensive evaluations of physicochemical properties, structural validation, and molecular dynamics simulations to ensure robust findings.
• Immune simulations revealed that the multi-epitope construct may exhibit a stronger affinity for the CD40 receptor, indicating improved interactions with the immune system.
• These findings suggest that our innovative multi-epitope vaccine could offer a promising strategy for combating infections and cancers, laying the groundwork for future vaccine development.

Ethics Approval

Not applicable.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

All data generated or analyzed during this study are included in this published article.

Authors’ Contributions

Conceptualization: FH, AB; Investigation: FH, MM; Software: SAS, HB, FH; Supervision: FH, AB; Writing original draft: SAS, FH; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Figure 1.
Final CD40 ligand (CD40L) multi-epitope construct. Dominant linear B-cell (LBL), cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were linked using an AYY linker to design the multi-epitope construct.
Figure 1. Final CD40 ligand (CD40L) multi-epitope construct. Dominant linear B-cell (LBL), cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were linked using an AYY linker to design the multi-epitope construct.  
Figure 2.
Secondary structure analysis of vaccine constructs. (A) CD40 ligand (CD40L) whole sequence secondary structure; (B) CD40L multi-epitope secondary structure. The multi-epitope construct showed a notable increase in alpha helices, beta sheets, and beta turns compared to the whole sequence.
Figure 2. Secondary structure analysis of vaccine constructs. (A) CD40 ligand (CD40L) whole sequence secondary structure; (B) CD40L multi-epitope secondary structure. The multi-epitope construct showed a notable increase in alpha helices, beta sheets, and beta turns compared to the whole sequence.  
Figure 3.
Structural validation of the CD40 ligand (CD40L) multi-epitope construct. (A) Three-dimensional (3D) structure generated by the Robetta server and refined by GalaxyRefine; (B) ERRAT error values, with a quality factor of 91.25; (C) Ramachandran plot analysis by PROCHECK, showing 92% of residues in favored regions; (D) Verify3D plot, with 82.71% of residues scoring ≥0.1, meeting Verify3D’s acceptance criteria.
Figure 3. Structural validation of the CD40 ligand (CD40L) multi-epitope construct. (A) Three-dimensional (3D) structure generated by the Robetta server and refined by GalaxyRefine; (B) ERRAT error values, with a quality factor of 91.25; (C) Ramachandran plot analysis by PROCHECK, showing 92% of residues in favored regions; (D) Verify3D plot, with 82.71% of residues scoring ≥0.1, meeting Verify3D’s acceptance criteria.  
Figure 4.
Structural validation of the CD40 ligand (CD40L) whole sequence construct. (A) Three-dimensional (3D) structure generated by the Robetta Baker server and refined by GalaxyRefine; (B) ERRAT error values, with a quality factor of 87.288; (C) Ramachandran plot analysis by PROCHECK, showing 90.7% of residues in favored regions; (D) Verify3D plot, with 80.22% of residues scoring ≥0.1, within acceptable limits.
Figure 4. Structural validation of the CD40 ligand (CD40L) whole sequence construct. (A) Three-dimensional (3D) structure generated by the Robetta Baker server and refined by GalaxyRefine; (B) ERRAT error values, with a quality factor of 87.288; (C) Ramachandran plot analysis by PROCHECK, showing 90.7% of residues in favored regions; (D) Verify3D plot, with 80.22% of residues scoring ≥0.1, within acceptable limits.  
Figure 5.
Molecular docking of vaccine constructs with the CD40 receptor. (A) Protein-protein docking of the CD40 ligand (CD40L) whole sequence construct and CD40 receptor; (B) Docking of the CD40L multi-epitope construct and CD40 receptor. Blue indicates the vaccine construct; red indicates the receptor. The multi-epitope construct displayed lower energy and a stronger docking score with CD40 compared to the whole sequence construct.
Figure 5. Molecular docking of vaccine constructs with the CD40 receptor. (A) Protein-protein docking of the CD40 ligand (CD40L) whole sequence construct and CD40 receptor; (B) Docking of the CD40L multi-epitope construct and CD40 receptor. Blue indicates the vaccine construct; red indicates the receptor. The multi-epitope construct displayed lower energy and a stronger docking score with CD40 compared to the whole sequence construct.  
Figure 6.
Protein–ligand interactions analyzed with LigPlot. (A) CD40 ligand (CD40L) whole sequence construct and CD40 receptor; (B) CD40L multi-epitope construct and CD40 receptor. Ligands and protein side chains are depicted in ball-and-stick format, with ligand bonds in purple. Hydrogen bonds are shown as green dashed lines, and spoked arcs represent non-bonded contacts. Both constructs interact with CD40 via hydrophobic and hydrogen bonds, but the multi-epitope construct forms more hydrogen bonds, potentially enhancing CD40/CD40L signaling and immune activation.
Figure 6. Protein–ligand interactions analyzed with LigPlot. (A) CD40 ligand (CD40L) whole sequence construct and CD40 receptor; (B) CD40L multi-epitope construct and CD40 receptor. Ligands and protein side chains are depicted in ball-and-stick format, with ligand bonds in purple. Hydrogen bonds are shown as green dashed lines, and spoked arcs represent non-bonded contacts. Both constructs interact with CD40 via hydrophobic and hydrogen bonds, but the multi-epitope construct forms more hydrogen bonds, potentially enhancing CD40/CD40L signaling and immune activation.  
Figure 7.
Immune simulation for the CD40 ligand (CD40L) whole sequence construct. (A) Evolution of B-cell populations after 3 injections; (B) active B-cell populations post-vaccination; (C) T helper (Th) cell populations; (D) generation of T cells; (E) active Th cell population; (F) cytotoxic T cells after vaccination. RESTING indicates cells not exposed to antigen, ANERGIC indicates antigen tolerance; (G) cytokine profile, showing increased interferon (IFN)-γ and interleukin (IL)-2; (H) immunoglobulin (Ig) production, indicating robust immune response. Each injection triggered strong secondary responses, enhanced antigen clearance, and promoted immune memory.
TGF, transforming growth factor; TNF, tumor necrosis factor.
Figure 7. Immune simulation for the CD40 ligand (CD40L) whole sequence construct. (A) Evolution of B-cell populations after 3 injections; (B) active B-cell populations post-vaccination; (C) T helper (Th) cell populations; (D) generation of T cells; (E) active Th cell population; (F) cytotoxic T cells after vaccination. RESTING indicates cells not exposed to antigen, ANERGIC indicates antigen tolerance; (G) cytokine profile, showing increased interferon (IFN)-γ and interleukin (IL)-2; (H) immunoglobulin (Ig) production, indicating robust immune response. Each injection triggered strong secondary responses, enhanced antigen clearance, and promoted immune memory.  
Figure 8.
Immune simulation for the CD40 ligand (CD40L) multi-epitope construct. (A) Evolution of B-cell populations after 3 injections; (B) active B-cell populations; (C) T helper (Th) cell populations; (D) generation of T cells; (E) active Th cell population; (F) cytotoxic T cells post-vaccination. RESTING indicates unexposed cells; ANERGIC indicates tolerance. (G) Cytokine profile, with increased interferon (IFN)-γ and interleukin (IL)-2; (H) immunoglobulin (Ig) production. Each injection elicited potent secondary immune responses, improved antigen clearance, and strengthened immune memory.
TGF, transforming growth factor; TNF, tumor necrosis factor.
Figure 8. Immune simulation for the CD40 ligand (CD40L) multi-epitope construct. (A) Evolution of B-cell populations after 3 injections; (B) active B-cell populations; (C) T helper (Th) cell populations; (D) generation of T cells; (E) active Th cell population; (F) cytotoxic T cells post-vaccination. RESTING indicates unexposed cells; ANERGIC indicates tolerance. (G) Cytokine profile, with increased interferon (IFN)-γ and interleukin (IL)-2; (H) immunoglobulin (Ig) production. Each injection elicited potent secondary immune responses, improved antigen clearance, and strengthened immune memory.  
Figure 9.
Molecular dynamics simulation of the vaccine construct-CD40 complex: (A) Root mean square deviations (RMSD) plots, showing that the proteins maintained comparable stability. Each protein’s replicates exhibited only slight variations in their RMSD values, (B) root mean square fluctuation (RMSF) plot; A comparison of fluctuations between the CD40 ligand (CD40L) multi-epitope sequence and the CD40L whole sequence highlights that proline residues within amino acids 20 to 60 of the multi-epitope sequence contribute to increased local flexibility, resulting in greater RMSF variations in this region. (C) Radius of gyration (Rg) plotted against simulation time, showing that the CD40L multi-epitope sequence exhibited a larger Rg than the CD40L whole sequence, suggesting that it possesses greater stability.
Figure 9. Molecular dynamics simulation of the vaccine construct-CD40 complex: (A) Root mean square deviations (RMSD) plots, showing that the proteins maintained comparable stability. Each protein’s replicates exhibited only slight variations in their RMSD values, (B) root mean square fluctuation (RMSF) plot; A comparison of fluctuations between the CD40 ligand (CD40L) multi-epitope sequence and the CD40L whole sequence highlights that proline residues within amino acids 20 to 60 of the multi-epitope sequence contribute to increased local flexibility, resulting in greater RMSF variations in this region. (C) Radius of gyration (Rg) plotted against simulation time, showing that the CD40L multi-epitope sequence exhibited a larger Rg than the CD40L whole sequence, suggesting that it possesses greater stability.  
Figure 10.
Schematic representation of in silico cloning of vaccine candidate constructs within the pET24a(+) prokaryotic expression vector: (A) CD40 ligand (CD40L) whole sequence cloned into pET24a(+) vector by BamHI/HindIII restriction enzymes, (B) CD40L multi-epitope sequence cloned into pET24a(+) vector by BamHI/HindIII restriction enzymes. For the CD40L whole sequence, we recorded a codon adaptation index (CAI) of 0.97 and a GC content of 47.31%. In comparison, the CD40L multi-epitope sequence showed a CAI of 1.0 and a GC content of 46.60%. These findings indicate that the optimized nucleotide sequence of the CD40L multi-epitope construct is likely to achieve more efficient expression than the CD40L whole sequence construct.
Figure 10. Schematic representation of in silico cloning of vaccine candidate constructs within the pET24a(+) prokaryotic expression vector: (A) CD40 ligand (CD40L) whole sequence cloned into pET24a(+) vector by BamHI/HindIII restriction enzymes, (B) CD40L multi-epitope sequence cloned into pET24a(+) vector by BamHI/HindIII restriction enzymes. For the CD40L whole sequence, we recorded a codon adaptation index (CAI) of 0.97 and a GC content of 47.31%. In comparison, the CD40L multi-epitope sequence showed a CAI of 1.0 and a GC content of 46.60%. These findings indicate that the optimized nucleotide sequence of the CD40L multi-epitope construct is likely to achieve more efficient expression than the CD40L whole sequence construct.  
Immunoinformatics study of CD40 ligand-targeting vaccine constructs: a novel immunotherapeutic approach
Table 1.
MHC alleles used for peptide-protein docking
Table 1.
MHC-I MHC-II
Allele PDB code Allele PDB code
HLA-A01:01 4NQV DRB1:0101 4AH2
HLA-A02:01 4UQ3 DRB1:0301 2Q6W
HLA-A03:01 3RL2 DRB1:0401 5LAX
HLA-A11:01 1×7Q DRB1:1101 6CPL
HLA-A24:02 5HGA DRB1:1501 5V4M
HLA-B07:02 5EO1 DRB5:0101 1FV
HLA-B08:01 3SPV
HLA-B27:05 1OGT
HLA-B35:01 3LKN
HLA-B51:01 1E+27

MHC, major histocompatibility complex; PDB, Protein Data Bank; HLA, human leukocyte antigen.

Table 2.
MHC-I processing prediction and immunogenicity scores of CD40L CTL epitopes
Table 2.
Protein name Position Epitope sequence TAP transport efficiency scorea) Proteasomal C-terminal cleavage scoreb) Epitope identification scorec) Immunogenicity scoresd)
CD40L 1–13 MIETYSQPSPRSV 1.547 0.585 0.125 –0.44126
4–17 TYSQPSPRSVATGL 0.722 0.976 0.225 –0.33407
17–27 LPASMKIFMYL 2.331 0.905 0.442 –0.44533
34–45 TQMIGSVLFAVY 2.787 0.869 0.422 0.14556
158–170 KQLTVKREGLYYV 2.968 0.796 0.35 0.03084
84–95 SQRPFIVGLWLK 1.277 0.974 0.5 0.49246

MHC, major histocompatibility complex; CD40L, CD40 ligand; CTL, cytotoxic T lymphocyte.

a)A higher score indicates a better quality of TAP transport efficiency.

b)A higher score indicates a better quality of proteasomal cleavage.

c)Higher rates indicate a better quality of epitope identification.

d)A higher score indicates a greater probability of eliciting an immune response.

Table 3.
Prediction of dominant T and B-cell epitopes of the CD40L
Table 3.
Epitopes Methods Location Sequence Average rank scoresa) Toxicity Antigenicityb) Allergenicity
B cell ElliPro 1–33 MIETYSQPSPRSVATGLPASMKIFMYLLTVFLI 0.862 No 0.6137 Non
178–187 SNREPSSQRP 0.71 No 1.1488 Non
HTL NetMHCIIpan 90–108 QFEDLVKDITLNKEEKKENS 2.684 No 0.562 Non
135–154 SVLQWAKKGYYTMKSNLVML 1.333 No 0.9495 Non
139–157 WAKKGYYTMKSNLVMLENG 1.401 No 0.9215 Non
141–160 KKGYYTMKSNLVMLENGKQL 1.610 No 0.7886 Non
228–249 FELQAGASVFVNVTEASQVIHR 2.758 No 0.6329 Non
CTL NetCTLpan 1–13 MIETYSQPSPRSV 0.905 No 0.7404 Non
4–17 TYSQPSPRSVATGL 0.897 No 1.1382 Non
17–27 LPASMKIFMYL 0.982 No 0.6488 Non
34–45 TQMIGSVLFAVY 0.965 No 0.6284 Non
158–176 KQLTVKREGLYYV 0.989 No 0.7354 Non
84–95 SQRPFIVGLWLK 0.908 No 0.8084 Non

CD40L, CD40 ligand; HTL, helper T lymphocyte; CTL, cytotoxic T lymphocyte.

a)Lower rates show better binding affinity.

b)Higher rate shows high degree of peptide antigenicity.

Table 4.
Binding affinities and interaction between selected CTL epitopes and HLA alleles
Table 4.
Epitope HLA A0101 HLA A0201 HLA A0301 HLA A2402 HLA A1101 HLA B0702 HLA B0801 HLA B2705 HLA B3501 HLA B5101 Average of interaction similarity scorea)
CD40L (1–13) 162 214 187 216 171 187 211 192 189 207 193.6
CD40L (4–17) 176 203 183 214 180 195 183 179 209 247 196.9
CD40L (17–27) 192 242 206 229 206 215 221 233 251 272 226.7
CD40L (34–45) 192 249 226 222 207 208 216 210 245 235 221
CD40L (84–95) 237 266 252 249 254 253 241 247 268 289 255.6
CD40L (158–176) 190 227 207 202 211 229 220 218 257 241 220.2

CTL, cytotoxic T lymphocyte; HLA, human leukocyte antigen; CD40L, CD40 ligand.

a)Higher rate shows better quality of peptide-major histocompatibility complex interactions.

Table 5.
Binding affinities and interaction between selected HTL epitopes and HLA alleles
Table 5.
Epitope DRB1–0101 DRB1–0301 DRB1–0401 DRB1–1101 DRB1–1501 DRB5–0101 Average of Interaction similarity scorea)
CD40L (90–108) 125 131 117 117 117 117 120.6
CD40L (135–154) 141 150 141 141 141 141 142.5
CD40L (139–157) 141 143 141 141 141 141 141.3
CD40L (141–160) 142 143 135 135 135 135 137.5
CD40L (228–249) 141 131 135 131 151 131 136.6

HTL, helper T lymphocyte; HLA, human leukocyte antigen; CD40L, CD40 ligand.

a)Higher rate shows better quality of peptide-major histocompatibility complex interactions.

Table 6.
Cytokine production of CD40L HTL epitopes
Table 6.
Protein name Position Epitope sequence IL-10 production SVM scores IL-10 induction IL-4 production SVM scores IL-4 induction IFN-ɣ production SVM scores IFN-ɣ induction
CD40L 90–108 QFEDLVKDITLNKEEKKENS –0.211 Non- inducer 1.15 Inducer 1.02 Inducer
135–154 SVLQWAKKGYYTMKSNLVML 0.462 Inducer –0.03 Non-inducer 1.82 Inducer
139–157 WAKKGYYTMKSNLVMLENG 0.290 Non- inducer 0.10 Non-inducer 1.22 Inducer
141–160 KKGYYTMKSNLVMLENGKQL 0.708 Inducer –1.02 Non-inducer 1.39 Inducer
228–249 FELQAGASVFVNVTEASQVIHR 0.115 Non- inducer 0.55 Inducer 2.19 Inducer

CD40L, CD40 ligand; HTL, helper T lymphocyte; IL, interleukin; IFN, interferon; SVM, support vector machine.

Table 7.
Population coverage of CD40L CTL epitopes
Table 7.
Area CD40L1–13 (%) CD40L4–17 (%) CD40L17–27 (%) CD40L34–45 (%) CD40L84–95 (%) CD40L158–176 (%)
Central Africa 63.96 54.25 51.08 45.85 63.10 49.82
Central America 4.14 2.77 2.77 3.37 4.72 2.77
East Africa 71.38 63.91 61.98 49.84 69.40 60.77
East Asia 94.86 87.36 91.01 89.00 96.00 74.39
Europe 98.34 90.79 92.53 85.42 97.48 92.83
Iran 90.81 77.40 78.49 68.71 89.44 84.13
North Africa 86.43 80.22 73.43 67.68 83.24 70.69
North America 95.01 86.84 87.43 83.24 94.17 83.49
Northeast Asia 87.92 64.89 70.15 70.85 90.66 77.57
Oceania 94.15 81.84 81.62 80.42 93.31 72.26
South Africa 76.68 64.05 61.71 40.21 69.61 60.43
South America 81.50 61.96 61.03 59.58 77.20 48.40
South Asia 89.65 67.93 69.37 59.33 83.59 73.90
Southeast Asia 88.86 72.57 73.43 82.36 92.95 69.56
Southwest Asia 89.00 76.98 73.94 65.88 85.66 72.77
West Africa 75.35 68.99 69.21 59.32 70.65 66.92
West Indies 92.08 82.56 81.61 79.79 91.79 76.55
World 95.11 84.26 85.92 80.13 94.01 85.20

CD40L, CD40 ligand; CTL, cytotoxic T lymphocyte.

Table 8.
Population coverage of CD40L HTL epitopes
Table 8.
Area CD40L90–108 (%) CD40L135–154 (%) CD40L139–157 (%) CD40L141–160 (%) CD40L228–249 (%)
Central Africa 88.56 99.98 99.96 99.96 94.99
Central America 99.86 100.00 100.00 100.00 99.85
East Africa 94.81 99.98 99.97 99.97 96.86
East Asia 87.83 99.58 99.22 99.22 93.57
Europe 89.85 100.00 100.00 100.00 93.28
Iran 83.40 95.78 96.15 96.15 90.49
North Africa 86.55 99.22 98.32 98.32 93.73
North America 97.76 100.00 100.00 100.00 97.99
Northeast Asia 92.29 99.74 99.72 99.72 95.75
Oceania 95.40 99.97 99.90 99.90 97.85
South Africa 45.98 7.65 7.65 7.65 7.65
South America 99.23 100.00 100.00 100.00 98.61
South Asia 87.87 99.97 99.96 99.96 86.76
Southeast Asia 84.89 98.00 98.03 98.03 90.61
Southwest Asia 81.09 99.30 98.51 98.51 89.04
West Africa 98.14 99.97 99.97 99.97 98.12
West Indies 75.91 95.15 95.60 95.60 94.42
World 93.18 99.91 99.91 99.91 94.10

CD40L, CD40 ligand; HTL, helper T lymphocyte.

Table 9.
Physicochemical properties of CD40L-based vaccine constructs
Table 9.
Construct Molecular weight (kDa) Theoretical pI Half-life (h) Stability Solubility >0.45 Allergenicity Antigenicity >0.4
CD40L whole sequence 29.37 8.26 >20 (mammalian), >10 (Escherichia coli) 46.29 (unstable) In soluble (0.361) Non-allergen 0.504
CD40L multi-epitopes 28.70 9.56 >20 (mammalian), >10 (E. coli) 35.73 (stable) Soluble (0.713) Non-allergen 0.567

CD40L, CD40 ligand; pI, isoelectric point.

Table 10.
Secondary structure analysis of the CD40L-based vaccine constructs
Table 10.
Construct Alpha helix (%) Beta sheet (%) Beta turn (%) Random coil (%)
CD40L whole sequence 32.00 21.92 5.77 39.69
CD40L multi-epitope 40.38 21.96 6.27 31.37

CD40L, CD40 ligand.

Table 11.
Predicted discontinuous epitopes of CD40 ligand construct
Table 11.
No. Residues No. of residues Score
1 A:M1, A:I2, A:E3, A:T4, A:Y5 5 0.988
2 A:S6, A:Q7, A:P8, A:S9, A:P10, A:R11, A:S12, A:V13, A:A14, A:T15, A:G16, A:L17, A:P18, A:A19, A:S20, A:M21, A:K22, A:I23, A:F24, A:M25, A:Y26, A:L27, A:L28, A:T29, A:V30, A:F31, A:L32, A:I33, A:M36, A:I37, A:V40 31 0.811
3 A:E54, A:E55, A:E56, A:V57, A:N58, A:L59, A:H60, A:E61, A:D62, A:F63, A:V64, A:F65, A:I66, A:K67, A:K68, A:L69, A:K70, A:R71, A:C72, A:N73, A:K74, A:G75, A:E76, A:G77, A:S78, A:L79, A:S80, A:L81, A:L82, A:N83, A:C84, A:E85, A:E86, A:M87, A:R88, A:R89, A:Q90, A:F91, A:E92, A:D93, A:L94, A:V95, A:K96, A:D97, A:I98, A:T99, A:L100, A:N101, A:E104 49 0.79
4 A:S178, A:N179, A:E181, A:P182, A:S183, A:S184, A:Q185, A:R186, A:P187, A:T210, A:H211, A:S212, A:S213, A:S214, A:Q215, A:L216, A:C217 17 0.677
5 A:F110, A:E111, A:R114, A:G115 4 0.569
Table 12.
Interacting amino acids of CD40L whole sequence, CD40L multi-epitopes and CD40 receptor complexes by LigPlot software
Table 12.
Complexes Interacting amino acids of ligand Interacting amino acids of receptor No. of H-bonds
CD40L whole sequence-CD40 Arg 249, Gln 219, Arg 49, Gln 35, Glu 218, His 47, Try 26 Glu 117, Glu 107, Glu 117, Cys 116, Lys 81, Ala 115, Gln 79, Asp 69, Thr 70 12
CD40L multi-epitopes-CD40 Try 59, Gln 49, Try 47, Try 73, Try 62, Glu 70, Arg 69, Trh 66, Try 78 Gln 93, Thr 136, Glu 107, Glu 74, Glu 98, Lys 29, Asp 69, Glu 21 10

CD40L, CD40 ligand.

  • 1. Karnell JL, Rieder SA, Ettinger R, et al. Targeting the CD40-CD40L pathway in autoimmune diseases: hgumoral immunity and beyond. Adv Drug Deliv Rev 2019;141:92−103.
  • 2. Fadul CE, Mao-Draayer Y, Ryan KA, et al. Safety and immune effects of blocking CD40 ligand in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 2021;8:e1096.
  • 3. Lacy M, Burger C, Shami A, et al. Cell-specific and divergent roles of the CD40L-CD40 axis in atherosclerotic vascular disease. Nat Commun 2021;12:3754.
  • 4. Daoussis D, Andonopoulos AP, Liossis SN. Targeting CD40L: a promising therapeutic approach. Clin Diagn Lab Immunol 2004;11:635−41.
  • 5. Zhang B, Wu T, Chen M, et al. The CD40/CD40L system: a new therapeutic target for disease. Immunol Lett 2013;153:58−61.
  • 6. Zhang S, Breidenbach JD, Russell BH, et al. CD40/CD40L signaling as a promising therapeutic target for the treatment of renal disease. J Clin Med 2020;9:3653.
  • 7. Yurina V, Adianingsih OR. Predicting epitopes for vaccine development using bioinformatics tools. Ther Adv Vaccines Immunother 2022 May 21 [Epub]. https://doi.org/10.1177/25151355221100218.
  • 8. De Groot AS, Moise L, McMurry JA, et al. Epitope-based immunome-derived vaccines: a strategy for improved design and safety. In: Falus A, editor. Clinical applications of immunomics. Springer; 2009. p. 39−69.
  • 9. Malonis RJ, Lai JR, Vergnolle O. Peptide-based vaccines: current progress and future challenges. Chem Rev 2020;120:3210−29.
  • 10. Oscherwitz J. The promise and challenge of epitope-focused vaccines. Hum Vaccin Immunother 2016;12:2113−6.
  • 11. Palma M. Epitopes and mimotopes identification using phage display for vaccine development against infectious pathogens. Vaccines (Basel) 2023;11:1176.
  • 12. Arya H, Bhatt TK. Role of bioinformatics in subunit vaccine design. In: Coumar MS, editor. Molecular docking for computer-aided drug design. Elsevier; 2021. p. 425−39.
  • 13. Nandy A, Basak SC. Bioinformatics in design of antiviral vaccines. Encycl Biomed Eng 2019;280−90.
  • 14. Collins KA, Snaith R, Cottingham MG, et al. Enhancing protective immunity to malaria with a highly immunogenic virus-like particle vaccine. Sci Rep 2017;7:46621.
  • 15. Jain R, Jain A, Verma SK. Prediction of epitope based peptides for vaccine development from complete proteome of novel corona virus (SARS-COV-2) using immunoinformatics. Int J Pept Res Ther 2021;27:1729−40.
  • 16. Sharma AD, Magdaleno JS, Singh H, et al. Immunoinformatics-driven design of a multi-epitope vaccine targeting neonatal rotavirus with focus on outer capsid proteins VP4 and VP7 and non structural proteins NSP2 and NSP5. Sci Rep 2025;15:11879.
  • 17. Randriamamisolonirina NT, Razafindrafara MS, Maminiaina OF. Design of a multi-epitope vaccine against the glycoproteins of Newcastle disease virus by using an immunoinformatics approach. ACS Omega 2025;10:4007−18.
  • 18. Larsen MV, Lundegaard C, Lamberth K, et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatic 2007;8:424.
  • 19. Andreatta M, Nielsen M. Bioinformatics tools for the prediction of T-cell epitopes. Methods Mol Biol 2018;1785:269−81.
  • 20. Stranzl T, Larsen MV, Lundegaard C, et al. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 2010;62:357−68.
  • 21. Gupta S, Kapoor P, Chaudhary K, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013;8:e73957.
  • 22. Dhanda SK, Gupta S, Vir P, et al. Prediction of IL4 inducing peptides. Clin Dev Immunol 2013;2013:263952.
  • 23. Nagpal G, Usmani SS, Dhanda SK, et al. Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci Rep 2017;7:42851.
  • 24. Bui HH, Sidney J, Dinh K, et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 2006;7:153.
  • 25. Dimitrov I, Bangov I, Flower DR, et al. AllerTOP v.2: a server for in silico prediction of allergens. J Mol Model 2014;20:2278.
  • 26. Hebditch M, Carballo-Amador MA, Charonis S, et al. Protein-Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 2017;33:3098−100.
  • 27. Garg VK, Avashthi H, Tiwari A, et al. MFPPI: multi FASTA ProtParam Interface. Bioinformation 2016;12:74−7.
  • 28. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995;11:681−4.
  • 29. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 2004;32(Web Server issue):W526−31.
  • 30. Seok C, Baek M, Steinegger M, et al. Accurate protein structure prediction: what comes next. Biodesign 2021;9:47−50.
  • 31. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511−9.
  • 32. Laskowski R, MacArthur M, Thornton J. PROCHECK: validation of protein-structure coordinates. Wiley; 2006.
  • 33. Mukonyora M. A review of important discontinuous B-cell epitope prediction tools. J Clin Cell Immunol 2015;6:358.
  • 34. Cheng J, Randall AZ, Sweredoski MJ, et al. SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 2005;33(Web Server issue). W72−6.
  • 35. Kozakov D, Hall DR, Xia B, et al. The ClusPro web server for protein-protein docking. Nat Protoc 2017;12:255−78.
  • 36. Yan Y, Zhang D, Zhou P, et al. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res 2017;45(W1). W365−73.
  • 37. Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model 2011;51:2778−86.
  • 38. Rapin N, Lund O, Bernaschi M, et al. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One 2010;5:e9862.
  • 39. Sami SA, Marma KK, Mahmud S, et al. Designing of a multi-epitope vaccine against the structural proteins of Marburg virus exploiting the immunoinformatics approach. ACS Omega 2021;6:32043−71.
  • 40. Lopez-Blanco JR, Aliaga JI, Quintana-Orti ES, et al. iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res 2014;42(Web Server issue). W271−6.
  • 41. Lopez-Blanco JR, Garzon JI, Chacon P. iMod: multipurpose normal mode analysis in internal coordinates. Bioinformatics 2011;27:2843−50.
  • 42. Ghahremanian S, Rashidi MM, Raeisi K, et al. Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: a structural review. J Mol Liq 2022;354:118901.
  • 43. Best RB, Zhu X, Shim J, et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J Chem Theory Comput 2012;8:3257−73.
  • 44. Berendsen HJ, Postma Jv, Van Gunsteren WF, et al. Molecular dynamics with coupling to an external bath. J Chem Phys 1984;81:3684−90.
  • 45. Petersen HG. Accuracy and efficiency of the particle mesh Ewald method. J Chem Phys 1995;103:3668−79.
  • 46. Grote A, Hiller K, Scheer M, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005;33(Web Server issue). W526−31.
  • 47. Kar T, Narsaria U, Basak S, et al. A candidate multi-epitope vaccine against SARS-CoV-2. Sci Rep 2020;10:10895.
  • 48. Mugunthan SP, Harish MC. Multi-epitope-based vaccine designed by targeting cytoadherence proteins of Mycoplasma gallisepticum. ACS Omega 2021;6:13742−55.
  • 49. Nezafat N, Ghasemi Y, Javadi G, et al. A novel multi-epitope peptide vaccine against cancer: an in silico approach. J Theor Biol 2014;349:121−34.
  • 50. Tahir Ul Qamar M, Rehman A, Tusleem K, et al. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: immunoinformatics and in silico approaches. PLoS One 2020;15:e0244176.
  • 51. Li M, Zhu Y, Niu C, et al. Design of a multi-epitope vaccine candidate against Brucella melitensis. Sci Rep 2022;12:10146.
  • 52. Aleksandrov A, Myllykallio H. Advances and challenges in drug design against tuberculosis: application of in silico approaches. Expert Opin Drug Discov 2019;14:35−46.
  • 53. Chakraborty AK, Dustin ML, Shaw AS. In silico models for cellular and molecular immunology: successes, promises and challenges. Nat Immunol 2003;4:933−6.
  • 54. Auten MW, Huang W, Dai G, et al. CD40 ligand enhances immunogenicity of vector-based vaccines in immunocompetent and CD4+ T cell deficient individuals. Vaccine 2012;30:2768−77.
  • 55. Bullock TNJ. CD40 stimulation as a molecular adjuvant for cancer vaccines and other immunotherapies. Cell Mol Immunol 2022;19:14−22.
  • 56. Godot V, Tcherakian C, Gil L, et al. TLR-9 agonist and CD40-targeting vaccination induces HIV-1 envelope-specific B cells with a diversified immunoglobulin repertoire in humanized mice. PLoS Pathog 2020;16:e1009025.
  • 57. Heidarnejad F, Namvar A, Sadat SM, et al. In silico designing of novel epitope-based peptide vaccines against HIV-1. Biotechnol Lett 2024;46:315−54.
  • 58. Tang YC, Thoman M, Linton PJ, et al. Use of CD40L immunoconjugates to overcome the defective immune response to vaccines for infections and cancer in the aged. Cancer Immunol Immunother 2009;58:1949−57.
  • 59. Zhang L, Tang Y, Akbulut H, et al. An adenoviral vector cancer vaccine that delivers a tumor-associated antigen/CD40-ligand fusion protein to dendritic cells. Proc Natl Acad Sci U S A 2003;100:15101−6.
  • 60. Medina-Echeverz J, Hinterberger M, Testori M, et al. Synergistic cancer immunotherapy combines MVA-CD40L induced innate and adaptive immunity with tumor targeting antibodies. Nat Commun 2019;10:5041.
  • 61. Lookian PP, Zhao D, Medina R, et al. Mannan-BAM, TLR ligands, anti-CD40 antibody (MBTA) vaccine immunotherapy: a review of current evidence and applications in glioblastoma. Int J Mol Sci 2021;22:3455.
  • 62. Stone GW, Barzee S, Snarsky V, et al. Multimeric soluble CD40 ligand and GITR ligand as adjuvants for human immunodeficiency virus DNA vaccines. J Virol 2006;80:1762−72.
  • 63. Khan N, Rehman B, Almanaa TN, et al. A novel therapeutic approach to prevent Helicobacter pylori induced gastric cancer using networking biology, molecular docking, and simulation approaches. J Biomol Struct Dyn 2024;42:13876−89.
  • 64. Ahmad S, Almanaa TN, Khan S, et al. Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate. J Biomol Struct Dyn 2025;43:1240−8.
  • 65. Momajadi L, Khanahmad H, Mahnam K. Designing a multi-epitope influenza vaccine: an immunoinformatics approach. Sci Rep 2024;14:25382.
  • 66. Jaan S, Shah M, Ullah N, et al. Multi-epitope chimeric vaccine designing and novel drug targets prioritization against multi-drug resistant Staphylococcus pseudintermedius. Front Microbiol 2022;13:971263.
  • 67. Sarfraz A, Wara TU, et al. Structural informatics approach for designing an epitope-based vaccine against the brain-eating Naegleria fowleri. Front Immunol 2023;14:1284621.
  • 68. Jain N, Shankar U, Majee P, et al. Scrutinizing the SARS-CoV-2 protein information for designing an effective vaccine encompassing both the T-cell and B-cell epitopes. Infect Genet Evol 2021;87:104648.
  • 69. Lam JH, Smith FL, Baumgarth N. B cell activation and response regulation during viral infections. Viral Immunol 2020;33:294−306.
  • 70. Fleri W, Paul S, Dhanda SK, et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front Immunol 2017;8:278.
  • 71. Barazesh M, Abbasi M, Mohammadi M, et al. Bioinformatics analysis to design a multi-epitope mRNA vaccine against S. agalactiae exploiting pathogenic proteins. Sci Rep 2024;14:28294.
  • 72. Hosseini Khorami SH, Nejatollahi F, Davarpanah MA. Serum levels of interleukin-4, interleukin-10 and interferon-γ in patients with chronic hepatitis B infection. Hepat Mon 2018;48:e60377.
  • 73. Valentin A, Lu W, Rosati M, et al. Dual effect of interleukin 4 on HIV-1 expression: implications for viral phenotypic switch and disease progression. Proc Natl Acad Sci U S A 1998;95:8886−91.
  • 74. Browne EP. The role of toll-like receptors in retroviral infection. Microorganisms 2020;8:1787.
  • 75. Csiszar A, Nagy G, Gergely P, et al. Increased interferon-gamma (IFN-gamma), IL-10 and decreased IL-4 mRNA expression in peripheral blood mononuclear cells (PBMC) from patients with systemic lupus erythematosus (SLE). Clin Exp Immunol 2000;122:464−70.
  • 76. Osuji FN, Onyenekwe CC, Ahaneku JE, et al. The effects of highly active antiretroviral therapy on the serum levels of pro-inflammatory and anti-inflammatory cytokines in HIV infected subjects. J Biomed Sci 2018;25:88.
  • 77. Stylianou E, Aukrust P, Kvale D, et al. IL-10 in HIV infection: increasing serum IL-10 levels with disease progression: down-regulatory effect of potent anti-retroviral therapy. Clin Exp Immunol 1999;116:115−20.
  • 78. Yuan S, Jiang SC, Zhang ZW, et al. Quantification of cytokine storms during virus infections. Front Immunol 2021;12:659419.
  • 79. Lovell SC, Davis IW, Arendall WB 3rd, et al. Structure validation by Calpha geometry: phi,psi and Cbeta deviation. Proteins 2003;50:437−50.
  • 80. Athanasiou E, Agallou M, Tastsoglou S, et al. A Poly(Lactic-co-Glycolic) acid nanovaccine based on chimeric peptides from different Leishmania infantum proteins induces dendritic cells maturation and promotes peptide-specific IFNγ-producing CD8+ T cells essential for the protection against experimental visceral leishmaniasis. Front Immunol 2017;8:684.
  • 81. Dolenc I, Seemuller E, Baumeister W. Decelerated degradation of short peptides by the 20S proteasome. FEBS Lett 1998;434:357−61.
  • 82. Jahantigh HR, Stufano A, Lovreglio P, et al. In silico identification of epitope-based vaccine candidates against HTLV-1. J Biomol Struct Dyn 2022;40:6737−54.
  • 83. Yang Y, Sun W, Guo J, et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum Vaccin Immunother 2015;11:795−805.
  • 84. Hossan MI, Chowdhury AS, Hossain MU, et al. Immunoinformatics aided-design of novel multi-epitope based peptide vaccine against Hendra henipavirus through proteome exploration. Inform Med Unlocked 2021;25:100678.
  • 85. Velders MP, Weijzen S, Eiben GL, et al. Defined flanking spacers and enhanced proteolysis is essential for eradication of established tumors by an epitope string DNA vaccine. J Immunol 2001;166:5366−73.
  • 86. Khatoon N, Pandey RK, Prajapati VK. Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach. Sci Rep 2017;7:8285.
  • 87. Naz A, Awan FM, Obaid A, et al. Identification of putative vaccine candidates against Helicobacter pylori exploiting exoproteome and secretome: a reverse vaccinology based approach. Infect Genet Evol 2015;32:280−91.
  • 88. Mathur D, Prakash S, Anand P, et al. PEPlife: a repository of the half-life of peptides. Sci Rep 2016;6:36617.
  • 89. Pandey RK, Bhatt TK, Prajapati VK. Novel immunoinformatics approaches to design multi-epitope subunit vaccine for Malaria by investigating anopheles salivary protein. Sci Rep 2018;8:1125.
  • 90. Guruprasad K, Reddy BV, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng 1990;4:155−61.
  • 91. Chang KY, Yang JR. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS One 2013;8:e70166.
  • 92. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982;157:105−32.
  • 93. Almofti YA, Abd-Elrahman KA, Eltilib EE. Vaccinomic approach for novel multi epitopes vaccine against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). BMC Immunol 2021;22:22.
  • 94. Magnan CN, Randall A, Baldi P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics 2009;25:2200−7.
  • 95. Kwok SC, Mant CT, Hodges RS. Importance of secondary structural specificity determinants in protein folding: insertion of a native beta-sheet sequence into an alpha-helical coiled-coil. Protein Sci 2002;11:1519−31.
  • 96. Santhoshkumar R, Yusuf A. In silico structural modeling and analysis of physicochemical properties of curcumin synthase (CURS1, CURS2, and CURS3) proteins of Curcuma longa. J Genet Eng Biotechnol 2020;18:24.
  • 97. Meza B, Ascencio F, Sierra-Beltran AP, et al. A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach. Infect Genet Evol 2017;49:309−17.
  • 98. Kuruvilla Thomas R, Balasundaram A, Fathima G, et al. Identification and validation of B-cell epitopes on the VP1 protein of parvovirus B19 through molecular docking and dynamics simulation. ACS Omega 2025;10:3598−609.
  • 99. Ara A, Ahmed KA, Xiang J. Multiple effects of CD40-CD40L axis in immunity against infection and cancer. Immunotargets Ther 2018;7:55−61.
  • 100. Elgueta R, Benson MJ, de Vries VC, et al. Molecular mechanism and function of CD40/CD40L engagement in the immune system. Immunol Rev 2009;229:152−72.
  • 101. Sharma MD, Leite de Moraes M, Zavala F, et al. Induction and inhibition of CD40-CD40 ligand interactions: a new strategy underlying host-virus relationships. J Immunol 1998;161:5357−65.
  • 102. Tang T, Cheng X, Truong B, et al. Molecular basis and therapeutic implications of CD40/CD40L immune checkpoint. Pharmacol Ther 2021;219:107709.
  • 103. Pace CN, Fu H, Lee Fryar K, et al. Contribution of hydrogen bonds to protein stability. Protein Sci 2014;23:652−61.
  • 104. Zhou S, Liu Y, Wang S, et al. Effective prediction of short hydrogen bonds in proteins via machine learning method. Sci Rep 2022;12:469.
  • 105. Smith SO, Eilers M, Song D, et al. Implications of threonine hydrogen bonding in the glycophorin A transmembrane helix dimer. Biophys J 2002;82:2476−86.
  • 106. Sarkar B, Ullah MA, Araf Y, et al. Immunoinformatics-guided designing and in silico analysis of epitope-based polyvalent vaccines against multiple strains of human coronavirus (HCoV). Expert Rev Vaccines 2022;21:1851−71.
  • 107. Sher H, Sharif H, Zaheer T, et al. Employing computational tools to design a multi-epitope vaccine targeting human immunodeficiency virus-1 (HIV-1). BMC Genomics 2023;24:276.
  • 108. Delves PJ. Innate and adaptive systems of immunity. In: Rose NR, Mackay IR, editors. The autoimmune diseases. 6th ed. Elsevier; 2020. p. 45−61.

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Immunoinformatics study of CD40 ligand-targeting vaccine constructs: a novel immunotherapeutic approach
Osong Public Health Res Perspect. 2025;16(4):311-332.   Published online August 11, 2025
DOI: https://doi.org/10.24171/j.phrp.2025.0077
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Immunoinformatics study of CD40 ligand-targeting vaccine constructs: a novel immunotherapeutic approach
Osong Public Health Res Perspect. 2025;16(4):311-332.   Published online August 11, 2025
DOI: https://doi.org/10.24171/j.phrp.2025.0077
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