Introduction

Leishmaniasis is a vector-borne disease caused by the protozoan Leishmania, which can be zoonotic or anthroponotic.^1,2 The disease is transmitted through female sandflies (Phlebotomus and Lutzomyia),³ with humans and animals as hosts, and it primarily affects dogs and cats.^2,4,5 Leishmaniasis can lead to renal disease, epistaxis, and several other clinical abnormalities,^4,6 particularly in vulnerable populations such as immunocompromised, malnourished, and poor communities. The World Health Organization classifies leishmaniasis as a neglected tropical disease (NTD), with a global prevalence of 12 million cases and an incidence of up to 1.2 million cases per year across 98 countries.^1,3 Its prevalence and distribution are strongly influenced by factors such as climate and environment, contributing to widespread endemicity in regions including Asia, Africa, Europe, and the Americas.^2,7

Different types of leishmaniasis have been managed using various treatment approaches. Clinically simpler lesions are treated with thermotherapy, photo- or laser therapy, and intralesional injections, whereas more complex lesions are managed with amphotericin, pentamide, and oral therapies. Pentavalent antimonials are considered the standard treatment for severe cases of leishmaniasis; however, resistance to these drugs has developed in many Leishmania species, making them a less favorable option. Currently, immunocompromised patients are treated with liposomal amphotericin B in combination with other therapies.^1,8 Despite these strategies, treatment of leishmaniasis remains in its nascent stages and frequently results in relapse in dogs.⁹

There is currently no successful human vaccine for leishmaniasis; however, some vaccine candidates for dogs have shown promise. The lack of effective human vaccines is most likely due to economic pressures and the absence of a suitable human adjuvant system for vaccine testing. Studies have also suggested that the use of whole Leishmania parasites in vaccine candidates may impair antigen presentation, thereby reducing vaccine efficacy.¹⁰ At present, several vaccine candidates are undergoing clinical trials, including one developed by the Infectious Disease Research Institute (IDRI, Washington, Seattle), which uses recombinant Leishmania proteins, L. infantum, and L. donovani. The Sabin Vaccine Institute Product Development Partnership (Sabin PDP) is also developing a recombinant L. donovani vaccine, and numerous other vaccine strategies are under investigation.¹

In this study, Pteridine Reductase was selected as a potential vaccine candidate because of its role in folate metabolism in the Leishmania parasite. The pteridine pathway reduces folate and pteridines, which are essential for nucleotide acquisition during DNA synthesis and are necessary for parasite growth and metacyclogenesis. Therefore, inhibition of Pteridine Reductase would impair DNA replication and lead to cell death.^11,12 Previous studies have suggested that it is a suitable drug-target candidate in vitro when tested against Iranian Lizard Leishmania.¹³ In this study, T-cell and B-cell epitopes were filtered, and vaccinomics was applied to design a multi-epitope vaccine candidate. Docking was performed with TLR-9, an intracellular pattern recognition receptor primarily localized in endosomes, where it recognizes unmethylated CpG motifs commonly found in microbial DNA. TLR-9 is expressed in immune cells such as plasmacytoid dendritic cells and macrophages, both of which play critical roles in anti-Leishmania responses. Activation of TLR-9 triggers the MyD88-dependent pathway, leading to NF-κB activation and the production of pro-inflammatory cytokines and type I interferons. TLR-9 expression is upregulated during Leishmania infection, and stimulation with synthetic CpG oligodeoxynucleotides (ODNs) has been shown to enhance protective Th1 responses, making it a promising immunological target in vaccine design. For this reason, TLR-9 was chosen as the docking receptor to evaluate the potential innate immune activation.¹⁴

The structural and immunological properties of the candidate were analyzed to validate its potential as an effective and safe vaccine against leishmaniasis. The methods applied in this study are illustrated in Figure 1.

Figure 1 Methods of study to design in-silico Leishmania vaccine candidate.

Materials and Methods

Retrieval and analysis of protein sequences for pteridine reductase

UniProt (https://www.uniprot.org/) was used to identify and download the sequence of the selected protein, Pteridine Reductase. The retrieved sequence was analyzed to confirm that its characteristics were suitable for vaccine viability. ExPasy ProtParam (https://web.expasy.org/protparam/) was used to calculate the GRAVY score and TMHMM (https://services.healthtech.dtu.dk/services/TMHMM-2.0/) was applied to predict transmembrane helices in the protein. Antigenicity was assessed using VaxiJen v2.0 (https://ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html), allergenicity using AllerTop v2.0 (https://www.ddg-pharmfac.net/AllerTOP/), and toxicity using ToxinPred 3.0 (https://webs.iiitd.edu.in/raghava/toxinpred/). AllerTop is an in-silico prediction server that outperforms other allergen prediction tools with 94% sensitivity. All sequences used in this study were obtained from public, non-sensitive repositories.

Epitope prediction

Identifying B-cell epitopes is crucial because of their role in antibody production, which is essential for humoral immunity.^10,15 The selected sequence was analyzed using the Immune Epitope Database and Analysis Resource (IEDB)v2.28 (https://www.iedb.org/) to predict the linear B-cell epitopes of the conserved domains. B-cell epitopes that were non-allergenic, antigenic, and non-toxic were selected as the predicted epitopes.

T-cell epitopes were also identified because of their crucial role in the adaptive immune system.^10.16 For T-cell prediction, IEDB was used to forecast epitopes for MHC class I and MHC class II. MHC class I epitopes with a predicted IC value ≤ 50 were selected for further screening, whereas MHC class II epitopes with a predicted IC value ≤ 500 were shortlisted for further analysis based on antigenicity, allergenicity, and toxicity.

Peptide analysis

Additional analysis of the predicted peptides was carried out to identify antigenic, non-allergenic, and non-toxic candidates. To assess antigenicity, the peptide sequences of the epitopes were subjected to VaxiJen v2.0, and those with antigenicity scores greater than 0.4 were selected for evaluation. AllerTop v2.0 was then used to determine allergenic properties, and ToxinPred was applied to identify non-toxic peptides. Epitopes that fulfilled all criteria were prioritized.

Population coverage analysis

Population coverage analysis was conducted to ensure the compatibility of the multipeptide vaccine with diverse HLA alleles across different ethnic groups and populations. The selected epitopes were analyzed using the IEDB population coverage web tool to determine the percentage of the global population with HLA alleles compatible with the vaccine.

Construction of vaccine construct

Vaccine construction was carried out by compiling all prioritized epitopes with adjuvants and linkers previously used in Leishmania candidate vaccines. Synthetic oligodeoxynucleotide (CpG ODN) was selected as an adjuvant because its stability was confirmed in a previous study.^11,16 Another study suggested that CpG ODN provides sustained cellular protection against immune challenges through dendritic cells, which are critical for effective vaccination against Leishmania.¹⁷ CpG ODN has also been reported as an effective and economical option, and it is a common adjuvant in vaccine development, making it an optimal choice over other adjuvants.¹⁸ The AAY linker was chosen to connect MHC I and MHC II epitopes because of its flexibility and neutrality, which preserve functionality and presentation. AAY linkers bind CTL epitopes to one another while maintaining their separation and alignment.¹⁹ The adjuvant was connected using the EAAAK linker, whereas the CPGPG linker was employed between B-cell epitopes and MHC I epitopes. Finally, a 6X-His tag was added to the C-terminus of the vaccine sequence.

Analysis of vaccine

Vaccine efficacy was evaluated through multiple analyses. Physicochemical properties were determined using ProtParam, while the Errat score and Ramachandran plot were assessed using UCLA DOE (https://saves.mbi.ucla.edu/). Antigenicity was confirmed with VaxiJen v2.0, allergenicity was assessed with AllerTop v2.0, and toxicity was evaluated using ToxinPred 3.0. A BLASTp search was also performed to identify potential autoimmune cross-reactivity and allergen mimicry. To gain a more detailed understanding of the vaccine structure, the Protein Structure Prediction Server (PSIPRED) 4.0 (http://bioinf.cs.ucl.ac.uk/psipred/) was used to visualize loops and helices in the secondary structure, and trRosetta (https://yanglab.qd.sdu.edu.cn/trRosetta/) was employed to predict the tertiary structure. trRosetta generated five structures in the form of Protein Data Bank (PDB) files, the most suitable of which was uploaded into CHIMERA for tertiary structure visualization. NetOGlyc 4.0 and NetPhos 3.1 were then used to screen the final vaccine construct for post-translational modifications.

Docking of vaccine with human TLR9

TLR9 was selected as a receptor because of its ability to activate dendritic cells (DCs) and induce host protection against Leishmania species.^20,21 DCs activate TLR9 through its ligand, CpG-ODN, thereby initiating the innate immune response. TLR9 is expressed on the endosomal membranes of B-cells and DCs, where it mediates proinflammatory cytokine responses and contributes to infection control.²² PDB files for the vaccine were generated using trRosetta 2, while the RCSB Protein Data Bank v4.2 (https://www.rcsb.org/) w was used to retrieve the PDB file for human TLR9. The active and passive sites of each structure were identified using castp 3.0 (http://sts.bioe.uic.edu/castp/indusing?3igg), and docking was performed with HDOCK (http://hdock.phys.hust.edu.cn/). The resulting complexes were visualized using UCSF Chimera 1.18 and BIOVIA Discovery Studio before proceeding to in-silico cloning. iMODS (https://imods.iqf.csic.es/) was then used to perform Normal Mode Analysis (NMA) in Internal Coordinates (IC) to derive eigenvalues, elastic networks, deformability plots, and covariance matrices. PRODIGY 2.2.2 (https://rascar.science.uu.nl/prodigy/) was used to predict Kd and Binding affinity.

Codon optimization and in-silico cloning

Codon optimization and in-silico cloning of the vaccine were performed to ensure efficient expression.^14,23 The Integrated DNA Technologies Codon Optimization tool (https://www.idtdna.com/pages/tools/codon-optimization-tool) was used to adapt codons for the selected expression host. Escherichia coli K12 was chosen as the host strain because of its availability, ease of growth, and economic advantages.²⁴ The optimized sequence obtained from the codon optimization tool was then input into SnapGene software and inserted into the vector pET-21a (+), enabling visualization of the cloning process.

Molecular dynamic simulation

This study utilized GROMACS 2024.02 to simulate a vaccine-docked protein complex under biologically relevant conditions. The vaccine structure was converted into a GRO file compatible with the OPLS-AA force field, while simulations were performed using the CHARMM36 force field for a 50 ns molecular dynamics (MD) run. The optimal ligand pose, selected based on docking scores, was prepared using an NVT ensemble with the V-rescale thermostat set at 303.15 K and equilibrated for 1 ns. A TIP3P water model was used to define a cubic box (100 × 100 × 100 Å) with a 10 Å buffer surrounding the protein, and physiological ionization states at pH 7.0 were maintained using K+ and Cl⁻ ions placed via Monte Carlo methods.²⁵

The system underwent steepest descent energy minimization (5,000 steps) followed by two equilibration phases (100 ps each) under NVT and NPT ensembles with Berendsen and Parrinello-Rahman barostats, respectively. The MD production phase employed periodic boundary conditions (PBC) for realistic molecular interactions, a 2 fs time step, and the Particle Mesh Ewald (PME) method for long-range electrostatics. Non-bonded interactions were truncated at 1.2 nm, and the LINCS algorithm was applied to constrain bond lengths. CHARMM36m force field parameters were used, and analyses included Root Mean Square Deviation (RMSD), radius of gyration (RoG), and binding energy calculations using the “gmmpbsa” module. Interaction distances were assessed with VMD, and visualizations were generated in PyMOL. A 1 ns NVT equilibration at 303.15 K was also performed. This comprehensive workflow accurately captured ligand-protein dynamics and energetics.²⁶

Results

Protein analysis

The selected protein, Pteridine Reductase, was evaluated and found to be stable and hydrophobic, as indicated by its positive GRAVY index. Furthermore, the protein was predicted to be non-allergenic and non-toxic (Table 1).

B-cell prediction

A list of predicted B-cell epitopes was obtained from IEDB and assessed for selection. The shortlisted B-cell epitopes were determined to be antigenic, non-allergenic, non-toxic, and soluble in water. Only one B-cell epitope, consisting of nine amino acids, fulfilled these criteria (Table 2).

T-cell epitope prediction

Epitopes for both MHC class I and class II were predicted, and the results were shortlisted to include only antigenic, non-toxic, water-soluble, and non-allergenic epitopes. Six epitopes were identified for MHC class I (Table 3), and 25 epitopes were identified for MHC class II (Table S1).

Population coverage analysis

To evaluate the global applicability of the selected epitopes, population coverage analysis was performed for T-cell epitopes across both MHC class I and MHC class II. The analysis indicated that the HLA alleles used to predict T cells could elicit an immune response in 81.8% of the global population, demonstrating encouraging potential for broad immunogenicity (Figure 2). Achieving high population coverage with a vaccine is essential for maximizing its effectiveness and ensuring widespread protection.

Figure 2 Combined coverage of T cell epitopes of combined MHC classes using IEDB.

Vaccine construction

The selected epitopes were used to construct an in-silico vaccine by combining the peptides with appropriate linkers and adjuvants. The construct refers to the selected targets, epitopes, adjuvants, and linkers that may serve as potential models for vaccines.^27,28 CpG Oligodeoxynucleotide (CpG ODN) was selected as an adjuvant and added to the N-terminus, while the AAY linker was chosen to connect MHC I and MHC II epitopes because of its ability to enhance vaccine functionality and structural integrity. The adjuvant was linked using the EAAAK linker, whereas the CPGPG linker was inserted between B-cell epitopes and MHC I epitopes. The CPGPG linker was used because of its ability to enhance the immune response elicited by a vaccine.²⁹ This combination of linkers between B-cell epitopes is known to intensify immune responses.³⁰ Finally, a 6X-His tag was added to the C-terminus of the vaccine sequence. The proposed construct consists of 484 amino acids (Figure 3). These linkers and adjuvants were incorporated with one B-cell epitope, six MHC class I epitopes, and 25 MHC class II epitopes, resulting in a potentially robust and stable vaccine.

Figure 3 Constructed vaccine consisting of linkers, adjuvants and peptides of B cells and T cells.

Prediction of secondary and tertiary structure of vaccine construct

The positions and percentages of helices, strands, and coils were predicted using PSIPRED 4.0 (Figure 4). Likewise, trRosetta 3.14 was employed to visualize the tertiary structure of the vaccine (Figure 5).

Figure 4 Graphical representation of the secondary structure features of the vaccine predicted using PSIPRED 4.0. Coils are shown in gray, helices in pink, and strands in white.

Figure 5 Visualization of the tertiary structure of vaccine construct on trRosetta.

Analysis of vaccine

Vaccine potency was confirmed by verifying that the construct met specific quality criteria. UCLA DOE analysis of the Ramachandran plot showed that 89.9% of residues were present in the most favorable region, while 8.9% of residues were present in the overall allowed region (Figure 6). Similarly, UCLA DOE analysis revealed an Errat score of 98.4615, indicating a high overall quality factor (Figure 7). The ProSA Z-score server reported a score of -5.56, and the local quality model indicated that most residues were located within the acceptable region. Furthermore, the vaccine was predicted to be antigenic, non-toxic, and non-allergenic, confirming that the construct was stable and potentially effective. BLASTp analysis against the human proteome (taxid:9606) revealed no significant similarity (defined as >35% identity over ≥80 amino acids), suggesting a minimal risk of autoimmune cross-reactivity. The vaccine construct was visualized using UCSF Chimera (Figure 8). NetOGlyc 4.0 identified 23 residues as positive glycosylation candidates, with scores exceeding the 0.5 threshold, the majority of which were located in the N-terminal or non-epitopic regions. Similarly, NetPhos 3.1 predicted phosphorylation sites primarily in non-epitopic or linker regions, reducing the risk of glycosylation masking epitopes.

Figure 6 Ramachandran plot showing the allowed and favored regions of dihedral angles in the vaccine construct, generated using UCLA DOE.

Figure 7 ERRAT score plot showing ERRAT value of 98.4615 using UCLA DOE.

Figure 8 Vaccine construct visualized through UCSF CHIMERA.

Docking of vaccine construct with human TLR-9

Molecular docking was performed to evaluate interactions between the TLR-9 ligand and the vaccine construct.²³ Human TLR-9, a pattern recognition receptor (PRR), was selected because of its critical role in activating the innate immune system.³¹ The active sites of TLR-9 and the vaccine construct were identified using CASTp, and docking was carried out with HDOCK to analyze the interactions between the two structures. The results indicated that the vaccine construct could bind to TLR-9 and was expected to trigger a strong immune response, as supported by the docking score and confidence score (Table 4). PRODIGY 1.15.8 predicted a Gibbs free energy (ΔG) of -11.8 kcal mol⁻¹ and a KD of 2.2e-09. These low values suggest that the vaccine construct was stable and exhibited strong binding affinity, supporting its potential as a successful vaccine candidate. The docked complex was visualized using UCSF Chimera (Figure 9).

Figure 9 Docked complex of vaccine construct (blue) with human TLR9 (yellowish-white) on UCSF Chimera.

Normal mode analysis

The docked complex was analyzed using iMODS to reproduce the collective functional motions of biological molecules.³² The individual distortion of each residue determined the deformability of the complex, represented by green hinges along the chain (Figure 10A). The eigenvalue of the complex was 2.06e-0.6, indicating a high degree of flexibility (Figure 10B). Similarly, the elastic network model was applied to assess flexibility by representing atoms as nodes connected through virtual springs (Figure 10C). Darker regions in the graph indicated greater stiffness between atoms. The covariance matrix illustrated the coupling between residue pairs, where red regions indicated correlated motions, blue regions indicated anti-correlated motions, and white regions indicated uncorrelated motions (Figure 10D).³³

Figure 10 iMODS analysis of the docked vaccine complex. (A) Residue-wise distortion representing deformability. (B) Eigenvalue plotted against mode index. (C) Elastic network model. (D) Covariance matrix.

Codon optimization and in-silico cloning

The IDTDNA web tool optimized codons and generated 1,452 nucleotides with improved expression levels³⁴ in the selected vector host. The pET plasmid system is commonly used to produce recombinant proteins in Escherichia coli.⁵ Among these, pET21a(+) is widely employed because its T7 promoter sequence maximizes protein expression in bacterial systems.^24,35 SnapGene was used to insert the vaccine fragment into pET-21a(+) and perform cloning, resulting in a complete clone of 3.420 kb (Figure 11).

Figure 11 Vaccine construct cloned for expression in E.coli using pET-21a (+) vector.

Molecular dynamic simulation

Molecular dynamics simulation was performed to analyze the behavior of the protein target in a biological system. In Figure 12A, a 50 ns simulation was conducted, yielding a maximum RMSD of 1.93 nm at 42 ns, with a mean RMSD of 1.3 nm observed at 21 ns. The RMSD initially started low (~0.5 nm) and gradually increased, indicating that the protein complex underwent structural adjustments before reaching stability.

Figure 12 Molecular dynamics simulation analysis of the vaccine–TLR4 docked complex. (A) RMSD graph showing a steady increase in fluctuations, reaching a maximum at ~43 ns before stabilizing at equilibrium. (B) RMSF plot with the highest fluctuations observed between residues 1000–2000, reaching ~1.5 nm. (C) Radius of gyration (Rg) plot showing overall compactness of the complex throughout the simulation.

Between 35 and 40 ns, the RMSD reached a plateau (~1.5–1.7 nm), suggesting that the system had stabilized and achieved equilibrium. Similarly, the RMSF plot in Figure 12B highlighted dynamic regions that may represent potential binding sites or regions contributing to interaction flexibility. Regions with higher fluctuations (~2.5–3.0 nm) corresponded to flexible loops, termini, or exposed regions of the protein complex, whereas lower RMSF values (~0.5–1.0 nm) indicated more rigid or structured regions, such as the protein core or binding interface.

Figure 12C illustrates the compactness and structural changes of the molecule over time, as determined by molecular dynamics simulation through the radius of gyration (Rg). The Rg was approximately 3.8–4.0 nm at the beginning, indicating an extended structure, but it dropped to about 3.0 nm during the simulation, suggesting folding or compaction. Minor variations were observed throughout, consistent with thermal fluctuations; however, the Rg stabilized at 2.8–3.0 nm after 40,000 ps, indicating that the system had reached a stable equilibrium state. This pattern reflects structural alterations such as folding or collapse, with the molecule ultimately stabilizing into a potentially native-like conformation. During the equilibration stage, the Parrinello–Rahman barostat was applied at a pressure of 1 atm and a temperature of 303.15 K using a constant NPT ensemble

Discussion

Leishmania, the parasite responsible for leishmaniasis, is the second leading cause of parasitic death worldwide after malaria, comprising more than 20 species that cause human disease across the globe. High levels of genetic variability among these species, combined with variable clinical manifestations and rapid adaptation in culture, complicate the development of standardized treatments, highlighting the urgent need for new therapies and preventive vaccines.³⁶ With 350 million people at risk, this neglected tropical disease poses a major global public health challenge.³⁷ Leishmania parasites have evolved mechanisms to subvert or inhibit the host immune response, making efficient treatment or prevention critically important. A thorough understanding of parasite biology and host immunology is essential for the development of effective vaccines. Immunoinformatics enables the generation of dynamic protein profiles, providing insights into interactions between the vaccine and the host immune system. This approach allows testing and analysis of immunogenic data to ensure vaccine safety and efficacy. Furthermore, in-silico methods enhance epitope specificity by selecting antigens predicted to elicit strong immune responses, while offering a cost- and time-effective strategy for the early stages of vaccine design.³⁸

The present study employed an immunoinformatics approach to design a vaccine construct targeting the Pteridine Reductase protein to induce an immune response. Previous studies have applied molecular docking and visualization to identify potential inhibitors of Leishmania as drug targets;^11,21 however, none have explored its potential as a vaccine target. Immunoinformatics has accelerated vaccine development by reducing both time and cost compared to traditional methods.³⁸ This approach enables accurate prediction of vaccine targets, stability, and efficacy. The antigenicity of Pteridine Reductase 1 was evaluated, with ideal antigenicity scores ranging from 0.4000 to 2.000.³⁹ The protein was selected based on its stability, estimated half-life, aliphatic index, GRAVY, theoretical pI, and antigenicity score.

B cells are responsible for the creation of antibody-secreting plasma cells and memory B cells that can recognize and respond to antigens in immediate and long-term immune responses, respectively. The B-cell and T-cell epitopes of the antigen were further prioritized based on their antigenicity, allergenicity, and toxicity. Epitopes with an immunogenicity score of less than 0.00 on the VaxiJen server indicated positive antigenicity and immunogenicity scores, which would be highly antigenic and exhibit a high affinity for interacting with MHC complexes and enhancing immune responses. B-cell epitopes with high affinity and antigenic properties were identified, whereas T-cell epitopes with appropriate IC values were considered to have high affinity.⁴⁰ One B-cell peptide and 31 T-cell peptides were identified. Thereafter, the peptides were combined with adjuvants and linkers to develop the vaccine construct. CpG ODN was utilized as an adjuvant and has been applied in other multi-epitope vaccines as well, including in a Mycobacterium tuberculosis vaccine designed by Wang et al.,⁴¹ where CpG ODN was used due to its ability to stimulate dendritic cells to produce cytokines such as TNF-a, IL-6, and IL-12, and to trigger T lymphocytes and IFN secretion as a Th1 response. CpG ODN has also been tested as an adjuvant in mice immunized with Leishmania, resulting in improved survival. The AAY linker was used between MHC I and MHC II, and a CPGPG linker was employed between B-cell epitopes and MHC I, as these linkers improve the presentation of epitopes by promoting their natural formation.²⁹ The EAAAK linker reduced the interaction of the vaccine with other protein regions, thereby increasing its stability. These linkers were also used in a study by Ramprasad et al.⁴² EAAAK is used to separate functional domains and maintain functional properties of epitopes in vaccine constructs. This is achieved through a peptide linker with a rigid alpha-helix structure, making EAAAK a popular choice as a linker in vaccines.²⁹ The peptides were synthesized using linkers and adjuvants to produce the final vaccine construct, which consisted of 484 amino acids.

The vaccine construct was inspected to determine allergenicity patterns, antigenicity, toxicity, physiochemical properties, and structural stability, and was found to meet the necessary criteria for an effective immune response with minimal adverse effects.⁴³ The Ramachandran plot revealed that 89.9% of the residues were present in the most favorable region and 8.9% of the residues were present in the overall allowed region, indicating that the structure was of high quality and stability. This score is on the borderline of the ideal threshold (< 90%) for acceptable stereochemical quality, which may result in minor but overall insignificant deviations.⁴⁴ A previously proposed Leishmania vaccine construct had a Ramachandran score of 81.5% in the most favored regions,⁴⁵ which is lower than the construct presented in this study, indicating superior structural stability and stereochemical quality. The Errat score was 98.4615, which further verified the dependability of the model. This was higher than another immunoinformatics study that reported an Errat score of 84.90.⁴⁶ Post-translational modifications were rarely observed in epitopic regions, making the vaccine less likely to contain masked epitopes or altered antigen processing.⁴⁷ Furthermore, population coverage of 81.8% was higher than that of a previously proposed Leishmania vaccine, which had a population coverage of 69.14%.⁴⁵

Visualization of the structures enabled the examination of the vaccine construct, and molecular docking was carried out using the human Toll-like receptor (TLR-9). A study found that TLR-9 signaling results in protective immunity against Leishmania due to its ability to induce proinflammatory cytokines such as IL-12 by dendritic cells, concluding that TLR-9 stimulation is essential for NK cell activation.⁴⁸ TLR-9 has been used in other studies and has been hypothesized to improve the efficacy of a vaccine against cutaneous leishmaniasis and to increase the level of T-cell response when used in a mouse model as an immunotherapeutic treatment against cutaneous leishmaniasis.⁴⁹ TLR-9 functions as an immune enhancer due to its ability to promote nuclear translocation and activation of nuclear factor-κB (NF-κB), resulting in an enhanced proinflammatory response, activation, and T-cell proliferation of DCs.⁵⁰ TLR-9 knockout mice experience a loss of protective immunity against the Leishmania parasite.

NMA is a computational method used for molecular refinement or replacement, capable of characterizing flexible conformations available to molecules around an equilibrium position. The eigenvalue, which represents the motion stiffness of the complex, was 2.06e-0.6. This low score indicates that the construct can easily undergo deformation and exhibits a high level of flexibility.^21,33 This analysis confirmed that the docked molecule achieved a successful interaction and that the resulting structure demonstrated high flexibility, making it an encouraging candidate. Codon optimization resulted in the generation of 1452 nucleotides. In-silico cloning into E. coli was performed, and the resulting clone was 3.420 kb long. The results of the study were consistent with the hypothesis that interactions between the vaccine and its targets were dynamic and stable. Additionally, Molecular Dynamic Simulation conducted using GROMACS 2024.2 revealed a stable configuration for the vaccine and TLR-9 docked complex.

While analysis has suggested promising results, the reliability of immunoinformatics is limited by the availability of datasets and remains challenged by epitope prediction errors, the complexity of the immune system, and genetic variability among individuals. These limitations make in vivo and in vitro validation crucial.^51,52 This study suggests that the vaccine candidate is stable and safe, making it a promising option against Leishmania. However, this candidate must be tested in vivo and in experimental trials using appropriate model organisms to validate the safety and efficacy of the vaccine.

Conclusion

Leishmania is a growing threat, with high mortality and morbidity rates in more than 98 countries. The development of an effective human vaccine is imperative for managing disease transmission, particularly given the absence of an existing vaccine. Using an immunoinformatics approach, a multi-epitope vaccine construct was designed to target both innate and adaptive immune responses. Potential vaccine targets were identified and investigated for allergenicity, immunogenicity, and antigenicity to ensure their safety and performance as vaccine candidates. Further analysis confirmed that the vaccine elicited antibody- and cell-mediated immune responses. The construct was then docked with human TLR-9 and visualized to verify that the interaction was stable and had high binding affinity. Finally, in-silico cloning was performed to determine vaccine expression in E. coli. Evaluation of the construct indicated that the vaccine is capable of inducing an immune response. However, in vivo and in vitro experiments are essential to confirm the safety and efficacy of the vaccine construct.