Immunoinformatic based Development of a Multi-Epitope Precision Vaccine Targeting Glycoprotein and RdRp of Oropouche Virus: An Innovative Approach to Counter Emerging Public Health Threats

Summary

Objectives:

To design high immunogenic vaccine against the Oropouche virus (OROV). The OROV is a neglected arbovirus endemic to Central and South America, causes oropouche fever, which can progress to severe complications such as meningitis and hemorrhagic symptoms.

Methods:

We utilized immunoinformatic and molecular dynamic simulation approaches to design highly immunogenic vaccines against OROV. This observational study, carried out between January and August 2024 in Saudi Arabia. As it involves an in-silico method, ethical consent was not required.

Results:

Docking analysis confirmed the stable interaction of the designed vaccines with human toll-like receptors (TLR)-3, producing binding scores of –300.78, –306.19, and –288.60 kcal/mol for the glycoprotein, ribonucleic acid (RNA)-dependent RNA polymerase (RdRp), and combined vaccine, respectively. Furthermore, molecular dynamics simulations supported the stability of the vaccine–TLR-3 complexes. The calculated total binding free energies were –107.44 kcal/mol for glycoprotein–TLR-3, –33.64 kcal/mol for RdRp –TLR-3, and –78.62 kcal/mol for the combined vaccine–TLR-3 interaction. The computed codon adaptation index (CAI) values for the vaccines were notably 0.96, with guanine-cytosine (GC) content ranging between 65% and 66%, suggesting strong potential for high expression in the pET28a+ vector. The analysis of immune simulation showed rapid antigen clearance, accompanied by sustained and elevated immunoglobulin (Ig)M and IgG responses.

Conclusion:

This study presents a potent and secure vaccine candidate to combat the emerging Oropouche virus infection, which requires further experimental validation.

Introduction

Oropouche virus (OROV) is an ribonucleic acid (RNA) virus with a negative-sense, single-stranded structure classified under the peribunyaviridae family and orthobunyavirus oropoucheense species. The virus genome is made up of 3 RNA segments that code for the RNA-dependent RNA polymerase (RdRp), nucleocapsid protein (N), and the glycoprotein of the envelope. The virus is enclosed in a helical nucleocapsid and a lipid envelop [1]. Since the initial identification of the OROV in the 1960s, more than 30 outbreaks have been recorded in countries such as Brazil, Peru, Panama, and Trinidad & Tobago, impacting an estimated 500,000 individuals [2,3]. Notably, 2024 has seen a significant increase in reported cases across South America compared to 2023. By May 2024, Brazil alone had reported 4,583 cases, a sharp rise from 835 positive cases in 2023 [4,5]. This upward trend continued, with confirmed cases reaching 8,078 by July 2024, including 7,284 in Brazil, 356 in Bolivia, 290 in Peru, and 74 across Colombia and Cuba [6]. The OROV belongs to the neglected category of arboviruses and is mainly encountered in Central and South America, where it is responsible for the bulk of cases of oropouche fever. This febrile disease is normally of mild nature and self-limiting, showing such symptoms as headache, chills, photophobia, nausea, vomiting, myalgia, and arthralgia [7]. However, in some cases, the disease can progress to severe complications, including meningitis, meningoencephalitis, hemorrhagic symptoms, and adverse pregnancy outcomes [8,9]. The OROV circulates primarily within an enzootic cycle, with culicoides paraensis midges serving as the primary vectors, transmitting the virus among non-human primates and various wild animals such as pale-throated sloths (bradypus tridactylus). Human-to-human transmission has never been observed in the case of OROV but infected arthropods are in the habit of biting humans causing them to be infected. Despite severe central nervous system (CNS) disease, brain histopathology shows mild inflammation, suggesting neuron replication causes minimal impairment. While OROV exhibits hepatotropic tendencies and spleen hyperplasia, hepatitis and spleen antigen detection remain undocumented [10]. The type I interferon (IFN) signaling network is important for defending against OROV and is linked to age-related mortality. The OROV infection is diagnoses using molecular virology techniques, alongside conventional methods such as viral isolation, serologic assays (complement fixation [CF], hemagglutination-inhibition [HI], neutralization tests [NT]), and antibody detection immunoglobulin (IgM, IgG) in convalescent sera [11,12,13]. Although OROV poses a considerable health threat, no approved vaccines or antiviral treatments exist, making preventive measures the primary approach to controlling OROV infection and oropouche fever. The development of a vaccine for this virus seems to be the most encouraging solution, as it will not only provide but also give communities power to take charge of their health. Computationally designed vaccines offer advantages over conventional methods, including precise epitope targeting, cost-effectiveness, improved safety, and faster development. Using immunoinformatics, we developed a safe, multi-epitope vaccine with high antigenicity against oropouche. The RdRp and glycoproteins were used to identify epitopes from B cell, cytotoxic T-lymphocyte (CTL), and human thymus lymphoid (HTL), which are linked with immunogenic linkers to make final vaccine. Molecular docking, binding free energy calculations, and molecular dynamics (MD) simulations confirmed stable interactions with the human toll-like receptors (TLR)-3. The immune responses were assessed by using the immune simulation approach. The overall workflow of this student is shown in the Fig. 1.

Fig. 1.

Schematics representation of the immunoinformatic based designing of vaccines. CTL: cytotoxic T-lymphocyte, HTL: human thymus lymphoid, GRAVY: grand average of hydropathicity index, AAY linker: ala-ala-tyr linker, GPGPG linker: glycine-proline-glycine-proline-glycine linker, KK linker: lysine-lysine, TLR-3: Toll-like receptors 3, RMSD: root mean square deviation, RMSF: root mean square fluctuation, Rg: radius of gyration.

Method

Proteins retrieval and high immune epitopes prediction

The Uniprot database was used to obtain the whole proteome (ID: UP000117676) of OROV, and among its proteins, only the glycoprotein (A0A0B5CLT0) and RdRp (A0A0B5CUJ1) were selected for epitope prediction [14]. Moreover, in order to anticipate the major antigenic CTL epitopes, the sequences of the glycoprotein and RdRp were uploaded to the NetCTL online server [15]. Additionally, to predict the high immunogenic HTL epitopes, we submitted the sequence to the IEDB server [16]. Furthermore, to selected the B cell epitopes we used the online server called the ABCPred [17]. This observational study, carried out between January and August 2024 in Saudi Arabia. As it involves an in-silico method, ethical consent was not required.

Shaping and characterization of final vaccine construct

The ultimate multi-epitope vaccine was built by incorporating chosen CTL, HTL, and B-cell epitopes with suitable linkers: ala-ala-tyr, glycine-proline-glycine-proline-glycine, and lysine-lysine (AAY, GPGPG, and KK) to guarantee correct epitope display. Human β-defensin-2 was fused to the N-terminus as an adjuvant to boost antigenicity and immune stimulation [18]. Moreover, the final vaccine constructs were analyzed for the antigenicity and allergenicity by using the VaxiJen and Algpred servers [19]. Finally, the physiochemical properties of the constructed vaccines were calculated by using the ProtParam server [20].

3. 3 dimensional (3D) structure modeling and validation

The 3D structural models of vaccines constructed from RdRp, glycoprotein, and combined construct, were generated by submitting their amino acid sequences to the Robetta web server [21]. Furthermore, to validate the quality of model protein we submitted the 3D structures to the to the online validation tools such as PROCHECK and ProSA- web [22].

Molecular docking and dissociation constant (KD) analysis of constructed vaccines and TLR-3 protein

The HDOCK server was used for the docking to assess the binding interactions between the designed vaccines and human TLR-3 (PDB ID: 1ZIW). Furthermore, to check the binding strength of the vaccine and TLR-3, we calculated the KD value by using the PRODIGY server [23].

Molecular dynamic simulation

The dynamic strength of the designed vaccine with the TLR-3 receptor, we performed the 200 ns and 100 ns molecular dynamics simulation respectively by using the AMBER 20 package [24]. The system was prepared by solvation in a transferable intermolecular potential with 3 points water box (10.0 Å), neutralized with sodium counter ions, and minimized in 2 steps (3000 and 6000 steps). After heating to 300 K and equilibration at 1 atmosphere pressure, CPPTRAJ and PTRAJ were employed to analyze structural parameters, including root mean square deviation (RMSD), radius of gyration (Rg), and root mean square fluctuation (RMSF), to evaluate vaccine stability and flexibility [25]. Finally, the molecular mechanics generalized born surface area pyhthon command we used for the calculation of binding free energy.

Codon optimization and cloning of vaccines sequences into pET-28a (+) vector

The vaccine protein sequence was reverse-translated and codon-optimized for Escherichia coli (E. coli) K12 using the Java Codon adaptation tool (JCat) server to improve expression efficiency by adjusting guanine-cytosine (GC) content and codon adaptation index (CAI) [26]. The optimized gene was then in silico cloned into the pET-28a(+) vector using SnapGene, with XhoI and NdeI restriction enzymes enabling precise insertion.

Immune simulation

The C-ImmSim server was used to model immune responses against the glycoprotein, RdRp, and combined vaccines. Immunizations were simulated on days 0, 17, and 56 using default parameters. The simulation evaluated antibody production, cytokine release, and interferon response, providing a comprehensive assessment of the vaccine-induced immune reactions in a human host context.

Result

Prediction of high antigenic CTL epitopes

Cytotoxic T cells (CD8⁺) are central to adaptive immunity, killing infected cells via perforin/granzymes or Fas- fas ligand (FasL), secreting antiviral cytokines, and forming memory cells for rapid future responses [27]. The glycoprotein and RdRp sequences were screened for potential CTL epitopes using the NetCTL 1.2 server. Three epitopes with the highest antigenic scores were selected based on their strong major histocompatibility complex (MHC) binding affinity and high predictive scores. For the glycoprotein, the selected epitopes were FTDYSYSNL₇₂₃ (1.4638), LSKDIKITY₄₈₅ (1.4258), and FSTMRQINY₈₉₈ (0.8506). For the RdRp protein, the chosen epitopes were STSLLSLNY₂₀₂₇ (1.0587), YTDYMSVKV₈₉₉ (0.8050), and SVEMWDFIY₄₈₅ (0.6854). Additionally, 2 CTL epitopes with the highest antigenic scores were selected from each protein to design a combined vaccine shown in Table 1.

Table 1.

Predicted antigenic epitopes (CTL, HTL, and B-cell).

CTL
Residue no	Peptide sequence	MHC binding affinity	Rescale Binding Affinity	C-terminal Cleavage Affinity	Transport Affinity	Prediction score	MHC-I binding	Antigenicity score (0.4)
Glycoprotein
723	FTDYSYSNL	0.4566	1.9385	0.7965	0.517	2.0839	YES	1.4638
485	LSKDIKITY	0.2988	1.2687	0.9755	2.96	1.5631	YES	1.4258
898	FSTMRQINY	0.5205	2.2098	0.206	2.667	2.374	YES	0.8506
RdRp
2027	STSLLSLNY	0.7617	3.2342	0.8863	3.053	3.5198	YES	1.0587
899	YTDYMSVKV	0.5289	2.2458	0.6562	0.042	2.3421	YES	0.8050
485	SVEMWDFIY	0.5877	2.4952	0.6856	2.965	2.7463	YES	0.6854
Combined
723	FTDYSYSNL	0.4566	1.9385	0.7965	0.517	2.0839	YES	1.4638
485	LSKDIKITY	0.2988	1.2687	0.9755	2.96	1.5631	YES	1.4258
2027	STSLLSLNY	0.7617	3.2342	0.8863	3.053	3.5198	YES	1.0587
899	YTDYMSVKV	0.5289	2.2458	0.6562	0.042	2.3421	YES	0.8050
HTL
S.no	Allele	Start	End	Peptides sequence		Score	Percentile Rank	Antigenicity score (0.4)
Glycoprotein
1	DRB1*15:01	147	161	MIGILKFASKLLDIN		0.8992	0.13	0.8873
2	DRB4*01:01	606	620	YNQKIDLSQLDESNY		0.8978	0.08	0.7366
3	DRB5*01:01	184	198	QINYNDITSYRKAIE		0.8736	0.05	0.7047
RdRp
1	DRB4*01:01	121	135	DFEIVIIRLDPSNMT		0.9424	0.04	1.3742
2	DRB1*03:01	90	104	DFKVSVDDRSSRITR		0.9792	0.04	0.9060
3	DRB4*01:01	302	316	SSPELELQIADEALR		0.8897	0.08	0.7661
Combined
1	DRB1*15:01	147	161	MIGILKFASKLLDIN		0.8992	0.13	0.8873
2	DRB4*01:01	606	620	YNQKIDLSQLDESNY		0.8978	0.08	0.7366
3	DRB4*01:01	121	135	DFEIVIIRLDPSNMT		0.9424	0.04	1.3742
4	DRB1*03:01	90	104	DFKVSVDDRSSRITR		0.9792	0.04	0.9060
B-cell
S.no	Position		Epitopes			Score		Antigenicity (0.4)
Glycoprotein
1	1008		GPSISINIEHNERCTG			0.93		0.9890
2	444		MHVQKESCKFSPRVNH			0.89		0.9548
3	1104		LEPIIGDKLSASFQNT			0.89		0.7363
RdRp
1	839		CIKIGDFEEEKKRKTK			0.91		1.4932
2	935		KHKIFHFGFFNKGQKT			0.94		1.2505
3	1669		KFEIKELTRFYQVCYD			0.92		0.7658
Combined
1	1008		GPSISINIEHNERCTG			0.93		0.9890
2	444		MHVQKESCKFSPRVNH			0.89		0.9548
3	839		CIKIGDFEEEKKRKTK			0.91		1.4932
4	935		KHKIFHFGFFNKGQKT			0.94		1.2505

CTL: cytotoxic T-lymphocyte, HTL: human thymus lymphoid, MHC-I: major histocompatibility complex class 1, RdRp: ribonucleic acid dependent RNA polymerase.

Prediction of high antigenic HTL epitopes

Selecting highly immunogenic and widely recognized HTL epitopes is crucial for broad population coverage [28]. To select the high antigenic HTL epitope among the predicted, we used the similar strategy as used in the CTL epitope selection. Using the lowest percentile rank, we selected 3 epitopes form each protein for final vaccine construction. The selected glycoprotein epitopes were MIGILKFASKLLDIN (0.8873), YNQKIDLSQLDESNY (0.7366), and QINYNDITSYRKAIE (0.7047). For the RdRp protein, the chosen epitopes were DFEIVIIRLDPSNMT (1.3742), DFKVSVDDRSSRITR (0.9060), and SSPELELQIADEALR (0.7661) (Table 1). Furthermore, 2 highly antigenic epitopes from each protein were selected for inclusion in the combined vaccine construct.

Prediction of high antigenic B cell epitopes

To identify B cell epitopes, we analyzed the glycoprotein and RdRp proteins, selecting 3 epitopes from each protein based on a cutoff score of 0.89. For the glycoprotein, the selected epitopes were GPSISINIEHNERCTG (0.9890), MHVQKESCKFSPRVNH (0.9548), and LEPIIGDKLSASFQNT (0.7363). For RdRp protein, the epitopes were CIKIGDFEEEKKRKTK (1.4932), KHKIFHFGFFNKGQKT (1.2505), and KFEIKELTRFYQVCYD (0.7658) (Table 1). For the combined vaccine, we selected 2 epitopes in a similar manner based on antigenic score: GPSISINIEHNERCTG and MHVQKESCKFSPRVNH from the glycoprotein, and CIKIGDFEEEKKRKTK and KHKIFHFGFFNKGQKT from RdRp.

Assembly of predicted epitopes into final vaccines constructs and physiochemical properties analysis

Computer-aided vaccine design enables rapid, precise, and cost-effective development by linking CTL, HTL, and B-cell epitopes with AAY, GPGPG, and KK linkers to enhance stability, folding, and antigen presentation [29]. Human β-defensin 2 was added as an N-terminal adjuvant via an EAAK linker to further boost vaccine specificity and immunogenicity [18]. Furthermore, the constructed vaccines were analyzed to confirm the antigenicity and non-allergenicity. The antigenicity scores of 0.517, 0.576, and 0.642 for the glycoprotein, RdRp, and combined vaccine constructs, were obtained through analysis. Optimizing physicochemical properties ensures vaccine safety, stability, and effectiveness with molecular weights of 30.1, 30.5, and 35.8 kilodalton (kDa), with instability indices below 40, high aliphatic indices (82–87) indicating thermostability, and half-lives over 10 hours, supporting stable expression in E. coli. The grand average of hydropathicity index (GRAVY) values (–0.208 for glycoprotein, –0.207 for RdRp, and –0.289 for the combined vaccine) indicate hydrophilicity, favoring interactions with water. Physicochemical analysis further confirmed their thermal stability, efficient E. coli expression, and ease of purification, consistent with earlier findings [30].

The 3D modeling and validation of constructed vaccines

The Robetta server was used for the 3D modelling which generated 5 3D structures and subjected to quality assessment for the selection of best one [21]. The 3D structures of modeled proteins are shown in the (Fig. 2a-c). Ramachandran analysis was used for the generated 3D structures, which evaluates residue distribution across structural regions. In the case of glycoprotein structure, the residues were distributed as follows: 91.4% in the favored regions, 7.7% in the additionally allowed regions, and just 0.8% in the disallowed regions. However, in case of RdRp model the residues distributions were 89.5% and 10.5%, with none in disallowed regions. In the case of the combined vaccine construct, 87.2% of residues fell within favored regions, 12.2% within additionally allowed regions, and 0.7% within disallowed regions. To further validate, the selected models were analyzed using ProSA-web, which provided Z-scores of –8.37 for the glycoprotein, –7.87 for the RdRp, and –6.84 for the combined vaccine construct. The Z-scores confirmed that the predicted structures were reliable.

Fig. 2.

Modelling and dynamic stability analysis of 3D structure of constructed vaccine. (a–c) Illustrate the three-dimensional structures of the glycoprotein, RdRp, and combined vaccine constructs. (d) Depicts the structural stability of the vaccines measured by RMSD. (e) Represents the compactness of the constructs via radius of gyration. (f) Shows the residue-level flexibility of the vaccines as determined by RMSF. 3D: three-dimensional, CTL: cytotoxic T-lymphocyte, HTL: human thymus lymphoid, RMSD: root mean square deviation, RMSF: root mean square fluctuation, RdRp: ribonucleic acid dependent RNA polymerase.

Folding dynamics analysis of the constructed vaccines

In order to assess the folding and stability of the constructed designs, we conducted a 200 nanoseconds (ns) molecular dynamic simulation. The findings exhibited different structural behaviors for the glycoprotein, RdRp, and the combined system that were identified through RMSD, Rg, and RMSF. The RMSD value of all 3 constructed are stabilized at 100 ns and then remain stable until the end of simulation. The glycoprotein exhibits the highest RMSD values (12–15 Å) with considerable structural conformational changes as compared to RdRp and combined vaccines. RdRp displays moderate RMSD values (~5–8 Å) with fewer fluctuations, suggesting a relatively stable structure. In contrast, the combined vaccine construct shows the lowest RMSD (~2–6 Å), reflecting enhanced structural stability (Fig. 2d). For Rg, the glycoprotein has the highest values (~24–28 Å), while RdRp shows intermediate Rg values (~22–26 Å), reflecting a relatively compact structure, while the combined vaccine system exhibits the lowest Rg (~20–24 Å), demonstrating the most compact conformation (Fig. 2e). Regarding RMSF, the glycoprotein has the highest RMSF value with significant fluctuations at different points such between 60–100 and 175–225 amino acids, with peaks up to ~12 Å, indicating highly dynamic regions. The RdRp shows lower flexibility (~2–6 Å), suggesting most residues are stable and rigid. The combined vaccine construct exhibits the lowest RMSF values, with significantly reduced residue flexibility (Fig. 2f).

Binding analysis of modelled vaccines with human TLR-3 receptor

The HDOCK docking of the designed vaccines with human TLR-3 resulted in scores of –300.78, –306.19, and –288.60 kcal/mol for glycoprotein, RdRp, and combined vaccines. The PDBsum analysis revealed key interface residues (Gln192-His108, Ser197-Glu203, Gln198-Ser155, Gln198-Asp153, Asp195-Ser179, Tyr-169-Glu363, Gln179-Asn288, Glu67-Gln174, Asn165-Tyr326, Lys3-Lys589 and Glu8-Lys589), with the glycoprotein-TLR-3 complex forming 3 salt bridges, 11 hydrogen bonds, and 213 non-bonded contacts (Fig. 3a). In case of RdRp-TLR-3 the interface analysis found the formation of 5 hydrogen and 179 non-bonded contacts. The residues in hydrogen bonds were Gln-277-Arg222, Met150-Lys272, Lys265-Asn247, Ala50-Gln352 and Gly46-His316 (Fig. 3b). A total of 258 non-bonded contacts, along with 10 hydrogen bonds and 5 salt bridges, were observed at the interface between TLR-3 and the combined vaccine construct. The hydrogen bonds were formed between Ala2-His32, Tyr173-Tyr326, Asn165-Asn252, Tyr166-Arg252, Lys205-Ser249, Asp212-Arg325, Ser209-Gln299 and Gln210-Asn275 amino acid residues (Fig. 3c). Additionally, KD analysis confirmed strong binding of glycoprotein, RdRp, and combined vaccines with TLR-3, with KD values of 3.8 × 10^–12, 6.1 × 10^–11, and 4.6 × 10^–10.

Fig. 3.

Molecular docking and simulation analysis of designed vaccines and TLR3. (a–c) Showing interface analysis of vaccines-TLR-3 complexes. (d) Showing the Dynamic stability of constructed vaccines-LTR-3 complexes. (e) Depicts compactness of constructed vaccines-TLR-3 complexes. (f) Depicts average hydrogen bonds constructed vaccines-TLR-3 complexes. (g) Showing the residual fluctuation of vaccines-TLR-3 complexes. RMSD: root mean square deviation, RMSF: root mean square fluctuation, RdRp: ribonucleic acid dependent RNA polymerase, TLR3: toll-like receptor 3, Rg: radius of gyration.

Molecular dynamics simulation analysis of vaccines-TLR-3 complexes

The RMSD analysis validated binding efficacy, conformational stability, and the reliability of predicted interactions [31]. The RMSD analysis over the 100 ns simulation demonstrated the structural stability of all vaccine-TLR-3 complexes after initial adjustments. The glycoprotein-TLR-3 complex stabilized around 4 Å, indicating a relatively stable conformation. The RdRp–TLR-3 complex showed higher flexibility, rising to ~8 Å, while the combined vaccine-TLR-3 complex fluctuated around 6 Å (Fig. 3d). These results imply a stable and adaptable binding interface, critical for potential immunogenicity. The Rg analysis was used to evaluate the compactness of vaccine-TLR3 complexes. A decrease in Rg indicates tighter, more stable binding, while variations suggest conformational changes or flexibility, providing insights into binding strength, structural integrity, and validating docking results [32]. Fig. 3e display the Rg data, showing the compactness of the glycoprotein, RdRp, and combined vaccines. The glycoprotein-TLR-3 complex exhibits relatively stable Rg with minor fluctuations between 40–70 ns, indicating consistent structural integrity. The RdRp starts with a higher Rg around 36.5 Å and gradually decreases over time, stabilizing at ~35.0 Å by the end of the simulation. The Rg of the combined vaccine system is positively indicating a structural expansion. The average hydrogen bonds after simulation for the glycoprotein-TLR-3, RdRp-TLR-3, and combined-TLR-3 complexes were mentioned as 396, 376, and 386 (Fig. 3f). The RMSF analysis evaluated residue-level flexibility in the vaccine-TLR-3 complexes, showing that residues 1–300 exhibited higher fluctuations, reflecting greater structural flexibility in this region. The remaining residues-maintained stability with an average RMSF around 2 Å. (Fig. 3g). These results indicated the stability of vaccine-receptor complexes.

Binding free energies calculation of constructed vaccine-TLR-3 complexes

To the binding strength of vaccine-receptor complexes the binding free energies estimation approach. By decomposing electrostatic, van der Waals, and solvation contributions, it provides total binding free energy and insights into interaction stability. The van der Waals contributions to binding for the glycoprotein, RdRp, and combined vaccine complexes with TLR-3 were calculated as –248.83, –131.30, and –137.49 kcal/mol. Corresponding electrostatic energy components were –260.00, –23.20, and –652.64 kcal/mol. Overall, the total binding free energies were determined to be –107.44 kcal/mol for the glycoprotein–TLR-3 complex, –33.64 kcal/mol for the RdRp–TLR-3 complex, and –78.62 kcal/mol for the combined vaccine–TLR-3 complex (Table 2). In conclusion, the binding free energy analysis highlights that the glycoprotein-TLR-3 complex exhibited the strongest interaction.

Table 2.

List of energies contributed to the strength complexes.

MM/GBSA
Energies	Glycoprotein-TLR-3	RdRp-TLR-3	Combined-TLR-3
ΔEvdw	–248.83	–131.30	–137.49
ΔEele	–260.00	–23.20	–652.64
EGB	437.05	137.54	730.59
ESURF	–35.65	–16.67	–19.06
Delta G Gas	–508.84	–154.51	–790.14
Delta G Solv	401.39	120.86	790.52
ΔG total	–107.44	–33.64	–78.62

MM/GBSA: molecular mechanics/generalized born surface area, TLR-3: Toll-like receptors 3, ΔEvdw: van der waals, ΔEele: electrostatic, EGB: generalized born electrostatic energy, ESURF: solvent accessible surface area energy, Delta G Gas: nonpolar (van der Waals) solvation free energy, Delta G Solv: total solvation free energy, ΔG total: gibbs free energy, RdRp: ribonucleic acid dependent RNA polymerase.

In-silico cloning and codon optimization

To ensure optimal expression in an E. coli K12 host, the deoxyribonucleic acid (DNAs) encoding the vaccine protein sequences were codon-optimized by using the JCat. Optimization targeted key parameters, including GC content (30–70%) and the CAI, with a score near 1.0 indicating strong adaptation for high-level expression. The analysis revealed the CAI value of 0.96 for the glycoprotein and RdRp vaccines while 0.95 was recorded for the combined vaccine construct. However, the GC content of glycoprotein, RdRp and combined vaccine construct was recorded to be 65.8%, 66.5% and 65.1%. To sum up, the findings proved that the created vaccine could be continuously produced in the E. coli system. Then the optimized gene sequences were uploaded to the pET-28a(+) expression vector (Fig. 4a-c).

Fig. 4.

Cloning and immune simulation analysis of designed vaccines. (a-c) Illustrate the insertion of the vaccine sequences into the pET28a(+) expression vector. (d-f) Display the antibody responses generated against the administered vaccines. (g-i) Present the levels of interleukins and cytokines induced by the vaccine constructs.

Immune simulation of constructed vaccine

Immune simulation was employed to evaluate the vaccine's ability to induce strong and lasting immune responses, ensuring its safety and efficacy. As shown in Fig. 4d-f, antigen levels peak immediately after administration and are quickly cleared, demonstrating efficient immune activation. The IgM dominates the primary response, while IgG (particularly IgG1 and IgG2) rises strongly after booster doses on days 15 and 75, ensuring long-term protection. Following the third booster, combined IgG+IgM titers reached ~680,000/ml for glycoprotein and RdRp vaccines, and ~570,000/ml for the combined vaccine. The IgM titers were ~430,000/ml (glycoprotein), ~380,000/ml (RdRp), and ~340,000/ml (combined), whereas IgG1+IgG2 levels were ~230,000/ml, ~300,000/ml, and ~250,000/ml, confirming strong and sustained humoral immunity. Fig. 4g-i demonstrates sharp early peaks in interferon-gamma (IFN-γ) and interleukin (IL-2), indicating effective and balanced immune activation. The IFN-γ levels remained steady at ~440,000 ng/ml during the first 2 doses but declined to ~330,000 ng/ml after the third booster for RdRp and combined vaccines, with the glycoprotein vaccine showing slightly lower values. In contrast, IL-2 increased markedly after the first booster, reaching ~485,000 ng/ml for glycoprotein, ~510,000 ng/ml for RdRp, and ~580,000 ng/ml for the combined vaccine, highlighting strong T-cell stimulation. Early activation of transforming growth factor-beta (TGF-β), IL-10, and IL-12 was also observed, with IL-10 and TGF-β regulating inflammation, while IL-2 supported T-cell proliferation and sustained immune responses [34,35]. As a whole, the vaccine activates the immune system in many steps and stimulates strong and long-lasting immunity, with the addition of booster shots further enhancing memory and regulation.

Discussion

The re-emergence and wide geographical distribution of OROV highlight the necessity for the immediate development of effective vaccines, given that there are still no licensed vaccines or antiviral therapies available and the virus is becoming a major cause of neurological disorders [2,3,6]. A multi-epitope subunit vaccine (MESV) targeting the OROV glycoprotein and RdRp was designed in this study through computational approach. The rationale for targeting both proteins is based on the fact that while glycoproteins are involved in the processes of host cell entry and antibody neutralization, the highly conserved RdRp provides support for T-cell–mediated immunity through the generation of robust T-cell responses. Epitopes of helper t lymphocyte (CD4⁺) are critical in vaccination, where they activate T cells, and in turn, support B-cell antibody production, aid CTL responses, and stimulate memory T cells [36]. B-cell epitopes lead to pathogen neutralization, and cytotoxicity through antibodies, whereas cytotoxic T cells (CD8⁺) kill the infected cells and acquire memory for future responses [37,38]. The combination of CTL, HTL, and B-cell epitopes produced vaccine constructs that were not only antigenic but also non-allergenic, structurally stable, and interactive with the host receptors in a favorable way. Structural modeling validated that all the vaccines designed were able to form stable and dependable 3D structures, which was corroborated by the Ramachandran and ProSA-web validations. Molecular dynamics simulations indicated that the combined vaccine was significantly more stable in terms of structure as compared to the individual glycoprotein or RdRp-based constructs, thereby exhibiting lower fluctuations and a more compact structure. The advantages of this research is the investigation into the interactions between the vaccine and human TLR-3, which is an important receptor in antiviral immunity. The docking and molecular dynamics results revealed a strong and stable binding of all vaccine constructs to TLR-3, while the glycoprotein-based vaccine exhibited the highest affinity. Immune simulation has further backed the immunogenic potential of the vaccines that were designed. The rapid clearing of the antigen, the strong primary IgM responses, and the robust secondary IgG responses after the booster doses are all signs of good immune priming and memory formation. The increased levels of IFN-γ and IL-2 imply the strong activation of T-helper and cytotoxic T-cell responses, whereas the production of T-regulatory cytokines such as IL-10 and TGF-β suggests that the immune activation is controlled, thereby minimizing the risk of inflammation being too much [34,35]. The combined vaccine, significantly, showed a balanced cytokine profile and maintained high levels of antibodies, thus, its potential as a universally protective candidate was further strengthened. Codon optimization and in-silico cloning experiments provided positive results in terms of CAI values and GC content, which, in turn, suggested that the vaccine constructs would be suitable for E. coli expression system and could be expressed efficiently. This increases the translational feasibility of the new vaccines by making it possible to produce them not only in large quantities but also at a lower cost. The study presents encouraging outcomes, but its in-silico nature is a limitation, and laboratory tests are necessary to verify vaccine safety, immunogenicity, and protective efficacy. Future studies should include in vitro and in vivo testing, along with population coverage and strain diversity analysis.

In conclusion, despite the burden of OROV, no vaccines or antivirals exist, making vaccine development important for improved public health. Therefore, the computational approach were used to design the potent vaccine for the OROV. By predicting the CTL, HTL and B cell epitopes form the glycoprotein and RdRp protein, we constructed vaccines that demonstrated stable interaction with human TLR-3, as validated by molecular docking, dissociation constant analysis and molecular dynamics simulations. The calculated total binding free energies supported the strong affinity of the designed vaccines for human TLR-3. Additionally, the favorable GC content and high CAI values indicated efficient and stable expression of the vaccines in the pET28a(+) vector. Immune simulations showed that the antigen was eliminated quickly and strong humoral responses were developed after the booster dose, thus confirming the immunogenicity of the vaccines. All this evidence together strongly indicates these vaccine types as the future preventive measures against OROV. But still, more experiments are needed to be done to test in vitro and in vivo their safety and efficacy.

Acknowledgement

The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/03/29385). The authors would like to acknowledge ServiceScape (https://www.servicescape.com) for the English language editing.

Disclosure

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research work was funded by Prince Sattam bin Abdulaziz University through the project number (PSAU/2024/03/29385).

Supplementary data

Supplementary data to this article can be found online at https://smj.researchcommons.org/cgi/viewcontent.cgi?filename=0&article=8797&context=journal&type=additional&preview_mode=1.