Multi-Epitope mRNA Vaccine Targeting Dabie Bandavirus Glycoprotein: An Immunoinformatics-Based Study

Abstract

Dabie bandavirus (DBV), also called severe fever with thrombocytopenia syndrome virus (SFTSV), is a tick-borne virus that leads to a serious illness with high fever, low platelets, bleeding risks, and organ damage. Death rates can reach 5% to 30% in affected areas like China, South Korea, and Japan, where cases continue to rise. No approved vaccines or specific treatments are available, prompting the World Health Organization to mark it as a priority emerging infectious disease. This in silico study designs a multi-epitope mRNA vaccine candidate targeting the virus’s membrane glycoprotein. After reviewing 1042 virus sequences, 9 cytotoxic T-cell (cytotoxic T lymphocyte [CTL]), 6 helper T-cell (helper T lymphocyte [HTL]), and 5 B-cell epitopes were chosen for their high conservation, predicted antigenicity, safety (non-toxic, low allergenicity), and lack of similarity to human proteins, achieving 94.77% global population coverage. The construct included beta-defensin-3 as an adjuvant. Computational predictions indicated favorable features: structural stability, potential binding to immune receptors (Toll-like receptor 3 [TLR3] and Toll-like receptor 4 [TLR4]), consistent behavior in molecular dynamics simulations (100 ns), encouraging patterns in immune response modeling, and good codon optimization for possible expression. These results are hypothesis-generating and based solely on in silico tools and require experimental validation, including in vitro studies, immunogenicity tests, and animal challenge models to assess any real-world potential.

Keywords

immunoinformatics membrane glycoprotein MM/GBSA mRNA vaccine

Introduction

Numerous tick vectors are responsible for transmitting a wide range of pathogens to hosts, resulting in various tick-borne viral diseases.¹ Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) is an emerging tick-borne pathogen that causes viral hemorrhagic fever and is primarily transmitted through tick bites. The SFTSV is caused by the recently identified Dabie bandavirus (DBV).² According to the International Committee on Taxonomy of Viruses (ICTV), DBV belongs to the genus Bandavirus, order Bunyavirales, and family Phenuiviridae. The virus is also referred to as the SFTSV, Huaiyangshan virus (HYSV), or novel bunyavirus (NBV). As most members of the Phenuiviridae family are vector-borne, ticks are considered the principal agents responsible for DBV transmission.³

Among tick species, Haemaphysalis longicornis, also known as the Asian longhorned tick, is considered the primary vector of SFTSV transmission.³ Other species of ticks were also reported to carry the SFTSV RNA, such as Haemaphysalis flava, Hyalomma asiaticum, Dermacentor nuttalli, Ixodes nipponensis, and Amblyomma testudinarium, which suggests their connection to the circulation of the virus in endemic areas.^4-6 In addition to the transmission by the use of vectors, DBV can also be spread by direct contact with infected blood of humans or animals.⁶

The SFTSV infection triggers multiple pathogenic processes, including viral replication, immune dysregulation, and inflammatory damage.⁷ At the initial stage of the SFTSV infection, mononuclear phagocytic systems, such as monocytes, macrophages, and dendritic cells (DCs), became infected. Viral replication in these cells leads to depressed innate immunity and inhibition of antiviral defense mechanisms, especially interferon-mediated responses.⁸ Myeloid DCs have defective maturation and impaired antigen presentation and therefore are limited in the development of effective humoral immune responses. Although the exact role of natural killer (NK) cells in DBV infection is not well defined, differences in the number of NK cells have been linked to disease progression.⁹ Moreover, enhanced type I interferon signaling induces the expression of interferon-stimulated genes, contributing to dysregulated immune responses. The dysregulated immune response results in the excessive production of inflammatory cytokines that leads to clinical symptoms.²

The DBV is an enveloped spherical virus with a diameter of approximately 80 to 100 nm.¹⁰ Its negative-sense RNA genome consists of 3 segments: small (S), medium (M), and large (L).¹¹ The M segment encodes 2 surface glycoproteins, Gn and Gc, which are major antigenic determinants on the viral envelope.¹² Compared with non-structural proteins encoded by the S segment, the DBV envelope glycoproteins (Gn and Gc) are more exposed to neutralizing antibodies and provide better protective immunity in animal models; thus, preclinical studies have supported the preferential selection of the DBV envelope glycoproteins as vaccine targets.¹³ Upon viral entry, infection initially involves skin-resident cells, such as keratinocytes, mast cells, immature Langerhans cells, and epidermal cells. In the early stages of disease, infection of monocytes, macrophages, and DCs results in suppression of first-line immune responses, thereby facilitating viral dissemination.¹⁴

The initial outbreak of SFTSV occurred in rural central China between the end of March and mid-July 2009 and was linked to severe clinical cases and a high mortality rate of an estimated 30%.¹⁵ The virus then spread to several other provinces in China, having over 2500 reported cases and an average case-fatality rate of 7.3%.¹⁶ Later, the illness spread to other nations like Japan, South Korea, and Vietnam.¹ By the end of 2019, China, Korea, and Japan had reported more than 14 500 cases.¹⁷ Patients in the United States were also found to have a genetically related virus, the Heartland virus.¹⁶ The transmission of SFTSV is usually during the period between March and November, with high transmission rates in May to July. More recently, from April 2023 to August 2024, 10 cases of DBV infection were reported in Suzhou City, China, 3 of which resulted in death during hospitalization.¹⁸

Common clinical symptoms associated with an SFTSV infection include hemorrhagic fever, fatigue, vomiting, dizziness, myalgia, nausea, and diarrhea. Severe instances of DBV infection are characterized by thrombocytopenia, leukopenia, liver dysfunction, increased levels of hepatic enzymes, renal impairment, neurological complications, and gastrointestinal disorders.^3,19 During the acute phase, patients, particularly those older than 40 years of age, can suffer from hemorrhage, neurological deficits, respiratory failure, and, in some cases, death.^19,20 Due to its increasing incidence and persistently high mortality rate, the World Health Organization in 2018 classified the SFTSV as an emerging infectious disease.^21,22

Despite the increasing number of reported cases of SFTS, there is currently no approved vaccine or antiviral therapy against SFTSV infection.²³ Previous vaccine development attempts have faced serious challenges such as mortality rates, geographic distribution, nucleotide diversity, and the zoonotic nature of the virus.^24,25 Although recent studies have led to an increased understanding of the molecular structure, epidemiology, and pathogenesis of DBV, effective immunization strategies are limited.²⁶ Certain strains have been suggested as potential vaccine candidates, like HB29;^10,26 however, the experimental studies using mouse models, including C57BL/6 mice, have not been able to elicit protective immune responses against nonstructural proteins of the S segment.²⁷

Vaccine development against tick-borne viruses is faced with further difficulties as compared to directly transmitted viral pathogens. Tick-borne viruses have a vector and animal reservoir, which makes it difficult to identify the antigen.²⁸ Moreover, immunomodulatory components found in tick saliva are capable of suppressing host immune responses, which facilitates the transmission of the virus.²⁹ Antigenic diversity between circulating strains also further complicates the development of broadly protective vaccines, and research on the longer-term immune protection against tick-borne viruses is limited.

Given the lack of approved vaccines, the limited availability of high-containment experimental models, and the genetic diversity of circulating DBV strains, immunoinformatics offers a rapid, cost-effective, and hypothesis-driven platform for immunogen prioritization of vaccine candidates at the early stages.^2,30,31 In recent years, various approaches have been used for vaccine research, including live-attenuated, inactivated, and subunit vaccines. Nevertheless, only a few vaccines are available against tick-borne viral diseases, although their global distribution is increasing.³² Epitope-based vaccine approaches have attracted interest because of their potential to generate long-lasting immune responses. Peptide- and DNA-based vaccines have the advantage of speed and flexibility, but they are often less immunogenic and have the risk of genomic integration.^33,34 In comparison, mRNA vaccines have demonstrated higher efficacy, improved safety profiles, and reduced adverse effects.^35,36 The mRNA vaccination strategy allows for endogenous antigen expression by both major histocompatibility complex (MHC) class I and MHC class II pathways, resulting in activation of CD8+ cytotoxic T lymphocytes and CD4+ helper T cells, as well as the induction of neutralizing antibody responses that are often difficult to obtain using subunit vaccines. Importantly, mRNA vaccines can be developed quickly, without requiring viral culture or protein purification, which makes them especially appropriate for emerging threats posed by tick-borne viruses.^37,38 The combination of immunoinformatics and the use of a multi-epitope mRNA vaccine design enable the optimization of B-cell and T-cell epitopes into 1 immunogen, which has been used successfully for the development of vaccines against emerging pathogens, including the novel coronavirus (SARS-CoV-2).^39,40

Previous computational studies have been conducted for multi-epitope protein-based vaccine design against DBV, including the selection of epitopes, immune response prediction, and protein interaction analysis.⁴¹ However, these strategies were mostly based on recombinant protein expression systems. Viral glycoproteins are important immunogenic targets in enveloped viruses because of their involvement in host cell attachment and membrane fusion as well as the fact that they contain conserved regions that can trigger a neutralizing antibody and T-cell response. Glycoprotein-based epitopes have been used previously in the fight against other emerging viruses with promising results.⁴²

In this context, mRNA-based multi-epitope vaccine strategies have a unique advantage because they stimulate both humoral and cellular immune responses by expressing antigens endogenously. To date, this strategy for DBV has not been explored. In the current study, a holistic immunoinformatics framework was used to identify and evaluate appropriate B-cell, helper T lymphocyte (HTL) and cytotoxic T lymphocyte (CTL) epitopes in terms of antigenicity, conservancy, non-toxicity, non-homology, allergenicity, and physicochemical characteristics. The secondary and tertiary structures of the designed vaccine construct were predicted, improved, and validated. Furthermore, molecular docking with Toll-like receptor 3 (TLR3) and Toll-like receptor 4 (TLR4), molecular mechanics with generalized Born surface area (MM/GBSA) analysis, molecular dynamics (MD) and immune simulations, computational cloning, and mRNA structure analyses were conducted to assess the predicted stability, immunogenic potential, and translational feasibility of a proposed mRNA vaccine candidate, generating hypotheses for future experimental validation.

Methodology

Online-based servers and tools were utilized for the development of the multi-epitope mRNA vaccine. The methodology for building an epitope-based mRNA vaccine is summarized in a diagram in Figure 1.

Figure 1.

A diagrammatic pathway illustrating the construction of an mRNA vaccine focusing on the diverse epitopes of Dabie bandavirus (DBV).

Obtaining protein sequence and physicochemical properties assessment

The glycoprotein of Banda virus is encoded by the M segment, which is responsible for host entry. We targeted the membrane glycoprotein polyprotein (Gn and Gc), comprising 1073 amino acids (Accession ID: YP_009666134), which was retrieved from the National Center for Biotechnology Information (NCBI) database for vaccine construction. The target protein’s antigenicity was analyzed via version 2.0 of the VaxiJen server.⁴³ VaxiJen 2.0, an online-based tool that estimates the protein’s or peptide’s antigenicity from various bacterial, viral, and fungal origins, where 0.4 was the cut-off value.⁴³ Another web server, ExPASy ProtParam, was employed to analyze a protein’s physicochemical characteristics.⁴⁴

Mapping of T-cell epitopes

T cells recognize antigens via antigen-presenting cells (APCs) through interactions with MHC molecules.⁴⁵ The CTLs detect endogenous peptides presented by MHC class I and eliminate infected cells, while HTLs recognize exogenous peptides on MHC class II and orchestrate adaptive immunity by activating immune cells and secreting cytokines such as interleukin (IL)-4, IL-10, and interferon-gamma (IFN-γ).^45,46 Consequently, HTL epitopes play a critical role in vaccine and immunotherapy development. The Immune Epitope Database (IEDB), which compiles experimental data on antibodies and epitopes, was used for T-cell epitope prediction.⁴⁷ The NetMHCpan 4.1 EL method within IEDB predicted epitopes for both MHC class I and II molecules. Peptides of 9-mer and 15-mer lengths were selected for CTL and HTL prediction, respectively, as these lengths optimize MHC binding and recognition.⁴⁸ A panel of 27 MHC class I and 10 HLA class II alleles (Supplementary File 1), representative of global populations, was used for the analysis.

Mapping of B-cell epitopes

B-cells are involved in humoral immunity and are stimulated by the B-cell receptors (BCRs) to recognize antigens. When B-cell epitopes bind to cell membrane BCRs, lymphocytes produce neutralizing antibodies that assist the immune system in fighting off viral infections.⁴⁹ B-cells with the class II molecules deliver antigenic peptides to CD4⁺ follicular helper T (T_FH) cells, thereby producing memory B-cells and plasma cells.^49,50 This study utilized the Bepipred Linear Epitope Prediction 2.0 tool available on the IEDB website to identify linear B-cell epitopes.⁵¹ The 7 to 35 amino acid residues comprising the B-cell epitopes were considered for further analysis. Conformational B-cell epitopes were analyzed separately based on the tertiary structure of the final vaccine construct.

Evaluating epitopes according to their physical characteristics

A systematic methodology was employed to identify the most suitable vaccine candidates, focusing on the expected CTL, HTL, and B-cell epitopes. This procedure was carried out following established criteria that encompass immunological relevance, safety considerations, and physicochemical characteristics. The process of identifying T-cell epitopes commenced with an evaluation of their percentile rank, selecting CTL (MHC-I) ⩽ 0.5 and HTL (MHC-II) ⩽ 2.0 as strong MHC binders.⁵² All selected epitopes, including both T-cell and B-cell types, underwent a thorough evaluation of their antigenic characteristics using VaxiJen v2.0 (with a threshold of ⩾0.4). Furthermore, the evaluation of their allergenic potential was conducted using AllerTOP v2.1, while toxicity assessments were performed through ToxinPred. Peptides recognized as non-immunogenic, exhibiting less allergenic potential and non-toxic, were subsequently omitted from further examination.^43,53,54 The epitopes that met these criteria underwent additional examination concerning their physicochemical characteristics, such as GRAVY, aliphatic index, instability index, and expected half-life. This analysis was conducted using ExPASy ProtParam to evaluate their stability and appropriateness for vaccine development.⁴⁴ In conclusion, the evaluation of HTL epitopes was conducted to determine their ability to stimulate IFN-γ, IL-4, and IL-10 through the utilization of IFNepitope, IL4pred, and IL-10pred servers, respectively. This assessment was crucial in confirming their potential to provoke significant adaptive immune responses.^55-57 Only those epitopes that fulfilled all established criteria were progressed for subsequent vaccine development.

Epitope conservancy and human proteome homology analysis

Furthermore, epitope conservation percentages were explored utilizing the web-based conservation analysis server of IEDB.⁴⁷ In this study, the percentage of conservancy was evaluated against 1042 glycoprotein sequences of DBV retrieved from the NCBI database, which contains a minimum identity threshold of 100%.⁵⁸ These sequences represent all publicly available DBV glycoprotein variants reported up to 2025, enabling a comprehensive evaluation of epitope conservation across circulating and emerging strains and addressing the potential for strain-specific immune escape.

The BLAST protein (BLASTp) tool was used to assess human proteome homology by comparing each epitope against the human proteome (taxid: 9606). The E-value threshold was set at 0.05 using default parameters.⁵⁹ Epitopes with E-values greater than 0.05 were considered non-homologous to human proteins and were therefore preferred due to their reduced risk of autoimmunity and enhanced immunogenic potential.⁶⁰

Population coverage analysis

The IEDB population coverage tool was employed to assess the distribution of anticipated HTL and CTL epitopic alleles among various populations.⁶¹ Due to the high polymorphism of HLA alleles, population coverage was evaluated in regions where DBV is endemic, particularly in East Asian nations such as China, South Korea, and Japan, identified as primary risk areas for infection.⁶² Population coverage was also analyzed in non-endemic regions to investigate the wider applicability of the chosen epitopes across varied worldwide populations.⁶³ This integrated strategy assures that the recommended vaccine candidates are appropriate for both groups at high epidemiological risk and for worldwide use.

Vaccine development and biophysical property assessment

After screening for epitopes, specific linkers (EAAAK, AAY, GPGPG, and KK) and an adjuvant were connected to merge the epitopes.⁶⁴ Adjuvants enhance immunogenicity and activate TLRs, thereby improving the vaccine’s immune response.⁶⁵ Adjuvant beta-defensin-3 was inserted at the N-terminal end of the constructed sequence.⁶⁴ The EAAAK linker added the adjuvant to CTL epitopes to promote structural rigidity and immune response. In addition, to ensure flexibility and preserve immunogenic activity, 3 other linkers were applied to assemble CTL, HTL, and B-cell epitopes.⁶⁵ Post-construction evaluations, including antigenicity and allergenicity for the new vaccine sequence, were executed to ascertain the physicochemical stability by using online tools. Afterwards, the ProtParam server examined the instability index, in vivo half-life, molecular weight, instability index, GRAVY, and aliphatic index for high accuracy of the new vaccine.⁴⁴

Vaccine secondary structure prediction

The SOPMA and PSIPRED web servers, both well-established and highly accurate computational tools, were employed to predict the secondary structure of the designed vaccine construct.^66,67 These methods estimate the relative proportions of α-helices, β-strands, and random coil regions within the amino acid sequence, providing insight into the structural organization of the protein.

Vaccine tertiary structure prediction, refinement, and validation

The algorithm-based Iterative Threading ASSEmbly Refinement (I-TASSER) tool used the amino acid sequences to build the 3-dimensional (3D) structure of the developed vaccine.⁶⁸ Then, it used C-scores (confidence scores) to determine which model was more accurate (a higher C-score means higher quality and accuracy). The alignment algorithms identify the template structures from the PDB (Protein Data Bank), and fragment assembly simulations generated some structural models.⁶⁹ Subsequently, the designed vaccine models were refined through the refinement tool GalaxyRefine server of GalaxyWEB,⁷⁰ which was validated through CASP10.⁷¹ The side chains are primarily reassembled and repacked in this method, and subsequently, relaxation was applied to the overall structure via a molecular dynamic simulation.⁷¹ The PROCHECK suite was used to evaluate the structural quality after each refinement by the visualization of the Ramachandran Plot.⁷² The percentages of phi (φ) degree and psi (ψ) degree of amino acids represent the allowed and disallowed areas located at the Ramachandran plot that calculate the radii generated by van der Waals for side chains.^70,71 If the number of residues is at a higher level, it would indicate the better structural stability of the most favorable position. In addition, the overall protein structure quality was calculated by the Z-score of the ProSA-web server. It reflects the model attributes by determining the total energy fluctuation level generated by the energy distribution of random conformations. When the Z-score falls within the range typical of native proteins, the model would be considered reliable.⁷³

Conformational B-cell prediction

The ElliPro server identified the discontinuous or conformational B-cell epitopes. It applies different algorithms to predict and visualize antibody epitopes from the antibody-protein complexes of 3D structures.⁷⁴ Default parameters were used, including a maximum distance of 6 Å and a minimum score of 0.5.

Disulfide engineering of the vaccine

Disulfide engineering changes some protein structural residues to cysteine, which makes it easier for new disulfide bonds to form. Lowering the entropy of the unfolded state makes proteins more stable since synthetic disulfide linkages are covalent.⁷⁵ DbD2, an online platform, estimated the cysteine residue pairs in a protein’s structure from the tertiary structure of the developed vaccine.⁷⁶ In his study, the Cα-Cβ-Sγ angle was default, and it was maintained at 114.6° ± 10, while the χ³ angle was modified to −87° or +97°. Residues were converted to cysteine residues to form disulfide bridges when the level of energies was 2.5 kcal/mol.⁷⁶

Molecular docking

Docking studies estimate the stability, binding affinity, and binding orientation of a ligand relative to a receptor when forming a stable complex.⁷⁷ In this context, TLRs are essential human immune receptors that identify pathogens and trigger immune responses. The ClusPro 2.0 online tool (https://cluspro.bu.edu) was utilized to get information on the binding molecules between vaccine-TLR complexes, where the PDB IDs of TLR3 and TLR4 were 2A0Z and 4G8A in the PDB (https://www.rcsb.org/). Discovery Studio software was used to preprocess the TLRs’ 3D structures, and the PDBsum tool evaluated the 2-dimensional interactions that occur by the combined vaccine-TLR complex molecules.⁷⁸ The 2 most appropriate docked complexes, one associated with TLR3 and the other with TLR4, were selected for further analysis due to their effective docking and low binding energy.

Post-docking molecular mechanics with generalized Born surface area–based free energy calculations

Following molecular docking, MM-GBSA binding free energy calculations were performed using the HawkDock web server to evaluate the stability of the docked protein-peptide complexes.⁷⁹ The binding free energy (ΔG) was estimated by integrating key molecular mechanics energy components, including van der Waals interactions (ΔVDW), electrostatic energy (ΔELE), generalized Born solvation energy (ΔGBE), surface area energy (ΔSA), and the total binding free energy (ΔTOTAL).⁸⁰

Molecular dynamics simulation of the vaccine-Toll-like receptor complexes

The MD simulation experiments were executed for exploring the configurations of both vaccine-TLR complexes’ stability and the tiny interactions between the molecules.⁸¹ In this study, the YASARA dynamics suite was navigated to calculate conformational change and to predict the interactions.⁸² All MD simulations employed AMBER 14 software to conduct simulations at a fixed temperature (298 K) under the NVT ensemble.⁸³ A 0.9% NaCl solution, supplemented with water molecules (0.998 g/cm³), was added to neutralize the system. A cuboidal simulation box with dimensions 20 Å larger than the complex was applied, and periodic boundary conditions were considered to run the simulation. Long-range electrostatic interaction was calculated by the method named Particle-mesh Ewald (PME), where 8.0 Å was set as the cut-off radius.⁸⁴ The total simulation time for each case was fixed to 100 ns with trajectories recorded at 100 ps intervals.

Immune simulation

Machine learning and PSSM (position-specific scoring matrices) were used in the C-ImmSim server that calculates the immune response profile of the vaccine.⁸⁵ This online tool provides the mechanism of specific immune epitopes’ functions.⁸⁶ During the process, 3 injections were scheduled at 15-day intervals, and 1-45-90 were the time steps, when each step represents 8 hours. Moreover, 1050 simulation steps were set up over a 1-year period to analyze the immunologic responses of the administered vaccine. The C-ImmSim models immunological responses with an agent-based framework that incorporates immunoinformatics and epitope prediction methodologies.⁸⁵ Similar to previous agent-based immunological models, it employs simplified representations of intricate biological processes and omits numerous mechanistic details; thus, its outputs indicate prediction trends rather than direct biological responses and necessitate experimental validation.⁸⁷

Constructed vaccine sequence cloning and codon optimization

Computer-based cloning validates optimized vaccine-protein expression and clones it into an appropriate expression vector. The reverse translation process was used to create DNA for cloning using the Java Codon Adaptation Tool (JCaT).⁸⁸ The Codon Adaptation Index (CAI) indicates how closely an optimized sequence aligns with host-specific codon preferences, with values ranging from 0.8 to 1 expressing enhanced translational efficiency. In addition, GC content, representing the ratio of guanine and cytosine nucleotides, was evaluated to ensure a balanced nucleotide composition that promotes mRNA stability and efficient translation within the optimal range of 30% to 70%.⁸⁹ SnapGene, a free online tool, is utilized to generate the cloning of the optimized gene sequence into the pET-28a (+) expression vector. The Eco53KI and EcoRV restriction sites of the vector were added to ensure correct orientation and reading frame. The length and restriction map of the recombinant plasmid construct were assessed through in silico analysis.

Structural stability assessment of vaccine-encoding mRNA

The newly developed DNA sequence was modified with the corresponding RNA sequence through the DNA-RNA-protein conversion process utilizing the Biomodel server. Subsequently, the RNA sequence was submitted to another web server, RNAfold, that thermodynamically generates the secondary structure of mRNA with minimum free energy (MFE; ΔG, kcal/mol).^90,91

Results

Retrieval and immunological properties exploration of the target protein sequence

The membrane glycoprotein (Accession ID: YP_009666134) of DBV was retrieved from the NCBI database and consists of 1073 amino acids. The VaxiJen v2.0 predicted antigenic score was 0.4807, higher than the threshold value (0.4), indicating an antigenic nature. Allergenicity analysis using AllerTOP v2.1 classified the protein as non-allergenic. In addition, the ProtParam server provides the profile of the protein that contains thermostable, slightly alkaline, and hydrophilic properties, suggesting potential suitability for immunological applications (Table 1). The thermostability score and alkalinity score indicate the structural robustness, while hydrophilic character indicates the solubility in aqueous solvents and epitope exposure.

Table 1.

Physicochemical properties of the selected Dabie bandavirus glycoprotein employed in vaccine development.

Accession ID	YP_009666134
Number of amino acids	1073
Molecular weight (Da)	116 650.18
Instability index	40.17
Aliphatic index	79.19
Theoretical isoelectric point (pI)	8.15
GRAVY	−0.061
Half-life (Escherichia coli)	>10 hours

Prediction and selection of T-cell and B-cell epitopes

Epitope identification

The DBV glycoprotein was examined for immunogenic regions, yielding 28 756 CTL (9-mer), 10 591 HTL (15-mer), and 34 linear B-cell epitopes. T-cell epitopes were selected based on a CTL (MHC-I) percentile rank of ⩽0.5 and an HTL (MHC-II) percentile rank of ⩽ 2.0 to identify potent MHC binders, whereas B-cell epitopes ranging from 7 to 35 amino acids were chosen for their relevance to antibody binding.

Screening and physicochemical evaluation

All candidates that are antigenic (⩾0.4), safe, less allergenic, and non-homologous to host proteins (Blast E-value > 0.05) were evaluated, and their physicochemical properties were assessed using ProtParam. The HTL epitopes were also evaluated for their capacity to elicit IFN-γ, IL-4, and IL-10 responses.

Final epitope selection

Nine CTL, 6 HTL, and 5 B-cell epitopes were finalized based on these criteria (Tables 2 and 3). Mechanistically, CTL epitopes may induce cytotoxic responses, HTL epitopes facilitate HTL activation, and B-cell epitopes promote neutralizing antibody formation, thus engendering comprehensive cellular and humoral defense.

Table 2.

Record of 9 cytotoxic T-lymphocyte (CTL) and 6 helper T lymphocyte (HTL) epitopes used in the vaccine sequence.

Types	Allele	Epitope	Antigenicity	Toxicity	Conservancy
CTL Epitopes	HLA-A*24:02,	MYSPVILTI	0.73	No	80.71%
	HLA-A*23:01	MYSPVILTI	0.73	No	80.71%
	HLA-B*08:01	KIKVKRINL	0.84	No	88.77%
	HLA-A*68:01,	SSYFVPDAR	0.84	No	96.07%
	HLA-A*31:01	SSYFVPDAR	0.84	No	96.07%
	HLA-B*58:01,	NAAPSCIFW	0.92	No	96.45%
	HLA-B*53:01,
	HLA-B*57:01
	HLA-A*26:01,	TSVEAVANY	0.49	No	83.30%
	HLA-B*35:01	TSVEAVANY	0.49	No	83.30%
	HLA-A*11:01,	SSKDFVCYK	0.84	No	86.28%
	HLA-A*03:01,
	HLA-A*31:01,
	HLA-A*68:01,
	HLA-A*30:01
	HLA-A*02:03,	KLMDRGRQV	1.37	No	84.74%
	HLA-A*02:06,
	HLA-A*02:01
	HLA-A*30:02,	VFKGVSVTY	1.14	No	81.86%
	HLA-B*15:01	VFKGVSVTY	1.14	No	81.86%
	HLA-A*24:02,	NYSAIPTTI	0.82	No	84.26%
	HLA-A*23:01	NYSAIPTTI	0.82	No	84.26%
HTL Epitopes	HLA-DRB1*15:01,	HSQFQGYVGQRGGRS	0.95	No	83.49%
	HLA-DRB1*01:03	HSQFQGYVGQRGGRS	0.95	No	83.49%
	HLA-DRB1*15:01	PGEVVVSYGGMRVRP	1	No	79.65%
	HLA-DRB1*01:03	GSLVGKTAIHVKGAL	0.85	No	75.05%
	HLA-DRB1*15:01	VEAVANYSAIPTTIG	0.84	No	83.40%
	HLA-DRB4*01:01	QGKISGSPLDITAIR	1.07	No	83.21%
	HLA-DRB1*01:01	SGCYSCMTGAKVSIK	0.96	No	77.45%

Table 3.

Shortlisted 5 linear B-epitopes used in the vaccine sequence.

Types	Epitope	Antigenicity	Toxicity	Conservancy
B-cell	TLTSELK	0.9	No	74.76%
	HSGSLVGKTAIH	0.73	No	75.82%
	ITSVEAVANYSAIPTTIGGLRFERSHDSQG	0.69	No	82.05%
	HSNKSADIPHLLGYSEKICQ	0.58	No	49.90%
	GVHSYSV	0.46	No	8.54%

Conservancy analysis

The epitope conservancy was measured utilizing the conservancy analysis tool existing at the IEDB server. The conservancy was against 1042 membrane glycoprotein polyprotein sequences of DBV, retrieved from the NCBI database (Supplementary File 2). Both Tables 2 and 3 summarize the conservancy score, where most epitopes are greater than 70%. This high score of conservancy suggests the evolutionary stability by reducing the viral mutations, and they are conserved.

Population coverage

Population coverage analysis was estimated based on MHC molecule epitope binding predictions across different populations. It indicates the immunological impact of the newly designed vaccine. Globally, the vaccine attained 92.51% MHC-I, 30.17% MHC-II, and 94.77% combination coverage, indicating a broad population-level immunogenetic suitability. This high coverage value could address the theoretical inclusion of both cytotoxic and HTL-mediated immune pathways at the population level, highlighting the worldwide relevance and inclusive immunogenetic applicability of the predicted epitopes. Importantly, these may be relevant for SFTSV viral infections, where cellular immunity is considered important for controlling viral replication and potentially reducing disease severity. Countries including Peru (MHC-I: 99.98%, MHC-II: 1.12%, Combined: 99.98%), Papua New Guinea (97.63%, 22.64%, 98.17%), Germany (95.97%, 43.28%, 97.72%), South Korea (95.63%, 26.44%, 99.37%), Japan (96.45%, 19.83%, 97.16%), Poland (95.54%, 36.79%, 97.18%), and the United States (94.55%, 36.77%, 96.55%) had very high combined coverage, suggesting a higher likelihood of epitope presentation across these populations. European countries like England (94.43%, 44.49%, 96.91%) and France (92.96%, 35.84%, 95.48%) also displayed strong scores. On the contrary, several populations showed reduced coverage, including India (80.68%, 25.10%, 85.52%), Central Africa (73.12%, 4.52%, 74.33%), Vietnam (86.18%, 7.54%, 87.22%), and Zimbabwe (85.26%, 4.35%, 85.91%). Notably, high combined coverage was also observed in endemic regions for SFTSV, including China, Japan, and South Korea (Figure 2 and Supplementary Table 4).

Figure 2.

Global population coverage of MHC molecules across representative countries.

Biophysical property assessment after the vaccine construction

The 6 most favored HTL, 7 CTL, and 5 linear B-cell epitopes were connected utilizing AAY, GPGPG, and KK linkers, with beta-defensin-3 (GIINTLQKYYCRVRGGRCAVLSCLPKEEQIGKCSTRGRKCCRRKK) affixed at the N terminal by an EAAAK linker. Beta-defensin-3 acts as an adjuvant, enhancing innate immune function without causing damage.

The constructed vaccine comprises 363 amino acids (Figure 3), demonstrating an antigenicity score of 0.6294, an aliphatic index of 72.87, an instability index of 27.98, and a negative GRAVY score of −0.247, indicating thermodynamic stability, hydrophilic characteristics, and potential for considerable immunogenicity. The lack of allergens suggests a reduced probability of hypersensitivity. The anticipated half-life in Escherichia coli (exceeding 10 hours) facilitates viable expression for experimental verification. The physicochemical qualities collectively indicate that the vaccine design is structurally stable, likely to maintain immunogenic conformations, and appropriate for in vivo applications.

Figure 3.

Schematic representation of the constructed multi-epitope vaccine, incorporating the beta-defensin-3 adjuvant and HTL, CTL, and B-cell epitopes linked by AAY, GPGPG, and KK linkers.

Secondary structure prediction

The SOPMA and PSIPRED tools provide the secondary configuration for the constructed vaccine, and it was indicated that among the 363 residues, 182 (50.14%) formed random coils, 92 (25.34%) formed beta-sheets, 60 (16.53%) formed alpha-helices, and 29 (7.99%) formed turns. The high density of coils indicates that the structure was flexible (Figure 4). It suggests the probability of structural flexibility and structural integrity, boosting the epitope attainability and immune recognition.

Figure 4.

Predicted secondary structure of the constructed multi-epitope vaccine, illustrating the allocation of alpha-helices, beta-sheets, turns, and random coils as assessed by PSIPRED and SOPMA.

Prediction of the 3-dimensional structure, refinement, and validation of the vaccine model

The best 5 3D models were predicted by the I-TASSER server, and Model I was chosen for further analysis with a C-score (confidence score) of −2.09 and a Z-score of −2.38. From the Ramachandran plot, the number of residues was 66.1%, 22.6%, 7.9%, and 3.4% for the most favored regions, additional allowed regions, generously allowed regions, and disallowed regions, respectively (Supplementary File 3). Model 1 underwent 3 iterations of improvement using the GalaxyWEB server to enhance structural reliability. After refining to improve structural quality, Model 1 exhibited a Z-score of −3.05, with the number of residues in the most favored regions, additional allowed regions, generously permitted zones, and disallowed sections being 86.6%, 8.6%, 1.7%, and 3.1%, respectively (Figure 5). The refinement process could increase the stereochemical accuracy, stability of the structure, and suitability for future structural and interaction analysis.

Figure 5.

Refined 3D structure of the designed multi-epitope vaccine with structural validation. (A) Refined 3D model of the vaccine. (B) Ramachandran plot illustrating residue distribution in most favored (red), additional allowed (yellow), generously allowed (light yellow), and disallowed (white) regions. (C) Z-score plot indicating the overall structural quality of the refined vaccine model.

Assessment of the discontinuous B-epitope

Seven conformational B-cell epitopes were identified through the utilization of the ElliPro server, exhibiting residue lengths that vary from 4 to 99. Epitope 1 demonstrated the highest prediction score of 0.745, indicating significant surface accessibility and a strong potential for antibody recognition, whereas Epitope 7 recorded the lowest score of 0.552. The remaining epitopes exhibited scores that fall within an acceptable range, suggesting their capability to provoke humoral immune responses against DBV (Figure 6 and Supplementary Table 5).

Figure 6.

A structural diagram of the 7 expected conformational B-cell epitopes that were predicted by ElliPro (Epitopes 1-7). The yellow areas show the predicted epitopes, while the gray areas show the rest of the vaccine that has been created.

Protein disulfide bond prediction

Disulfide bond engineering was accomplished using the DbD2 that minimizes conformational entropy to amplify the structural stability of the 3D vaccine. In total, 34 amino acid residue pairs were found during the analysis (Supplementary Table 6), among which 6 pairs—145ALA-151VAL, 47ALA-155ILE, 172GLY-175SER, 225ALA-239PRO, 259PRO-263CYS, and 334SER-337ALA—were selected to shape the disulfide bond (Figure 7). The residues had good angle and energy values (all less than 2.5 kcal/mol), which made them suitable for forming disulfide bonds in the mutated version. It may verify the presence of stabilizing cysteine pairings, bridging the intramolecular disulfide distances. The predicted disulfide bonds could amplify the structural rigidity of the epitopes, folding efficiency, and resistance against thermal and enzymatic degradation.

Figure 7.

Structural representation of disulfide bond modifications in the constructed vaccine. (A) Initial 3D model of the constructed vaccine, devoid of any disulfide bonds. (B) Modified model representing 6 selected residue pairs that form disulfide bonds, hence enhancing structural stability, folding efficacy, and resistance to heat and enzymatic degradation.

Molecular docking

Molecular docking of the developed vaccine with TLR3 and TLR4 was conducted to anticipate probable receptor interaction. The optimal docking model for TLR3 was identified in cluster 4, demonstrating a central energy score of −1077.3 kcal/mol. Interface analysis with PDBsum indicated that 44 residues of the vaccine interacted with 49 residues of TLR3, including interface regions of 2356 Å² and 2212 Å², respectively. The interactions were stabilized by 9 salt bridges, 33 hydrogen bonds, and 260 non-bonded contacts (Supplementary Table 7).

In a similar manner, docking with TLR4 revealed the best model from cluster 0, with a center energy score of −1181.3 kcal/mol and a lowest energy score of −1302.7 kcal/mol. In this interaction, 41 vaccine residues and 54 TLR4 residues contributed to interface areas of 2536 Å² and 2241 Å², respectively, stabilized by 11 salt bridges, 37 hydrogen bonds, and 311 non-bonded contacts (Supplementary Table 8).

The expected interactions suggest potential binding and engagement of TLR3 and TLR4, suggesting potential recognition by innate immune receptors. The discovered non-covalent interactions, including hydrogen bonds and salt bridges, may facilitate receptor identification and activate downstream signaling pathways, thereby enhancing both innate and adaptive immune responses to DBV (Figure 8).

Figure 8.

Visualization of the vaccine-receptor docked complexes having TLR3 (A) and TLR4 (B) receptors and also highlighting hydrogen bonds, disulfide bonds, salt bridges, and non-bonded interactions.

Post-docking molecular mechanics with generalized Born surface area free energy calculations

To evaluate the binding energetics of the vaccine-receptor complexes, MM/GBSA calculations were performed for both TLR3 and TLR4 interactions. For the vaccine-TLR3 complex, the total binding energy, van der Waals (VDW), electrostatic (ELE), generalized Born (GB), and surface area (SA) contributions were estimated as −192.33, −249.75, −3523.45, 3615.53, and −34.66 kcal/mol, respectively (Figure 9 and Supplementary Table 9). The vaccine-TLR4 complex demonstrated values of −244.61, −246.81, −7331.72, 7368.39, and −34.48 kcal/mol (Supplementary Table 10).

Figure 9.

MM/GBSA binding free energy analysis of the vaccine-receptor complexes. The contributions of van der Waals (VDW), electrostatic (ELE), generalized Born (GB), surface area (SA), and total binding energy were computed for TLR3-vaccine (A) and TLR4-vaccine (B) complexes using the HawkDock server.

The results demonstrate advantageous thermodynamic interactions and stable complex formation, corroborating the docking predictions. The calculated energies suggest that the vaccine is likely to bind well with TLR3 and TLR4, potentially activating innate immune signaling pathways in response to DBV.

Molecular dynamics simulation

Molecular dynamics simulations were performed for 100 ns to assess the structural stability and interaction dynamics of the multi-epitope vaccine with immune receptors TLR3 and TLR4 (Supplementary Table 11). The TLR3-vaccine complex exhibited a mean root mean square deviation (RMSD) of 4.253 ± 0.60 Å, with a range of 0.527 Å to 5.317 Å, whereas the TLR4-vaccine complex had a mean RMSD of 4.775 ± 0.55 Å, ranging from 0.540 Å to 5.522 Å (Figure 10A and A1). The consistent RMSD profiles indicate that both complexes maintained their conformational integrity with minimal variations, thereby confirming the reliability of the docking predictions.

Figure 10.

Molecular dynamics simulation-based assessment of structural stability and flexibility in docked complexes: (A-D) represent the vaccine-TLR3 complex showing (A) Root Mean Square Deviation (RMSD), (B) Radius of Gyration (Rg), (C) Solute-Solvent Hydrogen Bonds, and (D) Root Mean Square Fluctuation (RMSF); and (A1-D1) represent the vaccine-TLR4 complex illustrating (A1) RMSD, (B1) Rg, (C1) Solute-Solvent Hydrogen Bonds, and (D1) RMSF.

The radius of gyration (Rg) for the TLR3-vaccine complex was 33.506 ± 0.27 Å (range = 32.389-34.112 Å), while for the TLR4-vaccine complex, it was 33.889 ± 0.31 Å (range = 32.580-34.755 Å) (Figure 10B and B1), indicating adequate folding, structural compactness, and epitope accessibility for immune recognition.

Hydration and interface stability were assessed through the number of solute-solvent hydrogen bonds. The TLR3-vaccine complex exhibited an average of 1839.97 ± 32.20 hydrogen bonds (min: 1502, max: 1912), whereas the TLR4-vaccine complex maintained 1747.52 ± 33.71 hydrogen bonds (min: 1333, max: 1811) (Figure 10C and C1). The enduring hydrogen bonding configurations enhance the structural stability of the complexes and their ability to preserve epitope shape.

Investigations into root-mean-square fluctuation (RMSF) revealed residue flexibility across the complexes. The vaccine exhibited more flexibility in relation to TLR4 (average RMSF = 3.66 ± 1.83 Å; min = 1.32 Å, max = 13.41 Å) compared to TLR3 (average RMSF = 3.09 ± 2.00 Å; min = 1.21 Å, max = 16.44 Å). The TLR residues demonstrated a degree of stability (TLR4 = 1.80 ± 0.68 Å; TLR3 = 1.48 ± 0.50 Å) (Figure 10D and D1), thereby confirming the structural integrity of the receptor-ligand interaction.

Simulated immunogenic studies of the selected vaccine

The immunological simulation study was conducted over a period of 350 days, with dosages administered on days 1, 15, and 30. After the initial dosage, a primary immunological response was observed, marked by rapid antigen clearance and a rise in IgM antibodies (Figure 11A), signifying early humoral activation. This reaction was associated with the activation of B cells (Figure 11B) and T helper cells (Figure 11C), along with a modest production of cytokines, such as IL-2 and IFN-γ (Figure 11D), indicating the initiation of adaptive immunological pathways. Following the administration of the second dosage, there was a notable enhancement in the immunological response, as indicated by a significant increase in IgG1 and IgG2 antibodies (Figure 11A), a heightened proliferation of B and T cells (Figure 11B and 11), and an elevation in cytotoxic T cell activity (Figure 11E). Simultaneously, there was an increase in cytokine levels (Figure 11D), indicating robust cellular immunity and the establishment of immunological memory. After the administration of the third dosage, there was a notable peak in antibody titers (Figure 11A), accompanied by the migration of B and T cells into resting and memory stages (Figure 11B and C), suggesting the establishment of long-term immune protection. Cytokine levels stabilized (Figure 11D), and DCs remained continuously active (Figure 11F), suggesting prolonged antigen presentation. Collectively, these results suggest that the multi-epitope vaccination may produce significant humoral and cellular immune responses with persistent immunological memory against DBV.

Figure 11.

Immune response simulation over 350 days following 3 vaccine doses. (A) Antigen levels and antibody titers showing initial IgM response and rising IgG1/IgG2 after booster doses; (B) B cell states showing activation and memory formation; (C) T helper cell dynamics with strong activation post-injection; (D) cytokine levels peaking after second and third doses; (E) cytotoxic T cell activation and reduced energy over time; and (F) dendritic cells remained active, ensuring sustained antigen presentation.

In silico codon adaptation and sequence cloning in the Escherichia coli

The JCat server optimized the codon for E coli (strain K12) of the final vaccine protein sequence, where the codon sequence length was optimized to 1089 base pairs, with 0.97 for the CAI score. The GC content was adjusted to 49.95%, which supports the transcriptional stability and lowers the risk of secondary structure formation. It implies that the designated host organism could have a high degree of expression feasibility for host-specific preferences. The SnapGene application introduced the optimized sequence into the pET-28a (+) vector for in silico cloning (Figure 12A). A Kozak sequence was incorporated at the N-terminal to boost potential future eukaryotic expression (Figure 12C). The insertion was performed between the restriction sites Eco53KI (position 188) and EcoRV (position 1281), generating a recombinant plasmid that contains 5077 base pairs (Figure 12B). The successful insertion suggests the appropriate orientation and compatibility, which may facilitate future recombinant expression studies of the constructed vaccine against the DBV.

Figure 12.

In silico codon optimization and cloning of the vaccine sequence into the pET-28a(+) vector. (A) Circular map of the native pET-28a(+) plasmid (5369 bp); (B) recombinant plasmid map showing the in silico insertion of the codon-optimized vaccine sequence between Eco53KI (188 bp) and EcoRV (1281 bp); and (C) linear representation of the codon-optimized vaccine nucleotide sequence.

Structural stability assessment of vaccine-encoding mRNA

The RNAfold analysis reveals that the codon-optimized mRNA vaccine construct exhibits a predicted secondary structure characterized by an MFE of −314.40 kcal/mol. This observation indicates a configuration that could be thermodynamically advantageous, potentially enhancing both structural stability and translation efficiency. The thermodynamic ensemble indicated a free energy of −334.13 kcal/mol, potentially illustrating the average stability among all conceivable secondary structures. The centroid structure exhibited a free energy of −222.87 kcal/mol, indicating that it may represent the most probable ensemble conformation, which implies potential structural stability and physiological relevance (Figure 13).

Figure 13.

RNAfold-based secondary structure analysis of the mRNA vaccine after codon optimization. (A and C) Centroid and minimum free energy (MFE) structures colored by base-pair probability; (B and D) centroid and MFE structures colored by positional entropy; and (E) overlay of free energy profiles (MFE, ensemble, centroid) along with nucleotide-wise entropy distribution.

Discussion

A dominant tick-borne pathogen, DBV binds to the surface of skin resident with immune cells and later infects these cells via receptor-mediated entrance. As viral replication progresses, DBV disrupts innate immune signaling, which results in immune dysregulation and severe clinical manifestations. Multiple disease outcomes have been linked to DBV infection, leading the World Health Organization to classify it as a priority emerging infectious disease with major public health implications for the world.^16,92 In the absence of licensed vaccines or targeted antiviral therapies, there is an urgent need for rational vaccine design approaches. This study aimed to assess the computational feasibility of developing a multi-epitope mRNA vaccine for DBV using an immunoinformatics method.

Recent advancements in multi-epitope mRNA vaccine development highlight the importance of computational immunology in the systematic design of vaccines for various diseases. A systematic methodology encompassing epitope prediction, conservancy analysis, population coverage assessment, physicochemical characterization, structural modeling, docking, MD simulations, and immune simulations has facilitated the creation of mRNA vaccines designed with multiple epitopes targeting bacterial (Pseudomonas aeruginosa, meningitis pathogens, Campylobacter jejuni, Clostridium perfringens) and parasitic (Theileria annulata, Eimeria tenella) pathogens.^93-98 This study presents an in silico mRNA vaccine candidate for DBV, an emerging infectious disease, following the recent advancements in multi-epitope mRNA vaccine development, utilizing a comprehensive immunoinformatics framework.

The membrane glycoprotein polyprotein was chosen as the vaccine target. It is based on its known role in attachment to the host cell, transcription, and replication, playing a direct role in DBV pathogenicity.^99,100 Consequently, the antigenicity and biophysical characteristics of the protein were examined, indicating that it was antigenic, less allergenic, stable, and hydrophilic. Glycoproteins have been widely considered immunodominant antigens because they are expressed on the surface and accessible to neutralizing antibodies, especially in enveloped and tick-borne viruses. Protein antigenicity, which is defined as the elicitation of an immune response, is an important parameter for determining vaccine appropriateness.¹⁰¹

Potential B-cell, CTL, and HTL epitopes were identified to stimulate the immune system, such as antibody-mediated and T-cell-mediated responses. They are crucial for effective antiviral immunity.^102,103 The epitopes conservancy analysis was done on 1042 DBV glycoprotein sequences, including recent circulating forms. This approach increases theoretical strain coverage and minimizes the possibility of an escape of epitopes, although this does not eliminate the risk of evolution. Recent literature emphasizes that large-scale conservancy analysis is increasingly essential for RNA viruses with ongoing evolution.¹⁰⁴ Population coverage analysis showed good coverage of the selected epitopes of MHC class I and combined (MHC-I + MHC-II) across all endemic and non-endemic regions, indicating widespread inclusion of well-represented HLA alleles and the global relevance of the proposed vaccine construct. Importantly, comparable levels of coverage between endemic and non-endemic populations indicate limited geographic bias for the choice of epitopes.⁶¹ In contrast, the steady lower MHC class II coverage noted across all regions is in line with the extensive polymorphism and the stringent peptide-binding properties of MHC-II molecules and does not adversely affect the good combination of population coverage.⁶¹

Linkers were incorporated to ensure structural integrity, epitope separation, and proper antigen processing. The EAAAK, GPGPG, AAY, and KK linkers have been adopted based on their known roles in stabilization of fusion proteins and enhancement of immunogenic organization.^105,106 The AAY linker has been shown to increase CTL and HTL epitope processing,^107,108 and EAAAK facilitates efficient fusion between adjuvants and epitopes, increasing expression and functional stability.^109,110 Although it has been reported that GPGPG linkers can be used to decrease functional immunogenicity in certain experimental settings,¹¹¹ the use of such a linker in this study was designed to represent a balance between flexibility and epitope accessibility. Beta-defensin-3 was adopted as an adjuvant because of its immune-stimulatory effect and its having been previously validated in experimental vaccine studies.^112-114 Physicochemical evaluation suggested that the designing construct is antigenic, stable, hydrophilic, and less allergenic, which are favorable characteristics for downstream expression and formulation.

Structural assessment provided the information on the structural plausibility of the vaccine construct rather than the confirmation of biological function. Secondary structure analysis suggested a predominance of random coil regions that could increase accessibility of epitopes and also α-helices and β-sheets that contribute to structural stability.^115,116 Tertiary structure modeled by I-TASSER followed by refinement resulted in acceptable stereochemical quality as assessed using the Ramachandran plot and ProSA Z-score analysis. Surface-exposed antigenic regions that might be accessible for antibody binding have been identified by epitope mapping with the Ellipro.¹¹⁷ Disulfide engineering also improved conformational stability by lowering structural entropy, which had previously been shown to increase protein robustness.⁷⁶

Molecular docking and MD studies were performed to identify the possible interactions between this vaccine construct and the innate immune receptors TLR3 and TLR4. Stable hydrogen bond and salt bridge interactions were observed, and negative MM/GBSA binding free energy values indicated computationally thermodynamically favorable associations. The MD simulations (100 ns) of the TLR3 and TLR4 vaccine complexes revealed stable RMSD and RMSF values, which suggest overall stability of the structures with minor local flexibility.^118,119 The Rg analysis verified insignificant structural expansion or unfolding.¹²⁰ Solvent-solute hydrogen bond analysis further supported interactions to be stable.¹²¹ Dual evaluation of TLR3 and TLR4 is a methodological extension over many of the recent immunoinformatics studies on vaccines, which generally evaluate a single innate receptor. This is particularly relevant in the case of mRNA vaccines, because RNA sensing by TLR3 and adjuvant-like activation by TLR4 may act in a synergistic way in the early immune priming and innate signaling. The TLR3 recognition of the RNA structures leads to activation of downstream type I interferon pathways,⁶¹ and TLR4 engagement by lipid nanoparticle components can contribute to enhanced nuclear factor kappa B (NF-κB) and IRF signaling.¹²²

Immune simulation analyses indicated the improved trends in both humoral and cellular immune responses after booster administration, including improved levels of antibody response, cytotoxic T-cells (CTLs), and cytokines such as IFN-γ, IL-4, and IL-10.^123,124 Codon optimization analysis showed good CAI and GC content values, indicating compatibility with E coli expression systems, while RNA secondary structure prediction analysis showed a value of thermodynamic stability suitable for efficient translation.¹²⁵ Cloning into the pET28a (+) vector was suggested to ease recombinant protein expression and purification for experimental validation.^126,127 After cellular uptake, the mRNA vaccine activates innate immunity through RNA sensing by pattern recognition receptors, including TLRs, which triggers type I interferon and pro-inflammatory signaling; this innate activation facilitates antigen presentation and drives adaptive immune responses involving cytotoxic T cells, helper T cells, and antibody production.¹²⁸

Previous DBV vaccine studies have been focused on using experimental mRNA platforms to express full-length or subunit glycoproteins, which showed high immunogenicity and protective efficacy in animal models.^13,129 While such approaches have demonstrated the feasibility of mRNA-based DBV vaccines, they largely use single- or dual-antigen expression and do not specifically address viral sequence diversity or population-level HLA variability. Parallel immunoinformatics studies suggested multi-epitope constructs based on multiple viral proteins, but these were limited due to smaller sequence datasets, single innate receptor interaction, or lack of analysis of conservation with recent variants.¹³⁰

In contrast, the present study introduces a conservation-driven, multi-epitope mRNA vaccine design based on the analysis of 1042 glycoprotein sequences, incorporating the recent DBV variants explicitly to mitigate the immune escape. Unlike previous in silico designs, the construct engages both TLR3 and TLR4 simultaneously and thereby provides a more comprehensive model of innate immune engagement that has been under-explored in previous DBV vaccine studies. Furthermore, the integrated population coverage analysis shows strong global applicability across endemic and non-endemic regions via MHC-I and combined MHC coverage, overcoming an important translational limitation of prior computational reports.

Despite promising results of the computational outcomes, this study has limitations that mainly rely on in silico approaches. Immunoinformatics predictions are not a certainty for effective antigen processing, MHC presentation, and immunodominant recognition in vivo. Risks of HLA restriction, epitope escape, conformational mismatch of B-cell epitopes, and inefficient protein folding are challenging problems, as indicated in the recent literature.^131,132

In conclusion, this study proposes a structured immunoinformatics framework for the theoretical possibility of a multi-epitope mRNA vaccine targeting DBV. Although the findings are consistent with hypothesis generation and rational design, rigorous experimental validation is essential before any conclusions about protective efficacy or translational applicability can be made.

Conclusion

A multiple mRNA vaccine of the DBV was computationally designed using an immunoinformatics approach targeting the membrane glycoprotein. The 363-amino-acid construct exhibited predicted antigenicity (0.6294) and theoretical global population coverage of 94.77%. Structural modeling suggested acceptable stability, with 86.6% of residues located in preferred Ramachandran regions and a Z-score of −3.05, supported by disulfide engineering. Docking, MM/GBSA, and MD simulations suggested potential interactions with TLR3 and TLR4 (ΔG: −192.33 and −244.61 kcal/mol), indicating possible immune recognition at the in silico levels. Codon optimization (CAI: 0.97) and mRNA secondary structure analysis (MFE: −314.40 kcal/mol) supported the theoretical expressibility and stability of the construct. This work demonstrates the computational viability of vaccine design and formulates hypotheses, but immunogenicity, safety, and efficacy must be validated in vitro and in vivo prior to any experimental or clinical use.

Supplemental Material

sj-docx-1-bbi-10.1177_11779322261438994 – Supplemental material for Multi-Epitope mRNA Vaccine Targeting Dabie Bandavirus Glycoprotein: An Immunoinformatics-Based Study

Supplemental material, sj-docx-1-bbi-10.1177_11779322261438994 for Multi-Epitope mRNA Vaccine Targeting Dabie Bandavirus Glycoprotein: An Immunoinformatics-Based Study by Sk Faisal Ahmed, Md Shah Paran, Md Masudur Rahman Munna, Enam Ahmed, Upom Majumder, Monisha Rahmatullah and Md Zulfekar Ali in Bioinformatics and Biology Insights

Supplemental Material

sj-docx-2-bbi-10.1177_11779322261438994 – Supplemental material for Multi-Epitope mRNA Vaccine Targeting Dabie Bandavirus Glycoprotein: An Immunoinformatics-Based Study

Supplemental material, sj-docx-2-bbi-10.1177_11779322261438994 for Multi-Epitope mRNA Vaccine Targeting Dabie Bandavirus Glycoprotein: An Immunoinformatics-Based Study by Sk Faisal Ahmed, Md Shah Paran, Md Masudur Rahman Munna, Enam Ahmed, Upom Majumder, Monisha Rahmatullah and Md Zulfekar Ali in Bioinformatics and Biology Insights

Footnotes

Acknowledgements

The authors thank the Dawn of Bioinformatics Ltd., Dhaka 1360, Bangladesh, for the valuable suggestion and support.

ORCID iD

Sk Faisal Ahmed

Ethical Considerations

The authors did not use any animal model in our research.

Author Contributions

Sk Faisal Ahmed: Supervision; Conceptualization; Software.

Md Shah Paran: Methodology; Formal analysis; Visualization; Writing—original draft; Conceptualization.

Md Masudur Rahman Munna: Conceptualization; Supervision; Writing—review & editing.

Enam Ahmed: Data curation; Formal analysis; Visualization; Writing—original draft.

Upom Majumder: Data curation; Formal analysis; Visualization; Writing—original draft.

Monisha Rahmatullah: Formal analysis; Data curation; Writing—original draft.

Md Zulfekar Ali: Methodology; Writing—original draft; Formal analysis.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

All data associated with this study are present in the article and its supplementary files.

AI Statement

During the preparation of the manuscript, the authors used artificial intelligence (AI) for checking grammatical errors, improving the sentence structures and flow of different paragraphs.

Supplemental Material

Supplemental material for this article is available online.

References

Mansfield

Jizhou

Phipps

Johnson

Emerging tick-borne viruses in the twenty-first century. Front Cell Infect Microbiol. 2017;7:298. doi:10.3389/fcimb.2017.00298

Lin

Dai

Qian

Recent research advances in the development of Dabie Banda virus vaccines. PLoS Negl Trop Dis. 2024;18:e0012411. doi:10.1371/JOURNAL.PNTD.0012411

Liang

Zhang

, et al. Fever with thrombocytopenia associated with a novel bunyavirus in China. N Engl J Med. 2011;364:1523-1555. doi:10.1056/NEJMoa1010095

Kang

Chae

, et al. Prevalence of severe fever with thrombocytopenia syndrome virus in ticks collected from national parks in Korea. Vector Borne Zoonotic Dis. 2019;19:284-289. doi:10.1089/VBZ.2018.2338

Yun

Lee

Ryou

, et al. Severe fever with thrombocytopenia syndrome virus in ticks collected from humans, South Korea, 2013. Emerg Infect Dis. 2014;20:1358-1361. doi:10.3201/EID2008.131857

Zhu

Yin

Moming

, et al. First case of laboratory-confirmed severe fever with thrombocytopenia syndrome disease revealed the risk of SFTSV infection in Xinjiang, China. Emerg Microbes Infect. 2019;8:1122-1125. doi:10.1080/22221751.2019.1645573

Kim

Lai

Cha

Jung

JU.

Current progress of Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) vaccine development. Viruses. 2024;16:1-20. doi:10.3390/v16010128

Yang

Huang

Jiang

Overview of the immunological mechanism underlying severe fever with thrombocytopenia syndrome (Review). Int J Mol Med. 2022;50:118. doi:10.3892/ijmm.2022.5174

Liu

Zhai

Dai

Sun

Advances in research on severe fever with thrombocytopenia syndrome virus. Front Microbiol. 2025;16:1-9. doi:10.3389/fmicb.2025.1622394

10.

Liu

Zhu

Zhang

Genetic variants of Dabie bandavirus: classification and biological/clinical implications. Virol J. 2023;20:1-12. doi:10.1186/S12985-023-02033-Y/FIGURES/3

11.

Kwak

Kim

Il Park

, et al. Development of a SFTSV DNA vaccine that confers complete protection against lethal infection in ferrets. Nat Commun. 2019;10:3836. doi:10.1038/s41467-019-11815-4

12.

Zhu

Gao

, et al. Structures of phlebovirus glycoprotein Gn and identification of a neutralizing antibody epitope. Proc Natl Acad Sci U S A. 2017;114:E7564-E7573. doi:10.1073/PNAS.1705176114/-/DCSUPPLEMENTAL

13.

Kim

Jeon

Park

, et al. mRNA vaccine encoding Gn provides protection against severe fever with thrombocytopenia syndrome virus in mice. NPJ Vaccines. 2023;8:167. doi:10.1038/s41541-023-00771-2

14.

Wang

Tan

Fang

Yue

The endless wars: severe fever with thrombocytopenia syndrome virus, host immune and genetic factors. Front Cell Infect Microbiol. 2022;12:808098. doi:10.3389/FCIMB.2022.808098/PDF

15.

D-x.

[Fever with thrombocytopenia associated with a novel bunyavirus in China]. Zhonghua Shi Yan He Lin Chuang Bing Du Xue Za Zhi. 2011;25:81-84. doi:10.1056/NEJMOA1010095/SUPPL_FILE/NEJMOA1010095_DISCLOSURES.PDF

16.

Liu

Huang

Wei

Zhu

XQ.

Severe fever with thrombocytopenia syndrome, an emerging tick-borne zoonosis. Lancet Infect Dis. 2014;14:763-772. doi:10.1016/S1473-3099(14)70718-2

17.

Chen

, et al. A new luciferase immunoprecipitation system assay provided serological evidence for missed diagnosis of severe fever with thrombocytopenia syndrome. Virol Sin. 2022;37:107-114. doi:10.1016/J.VIRS.2022.01.018

18.

Dong

Yuan

Xing

Zhang

Shen

Characterizations of cytokines and viral genomes in serum of patients with Dabie bandavirus infection. Front Virol. 2024;4:1529730. doi:10.3389/fviro.2024.1529730

19.

Zhang

Dai

, et al. Hemorrhagic fever caused by a novel bunyavirus in China: pathogenesis and correlates of fatal outcome. Clin Infect Dis. 2012;54:527-533. doi:10.1093/CID/CIR804

20.

Dong

Lin

Jiang

Liu

Clinical characteristics and risk factors of 267 patients having severe fever with thrombocytopenia syndrome-new epidemiological characteristics of fever with thrombocytopenia syndrome: epidemiological characteristics of SFTS. Medicine. 2022;101:e31947. doi:10.1097/MD.0000000000031947

21.

Yun

Park

Kim

, et al. Genetic and pathogenic diversity of severe fever with thrombocytopenia syndrome virus (SFTSV) in South Korea. JCI Insight. 2020;5:e129531. doi:10.1172/JCI.INSIGHT.129531

22.

Ding

Niu

, et al. Age is a critical risk factor for severe fever with thrombocytopenia syndrome. PLoS ONE. 2014;9:e111736. doi:10.1371/JOURNAL.PONE.0111736

23.

Wan

Mao

Liu

Deng

Hou

Wang

Bergamottin, a natural bioactive compound, inhibits Dabie bandavirus infection in vitro and in vivo. J Med Virol. 2025;97:e70495. doi:10.1002/JMV.70495

24.

Liu

Wang

, et al. Establishment of a genotyping criteria for bandavirus dabieense and confirmation of new genotypes. Sci Rep. 2025;15:1-12. doi:10.1038/S41598-025-94203-X

25.

Liu

, et al. The pathogen of SFTS: bandavirus dabieense phylogenetic analysis and development of an online tool for genotyping. Acta Trop. 2025;266:107625. doi:10.1016/J.ACTATROPICA.2025.107625

26.

Jia

Wang

, et al. Identification of a candidate standard strain of severe fever with thrombocytopenia syndrome virus for vaccine quality control in China using a cross-neutralization assay. Biologicals. 2017;46:92-98. doi:10.1016/J.BIOLOGICALS.2017.01.005

27.

Liu

Huang

Bai

, et al. Immunization with recombinant SFTSV/NSs protein does not promote virus clearance in SFTSV-infected C57BL/6J mice. Viral Immunol. 2015;28:113-122. doi:10.1089/VIM.2014.0100; WEBSITE

28.

Gong

Diao

, et al. A comprehensive review of tick-borne disease epidemiology, clinical manifestations, pathogenesis, and prevention. Anim Zoonoses. 2025;1:254-265. doi:10.1016/j.azn.2025.05.004

29.

Kleissl

Weninger

Winkler

Ruivo

Wijnveld

Strobl

Ticks’ tricks: immunomodulatory effects of ixodid tick saliva at the cutaneous tick-host interface. Front Immunol. 2025;16:1520665. doi:10.3389/fimmu.2025.1520665

30.

Nguyen

Kim

Immunoinformatics and computational approaches driven designing a novel vaccine candidate against Powassan virus. Sci Rep. 2024;14:1-15. doi:10.1038/s41598-024-56554-9

31.

Suleman

Asad

Arshad

, et al. Screening of immune epitope in the proteome of the Dabie bandavirus, SFTS, to design a protein-specific and proteome-wide vaccine for immune response instigation using an immunoinformatics approaches. Comput Biol Med. 2022;148:105893. doi:10.1016/J.COMPBIOMED.2022.105893

32.

Stachera

Szuba

Kim

, et al. What stage are we at in the development of vaccines against tick-borne diseases? Vaccines. 2025;13:990. doi:10.3390/vaccines13090990

33.

Soltanifar

Lashkarnevis

Ziaee

, et al. Investigating the effects and side effects of two antipsychotic drugs in the treatment of children and adolescents with Tourette syndrome: a semi-experimental research. Curr Drug Discov Technol. 2023;20:1-8. doi:10.2174/1570163820666230609095720

34.

Mohammadzadeh

Shivaee

Ohadi

Kalani

BS.

In silico insight into the dominant type II toxin–antitoxin systems and Clp proteases in listeria monocytogenes and designation of derived peptides as a novel approach to interfere with this system. Int J Pept Res Ther. 2020;26:613-623. doi:10.1007/S10989-019-09868-6/FIGURES/7

35.

Al Tbeishat

. Novel in silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. Sci Rep. 2022;12:4645. doi:10.1038/S41598-022-08506-4

36.

Asadollahi

Pakzad

Sadeghifard

, et al. Immunoinformatics insights into the internalin A and B proteins to design a multi-epitope subunit vaccine for L. monocytogenes. Int J Pept Res Ther. 2022;28:1-10. doi:10.1007/S10989-021-10359-W/FIGURES/7

37.

Naveed

Jabeen

Aziz

, et al. Computational design of a multi epitope vaccine against staphylococcus warneri for combatting recurrent UTIs and skin infections [published online ahead of print July 7, 2025]. Mol Biotechnol. doi:10.1007/s12033-025-01477-7

38.

Qian

Huang

, et al. Advancing mRNA vaccines: a comprehensive review of design, delivery, and efficacy in infectious diseases. Int J Biol Macromol. 2025;319:145501. doi:10.1016/J.IJBIOMAC.2025.145501

39.

Naveed

Saleem

Aziz

, et al. Exploring the therapeutic potential of Thymus vulgaris ethanol extract: a computational screening for antimicrobial compounds against COVID-19 induced mucormycosis. Sci Rep. 2025;15:1-17. doi:10.1038/s41598-025-00937-z

40.

Aziz

Naveed

Shabbir

, et al. Revolutionizing the virulent protein Internalin a in Listeria monocytogenes and designing multi epitope-based vaccine via immunoinformatic approaches. CYTA—J Food. 2024;22:1-11. doi:10.1080/19476337.2023.2296006

41.

Naveed

Asim

Aziz

, et al. Development of a broad-spectrum multiepitope vaccine against Dabie bandavirus through immunoinformatic approaches. Int Immunopharmacol. 2025;166:115492. doi:10.1016/J.INTIMP.2025.115492

42.

Naveed

Mahmood

Aziz

, et al. An mRNA-based reverse-vaccinology strategy to stimulate the immune response against Nipah virus in humans using fusion glycoproteins. Acta Biochim Pol. 2023;70:623-631. doi:10.18388/abp.2020_6721

43.

Doytchinova

Flower

DR.

VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:1-7. doi:10.1186/1471-2105-8-4/TABLES/2

44.

Gasteiger

Hoogland

Gattiker

, et al. Protein identification and analysis tools on the ExPASy server. In: Walker

, ed. The Proteomics Protocols Handbook. Humana Press; 2005:571-607. doi:10.1385/1-59259-890-0:571

45.

Nakayama

Antigen presentation by MHC-dressed cells. Front Immunol. 2014;5:120340. doi:10.3389/FIMMU.2014.00672/BIBTEX

46.

Luckheeram

Zhou

Verma

Xia

CD4 +T cells: differentiation and functions. Clin Dev Immunol. 2012;2012:925135. doi:10.1155/2012/925135

47.

Vita

Mahajan

Overton

, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2019;47:D339-D343. doi:10.1093/NAR/GKY1006

48.

Lin

Zhang

Tongchusak

Reinherz

Brusic

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics. 2008;9:1-10. doi:10.1186/1471-2105-9-s12-s22

49.

Chiu

Openshaw

PJ.

Antiviral B cell and T cell immunity in the lungs. Nat Immunol. 2014;16:18. doi:10.1038/NI.3056

50.

Crotty

Follicular Helper CD4 T cells (T FH). Annu Rev Immunol. 2011;29:621-663. doi:10.1146/ANNUREV-IMMUNOL-031210-101400

51.

Jespersen

Peters

Nielsen

Marcatili

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45:W24-W29. doi:10.1093/NAR/GKX346

52.

Reynisson

Alvarez

Nielsen

Paul

Peters

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 2020;48:W449-W454. doi:10.1093/nar/gkaa379

53.

Gupta

Kapoor

Chaudhary

, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS ONE. 2013;8:e73957. doi:10.1371/JOURNAL.PONE.0073957

54.

Dimitrov

Flower

Doytchinova

AllerTOP—a server for in silico prediction of allergens. BMC Bioinformatics. 2013;14:S4. doi:10.1186/1471-2105-14-S6-S4

55.

Dhanda

Vir

Raghava

GPS

. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct. 2013;8:30. doi:10.1186/1745-6150-8-30

56.

Dhanda

Gupta

Vir

Raghava

GP.

Prediction of IL4 inducing peptides. Clin Dev Immunol. 2013;2013:263952. doi:10.1155/2013/263952

57.

Singh

Hsu

ECY

. Ileukin10pred: a computational approach for predicting il-10-inducing immunosuppressive peptides using combinations of amino acid global features. Biology (Basel). 2022;11:5. doi:10.3390/BIOLOGY11010005/S1

58.

Bui

Sidney

Fusseder

Sette

Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics. 2007;8:361. doi:10.1186/1471-2105-8-361

59.

Johnson

Zaretskaya

Raytselis

Merezhuk

McGinnis

Madden

TL.

NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36:W5-W9. doi:10.1093/NAR/GKN201

60.

Altschul

Gish

Miller

Myers

Lipman

DJ.

Basic local alignment search tool. J Mol Biol. 1990;215:403-410. doi:10.1016/S0022-2836(05)80360-2

61.

Bui

Sidney

Dinh

Southwood

Newman

Sette

Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics. 2006;7:1-5. doi:10.1186/1471-2105-7-153/METRICS

62.

Kim

Park

SJ.

Emerging tick-borne Dabie bandavirus: virology, epidemiology, and prevention. Microorganisms. 2023;11:1-20. doi:10.3390/microorganisms11092309

63.

Janse van Rensburg

de Kock

Bester

Kloppers

JF.

HLA major allele group frequencies in a diverse population of the Free State Province, South Africa. Heliyon. 2021;7:e06850. doi:10.1016/J.HELIYON.2021.E06850

64.

Fadaka

Sibuyi

NRS

Martin

, et al. Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus. Sci Rep. 2021;11:19707. doi:10.1038/S41598-021-99227-7

65.

Davies

Pere

Bosschem

, et al. In silico adjuvant design and validation. Methods Mol Biol. 2017;1494:107-125. doi:10.1007/978-1-4939-6445-1_8

66.

Buchan

DWA

Minneci

Nugent

TCO

Bryson

Jones

. Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res. 2013;41:W349-W357. doi:10.1093/NAR/GKT381

67.

Geourjon

Deléage

SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci. 1995;11:681-684. doi:10.1093/BIOINFORMATICS/11.6.681

68.

Yang

Yan

Roy

Poisson

Zhang

The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015;12:7-8. doi:10.1038/nmeth.3213

69.

Yang

Zhang

I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res. 2015;43:W174-W181. doi:10.1093/NAR/GKV342

70.

Park

Heo

Seok

GalaxyWEB server for protein structure prediction and refinement. Nucleic Acids Res. 2012;40:W294-297. doi:10.1093/NAR/GKS493

71.

Heo

Park

Seok

GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 2013;41:W384-388. doi:10.1093/NAR/GKT458

72.

Lovell

Davis

Arendall

, et al. Structure validation by Cα geometry: φ, ψ and Cβ deviation. Proteins Struct Funct Genet. 2003;50:437-450. doi:10.1002/PROT.10286

73.

Wiederstein

Sippl

MJ.

ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007;35:W407-W410. doi:10.1093/nar/gkm290

74.

Ponomarenko

Bui

, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics. 2008;9:1-8. doi:10.1186/1471-2105-9-514/FIGURES/3

75.

Dombkowski

Sultana

Craig

DB.

Protein disulfide engineering. FEBS Lett. 2014;588:206-212. doi:10.1016/J.FEBSLET.2013.11.024

76.

Craig

Dombkowski

AA.

Disulfide by design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics. 2013;14:1-7. doi:10.1186/1471-2105-14-346/FIGURES/4

77.

Morris

Lim-Wilby

Molecular docking. Methods Mol Biol. 2008;443:365-382. doi:10.1007/978-1-59745-177-2_19

78.

Laskowski

Jabłońska

Pravda

Vařeková

Thornton

JM.

PDBsum: structural summaries of PDB entries. Protein Sci. 2018;27:129-134. doi:10.1002/pro.3289

79.

Weng

Wang

, et al. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 2019;47:W322-W330. doi:10.1093/NAR/GKZ397

80.

Sun

Tian

Hou

. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys. 2014;16:16719-16729. doi:10.1039/C4CP01388C

81.

Hansson

Oostenbrink

Van Gunsteren

WF.

Molecular dynamics simulations. Curr Opin Struct Biol. 2002;12:190-196. doi:10.1016/S0959-440X(02)00308-1

82.

Krieger

Vriend

New ways to boost molecular dynamics simulations. J Comput Chem. 2015;36:996-1007. doi:10.1002/JCC.23899

83.

Case

Cheatham

3rd Darden

, et al. The Amber biomolecular simulation programs. J Comput Chem. 2005;26:1668-1688. doi:10.1002/JCC.20290

84.

Darden

York

Pedersen

Particle mesh Ewald: an Nlog(N) method for Ewald sums in large systems Particle mesh Ewald: an N-log(N) method for Ewald sums in large systems. Cit J Chem Phys. 1993;98:10089. doi:10.1063/1.464397

85.

Rapin

Lund

Bernaschi

Castiglione

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS ONE. 2010;5:e9862. doi:10.1371/JOURNAL.PONE.0009862

86.

Omoniyi

Adebisi

Musa

, et al. Designing a multi-epitope vaccine against the Lassa virus through reverse vaccinology, subtractive proteomics, and immunoinformatics approaches. Informatics Med Unlocked. 2021;25:100683. doi:10.1016/J.IMU.2021.100683

87.

Bauer

Beauchemin

CAA

Perelson

AS.

Agent-based modeling of host-pathogen systems: the successes and challenges. Inf Sci (NY). 2009;179:1379-1389. doi:10.1016/j.ins.2008.11.012

88.

Grote

Hiller

Scheer

, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 2005;33:W526-W31. doi:10.1093/NAR/GKI376

89.

Demissie

Park

Moon

Lee

DY.

Comparative analysis of codon optimization tools: advancing toward a multi-criteria framework for synthetic gene design. J Microbiol Biotechnol. 2025;35:e2411066. doi:10.4014/jmb.2411.11066

90.

Mathews

Turner

Zuker

RNA secondary structure prediction. Curr Protoc Nucleic Acid Chem. 2007;Chapter 11:Unit 11.2. doi:10.1002/0471142700.NC1102S28

91.

Mathews

Sabina

Zuker

Turner

DH.

Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol. 1999;288:911-940. doi:10.1006/JMBI.1999.2700

92.

Huang

, et al. Longitudinal analysis of immunocyte responses and inflammatory cytokine profiles in SFTSV-infected rhesus macaques. Front Immunol. 2023;14:1143796. doi:10.3389/FIMMU.2023.1143796

93.

Shi

Zhu

Shang

, et al. Development of innovative multi-epitope mRNA vaccine against central nervous system tuberculosis using in silico approaches. PLoS ONE. 2024;19:e0307877. doi:10.1371/journal.pone.0307877

94.

Fattahi

Sadeghi Kalani

mRNA vaccine design using the proteome of Theileria annulata through immunoinformatics approaches. Msphere. 2025;10:e0080924. doi:10.1128/msphere.00809-24

95.

Al-Sallami

Alsultan

Raziq

Kalani

Clarke

SR.

Design of innovative divalent Cj1621 and CjaA multiepitope mRNA-based vaccine against foodborne campylobacter jejuni using in silico approaches. Vet Med Int. 2025;2025:3487209. doi:10.1155/vmi/3487209

96.

Fattahi

Khoshnood

Omidi

Kalani

BS.

Engineering a novel multi-epitope mRNA vaccine against major bacterial meningitis pathogens: E. coli K1, Group B Streptococcus, Listeria monocytogenes, Neisseria meningitidis, and Streptococcus pneumoniae. Int J Biol Macromol. 2025;317:144311. doi:10.1016/J.IJBIOMAC.2025.144311

97.

Fattahi

Shivaee

Bahraminia

Omidi

Kalani

BS.

Computational design of inhibitory peptides and an mRNA-Based multi-epitope vaccine targeting the MIC3 protein of Eimeria tenella. Exp Parasitol. 2025;275:108986. doi:10.1016/J.EXPPARA.2025.108986

98.

Asadollahi

Kalani

BS.

Novel toxin-based mRNA vaccine against Clostridium perfringens using in silico approaches. Toxicon. 2024;238:107584. doi:10.1016/J.TOXICON.2023.107584

99.

Tilston-Lunel

Acrani

Randall

Elliott

RM.

Generation of recombinant oropouche viruses lacking the nonstructural protein NSm or NSs. J Virol. 2016;90:2616. doi:10.1128/JVI.02849-15

100.

Adhikari

Tayebi

Rahman

MM.

Immunoinformatics approach for epitope-based peptide vaccine design and active site prediction against polyprotein of emerging oropouche virus. J Immunol Res. 2018;2018:6718083. doi:10.1155/2018/6718083

101.

Ali

Pandey

Khatoon

Narula

Mishra

Prajapati

VK.

Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Sci Rep. 2017;7:1-13. doi:10.1038/S41598-017-09199-W

102.

Khan

Ali

, et al. Immunoinformatics approaches to explore Helicobacter Pylori proteome (Virulence Factors) to design B and T cell multi-epitope subunit vaccine. Sci Rep. 2019;9:1-13. doi:10.1038/S41598-019-49354-Z

103.

Harder

Bartels

Christophers

Schröder

JM.

Isolation and characterization of human μ-Defensin-3, a novel human inducible peptide antibiotic. J Biol Chem. 2001;276:5707-5713. doi:10.1074/JBC.M008557200

104.

Bagherzadeh

Izadi

Baesi

Considering epitopes conservity in targeting SARS-CoV-2 mutations in variants: a novel immunoinformatics approach to vaccine design. Sci Rep. 2022;12:14017. doi:10.1038/s41598-022-18152-5

105.

Albutti

Proteome-wide and protein-specific multi-epitope vaccine constructs against the rift valley fever virus outbreak using integrated omics approaches. Front Microbiol. 2022;13:921683. doi:10.3389/FMICB.2022.921683

106.

Umar

Haque

Alghamdi

, et al. Development of a candidate multi-epitope subunit vaccine against klebsiella aerogenes: subtractive proteomics and immuno-informatics approach. Vaccines. 2021;9:1373. doi:10.3390/VACCINES9111373

107.

Naveed

Ali

Karobari

, et al. A vaccine construction against COVID-19-associated mucormycosis contrived with immunoinformatics-based scavenging of potential mucoralean epitopes. Vaccines. 2022;10:664. doi:10.3390/VACCINES10050664

108.

Naveed

Tehreem

Arshad

, et al. Design of a novel multiple epitope-based vaccine: an immunoinformatics approach to combat SARS-CoV-2 strains. J Infect Public Health. 2021;14:938-946. doi:10.1016/J.JIPH.2021.04.010

109.

Arai

Wriggers

Nishikawa

Nagamune

Fujisawa

Conformations of variably linked chimeric proteins evaluated by synchrotron X-ray small-angle scattering. Proteins Struct Funct Genet. 2004;57:829-838. doi:10.1002/PROT.20244

110.

Arai

Ueda

Kitayama

Kamiya

Nagamune

Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. 2001;14:529-532. doi:10.1093/PROTEIN/14.8.529

111.

Livingston

Crimi

Newman

, et al. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J Immunol. 2002;168:5499-5506. doi:10.4049/JIMMUNOL.168.11.5499

112.

Mohan

Mitra

Rao

DN.

Nasal delivery of PLG microparticle encapsulated defensin peptides adjuvanted gp41 antigen confers strong and long-lasting immunoprotective response against HIV-1. Immunol Res. 2014;58:139-153. doi:10.1007/S12026-013-8428-5

113.

Yang

Biragyn

Kwak

Oppenheim

JJ.

Mammalian defensins in immunity: more than just microbicidal. Trends Immunol. 2002;23:291-296. doi:10.1016/S1471-4906(02)02246-9

114.

Mohan

Sharma

Bhat

Rao

DN.

Modulation of HIV peptide antigen specific cellular immune response by synthetic α- and β-defensin peptides. Vaccine. 2013;31:1707-1716. doi:10.1016/J.VACCINE.2013.01.041

115.

Sauer

Harris

An effective COVID-19 vaccine needs to engage T cells. Front Immunol. 2020;11:581807. doi:10.3389/FIMMU.2020.581807

116.

Meza

Ascencio

Sierra-Beltrán

Torres

Angulo

A novel design of a multi-antigenic, multistage and multi-epitope vaccine against Helicobacter pylori: an in silico approach. Infect Genet Evol. 2017;49:309-317. doi:10.1016/j.meegid.2017.02.007

117.

Ferdous

Kelm

Baker

Shi

Martin

ACR

. B-cell epitopes: discontinuity and conformational analysis. Mol Immunol. 2019;114:643-650. doi:10.1016/j.molimm.2019.09.014

118.

Mahdeen

Arpa

Hasan

Barua

Ferdous

Masum

MHU

. Next-gen vaccine design: integrating proteomics and bioinformatics for Japanese encephalitis. Comput Struct Biotechnol Reports. 2025;2:100049. doi:10.1016/J.CSBR.2025.100049

119.

Firuzpour

Barancheshmeh

Ziarani

Karami

Aram

The HER2 target for designing novel multi-peptide vaccine against breast cancer using immunoinformatics and molecular dynamic simulation. Biochem Biophys Rep. 2025;43:102135. doi:10.1016/j.bbrep.2025.102135

120.

Naveed

Ali

Aziz

, et al. Computational design of a glycosylated multi-epitope vaccine against HAsV-1 and HAsV-2 astrovirus for acute gastroenteritis. Sci Rep. 2025;15:13954. doi:10.1038/S41598-025-96989-2

121.

Raj

Hossain

Zahin

, et al. A robust comprehensive immunoinformatics approach for designing a potential multi-epitope based vaccine against a reiterated monkeypox virus. Biochem Biophys Rep. 2025;43:102075. doi:10.1016/J.BBREP.2025.102075

122.

Zelkoski

Sukumar

, et al. Ionizable lipid nanoparticles of mRNA vaccines elicit NF-κB and IRF responses through toll-like receptor 4. NPJ Vaccines. 2025;10:73. doi:10.1038/s41541-025-01124-x

123.

Cao

Han

Shan

Next-generation sequencing and immuno-informatics for designing a multi-epitope vaccine against HSV-1-induced uveitis. Front Immunol. 2025;16:1461725. doi:10.3389/FIMMU.2025.1461725

124.

Ehsasatvatan

Baghban Kohnehrouz

Designing and immunomolecular analysis of a new broad-spectrum multiepitope vaccine against divergent human papillomavirus types. PLoS ONE. 2024;19:e0311351. doi:10.1371/JOURNAL.PONE.0311351

125.

Motamedi

Ari

Shahlaei

, et al. Designing multi-epitope vaccine against important colorectal cancer (CRC) associated pathogens based on immunoinformatics approach. BMC Bioinformatics. 2023;24:1-31. doi:10.1186/S12859-023-05197-0

126.

Bhowmik

Bhuyan

Gunalan

Kothandan

Kumar

In silico and immunoinformatics based multiepitope subunit vaccine design for protection against visceral leishmaniasis. J Biomol Struct Dyn. 2024;42:9731-9752. doi:10.1080/07391102.2023.2252901

127.

Rahman

Masum

MHU

Talukder

Akter

An in silico reverse vaccinology approach to design a novel multiepitope peptide vaccine for non-small cell lung cancers. Informatics Med Unlocked. 2023;37:101169. doi:10.1016/J.IMU.2023.101169

128.

Wei

Dong

Zhu

Wang

BZ.

mRNA Vaccine Nanoplatforms and Innate Immunity. Viruses. 2024;16:120. doi:10.3390/v16010120

129.

Zhang

, et al. The mRNA vaccine encoding Gc protein confers complete protection against severe fever with thrombocytopaenia syndrome virus. Vaccine. 2025;63:127631. doi:10.1016/j.vaccine.2025.127631

130.

Tao

Zhang

, et al. Computer-aided designing of a novel multi-epitope DNA vaccine against severe fever with thrombocytopenia syndrome virus. BMC Infect Dis. 2024;24:1-12. doi:10.1186/s12879-024-09361-6

131.

Naveed

Ali

Aziz

, et al. An Aedes–Anopheles Vaccine candidate supplemented with BCG epitopes against the Aedes and Anopheles genera to overcome hypersensitivity to mosquito bites. Acta Parasitol. 2024;69:483-504. doi:10.1007/s11686-023-00771-1

132.

Naveed

Jabeen

Aziz

, et al. Whole proteome analysis of MDR Klebsiella pneumoniae to identify mRNA and multiple epitope based vaccine targets against emerging nosocomial and lungs associated infections. J Biomol Struct Dyn. 2025;43:1915-1928. doi:10.1080/07391102.2023.2293266

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