Harnessing epitope reciprocity and multimeric epitope density for a novel multiepitope vaccine design against Acinetobacter baumannii

Abstract

Acinetobacter baumannii, a notorious nosocomial pathogen, presents a global health challenge due to its high antibiotic resistance and ability to cause various infections. Vaccine development against this pathogen is a pressing need, yet traditional methods have encountered limitations due to the immunogenic complexity and potential autoimmune risks associated with key antigenic components. In this study, we leveraged the reverse vaccinology approach, integrating epitope mapping, immunoinformatics, and structural modeling to design a novel multiepitope vaccine. Our methodology emphasizes the concept of epitope reciprocity, wherein B-cell and T-cell epitopes are co-localized to promote cross-epitope interactions, enhancing immunogenicity and immune response synergy. Additionally, utilizing a trimeric construct, we achieved a threefold increase in epitope density, further amplifying the immune activation potential. Key antigenic proteins implicated in the pathogenicity of A. baumannii were screened, excluding components like OmpA, which is known to induce autoimmune responses. After rigorous silico analyses, including structural validation and molecular docking, the final construct demonstrated promising interaction with Toll-like receptor 1 (TLR-1), suggesting efficient immune recognition. This multiepitope design, grounded in the principles of epitope reciprocity and enhanced by multimeric epitope density, offers a promising platform for developing a safe and effective vaccine against A. baumannii. Further experimental validation is warranted to assess its in vivo efficacy and safety.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-32476-y.

Introduction

The relentless rise of multidrug-resistant (MDR) pathogens presents one of the most urgent threats to global healthcare, with Acinetobacter baumannii emerging as a particularly dangerous adversary¹. This opportunistic pathogen, notorious for causing severe hospital-acquired infections such as ventilator-associated pneumonia and bloodstream infections, has developed resistance to a wide range of antibiotics². As a result, infections caused by A. baumannii are increasingly challenging to treat, often leading to higher mortality rates, prolonged hospital stays, and growing healthcare costs. Given the limitations of current antimicrobial therapies, the development of effective vaccines offers a promising alternative to manage and prevent A. baumannii infections^3,4.

Historically, confronting A. baumannii has proven challenging due to its complex resistance mechanisms, including biofilm formation, efflux pumps, and a propensity to acquire resistance genes⁵. Efforts in vaccine development have primarily focused on whole-cell inactivated vaccines, outer membrane vesicles (OMVs)^6–8, and subunit vaccines targeting specific virulence factors such as outer membrane proteins (OMPs)⁹ and capsular polysaccharides^10,11. However, while some progress has been made, these approaches face significant hurdles, particularly in terms of vaccine efficacy and the ability to generate long-lasting immune responses¹². Additionally, concerns about potential immunogenicity and safety further complicate the development of whole-cell or OMV-based vaccines¹².

Despite showing protective efficacy, whole-cell and OMV-based vaccines against A. baumannii have raised safety concerns because of their complex composition and high content of surface lipopolysaccharides. Such preparations may induce excessive inflammatory responses or autoimmune reactions, limiting their clinical applicability. Furthermore, variability in OMV composition across different bacterial strains hinders consistent antigen presentation^6–8. These risks emphasize the need for safer, more defined approaches such as peptide-based vaccines, which allow precise control over antigenic content and reduce unwanted reactogenicity.

The advent of reverse vaccinology has provided a more targeted and streamlined approach to vaccine design by utilizing genomic and proteomic data to identify potential vaccine candidates^13–15. This methodology, which includes steps such as proteome screening, epitope mapping, and multiepitope vaccine construction, has become the cornerstone of modern vaccine research against various pathogens, including A. baumannii^16,17. However, despite its effectiveness, reverse vaccinology has now become a routine practice, with many studies relying on similar epitope prediction algorithms and design strategies, resulting in a lack of novelty and incremental advancements.

To overcome these limitations, the present study introduces novel concepts such as epitope reciprocity^18–21 and epitope density^22–24 to enhance the immunogenic potential of a multiepitope vaccine candidate. Epitope reciprocity refers to the phenomenon where epitopes shared between the host and pathogen can lead to immune tolerance or evasion, a critical consideration in the design of vaccines¹⁸. Epitope reciprocity, as applied in this study, describes the deliberate spatial arrangement of B-cell and T-cell epitopes within a vaccine construct to promote cooperative immune signaling. By positioning complementary epitopes in close proximity, this design enhances cross-epitope interactions and synergistic immune activation, ultimately improving the magnitude and durability of the response while minimizing redundancy among predicted epitopes. By strategically selecting epitopes with minimal similarity to host proteins, our approach aims to maximize immune recognition while minimizing the risk of autoimmune responses. Additionally, epitope density, the concentration of epitopes on a given antigenic surface, plays a crucial role in determining the strength of the immune response²⁴. In traditional multiepitope vaccines, epitope density is often limited by the monomeric structure of the vaccine candidate. However, in our study, we utilized a trimeric vaccine construct, which allows for a threefold increase in epitope density compared to monomeric designs. This enhanced epitope density is expected to result in stronger and more durable immune responses by increasing the likelihood of B-cell receptor (BCR) cross-linking and subsequent activation.

In addition to leveraging epitope reciprocity and density, our approach is grounded in the concept that all components necessary for vaccine design can be derived from the pathogen’s proteome itself. This mirrors the traditional strategy of whole-cell vaccines, which utilize the entire pathogen to elicit a broad immune response. However, by focusing on the pathogen’s proteome, we can design a particular and tailored vaccine candidate that retains the broad immunogenicity of whole-cell vaccines while minimizing the risk of adverse effects. The present study demonstrates this approach in the development of a multimeric vaccine candidate targeting A. baumannii, a pathogen for which current vaccine strategies remain inadequate. Therefore, by applying the concepts of epitope reciprocity and density in a trimeric multiepitope vaccine construct, this study aims to overcome the limitations of traditional reverse vaccinology approaches and provide a potent and targeted immunogenic candidate against A. baumannii, showcasing the potential for intellectual advancement in the field of computational vaccine design.

Methods

Epitope design and reciprocity approach

After identifying optimal B-cell and T-cell epitopes using immunoinformatics tools, comprehensive epitope mapping was performed to ensure their precise placement, maximizing the potential for interaction.

Given the routine nature of reverse vaccinology, a brief explanation of the standard methods applied is provided in the main text. For reproducibility and to offer a comprehensive understanding of the approach, the complete step-by-step methodology, including proteome screening, allergenicity and toxicity prediction, structural modeling, docking, and immune response simulations, is available in the supplementary data 1.

Data acquisition and screening workflow

Protein sequences and the complete proteome of A. baumannii (ATCC 19606)²⁵ were obtained from the UniProt Knowledge Base²⁶. Using CLC Genomic Workbench version 23²⁷, protein data were extracted in FASTA format, and sequences shorter than 40 amino acids were excluded. To facilitate subcellular localization analysis, the proteome was segmented into subsets of 500 sequences.

Subcellular localization

To identify surface-exposed proteins within the A. baumannii proteome, four complementary servers, PSORTb v3.0²⁸, BUSCA²⁹, SignalP 5.0³⁰, and SOSUI-GramN³¹, were employed. Proteins were shortlisted when at least two out of these four tools consistently predicted extracellular or outer-membrane localization (Table 1). This consensus approach yielded 204 proteins that were carried forward for antigenicity evaluation. Focusing exclusively on extracellular and outer-membrane proteins ensured that the selected candidates represented the most immunologically accessible targets for vaccine design.

Table 1.

Summarizes the predicted subcellular localization of proteins, showing that 204 proteins were consistently identified by at least two tools as extracellular or outer-membrane targets.

Software	Cytoplasmic	Inner membrane	Periplasmic	Outer membrane	Extracellular	Unknown
Sosua	2,167	697	189	189	140	343
BUSCA	2,370	995	0	386	38	0
Psortb3	1,841	752	58	105	38	971

Antigenicity screening

Proteins with confirmed extracellular or membrane localization were evaluated for immunogenic potential using the bacterial model of VaxiJen v2.0 (threshold 0.4; sensitivity ≈ 91%, specificity ≈ 72%)^32–34. This cutoff value was chosen as it is recommended for bacterial antigens and provides a suitable balance between prediction accuracy and reliability. These antigenic proteins were next assessed for broad genomic prevalence using BLASTP (restricted to A. baumannii taxon, BLOSUM62 matrix), with proteins achieving ≥ 100 hits across distinct A. baumannii genomes prioritized as the most widely conserved antigens.

From the 204 proteins initially screened, 189 exceeded this threshold and were ranked according to their antigenicity scores. The highest-scoring proteins were then assessed for prevalence across A. baumannii genomes using the Basic Local Alignment Search Tool for Proteins (BLASTP), with the search restricted to Acinetobacter spp.; higher BLAST hit counts indicated broader occurrence across strains. Proteins meeting both high-prevalence and high-antigenicity criteria progressed to allergenicity assessment (AlgPred2) and toxicity filtering (ToxinPred2, CSM-Toxins), ensuring that the final candidates were surface-exposed, immunogenic, widely conserved, and non-allergenic/non-toxic.

BLAST screening for protein prevalence

The 189 antigenic proteins were analyzed for prevalence across various A. baumannii genomes using BLASTP^35,36, restricted to A. baumannii taxonomic identifiers (NCBI). This provided insights into the conservation and prevalence of proteins within the genus, with higher BLAST hit counts indicating broader occurrence across genomes.

Allergenicity and toxicity filtering

To ensure the selection of safe vaccine candidates, allergenicity was assessed using AlgPred2, while toxicity was evaluated via ToxinPred2³⁷ and CSM-Toxins³⁸. Proteins with allergenic or toxic potential were excluded from further analysis.

Epitope prediction

Immunoinformatics tools were utilized to predict the Major Histocompatibility Complex-I (MHC) MHC-I and MHC-II binding epitopes for T-cells and B-cell binding epitopes. NetMHCpan-4.1 and NetMHCIIpan-4.3 were employed to predict MHC class I and II binding affinities, respectively^39,40,. B-cell epitopes were predicted using BepiPred-3.0⁴¹, while conformational epitopes were predicted from AlphaFold structures analyzed by DiscoTope-3.0^42–44.

Linear epitopes were predicted using BepiPred-3.0 (threshold = 0.5, specificity 74%, sensitivity 58%), while conformational epitopes were identified using DiscoTope-3.0 (threshold = − 3.7, specificity 47%, sensitivity 75%) applied to AlphaFold-predicted structures. Only peptides in the upper 5th percentile of prediction scores were retained. These candidate epitopes were further evaluated using VaxiJen v2.0 (bacterial model, threshold = 0.4) for antigenicity and screened for non-allergenicity (AlgPred2) and non-toxicity (ToxinPred2, CSM-Toxins).

Adjuvant selection

VaxinPAD⁴⁵ at https://webs.iiitd.edu.in/raghava/vaxinpad/protein.php is a computational tool designed to predict immunomodulatory peptides for use as vaccine adjuvants. It analyzes protein sequences and identifies regions with high potential for modulating the immune response. VaxinPAD also provides information on the physicochemical properties of these peptides, which can be valuable for selecting suitable adjuvant candidates.

Construct design

Following identification of candidate epitopes, four multiepitope constructs were assembled by integrating selected T-cell and B-cell determinants with peptide spacers. Epitope arrangement and composition were determined using both quantitative (VaxiJen v2.0; threshold ≥ 0.4 for bacterial antigens) and qualitative (ProtParam-derived physicochemical properties) criteria. Linker sequences were assigned based on experimentally documented structural roles: EAAK for rigid separation between the adjuvant and epitopes, GPGPG for flexible HTL presentation, AAY for CTL epitope spacing, and KK for B-cell epitope accessibility. Linker lengths were maintained between 3 and 5 residues to minimize strain and prevent steric clashes. Immunogenicity and safety of each epitope–linker combination were validated using AlgPred2 and ToxinPred2. The multimeric architecture and stability of each construct were subsequently evaluated using MultiFOLD and GalaxyHomomer.

Linker sequences

To connect the selected epitopes within the vaccine construct, various linker sequences were employed^46,47:

EAAK

This flexible linker sequence was used to create a spacer between the adjuvant and the rest of the construct, allowing for proper presentation of the epitopes.

K.K

This lysine-rich linker sequence was used to introduce flexibility between B-cell epitopes, potentially enhancing their accessibility to the immune system.

AAY

This linker sequence was used to create flexibility between cytotoxic T-lymphocyte (CTL) epitopes, facilitating their interaction with T-cell receptors.

GPGPG

This glycine-proline-rich linker sequence was used to create flexibility between helper T-lymphocyte (HTL) epitopes, promoting their interaction with antigen-presenting cells.

In silico prediction of multimerization potential

The potential of the designed protein constructs to form multimers was assessed using the iFrag server (https://sbi.upf.edu/)⁴⁸. This web server predicts protein-protein interaction interfaces based on sequence analysis and integrates information from protein-protein interaction databases. The amino acid sequences of the designed constructs were submitted to the iFrag server in FASTA format. The server predicted the potential interacting regions within each construct based on different scoring functions. The results, including predicted interface regions and associated scores, were downloaded and analyzed. MultiFOLD⁴⁹ and GalaxyHomomer⁵⁰ were used to predict multimeric configurations and optimize structural stability.

Post-design evaluation

The final construct underwent multiple computational assessments to verify its properties and stability. Sequence-based profiling included antigenicity scoring, physicochemical characterization, and allergenicity/toxicity screening. A three-dimensional model of the construct was generated to map potential conformational epitopes, which were subsequently characterized for immunogenic potential. Protein–receptor docking was performed against Toll-like receptors (TLRs) to identify interaction sites and obtain binding affinity scores.

Docking procedure

The structure of TLR-1 was used as the receptor, while the designed construct served as the ligand. The docking process focused on achieving an orientation where the construct binds TLR-1 through its adjuvant domain, aligning with our expected docking hypothesis. To evaluate the interaction between the designed vaccine construct and TLR-1, we performed protein-protein docking using LzerDock^51,52, a computational docking server available at https://lzerd.kiharalab.org. This tool is suited explicitly for exploring interactions in immunological research, as it offers scoring functions that assess binding stability and orientation. LzerDock evaluated each configuration using three scoring metrics:

GOAP (Generalized orientation and atomic Potential)

An energy function that estimates the stability of the binding.

DFIRE

A statistical potential score indicating the interaction energy based on residue-specific potential.

ITScore

An empirical score that combines atomic potential and geometric orientation to predict binding strength.

Each docking model received a cumulative ranking based on these metrics. The model with the most favorable score, indicative of stable and correct orientation, was selected for further analysis.

Interface residue identification using Mapiya

The Mapiya server (https://mapiya.lcbio.pl/about/) was employed to analyze the molecular interactions between the designed multiepitope construct and TLRs, specifically focusing on identifying key interface residues. Mapiya is designed to map the interface residues in protein-protein complexes, facilitating the identification of interacting surface residues. For this study, the designed vaccine construct was docked against the TLR structures, and the resulting complexes were submitted to the Mapiya server for interface residue mapping. The server utilizes contact maps to visualize interactions, based on distance thresholds, between residues of the two proteins involved in the complex. The contact map analysis was pivotal in identifying residues at the interface that contribute to molecular recognition and binding.

In Silico immune simulation

To determine the potential immunogenicity and immune response of the peptide vaccine, an in silico immune simulation was performed using the Cellular Immune Simulation (C-IMMSIM) server at https://kraken.iac.rm.cnr.it/C-IMMSIM/. This server uses an agent-based model that utilizes position-specific scoring matrices (PSSM) generated through machine learning methods to predict immune interactions⁵³. The administration of three doses of the vaccine, each containing 1000 antigen proteins and no LPS, was carried out in 1-, 84-, and 168-time steps, with a minimum recommended four weeks between the first and second doses. The simulation parameters, such as random seed, simulation volume, and simulation step, remained constant at 12,345, 10 µL, and 1050, respectively.

Results

Screening and selection

The complete proteome of Acinetobacter baumannii strain ATCC 19,606 (UniProt ID: UP000005476), comprising 3,598 proteins, was retrieved from the UniProt Knowledge Base as the foundational dataset for reverse vaccinology screening. Proteins were analyzed for subcellular localization using multiple prediction tools, and only those consistently identified as extracellular or outer-membrane by at least two independent localization algorithms were retained (Table 1). This stringent overlap criterion yielded 204 proteins, which were considered for subsequent immunogenicity evaluation. Of these 204 proteins, 189 exceeded the antigenicity score threshold, indicating strong potential to elicit an immune response.

In designing the vaccine constructs, the concept of epitope reciprocity was employed to enhance immunogenic potential. This strategy focuses on the strategic placement of B-cell and T-cell epitopes in proximity within the vaccine construct, facilitating cross-epitope interactions. The goal is to intensify immune system recognition by promoting synergistic responses between these two types of epitopes, ultimately leading to stronger and more sustained immune activation.

This prevalence analysis down-selected the list to 15 proteins (Supplementary Data 1 and Supplementary Data 2, and Table 2). Proteins failing any safety criterion were excluded. The remaining 11 proteins, characterized by high antigenicity, extracellular localization, broad genomic occurrence, and absence of allergenic/toxic features, were taken forward as the final vaccine targets. These final candidates are clearly denoted in Table 2 by bold lettering and were subsequently used for epitope mapping and construct design.

Table 2.

Lists the shortlisted 15 candidate proteins with their biological and physicochemical properties. From these, 11 proteins meeting all selection criteria were advanced for epitope prediction.

Query	Antigenicity	BLAST hits	Protein names	Gene names	Length
D0CDU1	0.7385	490	Ig-like domain-containing protein	HMPREF0010_02921	806
D0CBD7	0.5761	427	Type 1 secretion C-terminal target domain	HMPREF0010_02067	2750
D0CEI3	0.6437	163	Type 1 secretion C-terminal target domain	HMPREF0010_03163	2890
D0CES2	0.6477	103	Sel1 repeat protein (EC 3.5.2.6)	HMPREF0010_03252	230
D0CDH3	0.7545	102	Uncharacterized protein	HMPREF0010_02803	113
D0CG80	0.5052	101	Tip attachment protein J domain-containing protein	HMPREF0010_03760	728
D0CF71	0.758	100	Carbapenem susceptibility porin CarO	HMPREF0010_03537	299
D0C9R5	0.7523	100	OmpA family protein	HMPREF0010_01378	217
D0CE63	0.6749	100	Outer membrane porin, OprD family	HMPREF0010_03043	414
D0CB85	0.6672	100	Outer membrane porin, OprD family	HMPREF0010_02015	438
D0C690	0.6029	100	OmpW family protein	HMPREF0010_00270	370
D0C9Y2	0.5695	100	Outer membrane porin, OprD family	HMPREF0010_01445	448
D0C8C3	0.5543	100	DcaP-like protein	HMPREF0010_01003	428
D0C6T2	0.5327	100	Outer membrane porin, OprD family	HMPREF0010_00462	418
D0C913	0.4484	100	OmpA family protein	HMPREF0010_01571	259

Final vaccine candidate selection

Following antigenicity ranking and prevalence analysis, candidate proteins were subjected to safety filtering to remove allergenic or toxic components, ensuring suitability for vaccine design. Allergenicity and toxicity filtering narrowed down the 15 candidates to 11 final proteins (Table 2). These were characterized by high antigenicity scores, extracellular localization, broad genomic presence, and an absence of allergenic or toxic properties, making them optimal vaccine targets. This final panel represents diverse outer-membrane antigens with broad prevalence and safety, providing a robust foundation for epitope mapping. These selected proteins show promise as potential vaccine targets due to their extracellular localization, high antigenicity scores, and frequent occurrence in various strains of A. baumannii. Further experimental validation of these candidates could pave the way for effective vaccine development against this pathogen.

Epitope prediction and selection criteria

Both B-cell and T-cell epitopes were predicted and selected based on explicit scoring thresholds and biological relevance to ensure immunogenicity and safety.

B-cell epitopes

Linear and conformational B-cell epitopes were successfully identified and prioritized based on high predictive scores. The top-ranked epitopes were retained for construct integration (Supplementary Data 3).

T-cell epitopes

T-cell epitopes were identified to ensure coverage of both cytotoxic (MHC I-restricted) and helper (MHC II-restricted) immune responses. MHC I and MHC II binding peptides were predicted using NetMHCpan-4.1 and NetMHCIIpan-4.3, respectively. MHC I epitopes were retained as strong binders with percentile rank ≤ 0.5%, and MHC II epitopes with percentile rank ≤ 2%. Cytotoxic T-lymphocyte (CTL) epitopes were predicted with NetCTLpan-1.1, applying proteasomal cleavage score ≥ 0.8, TAP transport score ≥ 0.5, and MHC binding percentile ≤ 0.5%.

Conservation and safety filtering

All epitopes were assessed for sequence conservation across A. baumannii genomes, retaining those with ≥ 85% identity in homologous sequences. Potential cross-reactivity was evaluated by BLASTP against the human proteome, excluding epitopes showing > 35% identity over ≥ 9 consecutive residues.

This filtered set of epitopes formed the building blocks for subsequent multi-epitope construct design and adjuvant integration (Supplementary Data 3). The selected B-cell epitopes offer structural compatibility with T-cell epitopes for integration into multiepitope constructs.

Adjuvant selection

An immunomodulatory adjuvant sequence was selected to enhance vaccine potency by promoting immune activation. The YMGG-like Gly-zipper domain-containing protein, identified with the accession number D0CB14 and an antigenicity score of 1.173, was selected as the adjuvant for this study. The sequence of this protein is illustrated in Fig. 1. The selection of this protein was based on its high antigenicity, which exceeded the established threshold, and the presence of IgA epitopes. This adjuvant’s high antigenicity and IgA epitope content predict strong mucosal immune response potential.

Fig. 1.

Sequence details of the selected adjuvant. The whole sequence of the adjuvant is presented, with key regions marked by red arrows. The upper right section illustrates the optimized adjuvant sequence, highlighting modifications introduced for enhanced immunogenic properties.

Table 3 presents the N-terminal segment of the protein, predicted as an immunomodulatory region, along with its corresponding scores.

Table 3.

Sequences of the adjuvants before engineering: MNKKIGLISTVI.

Position	Peptide sequence	SVM Score	Prediction	Hydrophobicity	pI	Mol Wt
1	MNKKIGLIST	0.02	IM	−0.09	10.02	1104.5
3	KKIGLISTVI	0.07	IM	0.08	10.02	1071.49
78	KSAIGAGIGS	0	IM	0.08	9.11	860.1

After undergoing site-directed mutations, the sequence was optimized, resulting in the following engineered sequence. The properties of this modified sequence are shown in Table 4.

Table 4.

Sequences of the adjuvants after engineering: MAKKIRCCISRRIWK.

Position	Peptide sequence	SVM Score	Prediction	Hydrophobicity	pI	Mol Wt	Antigenicity
1	MAKKIRCCIS	2.67	IM	−0.22	9.53	1152.61	0.1546
2	AKKIRCCISR	2.6	IM	−0.42	10.12	1177.6	0.1359
3	KKIRCCISRR	2.87	IM	−0.62	10.94	1262.71	0.5453
4	KIRCCISRRI	3.02	IM	−0.44	10.79	1247.69	0.8574
5	IRCCISRRIW	3.06	IM	−0.29	10.43	1305.74	0.6157
6	RCCISRRIWK	3	IM	−0.47	10.79	1320.76	0.5085

Adjuvant design

The adjuvant peptide integrated into the final multi-epitope construct (Figs. 1 and 2; Tables 3 and 4) was derived from an A. baumannii protein harboring a YMGG-like Gly-zipper domain (UniProt ID: D0CB14). The native fragment exhibited strong predicted antigenicity (VaxiJen v2.0 score = 1.173) and included putative IgA-stimulatory epitopes. The most reactive 11-mer identified using VaxinPAD was optimized by site-directed peptide engineering, modifying residue composition to improve charge balance and hydrophobic stability while maintaining TLR-binding capacity. The optimization yielded a 15-mer peptide with increased predicted antigenicity (0.86 vs. 0.15 for the native peptide) and immunomodulatory SVM scores > 3.0.

Fig. 2.

Contact map and structural representation of the designed construct. The upper left panel displays the contact map score for the construct, with the amino acid sequence represented along the x- and y-axes. The color gradient ranges from blue (low score) to red (high score), with a red box indicating the probable interface region between two identical construct sequences. Green dots represent experimentally confirmed contacts extracted from the PDB database. The upper right section shows the complete amino acid sequence of the construct, with predicted interface residues highlighted in red, corresponding to the red box in the contact map. The lower panel provides a schematic illustration of the homotrimer assembly, demonstrating the integration of three identical subunits.

Docking simulations were conducted against TLR-4, TLR-1, and TLR-3 to assess broad receptor interaction potential. TLR-4 was included as the canonical receptor for bacterial immunomodulatory motifs; TLR-1 docking verified compatibility of the adjuvant surface within the vaccine construct; and TLR-3 docking evaluated endosomal receptor engagement. These analyses collectively indicated favorable binding affinities across receptor types, validating the peptide’s suitability as an optimized adjuvant sequence with multi-TLR activation capacity.

Structural design

To provide a context and rationale for the vaccine construct design, the overall approach combined immunological, structural, and physicochemical principles. The multiepitope construct was arranged to integrate B-cell and T-cell epitopes in reciprocal proximity, maintaining antigenic diversity while preserving structural compactness. This configuration was guided by predicted antigenicity (VaxiJen v2.0 scores ≥ 0.4), safety filtering (AlgPred2, ToxinPred2), and linker selection rules described earlier, ensuring optimal spatial presentation of immunologically active sites. The adjuvant was positioned at the N-terminus with an EAAK rigid linker to stabilize orientation and distinct domain separation. Construct length and composition were balanced to maintain molecular weight within 22–26 kDa and isoelectric pH near 9, parameters compatible with both structural feasibility and antigen accessibility (Fig. 3). These design decisions collectively ensured that the model possessed a plausible quaternary structure and predicted capacity to multimerize, justifying subsequent analyses by MultiFOLD and GalaxyHomomer for higher-order stability evaluation.

Fig. 3.

Detailed structural composition of the designed construct. Each region of the construct is labeled with specific tags, as indicated in the image. The color key at the bottom corresponds to each tagged region, providing a clear visual guide to the distinct sections within the construct’s sequence.

Following the selection of a set of epitopes, four constructs were designed, and their three-dimensional structures were predicted using multiple software tools. To maximize cooperative immune activation, the constructs were organized according to the principle of epitope reciprocity, which involves spatially associating B-cell and T-cell epitopes to promote cross-epitope signaling. Flexible linkers were strategically applied to preserve epitope conformation while positioning complementary epitopes in close proximity. This arrangement was predicted to enhance immune recognition and synergistic activation.

Each model was assessed through structural validation and physicochemical analyses, including MolProbity scoring, RMSD comparison, stability index, and VaxiJen v2.0 antigenicity values (Table 5). The construct demonstrated the highest structural quality and antigenicity, along with an instability index below 40, was selected as the best model and used for subsequent analyses. Figure 3 shows the details of this optimized construct.

Table 5.

Physicochemical characterization of four designed constructs.

Construct	Stability*	New Epitopes	Toxicity	Allergenicity	Antigenicity	Isoelectric pH	Molecular weight (kDa)	Length
1	9.63	×	×	×	0.79	9.43	25.31	218
2	17.81	×	×	×	0.80	9.28	24.09	219
3	17.62	×	×	×	0.79	9.43	22.57	204
4	12.61	×	×	×	0.79	9.41	25.72	221

*Proteins with an index below 40 are considered stable.

To explicitly assess trimerization capacity, the designed construct was analyzed using in silico quaternary-structure prediction platforms. The amino acid sequence was first submitted to MultiFOLD, which integrates consensus threading and ab initio modeling to evaluate oligomeric states. MultiFOLD predicted a strong tendency for homotrimer formation with favorable interface scores and high global model quality estimates. To independently validate this prediction, the monomeric structure refined in UCSF ChimeraX was subsequently processed through GalaxyHomomer, a template-based modeling server specialized for oligomeric assembly assessment. GalaxyHomomer consistently returned a stable homotrimer configuration, characterized by cohesive inter-chain contacts and minimal steric conflict. Together, these findings confirmed that the construct was structurally capable of adopting trimeric organization while maintaining the architectural integrity required for effective epitope presentation.

The results obtained from the MultiFold software suggested the possibility of multimeric behavior in the constructs. Upon providing the construct sequence, MultiFold predicted a homotrimer configuration, a protein structure comprising three identical polypeptide chains (Fig. 4).

Fig. 4.

The trimeric configuration of the designed construct is shown from three different perspectives (a-c), each highlighting structural organization and spatial arrangement. The color key beneath the figures (d) provides a schematic of the construct, with color harmonies consistently applied across all sections, representing functional regions accurately and uniformly.

To determine how the principle of epitope reciprocity influenced construct architecture, reciprocal epitope mapping was performed using the refined structural model. The final multiepitope design ensured that predicted B-cell epitopes were spatially adjacent to corresponding helper T-cell epitopes, fostering potential cross-communication during immune recognition. Structural visualization in UCSF ChimeraX revealed that reciprocity-based placement reduced steric hindrance and maximized surface exposure of paired epitopes, confirming their mutual accessibility. To evaluate the functional relevance of this design, additional comparative models were generated without reciprocity constraints. In these alternative arrangements, several key epitope pairs showed disrupted orientation and lower solvent-accessible surface areas, suggesting suboptimal antigen presentation. Quantitative interface analysis from GalaxyHomomer and MultiFOLD confirmed that reciprocity-optimized constructs displayed greater structural coherence and higher stability indices than non-reciprocal counterparts. These findings emphasize that epitope reciprocity is not only a conceptual design feature but also a structural determinant of epitope accessibility and overall vaccine architecture integrity.

To further optimize the structure and confirm the homotrimeric nature of the construct, the predicted monomers from MultiFold were isolated and refined using the UCSF ChimeraX software. The refined structures were then submitted to GalaxyHomomer for fourth structure prediction, with the software automatically determining the multimeric state. Once again, a homotrimer was predicted, reinforcing the likelihood that the designed construct adopts a homotrimeric form.

To assess whether the designed antigen could adopt a higher-order multimeric form while retaining monomeric integrity, we employed in silico quaternary-structure prediction tools. First, the amino acid sequence of the construct was analyzed using the MultiFOLD server, which predicts tertiary and quaternary structures by integrating multiple threading and ab initio models. MultiFOLD identified a strong propensity for homotrimer formation, evidenced by high interface scores and favorable global model quality estimates. To independently verify this prediction and optimize structural stability, the monomeric model generated was refined in UCSF ChimeraX and submitted to GalaxyHomomer, a template-based modelling platform specialized for oligomeric assembly prediction. GalaxyHomomer consistently returned a homotrimer configuration with stable inter-chain interfaces, supporting the likelihood that the designed construct would adopt this form under physiological conditions. Both analyses demonstrated that potential multimerization did not compromise predicted antigenicity or epitope accessibility, thereby supporting the inclusion of multimerization capacity as part of the construct design rationale.

Table 5 presents the properties of four vaccine constructs, evaluating various attributes such as stability, antigenicity, and molecular weight. Among the four designed constructs, comparative evaluation revealed that all candidates met baseline safety criteria (non-allergenic, non-toxic) and stability indices below 40. However, the construct 2 demonstrated the optimal overall profile, with the highest antigenicity score (0.80), ideal molecular weight (24.09 kDa), and moderate isoelectric point (pH 9.28). Its predicted three-dimensional organization showed compact folding and consistent trimeric assembly as suggested by MultiFOLD and GalaxyHomomer analyses. These features collectively supported selection of the construct 2 as the final vaccine model illustrated in Fig. 3, and Fig. 4. Table 5 retains comparative data for all four constructs to maintain transparency in the development and evaluation process.

Structural and functional validation

The finalized constructs demonstrated favorable properties, including stability indices below 40, molecular weights ranging from 22.57 to 25.72 kDa, and high antigenicity scores. No allergenic or toxic epitopes were detected in the constructs.

This streamlined pipeline from data acquisition to final construct design highlights the immunogenic potential of these targets, laying the groundwork for future experimental validation and potential clinical application as a multiepitope vaccine against A. baumannii.

Docking results

To ensure comprehensive receptor profiling, molecular docking simulations were performed against TLR-4, TLR-1, and TLR-3 using distinct computational tools optimized for each receptor type. The HADDOCK platform (Supplementary Data 1) was applied to TLR-3 due to its endosomal localization, while TLR-1 docking was performed with LZerDock to assess surface receptor engagement and adjuvant orientation. A parallel TLR-4 docking run served as an additional verification of canonical bacterial pattern–recognition motif compatibility. These combined analyses provided multi-receptor validation of the construct’s interaction potential and ensured consistency between the main text and supplementary materials.

To determine whether the engineered construct possessed attributes suitable for further preclinical evaluation, we conducted a comprehensive post-design analysis. This included structural mapping of conformational epitopes and simulation of receptor interactions to judge the likelihood of triggering effective immune responses.

The docking study with LZerDock generated several models, with the best configuration assigned for the model presented in Fig. 5. This configuration was notable for its alignment of the construct’s adjuvant domain toward the TLR-1 receptor, supporting the expected interaction hypothesis. The scoring results are detailed below:

Fig. 5.

The complex of the designed construct with Toll-like receptor 1 (TLR1). The correct orientation of the construct toward TLR1 is depicted, with the construct’s adjuvant region highlighted in red and the TLR1 receptor in dark blue. The construct is shown in a light blue-ribbon representation with a 70% transparent surface overlay, illustrating the spatial interaction and docking alignment between the construct and TLR1.

GOAP score

−106742.06, ranked 223 among all models.

DFIRE score

−93186.25, ranked 132.

ITScore

−45072.09, ranked 125.

Ranksum score

480.

These scores collectively indicate a favorable binding interaction between the construct and TLR-1. The negative GOAP and DFIRE values suggest a stable conformation, while the relatively high ITScore aligns with a strong binding interaction at the molecular level. The rank-sum score confirms the model as one of the best-fit configurations across all docking parameters.

The docking analysis via Mapiya revealed a significant interaction between the adjuvant segment of the vaccine construct and TLR residues. The contact map highlighted several critical interface residues. Notably, key residues from the adjuvant components of the construct were in proximity to residues in the TLR receptor, indicating a strong interaction (Fig. 2). These interactions were consistent with the intended immunogenicity design, which aimed to ensure the adjuvant’s proximity to TLRs for optimal immune activation.

Contact map findings

The contact map demonstrated that specific adjuvant residues form close contact with TLR residues. These predicted interactions from the in silico docking reinforce the anticipated role of the adjuvant in potentially triggering immune responses (Fig. 6), noting that these findings are computational predictions without experimental validation.

Fig. 6.

Contact map of the construct and TLR1 complex. The interactions between the adjuvant residues of the construct and TLR1 are highlighted in green, showing clear and significant contacts. This map provides a detailed view of the interface and specific points of contact between the adjuvant region of the construct and TLR1, confirming the intended binding orientation.

The molecular complex, visualized through a 3D image and analyzed via Mapiya, further supported the construct’s docking accuracy. The close alignment of these interacting residues emphasizes the effectiveness of the designed construct in interfacing with TLRs, which is essential for inducing a potent immune response. Figures 7 and 8 present the computationally simulated immune response following three hypothetical vaccine administrations, as predicted by the C-IMMSIM platform.

Fig. 7.

The graph representation of antibody titer and antigen count simulated for three virtual injections of the vaccine. X and Y axes are labeled, and the color key for defining the curves is presented in the image.

Fig. 8.

The graph represents the count of the active B-cell population.

Discussion

In the ongoing battle against multidrug-resistant pathogens like A. baumannii, identifying novel vaccine candidates has become the best option. This pathogen, responsible for a wide range of hospital-acquired infections, has evaded many traditional therapeutic interventions. Recent efforts have focused on immunoinformatics-based approaches like reverse vaccinology to combat such threats. While these methods have shown promise in identifying potential vaccine candidates, they are often limited by routine and lack novelty. In this study, we sought to push the boundaries of conventional vaccinology by introducing innovative concepts such as epitope reciprocity and increased epitope density through multimeric vaccine constructs.

Efforts to design multiepitope vaccines against A. baumannii have gained momentum due to the pathogen’s role in nosocomial infections and antibiotic resistance. Various studies have employed immunoinformatics and computational methods to identify and evaluate potential vaccine candidates. Below are key examples of these innovative approaches.

Researchers identified ten conserved antigenic proteins from databases like NCBI and UniProt, selecting eight epitopes for vaccine construction⁵⁴.

Another study utilized BLASTn to discover conserved antigens, leading to the development of a multiepitope construct that included twenty epitopes from outer membrane protein⁵⁵.

Advanced computational methods were employed to predict T-cell and B-cell epitopes, with molecular docking used to assess binding efficacy⁵⁶.

The design of a multiepitope vaccine candidate included β-defensin as an adjuvant and utilized various linkers to enhance immunogenicity⁵⁶.

In silico tools were crucial for predicting immunogenicity and assessing physicochemical properties, ensuring the selected epitopes were non-toxic and allergenic^56–58.

Two specific T-cell epitopes from outer membrane protein K were highlighted as promising candidates based on their high immunogenicity and conservation across Acinetobacter species.

While these studies present promising avenues for vaccine development, the transition from in silico findings to in vitro and in vivo validation remains a critical step for confirming the efficacy of these multiepitope vaccines.

One of the significant advancements in this study is the concept of epitope reciprocity, which strategically places B-cell and T-cell epitopes near each other to promote synergistic immune responses. By facilitating interactions between the two epitope types, this approach potentially enhances immunogenicity and triggers a more robust and prolonged immune response. The placement of B- and T-cell epitopes ensures optimal immune system activation, making the vaccine construct more likely to elicit protective immunity.

Moreover, the trimeric nature of the designed construct ensures a threefold increase in epitope density, thereby amplifying immune system recognition. Each epitope is presented three times within the structure, effectively boosting the chances of immune cells encountering and recognizing the antigenic sites. This dense arrangement not only strengthens immune activation but also contributes to epitope redundancy, which could lead to greater vaccine efficacy by compensating for immune escape mechanisms.

In terms of candidate selection, the epitopes were chosen from proteins that play crucial roles in the pathogenicity of A. baumannii. In previous studies, these proteins have been widely recognized as virulence factors. For instance, outer membrane proteins like Carbapenem susceptibility porin (CarO) and outer membrane porin OmpW are critical for bacterial survival and antibiotic resistance. By targeting such proteins, our design aligns with the pathogen’s most vital components, enhancing the likelihood of disrupting its pathogenic processes.

OmpA, although an essential virulence factor, was excluded from our design due to reported associations of OmpA-derived peptides with autoimmune responses. To ensure the absence of such risk in our construct, all selected epitopes were further screened against the human proteome using BLASTP, confirming that none shared significant sequence similarity (< 35% identity) with human proteins. This exclusion reflects the importance of careful epitope selection to minimize adverse effects while retaining immunogenic potential. Such considerations ensure that the designed vaccine prioritizes safety alongside efficacy.

The in silico immune simulation was conducted on the C-IMMSIM platform to predict adaptive immune responses following vaccination with the designed multiepitope construct. Three doses were administered at simulated weeks 0, 4, and 8, each containing 1000 antigen protein units and formulated without lipopolysaccharide to avoid endotoxin-mediated artefacts. The random seed was fixed at 12,345 to ensure reproducibility across runs, the simulation volume was set to 10 µL to approximate local tissue microenvironments, and the total simulation time was defined as 1050 steps to capture both primary and secondary immune kinetics. These parameters were selected based on platform guidelines and prior benchmarking for peptide vaccines, allowing visualization of antibody titers (Fig. 7) and active B-cell population dynamics (Fig. 8) under conditions representative of a standard booster immunization regimen.

Finally, the structural design of the vaccine construct, including the use of the pathogen’s proteins for adjuvant design, showcases an important principle: the pathogen itself holds all the tools required for effective vaccine development. By leveraging this internal logic, we were able to create a more refined vaccine candidate with enhanced immunogenicity and minimized safety concerns.

This study demonstrates the effectiveness of innovative approaches such as epitope reciprocity and increased epitope density in vaccine design. By strategically positioning epitopes and amplifying their presentation through a trimeric construct, we not only enhance the immune response but also introduce a novel and practical framework for future vaccine development. The results suggest that this strategy holds great potential for creating multimeric vaccine candidates against other challenging pathogens, and it opens the door for further in vivo experimentation to confirm its efficacy.

Conclusions

In conclusion, our study highlights the importance of moving beyond conventional epitope mapping techniques to incorporate more dynamic strategies that address both immunogenicity and safety. The inclusion of epitope reciprocity as a novel immunological concept has the potential to enhance vaccine efficacy by promoting stronger cross-epitope interactions. Additionally, by using multimeric constructs to increase epitope density, we maximize immune system recognition, potentially leading to more robust protective immunity.

The exclusion of potentially harmful components such as OmpA, which is associated with autoimmune responses, underlines the importance of careful antigen selection in vaccine design. This study demonstrates how computational tools, combined with innovative design concepts, can generate more targeted and effective vaccine candidates.

Future studies will be essential to validate these findings through in vitro and in vivo experiments, which will provide deeper insights into the immunogenic potential of the designed construct. Ultimately, the approaches presented here have broad implications, not only for vaccines targeting A. baumannii but also for the development of multiepitope vaccines against other emerging pathogens. The results emphasize that epitope-based vaccines, when designed with novel principles in mind, hold significant promise for addressing the pressing issue of antimicrobial resistance in a wide range of infectious diseases.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

MRR: Investigation, Writing, Review, Data curation, Editing, Visualization, Formal analysis. RM: Investigation, Writing, Review, Data curation, Editing, Visualization, Formal Analysis. HD: Investigation, Writing, Review, Data curation, Editing, Visualization, Formal Analysis. MZ: Investigation, Data curation, Formal analysis, Writing, Review, Editing. MHM: Resources, Conceptualization, Methodology, Project administration, Visualization, Funding acquisition, Supervision, Validation.

Funding

This work was supported by the Research and Technology Deputy of Shiraz University of Medical Sciences, Shiraz, Iran (Grant no. 29020).

Data availability

All data generated or analyzed during this study are included in this paper, its supplementary data, and in publicly available repositories, including UniProt Knowledge Base at [www.uniprot.org], and Protein Data Bank at [www.rcsb.org].

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The Ethics Committee of Shiraz University of Medical Sciences, Shiraz, Iran (IR.SUMS.REC. 1402.610) approved all procedures and protocols.