==== Front BMC Res Notes BMC Res Notes BMC Research Notes 1756-0500 BioMed Central London 6409 10.1186/s13104-023-06409-9 Research Note Two peptides derivate from Acinetobacter baumannii outer membrane protein K as vaccine candidates: a comprehensive in silico study Heidarinia Hana hana717m@gmail.com 1 Tajbakhsh Elahe ee_tajbakhsh@yahoo.com 1 Rostamian Mosayeb mosayeb.rostamian@gmail.com mosayeb.rostamian@kums.ac.ir 2 Momtaz Hassan hamomtaz@yahoo.com 1 1 grid.467523.1 0000 0004 0493 9277 Department of Microbiology, Faculty of Basic Sciences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran 2 grid.412112.5 0000 0001 2012 5829 Infectious Diseases Research Center, Health Institute, Kermanshah University of Medical Sciences, Imam Reza Hospital, Parastar Blvd, Kermanshah, 6714415333 Iran 30 6 2023 30 6 2023 2023 16 12811 2 2023 20 6 2023 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background The lack of appropriate vaccines is an obstacle to the effective management of A. baumannii infections. Peptide vaccines offer an attractive and promising preventive strategy against A. baumannii. Objective In this study, we identified specific T cell epitopes of A. baumannii outer membrane protein K (OMPK) using comprehensive bioinformatics and detailed molecular docking analysis. Methods Both class-I and class-II T cell epitopes of A. baumannii OMPK were predicted by three tools namely IEDB, SYFPEITHI, and ProPred. The predicted epitopes were shortlisted based on several analyses including prediction scoring, clustering, exclusion of human similarity, considering immunogenicity and cytokine production, and removal of toxic and/or allergen epitopes. The epitopic peptides with high prediction scores and appropriate properties containing both class-I and class-II T cell epitopes were selected. Two of these class I/II epitopic peptides were chosen for molecular docking studies and assessing their physicochemical properties as vaccine candidates. Results The results showed many T-cell epitopes of OMPK that could be evaluated for possible immunogenicity. Two of these epitopes (containing both class-I and II epitopes) had high prediction scores, were predicted by several tools, attached to several HLAs, and had the best docking score. They had different physicochemical properties and were conserved among Acinetobacter species. Discussion We identified the A. baumannii OMPK high immunogenic class-I and class-II T cell epitopes and introduced two promising high immunogenic peptides as vaccine candidates. It is recommended to perform in vitro/in vivo investigation of these peptides to determine their true efficacy and efficiency. Supplementary Information The online version contains supplementary material available at 10.1186/s13104-023-06409-9. Keywords Acinetobacter baumannii Epitope In silico Molecular docking Outer membrane protein K Shahrekord Branch of Islamic Azad Universityissue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2023 ==== Body pmcBackground Bacteria of the genus Acinetobacter are non-motile Gram-negative coccobacilli that are ubiquitous in nature but are mainly found in soil, water, and sewage [1]. Acinetobacter baumannii, an opportunistic pathogen responsible for hospital-acquired infections, is considered a serious threat to public health [2]. This bacterium causes pneumonia, urinary tract infection, blood infection, secondary meningitis, and wound infection [3]. In intensive care units (ICUs), A. baumannii is responsible for approximately 20.9% of all hospital-acquired infections in Europe, the Eastern Mediterranean, and Africa [4]. Hospital-acquired pneumonia is one of the most common clinical manifestations of A. baumannii infections and occurs mostly in patients receiving mechanical ventilation in ICUs [5]. A recent meta-analysis of 126 studies from 29 countries worldwide showed that multidrug-resistant A. baumannii was present in 79.9% of all cases of acquired pneumonia and ventilator-associated pneumonia (VAP) [6]. Acinetobacter baumannii can be spread through respiratory droplets such as respiratory secretions, sneezes, and saliva, as well as through person-to-person contact, skin picking, and contact with contaminated surfaces [7]. A. baumannii ability to propagate easily is mainly due to three characteristics: desiccation resistance, the ability to form biofilms on nonliving surfaces, and a propensity to adhere to host cells [8]. The increase in drug-resistant strains of A. baumannii has made it difficult to control using common antibiotics. Therefore, the development of effective vaccines is an alternative means to prevent infections caused by A. baumannii [7]. As a result of antibiotic shortages, the World Health Organization has designated A. baumannii as a key priority for the development of new medical countermeasures, including vaccine development [9]. Over the past decade, research efforts have identified several promising immunomodulatory strategies that could lead to a safe and effective vaccine against A. baumannii [9]. Several vaccine candidates have been tested for their ability to immunize model animals against A. baumannii infection. However, none of them have yet received approval for use in humans, and further research is still needed to develop a suitable vaccine candidate against A. baumannii [10]. Outer membrane proteins1 (OMPs) play a decisive role in antibiotic resistance and the pathogenicity of A. baumannii [11]. One of the challenges in targeting the outer membrane components of A. baumannii is the presence of a dense polysaccharide capsule that shields most of the outer membrane antigens from immune recognition [12]. OMPs are abundant in the outer membrane of bacteria and often protrude from the polysaccharide capsule, making them a suitable target for vaccines due to their ability to induce a strong antibody response, primarily IgG [13, 14]. The most effective A. baumannii vaccines are often composed of antigens present on the bacterial outer membrane, such as OMPA, OMP22, and OMPK [9]. Outer membrane protein K2 (OMPK), also known as TSX, is a 241 amino acid A. baumannii OMP that is specific for the nucleoside-forming ion channels. This protein is equivalent to OMPK in the fish pathogen Vibrio harveyi [15]. Previous studies on animal models have demonstrated that A. baumannii OMPK has significant immunogenic properties and is considered a promising vaccine candidate [15–17]. Both B cell and T cell responses seem to be involved in protection against A. baumannii, although no accurate information about OMPK epitopes vaccine potential can be found in the literature. The development of epitopic (peptide) vaccines could offer an attractive and viable treatment option for A. baumannii-related diseases. Today, in addition to molecular approaches, computer calculations and bioinformatics studies have provided a new method for vaccine creation through in silico epitope prediction. Numerous studies, similar to the present study, have utilized bioinformatics and sometimes laboratory analysis to investigate suitable vaccine candidates for various bacteria [18–23]. The present study aims to identify specific T-cell epitopes of A. baumannii OmpK through comprehensive bioinformatics and detailed molecular docking analysis, based on cell-dependent immune responses. In addition, this study introduces two T-cell epitopic peptides derived from OMPK, which exhibit suitable immunological and physicochemical properties, as potential A. baumannii vaccine candidates. Methods OMPK sequence The sequence of A. baumannii OMPK was retrieved from the protein database of NCBI (https://www.ncbi.nlm.nih.gov/protein, accession number: CRL96222.1). T cell epitopes prediction The most frequent human leukocyte antigen3 (HLA)-I (specific for class-I or CD8+ T cell-specific epitopes) and HLA-II (specific for class-II or CD4+ T cell-specific epitopes) alleles were determined using the Allele frequencies server (http://www.allelefrequencies.net/), the prediction servers, or using our previous experiences on K. pneumoniae antigens [24–26] (Additional file 1: Table S1). Similar to our previous studies [24, 27], the prediction of T cell epitopes was carried out using several epitope prediction tools including SYFPEITHI (http://www.syfpeithi.de/bin/MHCServer.dll/EpitopePrediction.htm), IEDB (http://tools.iedb.org), and ProPred (http://crdd.osdd.net/raghava/propred/). For the prediction of class-I and class-II epitopes, the percentile ranks of ≤ 1 and ≤ 10, respectively, were selected as the thresholds in IEDB. In ProPred-I and SYFPEITHI servers, the score of ≥ 10 was used as the threshold, whereas in ProPred-II all the predicted epitopes were chosen. Epitopes of 9 and 15 residues were chosen for the prediction of class-I and class-II epitopes, respectively. Regarding ≥ 70 similarities, the predicted epitopes were clustered with the IEDB clustering tool (http://tools.iedb.org/cluster/), and an epitope of each cluster was selected for advanced analyses based on its higher prediction score compared to the others. The immunogenicity of class-I epitopes was predicted by a specific IEDB tool (http://tools.iedb.org/immunogenicity/). The potency of class-II epitopes in interferon-gamma4 (IFN-γ) production was estimated by the IFNepitope server (http://crdd.osdd.net/raghava/ifnepitope/). Class II/class I windows and final T cell epitopes The class-II/class-I epitopic windows were found by the IEDB clustering tool by putting the cut-off at ≥ 70% similarity. The class-II epitopes possessing at least one class-I epitope were chosen. The final selection of T-cell epitopes was based on several criteria, including the epitopes predicted by multiple tools, those comprising both class-I and class-II epitopes, those with higher prediction scores, and class-II epitopes with IFN-γ production ability or class-I epitopes with higher immunogenicity scores. Toxicity, human similarity, allergenicity, and experimental records The T cell epitopes were assessed for possible toxicity using the ToxinPred server (https://webs.iiitd.edu.in/raghava/toxinpred/algo.php). The percentage of human similarity was estimated by two indicators of coverage and identity in the BLASTP server using the human proteome (taxid 9606) as the reference organism. The ≥ 90% similar epitopes to human proteome were excluded from further analyses. The AllerCatPro [28] and the Structural Database of Allergenic Proteins5 (SDAP) [29] tools, were used to predict possible allergenicity. Epitopes were searched for any experimental record using the IEDB home page (https://www.iedb.org/). Tertiary structures The structure of the OMPK protein was generated using the SWISS-MODEL server (https://swissmodel.expasy.org/, [30]), with the experimentally determined structure of the E. coli OMPK protein (ID: 1TLW) used as a template. The PEP-FOLD server [31] was applied to predict the tertiary structure of the epitopes. The structure of selected final HLA alleles was retrieved from the PDB databank with the following IDs: 5HHP for HLA-A*02:01 and 1BX2 for HLA-DRB1*15:01. The accuracy of the protein structure model was evaluated using ERRAT (https://saves.mbi.ucla.edu/, [32]), the Z-score plot provided by the ProSA-web server (https://prosa.services.came.sbg.ac.at/prosa.php, [33]), and the Ramachandran plot generated by the Molprobity server (http://molprobity.biochem.duke.edu/, [34]). Molecular docking studies For binding of class-I and -II epitopes to their HLA alleles, molecular docking studies were performed by HADDOCK 2.2 tool (http://haddock.science.uu.nl/services/HADDOCK2.2). Consensus prediction of interface residues in transient complexes (CPORT) (https://milou.science.uu.nl/services/CPORT/) was applied to accurate prediction of amino acid residues incorporated in the binding [35]. Docking was performed and the results were reported in terms of HADDOCK scores along with some secondary scores. As controls, the original ligands of HLA PDB files that were previously characterized were utilized. Prediction of physicochemical characteristics, structure analyses, and antigenicity The ProtParam tool (https://web.expasy.org/protparam/) [36] was used to estimate the physicochemical characteristics of the epitopes including molecular weight, isoelectric pH6 (pI), grand average of hydropathicity index7 (GRAVY), instability index, and the estimated half-life. The net charge and water solubility of the epitopes were predicted by PepCalc (https://pepcalc.com/) and the epitopes’ hydrophobicity was predicted by PEPTIDE 2.0 (https://www.peptide2.com/N_peptide_hydrophobicity_hydrophilicity.php). Epitope conservancy was checked via BLASTP and NCBI conserved domain search. The secondary structure prediction was done by the Stride Web interface (http://webclu.bio.wzw.tum.de/cgi-bin/stride/stridecgi.py). The structures and location of the epitopes were depicted by the Web3DMol server (http://web3dmol.net/). The simplified molecular-input line-entry system8 (SMILES) string of the peptides was obtained from the PepSMI server (https://www.novoprolabs.com/tools/convert-peptide-to-smiles-string) and used to depict 2D structures of the peptides. Antigenicity prediction of the final epitopes was carried out by the Vaxijen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html, [37]), and ANTIGENpro (https://scratch.proteomics.ics.uci.edu/) web servers. Results Prediction of T cell epitopes T cell epitopes were predicted and several analyses were conducted to screen the predicted epitopes as follows: (1) excluding duplicate epitopes and choosing one epitope from an epitope cluster, (2) choosing high-scored epitopes and/or the epitopes predicted by more prediction tools, (3) weighing positivity in IFN-γ production and higher immunogenicity score for class-II and class-I epitopes, respectively, as preferred criteria of epitope selection, and (4) excluding allergens, toxic epitopes, and the epitopes with human similarity of 90% or more. By employing these analyses, the dominant class-I and class-II epitopes were chosen, and also their class II/I windows were found by comparing both classes with each other. The number of initially predicted class-I and class-II epitopes were 608, and 441, respectively, which were reduced to 92 and 62 following clustering and 22 and 27 after screening strategies (Table 1).Table 1 The number of predicted T cell epitopes of A. baumannii OMPK before and after clustering and screening Prediction tool Class-I Class-II IEDB SYFPEITHI ProPred-I Total IEDB SYFPEITHI ProPred-II Total Before clustering 85 477 46 608 118 307 16 441 After clustering 28 37 27 92 13 41 8 62 After screeninga – – – 22 – – – 27 aScreening include excluding duplicates, toxic, allergens, and human peptides similar epitopes and choosing high-scored, more frequently predicted, potent in IFN-γ production, and immunogenic epitopes The characteristics of short-listed epitopes None of our predicted epitopes was toxic or allergen. Also, none of our selected epitopes had ≥ 90% similarity with the human peptides. In addition, no previous experimental records were found on the epitopes. The characteristics of class II/I epitopic windows Five class II/I epitopic windows were found (Table 2). The table also presents the prediction scores of each epitope by each prediction tool, as well as information on toxicity, allergenicity, and human similarity of the epitopes. Two class-II/class-I windows, named Ep1 and Ep2, were selected for further studies.Table 2 The class II/I epitopic windows of A. baumannii OMPK Class II/I epitopic windowb Class-II epitopes Epitope HLA allele Scorea SYFPEITHI ProPred-II IEDB IFNg Toxicity Allergenicity Docking Human similarity (coverage %, identity %) Test Control DYQMTFVYGIPFKIA DYQMTFVYGIPFKIA DRB1*15:01 20 N/A 7.3 + – – − 23.6 ± 16.1 − 21.4 ± 13.1 100, 62.5 QLKQLAATCALLSAT QLKQLAATCALLSAT DRB1*07:01 14 N/A 8.5 + – – N/A N/A 100, 45 AKVKYADVFFFMDRM AKVKYADVFFFMDRM DRB1*15:01 10 N/A N/A + – – N/A N/A 86, 57.1 IPYFQYANLNFYRAN IPYFQYANLNFYRAN DRB1*15:01 N/A + 4.6 – – – N/A N/A 40, 64.2 GKHISPDTRLYLGIE GKHISPDTRLYLGIE DRB1*15:01 20 N/A N/A – – – − 39.7 ± 16.1 − 41.4 ± 15.1 86, 53.8 Class II/I epitopic windowb Class-I epitopes Epitope HLA allele Scorea SYFPEITHI ProPredI IEDB Immunogenicity Toxicity Allergenicity Docking Human similarity (coverage %, identity %) Test Control DYQMTFVYGIPFKIA FVYGIPFKI A*02:01 18 12 0.03 0.13 – – − 20.9 ± 14.0 − 22.4 ± 11.0 88, 75 QLKQLAATCALLSAT KQLAATCAL A*02:01 16 28 N/A 0.09 – – N/A N/A 100, 77.8 AKVKYADVFFFMDRM KYADVFFFM A*24:02 100 N/A 0.01 0.35 – – N/A N/A 88, 58.3 IPYFQYANLNFYRAN IPYFQYANL B*35:01 20 20 0.64 0.03 – – N/A N/A 77, 77.8 GKHISPDTRLYLGIE ISPDTRLYL A*02:01 14 N/A N/A 0.09 – – − 22.5 ± 7.8 − 25.0 ± 7.8 100, 77.8 Only the highest scores obtained from prediction tools are presented here. Also, only the most repeated HLA allele is presented for epitopes N/A not available aFor IEDB, the lower percentile rank (number) means the higher score (thresholds: 1 for class-I and 10 for class-II epitopes); for SYFPEITHI and ProPred-I, the higher scores are shown by the higher numbers (thresholds are equal to 10 for both tools); for ProPred-II, the “+” indicates that the epitope is predicted by the tool; in immunogenicity, the more immunogenic epitope is shown by the more positive number; in IFNg column, the “+” shows that the epitope is able to produce IFNg; in Toxicity and Allergenicity columns, being not toxic or allergen is presented by the “–”; in Docking, HADDOCK scores (kcal/mol) are presented as affinity ± standard deviation (test is our complex and control is the complex of HLA with its original peptides); in Human similarity, the numbers show the highest percentage of coverage and identity values reported by BLASTP server bIn this column, the regions containing both class-II and class-I epitopes (the epitopic windows) are presented. The bold fonts show the class I epitope residing in the class II epitope Evaluation of protein/epitopes tertiary structures The tertiary structures of the OMPK protein and the final epitopes (Ep1 and Ep2) were modeled. The overall quality factor for Ep1, Ep2, and OMPK, as determined by ERRAT, was 79.21, 73.13, and 89.67, respectively. The Ramachandran plot showed that most amino acid residues in all three molecules (Ep1, Ep2, and OMPK) were located in the allowed and/or favored regions (Fig. 1). Moreover, the Z-scores for these molecules were 0.6, − 2.18, and -2.5 respectively, which fell within the range of scores typically observed for native proteins of similar size from different sources (X-ray, NMR) (Fig. 1).Fig. 1 Structural quality validation of A. baumannii OMPK and its final peptides derivate (Ep1 and Ep2). The upper parts of the figure show the Ramachandran plots of the modeled structures of Ep1, Ep2, and the full OmpK protein. The number of residues located in the favored and allowed regions of the Ramachandran plots are shown below each plot. The lower parts of the figure present the ProSA-web Z-score plots. These plots demonstrate that the models of Ep1, Ep2, and the full OmpK protein (represented by black points) all fall within the range of scores typically observed for native proteins of similar size (represented by colored regions). The Z-score of each model is also shown below its corresponding plot Molecular docking results Class-II epitopes located in Ep1 and Ep2 were predicted to bind HLA-DRB1*15:01. Similarly, class-I epitopes were bound to HLA-A*02:01. After retrieving the 3D structures of epitopes (ligands) and HLA molecules (receptors), ligand-receptor docking was performed by the HADDOCK tool. The docking scores of our class I epitope-HLA and class II epitope-HLA were higher than the docking scores of HLA combined with their original peptides (controls), showing the high affinity of our epitopes to their specific HLA molecules (Table 2). The structure, antigenicity, and physicochemical properties of the final peptides Table 3 presents the physicochemical properties of the two final epitopes (peptides). Both peptides consist of 15 amino acid residues. The Ep1 (DYQMTFVYGIPFKIA) peptide (1793.11 Dalton) had 53.33% hydrophobicity and a pI of 5.83. Its estimated half-life was 1.1 h in mammalian reticulocytes in vitro and > 10 min in E. coli, in vivo. The peptide is poorly water-soluble, but it is stable with a net charge of zero. The Ep2 (GKHISPDTRLYLGIE) peptide (1698.94Dalton) had 33.33% hydrophobicity and a pI of 6.75. Its estimated half-life was > 30 h in mammalian reticulocytes in vitro and > 10 min in E. coli, in vivo. The peptide is good water-soluble, but it is unstable with a net charge of 0.1.Table 3 The properties of final peptides derivated from A. baumannii OMPK Peptide name Sequence Number of amino acid residues Molecular weight (Da) Hydrophobicity (%) Estimated water solubility Net charge at pH = 7.0 pI GRAVY Instability index Estimated half-life Antigenicitya Vaxijen score ANTIGENpro score Ep1 DYQMTFVYGIPFKIA 15 1793.11 53.33 Poor 0 5.83 0.42 − 5.47, stable 1.1 h (mammalian reticulocytes, in vitro) 1.75 (antigen) 0.046 (non-antigen) 3 min (yeast, in vivo) > 10 min (Escherichia coli, in vivo) Ep2 GKHISPDTRLYLGIE 15 1698.94 33.33 Good 0.1 6.75 − 0.5 70.85, unstable 30 h (mammalian reticulocytes, in vitro) 0.42 (antigen) 0.077 (non-antigen) > 20 min (yeast, in vivo) > 10 min (Escherichia coli, in vivo) GRAVY grand average of hydropathicity index, pI isoelectric pH aAntigenicity was assessed by Vaxijen and ANTIGENpro. The threshold of Vaxijen was 0.4, so Ep1 and Ep2 which had higher scores were predicted as antigens. In ANTIGENpro, whatever the scores close to 1.0 they were assumed as more probable antigens. None of our epitopes had a high ANTIGENpro score, hence predicted as non-antigen Both Ep1 and Ep2 peptides were predicted to be antigens by the Vaxijen server, while neither of them was predicted to be an antigen by the ANTIGENpro server (Table 3). Both peptides are mainly composed of turns and they were both located on the lateral surfaces of the OMPK structure (Fig. 2). The peptides were conserved among Acinetobacter species, but not among other species except ion channel protein TSX of Klebsiella pneumoniae.Fig. 2 The structure and location of final peptides derivate (Ep1 and Ep2) from A. baumannii OMPK. The 3D structure of Ep1 (DYQMTFVYGIPFKIA) and Ep2 (GKHISPDTRLYLGIE) peptides, along with their corresponding simplified molecular-input line-entry system (SMILES) strings and locations in the 3D structure of OMPK, are presented. As shown, both Ep1 and Ep2 are located on the lateral surfaces of the OMPK structure Discussion Previous research on OMPs has shown that these proteins can trigger defense against A. baumannii infections through both cell-mediated and humoral immunity. OMPK was able to trigger both humoral (IgG antibodies) and cytokines responses, which provided defense against a fatal challenge with A. baumannii [15, 16, 38]. The efficiency of OMPK vaccination was improved when fused with another OMP, called OMP22, or when adjuvanted with MF59 [15, 16]. MF59 is an oil-in-water emulsion adjuvant that has been shown to elicit innate immunity, vaccine-specific IgG antibodies, and effector CD8 T cells [39–41]. In addition to MF59, various other adjuvants have been developed that can be used to boost immunogenicity. One such example is Toll-like receptor (TLR) agonists, which have been shown to possess potent adjuvant activity in our previous studies and others [42–46]. Host defense against A. baumannii infections likely involves significant contributions from humoral immune responses, including IgM, IgG, and IgA [47]. The T cell immune responses to A. baumannii infections are still poorly understood. According to a small number of experimental studies, A. baumannii infection severity was linked to higher levels of Th1 inflammatory cytokine responses and lower levels of IL-10, suggesting a protective effect of Th2 immune responses [48, 49]. However, the precise functions of Th1 and Th2 in host defense against A. baumannii infections are still unknown [50], demanding more research in this field. In the present study, we found two potent T cell epitopic peptides (Ep1 and Ep2) that possess many favorable properties as A. baumannii vaccine candidates. They were both immunogenic, non-toxic, non-allergen, and non-similar to the human proteome. In addition, they contain both class I and II T cell epitopes that were found to bind to their HLAs with high affinity. Ep1 was predicted to be able to produce IFN-γ while Ep2 could not, but it should not be assumed as a negative criterion since as mentioned above, the role of Th1 cytokines such as IFN-γ is not clearly understood in protection against A. baumannii [50]. In the present study, the modeled 3D structures underwent verification using in silico tools. The majority of amino acid residues in our modeled protein (OMPK) and its final epitopic regions (Ep1 and Ep2) were located in the allowed/favored regions of the Ramachandran plot, indicating the high quality of our models. Furthermore, we utilized the ERRAT assessment to identify any potential anomalies in the structure [32], which showed a high-quality factor for our model. A factor closer to 100 in the ERRAT assessment indicates a better model quality. Similarly, the quality of our models was demonstrated by the Z-score, which is correlated with experimentally determined X-ray and/or NMR structures [33]. The results of the present study showed that Ep1 and Ep2 peptides have different physicochemical properties so Ep1 is stable, poor water-soluble, more hydrophobic, and with a lower estimated half-life than Ep2. Being more hydrophilic is indicated by the negative GRAVY [36], as seen for Ep2 but not Ep1. The pI index is a crucial consideration when choosing a vaccine candidate and shouldn't fall within the range of body tissues’ pH (7.2–7.6) [51]. The pI of both Ep1 and Ep2 peptides was outside of the pH range of body tissues. These parameters suggested both peptides as potential vaccine candidates for more in vitro and in vivo studies. Notably, the properties of these peptides can be improved using various chemical/biochemical techniques. Furthermore, these epitopes can be incorporated into the design of multi-valent and multi-epitope vaccine candidates, which can potentially be more stable and effective. Previous studies have employed similar strategies for vaccination against A. baumannii [20, 23, 52, 53]. Both epitopes identified in this study are located on the surface of the OMPK protein, making them easily accessible to antibodies. This feature, in addition to their potential as vaccine candidates, also makes them suitable candidates for the diagnosis of A. baumannii-related diseases. The epitopes may be chemically synthesized or cloned/expressed and utilized as radiochemical probes in designing diagnostic kits [54]. The conservancy study of the Ep1 and Ep2 peptides showed that they do not belong to a conserved domain superfamily, but they are highly conserved among Acinetobacter species, hence it may induce immune responses against many species of this genus. Also, both peptides had 100% similarity to a sequence of ion channel protein TSX of K. pneumoniae, so they may induce immune responses against K. pneumoniae as well. In recent years, the number of bioinformatics studies on A. baumannii vaccines has increased significantly. OMPA [55, 56], DcaP [57], NucAb [58], and TonB-dependent copper receptor [59] can be mentioned among the OMP antigens of A. baumannii that have been studied in silico. Collectively, the results of our study, along with previous research provide promising prospects for the development of a suitable vaccine based on OMPs to combat A. baumannii infections in the future. Altogether, we identified the A. baumannii OMPK high immunogenic class-I and class-II T cell epitopes, which can be utilized in future research. Additionally, two high immunogenic peptides containing both T cell epitope classes as well as the necessary physicochemical properties for the development of an A. baumannii vaccine were introduced. It is recommended to do an in vitro/in vivo investigation of these peptides to determine their true efficacy and efficiency. Limitations Limited immunity due to several factors such as easy degradation and their recognition difficulty is linked to epitope-based vaccinations, such as our suggested Ep1 and Ep2 peptides. Conjugating them with conventional adjuvants and/or novel built-in adjuvants, such as new biomaterials or carriers, is one efficient technique to bypass this limitation [60]. Another limitation of our suggested vaccine candidate peptides is that they will primarily be given to older and immunocompromised individuals that are not able to mount strong immune responses [61]. Supplementary Information Additional file 1: Table S1. The list of HLA alleles used for A. baumannii OMPK T cell epitope prediction. Acknowledgements The support of this work by the Shahrekord Branch of Islamic Azad University and the Infectious Diseases Research Center of Kermanshah University of Medical Sciences is highly appreciated. Author contributions MR contributed to the study's conception and design. Data collection was done by HH, HM, and ET. Data analysis was performed by ET and MR. Funding acquisition and resource preparation were done by ET. The first draft of the manuscript was written by HH and MR. All authors read and approved the final manuscript. Funding None. Availability of data and materials All data generated or analyzed during this study are included in this published article. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that there is no competing interests. 1 OMPs: outer membrane proteins. 2 OMPK: outer membrane protein K. 3 HLA: human leukocyte antigen. 4 IFN-γ: interferon-gamma. 5 SDAP: Structural Database of Allergenic Proteins. 6 pI: isoelectric pH. 7 GRAVY: grand average of hydropathicity index. 8 SMILES: simplified molecular-input line-entry system. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Bergogne-Bérézin E Bacteria: acinetobacter Encyclopedia of food safety 2014 Amsterdam Elsevier 337 341 2. Garnacho-Montero J Timsit J-F Managing Acinetobacter baumannii infections Curr Opin Infect Dis 2019 32 69 76 10.1097/QCO.0000000000000518 30520737 3. Munoz-Price LS Weinstein RA Acinetobacter infection N Engl J Med 2008 358 1271 1281 10.1056/NEJMra070741 18354105 4. Ayobami O Willrich N Harder T Okeke IN Eckmanns T Markwart R The incidence and prevalence of hospital-acquired (carbapenem-resistant) Acinetobacter baumannii in Europe, Eastern Mediterranean and Africa: a systematic review and meta-analysis Emerg Microbes Infect 2019 8 1747 1759 10.1080/22221751.2019.1698273 31805829 5. Howard A O’Donoghue M Feeney A Sleator RD Acinetobacter baumannii Virulence 2012 3 243 250 10.4161/viru.19700 22546906 6. Mohd Sazlly Lim S Zainal Abidin A Liew SM Roberts JA Sime FB The global prevalence of multidrug-resistance among Acinetobacter baumannii causing hospital-acquired and ventilator-associated pneumonia and its associated mortality: a systematic review and meta-analysis J Infect 2019 79 593 600 10.1016/j.jinf.2019.09.012 31580871 7. Ibrahim S Al-Saryi N Al-Kadmy IMS Aziz SN Multidrug-resistant Acinetobacter baumannii as an emerging concern in hospitals Mol Biol Rep 2021 48 6987 6998 10.1007/s11033-021-06690-6 34460060 8. Gedefie A Demsiss W Belete MA Kassa Y Tesfaye M Tilahun M Acinetobacter baumannii biofilm formation and its role in disease pathogenesis: a review Infect Drug Resist 2021 14 3711 3719 10.2147/IDR.S332051 34531666 9. Gellings PS Wilkins AA Morici LA Recent advances in the pursuit of an effective Acinetobacter baumannii vaccine Pathogens 2020 9 1066 10.3390/pathogens9121066 33352688 10. Chen W Current advances and challenges in the development of Acinetobacter vaccines Hum Vaccin Immunother 2015 11 2495 2500 10.1080/21645515.2015.1052354 26158773 11. Kumar S Anwer R Azzi A Virulence potential and treatment options of multidrug-resistant (MDR) Acinetobacter baumannii Microorganisms 2021 9 2104 10.3390/microorganisms9102104 34683425 12. Wang-Lin SX Olson R Beanan JM MacDonald U Balthasar JP Russo TA The capsular polysaccharide of Acinetobacter baumannii is an obstacle for therapeutic passive immunization strategies Infect Immun 2017 85 e00591-17 10.1128/IAI.00591-17 28947651 13. Huang W Yao Y Wang S Xia Y Yang X Long Q Immunization with a 22-kDa outer membrane protein elicits protective immunity to multidrug-resistant Acinetobacter baumannii Sci Rep 2016 6 20724 10.1038/srep20724 26853590 14. Zhang X Yang T Cao J Sun J Dai W Zhang L Mucosal immunization with purified OmpA elicited protective immunity against infections caused by multidrug-resistant Acinetobacter baumannii Microb Pathog 2016 96 20 25 10.1016/j.micpath.2016.04.019 27133268 15. Yang A Yang H Guo S Xie Y MF59 adjuvant enhances the immunogenicity and protective immunity of the OmpK/Omp22 fusion protein from Acineterbacter baumannii through intratracheal inoculation in mice Scand J Immunol 2019 90 e12769 10.1111/sji.12769 31006127 16. Guo SJ Ren S Xie YE Evaluation of the protective efficacy of a fused OmpK/Omp22 protein vaccine candidate against Acinetobacter baumannii infection in mice Biomed Environ Sci 2018 31 155 158 29606195 17. Moriel DG Beatson SA Wurpel DJ Lipman J Nimmo GR Paterson DL Identification of novel vaccine candidates against multidrug-resistant Acinetobacter baumannii PLoS ONE 2013 8 e77631 10.1371/journal.pone.0077631 24116234 18. Dey J Mahapatra SR Raj TK Kaur T Jain P Tiwari A Designing a novel multi-epitope vaccine to evoke a robust immune response against pathogenic multidrug-resistant Enterococcus faecium bacterium Gut Pathog 2022 14 21 10.1186/s13099-022-00495-z 35624464 19. Dey J Mahapatra SR Lata S Patro S Misra N Suar M Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia Expert Rev Vaccines 2022 21 569 587 10.1080/14760584.2022.2021882 34932430 20. Mahapatra SR Dey J Jaiswal A Roy R Misra N Suar M Immunoinformatics-guided designing of epitope-based subunit vaccine from Pilus assembly protein of Acinetobacter baumannii bacteria J Immunol Methods 2022 508 113325 10.1016/j.jim.2022.113325 35908655 21. Chatterjee R Mahapatra SR Dey J Raj Takur K Raina V Misra N An immunoinformatics and structural vaccinology study to design a multi-epitope vaccine against Staphylococcus aureus infection J Mol Recognit 2023 36 e3007 10.1002/jmr.3007 36700877 22. Dey J Mahapatra SR Patnaik S Lata S Kushwaha GS Panda RK Molecular characterization and designing of a novel multiepitope vaccine construct against Pseudomonas aeruginosa Int J Pept Res Ther 2022 28 49 10.1007/s10989-021-10356-z 35069055 23. Touhidinia M Sefid F Bidakhavidi M Design of a multi-epitope vaccine against Acinetobacter baumannii using immunoinformatics approach Int J Pept Res Ther 2021 27 2417 2437 10.1007/s10989-021-10262-4 34483787 24. Rostamian M Farasat A Chegene Lorestani R Nemati Zargaran F Ghadiri K Akya A Immunoinformatics and molecular dynamics studies to predict T-cell-specific epitopes of four Klebsiella pneumoniae fimbriae antigens J Biomol Struct Dyn 2020 40 1 11 32469279 25. Nemati Zargaran F Akya A Ghadiri K Ranjbarian P Rostamian M Detecting the dominant T and B epitopes of Klebsiella pneumoniae ferric enterobactin protein (FepA) and introducing a single epitopic peptide as vaccine candidate Int J Pept Res Ther 2021 27 2209 2221 10.1007/s10989-021-10247-3 34226823 26. Zargaran FN Akya A Rezaeian S Ghadiri K Lorestani RC Madanchi H B cell epitopes of four fimbriae antigens of Klebsiella pneumoniae: a comprehensive in silico study for vaccine development Int J Pept Res Ther 2020 10.1007/s10989-020-10134-3 33250677 27. Akya A Farasat A Ghadiri K Rostamian M Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches Infect Genet Evol 2019 75 103953 10.1016/j.meegid.2019.103953 31284043 28. Maurer-Stroh S Krutz NL Kern PS Gunalan V Nguyen MN Limviphuvadh V AllerCatPro—prediction of protein allergenicity potential from the protein sequence Bioinformatics 2019 35 3020 3027 10.1093/bioinformatics/btz029 30657872 29. Ivanciuc O SDAP: database and computational tools for allergenic proteins Nucleic Acids Res 2003 31 359 362 10.1093/nar/gkg010 12520022 30. Waterhouse A Bertoni M Bienert S Studer G Tauriello G Gumienny R SWISS-MODEL: homology modelling of protein structures and complexes Nucleic Acids Res 2018 46 W296 303 10.1093/nar/gky427 29788355 31. Shen Y Maupetit J Derreumaux P Tuffery P Improved PEP-FOLD approach for peptide and miniprotein structure prediction J Chem Theory Comput 2014 10 4745 4758 10.1021/ct500592m 26588162 32. Colovos C Yeates TO Verification of protein structures: patterns of nonbonded atomic interactions Protein Sci 1993 2 1511 1519 10.1002/pro.5560020916 8401235 33. Wiederstein M Sippl MJ ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins Nucleic Acids Res 2007 35 Web Server W407 W410 10.1093/nar/gkm290 17517781 34. Chen VB Arendall WB Headd JJ Keedy DA Immormino RM Kapral GJ MolProbity: all-atom structure validation for macromolecular crystallography Acta Crystallogr Sect D Biol Crystallogr 2010 66 12 21 10.1107/S0907444909042073 20057044 35. de Vries SJ Bonvin AMJJ CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK PLoS ONE 2011 6 e17695 10.1371/journal.pone.0017695 21464987 36. Adhikari UK Tayebi M 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 10.1155/2018/6718083 30402510 37. Doytchinova IA Flower DR VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines BMC Bioinform 2007 8 4 10.1186/1471-2105-8-4 38. Chiang M-H Sung W-C Lien S-P Chen Y-Z Lo AF Huang J-H Identification of novel vaccine candidates against Acinetobacter baumannii using reverse vaccinology Hum Vaccin Immunother 2015 11 1065 1073 10.1080/21645515.2015.1010910 25751377 39. Ko E-J Kang S-M Immunology and efficacy of MF59-adjuvanted vaccines Hum Vaccin Immunother 2018 14 3041 3045 10.1080/21645515.2018.1495301 30015572 40. O’Hagan DT Ott GS De Gregorio E Seubert A The mechanism of action of MF59—an innately attractive adjuvant formulation Vaccine 2012 30 4341 4348 10.1016/j.vaccine.2011.09.061 22682289 41. O’Hagan DT Ott GS Van NG Rappuoli R Del GG The history of MF59 ® adjuvant: a phoenix that arose from the ashes Expert Rev Vaccines 2013 12 13 30 10.1586/erv.12.140 23256736 42. Rostamian M Bahrami F Niknam HM Vaccination with whole-cell killed or recombinant leishmanial protein and toll-like receptor agonists against Leishmania tropica in BALB/c mice PLoS ONE 2018 13 e0204491 10.1371/journal.pone.0204491 30248142 43. Rostamian M Niknam HM Evaluation of the adjuvant effect of agonists of toll-like receptor 4 and 7/8 in a vaccine against leishmaniasis in BALB/c mice Mol Immunol 2017 91 202 208 10.1016/j.molimm.2017.09.010 28963929 44. Kennerknecht K Noschka R Löffler F Wehrstedt S Pedersen GK Mayer D Toll like-receptor agonist Pam3Cys modulates the immunogenicity of liposomes containing the tuberculosis vaccine candidate H56 Med Microbiol Immunol 2020 209 163 176 10.1007/s00430-020-00657-3 32020284 45. Aly I Ibrahim EH Hamad RS Sayed HEL Attiyah SMN E-Komy W The protective role of toll-like receptor agonist monophosphoryl lipid A against vaccinated murine schistosomiasis Acta Parasitol 2020 65 652 660 10.2478/s11686-020-00204-3 32240492 46. Yang J-X Tseng J-C Yu G-Y Luo Y Huang C-YF Hong Y-R Recent advances in the development of toll-like receptor agonist-based vaccine adjuvants for infectious diseases Pharmaceutics 2022 14 423 10.3390/pharmaceutics14020423 35214155 47. Islam AHMS Singh K-KB Ismail A Demonstration of an outer membrane protein that is antigenically specific for Acinetobacter baumannii Diagn Microbiol Infect Dis 2011 69 38 44 10.1016/j.diagmicrobio.2010.09.008 21146712 48. de Breij A Eveillard M Dijkshoorn L van den Broek PJ Nibbering PH Joly-Guillou M-L Differences in Acinetobacter baumannii strains and host innate immune response determine morbidity and mortality in experimental pneumonia PLoS ONE 2012 7 e30673 10.1371/journal.pone.0030673 22347396 49. Luo G Lin L Ibrahim AS Baquir B Pantapalangkoor P Bonomo RA Active and passive immunization protects against lethal, extreme drug resistant-Acinetobacter baumannii infection PLoS ONE 2012 7 e29446 10.1371/journal.pone.0029446 22253723 50. Chen W Host innate immune responses to Acinetobacter baumannii infection Front Cell Infect Microbiol 2020 10 486 10.3389/fcimb.2020.00486 33042864 51. Campos-Pinto I Méndez L Schouten J Wilkins J Fedorova M Pitt AR Epitope mapping and characterization of 4-hydroxy-2-nonenal modified-human serum albumin using two different polyclonal antibodies Free Radic Biol Med 2019 144 234 244 10.1016/j.freeradbiomed.2019.05.008 31075498 52. Khalid K Irum S Ullah SR Andleeb S In-silico vaccine design based on a novel vaccine candidate against infections caused by Acinetobacter baumannii Int J Pept Res Ther 2022 28 16 10.1007/s10989-021-10316-7 34873398 53. Dey J Mahapatra SR Singh PK Prabhuswamimath SC Misra N Suar M Designing of multi-epitope peptide vaccine against Acinetobacter baumannii through combined immunoinformatics and protein interaction-based approaches Immunol Res 2023 10.1007/s12026-023-09374-4 37022613 54. Dutta J Naicker T Ebenhan T Kruger HG Arvidsson PI Govender T Synthetic approaches to radiochemical probes for imaging of bacterial infections Eur J Med Chem 2017 133 287 308 10.1016/j.ejmech.2017.03.060 28395216 55. Mehdinejadiani K Bandehpour M Hashemi A Ranjbar MM Taheri S Jalali SA In silico design and evaluation of Acinetobacter baumannii outer membrane protein A (OmpA) antigenic peptides as vaccine candidate in immunized mice Iran J Allergy Asthma Immunol 2020 10.18502/ijaai.v18i6.2178 56. Jahangiri A Rasooli I Owlia P Fooladi AAI Salimian J In silico design of an immunogen against Acinetobacter baumannii based on a novel model for native structure of Outer membrane protein A Microb Pathog 2017 105 201 210 10.1016/j.micpath.2017.02.028 28242426 57. Raoufi Z Abdollahi S Armand R DcaP porin and its epitope-based subunit promise effective vaccines against Acinetobacter baumannii; in-silico and in-vivo approaches Microb Pathog 2022 162 105346 10.1016/j.micpath.2021.105346 34864145 58. Garg N Singh R Shukla G Capalash N Sharma P Immunoprotective potential of in silico predicted Acinetobacter baumannii outer membrane nuclease NucAb Int J Med Microbiol 2016 306 1 9 10.1016/j.ijmm.2015.10.005 26614015 59. Abdollahi S Rasooli I Mousavi Gargari SL An in silico structural and physicochemical characterization of TonB-dependent copper receptor in A. baumannii Microb Pathog 2018 118 18 31 10.1016/j.micpath.2018.03.009 29524546 60. Lei Y Zhao F Shao J Li Y Li S Chang H Application of built-in adjuvants for epitope-based vaccines PeerJ 2019 6 e6185 10.7717/peerj.6185 30656066 61. Weinberger B Herndler-Brandstetter D Schwanninger A Weiskopf D Grubeck-Loebenstein B Biology of immune responses to vaccines in elderly persons Clin Infect Dis 2008 46 1078 1084 10.1086/529197 18444828