Molecular characterization and epitope-based vaccine predictions for ompA gene associated with biofilm formation in multidrug-resistant strains of A.baumannii


The present study was conducted to molecularly characterize the biofilm associated ompA gene from the drug resistant strains of A. baumannii and its immuno-dominant vaccine epitope predictions through immuno-informatic approach. ompA was amplified by PCR from the genomic DNA and was sequenced. Using the ORF, ompA protein sequence was retrieved and was subjected for IEDB T cell and B cell epitope analysis for the selection of the epitope peptides. Selected peptides were evaluated using appropriate servers and tools to assess the propensity for its antigenicity, solubility, physico-chemical property, toxigenicity and class-I immunogenicity. MHC class I and II restriction of HLA alleles was also performed. 48% (n = 24) of the strains possessed ompA gene. Protein structure was successfully retrieved with the selection of two epitopes viz., E1- FDGVNRGTRGTSEEGTLGNA and E2-KLSEYPNATARIEGHTDNTGPRKL. Final docking with TLR-2, showed E2 as the best epitope candidate predicted with the highest number of hydrogen bonds.


Acinetobacter baumannii is an opportunistic, aerobic, non-motile, pleomorphic gram-negative bacteria and is commonly associated with nosocomial infections (Howard et al. 2012). Over the last 15 years, it has been considered as the most burdensome gaining incredible attention due to its ability to acquire resistance determinants, threatening the current era of antibiotics (Peleg et al. 2008). A. baumannii is a saprophyte growing luxuriously in aquatic habitat while in the in-vitro cultures, it is mostly characterized from urine samples, respiratory secretions, and wounds and thus seems to be associated with a range of infections like urinary tract infection, meningitis, bacteremia, and pneumonia (Dexter et al. 2015). Having been declared as a priority pathogen by WHO (2017), the high-risk group are usually the patients in hospitals because A. baumannii is known to colonize on the in-built devices through its potent biofilm formation (Fournier et al. 2006).

Acinetobacter baumannii establishes the infection in hospitalized host mainly through the biofilm formation on both biotic and abiotic surfaces and thus the same fact is considered as a potent virulent factor (Eze et al. 2018). Formation of biofilms in A.baumannii is associated with various genes such as PER1, bap, csu, csgA, KpsMT, etc., amidst which ompA gene takes a huge role in biofilm and porin formation (Gaddy et al. 2009). A review by Muhammed Asif et al. (Asif et al. 2018), has documented that ompA in A.baumannii aids in improving adhesion to the respiratory tract, over-expression resulting in cell death and modulation in the host immune response. In addition, studies have also documented the prevalence of ompA due to its overexpression and up-regulation of the same, stimulating the formation and development of biofilm layers (Cassin and Tseng, 2019; Orme et al. 2006). The role of ompA and its proteomic chemistry in biofilm formation was detailed in B. fragilis and in Sodalis commensal bacteria (Cassin and Tseng, 2019). Studies also report the failure of the biofilm formation in the mutant strains of A. baumannii and in the strains that do not express the ompA production (Maltz et al. 2012). It is exciting to note that the ability of A. baumannii to survive in dry, harsh environment is highly associated with ompA based biofilms leading to a mortality rate ranging from 26 to 68% (Vijayakumar et al. 2016).

In this context, assessing the correlation of ompA in MDR strains and targeting the ompA gene/protein associated with virulence would be a promising alternating strategy to combat the menace of drug resistance. Conventional vaccine models yielding partial protection, the prediction of immuno-dominant peptides, and its antigenic potential by applying the bioinformatics tools and databases is a fascinating approach leading to peptide synthesis and further vaccine development. In this context, the present investigation is undertaken to molecularly characterize the ompA gene among the drug-resistant A. baumannii strains and to predict the immuno-dominant vaccine candidates by immune-informatic approach.


Extraction of genomic DNA

Fresh cultures of 50 multidrug-resistant strains of A. baumannii which were maintained at – 80 °C in 80%/20% (v/v) glycerol in LB medium from our repertoire used in our earlier studies (Smiline et al. 2019) was retrieved on Mac Conkey agar with incubation at 37 °C for 24 h. Genomic DNA was extracted using the Qiagen DNA extraction kit conforming to the instructions provided by the manufacturer. The procured DNA was stored at − 20 °C up to the time of further use.

PCR amplification of ompA

7.8 µl of 2 × master mix [Taraka, Japan] in 5.6 µl of double-distilled water, in addition to 0.31 µl of 100 pmol/ml concentration of the specific F’primer and R’primer [Eurofins Genomic India Pvt Ltd, Bangalore] of ompA gene was used to formulate 15 µl of the PCR reaction mixture. 1 µl of the extracted genomic DNA was combined with the master mix and PCR was performed for amplification. F: GCGCCACAACCAAGCAATTA and R: GCGCCACAACCAAGCAATTA primers were used for the same. Using the reaction condition of annealing temperature as 58 °C for 35 cycles, PCR amplification for ompAwas carried out in Eppendorfthermocycler, Germany. 1% agarose gel electrophoresis containing ethidium bromide was used to examine the resulting PCR amplicons. The results were visualized by the gel documentation system. The PCR amplicon size was assessed using the 100 bp DNA ladder.

Sequencing of the ompA for amplicon confirmation:

The using Big-Dye terminator cycle sequencing kit and 3730XL Genetic Analyzer is used to bi-directionally sequence the amplicon product of ompA. Bio-Edit Sequence Alignment Editor v7.2.5 was used to align the sequences from forward and reverse primers. For nucleotide similarity search it was subjected to BLAST (Basic Local Alignment Search Tool). ClustalW software version 1.83 was used to align the sequences using default parameters.

ORF and Blast-p similarity analysis

The genomic sequence of ompA obtained by molecular sequencing of the amplicon was further subjected to open reading frame (ORF) analysis in ORF finder and programmed for the protein detection. The obtained protein sequenced was then predicted for the specific ompA protein with further multiple sequence alignment for the protein and its phylogenetic evaluation with BLASTp. A BLASTp analysis was performed on the sequence against the non-redundant protein sequence database and a non-human homologous protein sequence database of Homosapiens taxid 9606 was interpreted and recorded. Phylogenetic similarity analysis was done using the protein sequence of the gene as evaluated using the gene sequence obtained from the sequence analysis.

Analysis of druggability, physico-chemical properties, and antigenicity

The protein of choice (ompA) was evaluated using the Drug bank database to check for potential drug targets. It was evaluated against all the drug targets. Using online tools such as PSORTb and CELLO v2.5 analysis of the predictions based on the concurrent location of the biological orientation of the druggable protein was performed. The prediction using these tools will render the location of the epitopes that can be applied during drug development. The Protparam expasy server was used to analyze the physicochemical properties, such as molecular weight, instability index, theoretical PI, aliphatic index, and GRAVY. To confirm whether the protein selected possesses any potent epitopes, VaxiJen webserver was used. A threshold value of 0.5 was set against which ompA was subjected to analysis for potent antigenicity predictions.

Prediction of T-cell epitopes and MHC class restrictions

NetCTL server [Predictions of MHC class I epitopes (Prediction threshold value 0.75 was set for epitope identification IEDB AR (T-cell epitopes were subjected to MHC-I binding prediction, using the immune epitope database analysis resource (IEDB AR). MHC molecules represent the antigenic peptides which are recognized by the T-cells. IEDB is recommended as a default prediction method and uses the consensus method consisting of ANN32, SMM33, and CombLib34 and NetMHCpan35. The tools provide the high combinatorial scores for on peptide’s intrinsic potential. It also integrates several predictions on various biological factors such as proteosomal cleavage mechanism, TAP transporter efficiency involved in antigen processing, affinity scores of MHC-I predictions. Identified T-cell epitopes with HLA alleles were selected by IC50 values and percentile rank. Lower the percentile rank the higher the interaction shown between the peptide and MHC molecules. IC50 values divided in three different categories: high binding affinity, IC50 value < 50 nM; intermediate binding affinity, IC50 value < 500 nM; and low binding affinity, IC50 value < 5000 nM36).

Cluster analysis of the MHC restricted alleles

For the further strengthening of the predictions done, the restricted MHC alleles were subjected to MHC clusterV2.0 server to identify the clusters to interpret the functional relationship between the selected epitope peptides and the HLA alleles. The interactions were obtained in the form of the graphical tree and static heat maps.

B-cell epitope mapping with antigenicity predictions

IEDB linear epitope B cell prediction tests can be performed to check if the epitopes can bind to B cell receptors and stimulate a humoral immune response. This prediction server predicts the peptide epitopes based on various biochemical properties involving the composition of amino-acid, affinity to water and secondary protein structures. The obtained score is termed as Protrusion Index, which is an average of overall epitope residues, and 3D structure of each protein is approximated through some ellipsoids and the structures yielding larger scores shows the solvent accessibility of the proteins. The ompA sequence is subjected to various standardized tests like BepiPred linear epitope prediction (Larsen et al. 2006), Karplus-Schulz flexibility prediction (Karplus and Schulz 1985), Chou-Fasman beta-turn prediction (Yang 1996), Kolaskar Tongaonkar antigenicity (As and Pc 1990), Emini surface accessibility prediction (Emini et al. 1985), and Parker hydrophilicity prediction (Jm et al. 1986). As the name implies each prediction tool evaluates the properties of the epitopes and further its antigenicity as observed by yellow peaks in the graphs. MHC class I and II predictions were made in the same way as that of T-cell epitope predictions in the IEDB server and the common epitopes were selected for further docking analysis. All the selected epitopes were subjected for the analysis of antigenicity (Magnan et al. 2010) and solubility by VaxiJenV2.0, and physico-chemical property by Protparam servers.

Interaction between protein and TLR2 receptor

The selected epitope peptides were finally subjected to interact with the TLR-2 receptor. TLR-2 is selected due to the fact that it can interact with huge number of non-TLR molecules and can recognize a wide variety of complex molecules from all microbial phyla. It is ubiquitous in most of the immune cells, endothelial and epithelial cells with vast roles and functions (Flo et al. 2001). Galaxy web server (Afgan et al. 2018) was used for analyzing possible interactions of the ompA predicted epitopes with the TLR2 receptor. Based on the energy obtained on the interpretation of the number of hydrogen bonds formed between the complexes, it will facilitate the designing of an efficient ompA vaccine.


Molecular characterization of the ompA gene and its correlation with multi-drug resistance

From the screened 50 genomes of MDR A.baumannii, 48% showed positive amplicons for the ompA gene associated with biofilm and porin formation with 531 bp as amplicon size (Fig. 1). The gene sequence obtained is shown in Fig. 2. Correlation of its occurrence was high in carbapenem-resistant strains, followed by aminoglycoside, fluoroquinolone, and efflux pumps. The correlation of ompA was high (100%; n = 24) in the resistant groups of beta-lactam inhibitors such as piperacillin and tazobactam; cephalosporins such as ceftazidime, cefepime, ceftriaxone; carbapenems such as doripenem, meropenem, and imipenem; and folates. It was followed by an occurrence of 83.3%, 50% and 33.3% among aminoglycosides, fluoroquinolones and efflux pumps mediated resistant strains respectively.

Fig. 1

Electrophoregram of ompA gene product of size 531 bp in lane 1 and 2 with marker ladderof size 1.5Kbp (M)

Fig. 2

Partial sequence alignment of ompA gene from the present study (Ab) with reference sequences available in the database. The deleted regions are depicted as dashes (–), mismatch as gap () and conserved sequences as star (*)

Preliminary evaluation of ompA epitopes

Using the ORF finder the obtained ompA sequence yielded potential protein encoding segments which was further confirmed with the sequence alignment (Fig. 3) and similarity through the phylogenetic tree. The obtained sequence of ompA of A.baumannii Q6RYW5 upon human similarity search by BLASTp did not yield any significant similar proteins. The selected protein had a 355aa length and was found to be located in the outer membrane by PSORTb and CELLO servers. GRAVY values were negative (− 0.388) with a high aliphatic index (80.93) and the protein was found to be stable with the predicted value < 40 (Table 1). Overall prediction on antigenicity was observed using VaxiJen server and was found to be a probable antigen with a score of 0.8524 (at a threshold value of 0.5).

Fig. 3

Phylogenetic similarity of the ompAprotein sequence obtained through the ORF mega search results, obtained from the input sequence of the ompAgene of A.baumannii

Table 1 Prediction of MHC class I epitopes ompA of A. baumannii proteins using NetCTL

T-cell dominant epitopes and MHC class-1 and class – II restrictions

Using the NetCTL server, the T-cell epitopes and its MHC class-I peptides were retrieved (Table 1). T cell class-I immunogenicity predictions were assessed using the scores obtained and the peptides of higher scores were considered as the probable antigens to elicit an immune response. Thus the top 5 epitopes with the higher scores were selected for further analysis (Table 2). Out of 5 epitopes, 2 epitopes were predicted as probable antigens with the antigenic score of > 0.5, SVM scores as non-toxins and yielded 100% conservancy (Table 3). Using the IEDB server, the MHC class-II restricted alleles were also predicted from where 5 epitopes were selected and were considered to elicit a proper humoral immune response. This selection was based on the lowest percentile ranks.

Table 2 Class I immunogenicity predictions from ompA of A.baumannii
Table 3 Toxigenicity, antigenicity and Class I conservancy of T cell epitopes from ompA of A.baumannii

Selection of B-cell epitopes and their antigenic properties

B-cell epitope prediction is crucial to peptide driven vaccine design in concert with the predictions on the MHC class II epitopes (Table 4). The recognition of epitopes by B cells is based on features such as hydrophilicity, surface accessibility, flexibility, linearity and presence of beta turns. Bepipred tool aids in the determination of single scale amino acid propensity profiles. B cell epitopes were thus determined by the highest peak yellow region (threshold value) which corresponds to beta turns of the epitope. Further, using the IEDB linear epitope predictions, 8 epitopes were totally selected towards the immuno-dominant B-cell epitope selection (Table 5). Further analysis and selection of the B-cell dominant epitopes done based on the biochemical parameters under the IEDB B-cell epitope analysis showed that the selected epitopes possessed good biochemical properties of flexibility, beta turns, antigenicity, accessibility, and hydrophilicity. The propensity of the antigenicity was measured above the threshold values and was observed as yellow coloured peaks. Figure 4 shows the graphical peaks with yellow color as interpreted as the B-cell antigenic epitopes. Based on the physico-chemical property, immunogenicity and antigenicity scores, (Table 5), two epitopes viz., E1- FDGVNRGTRGTSEEGTLGNA and E2- KLSEYPNATARIEGHTDNTGPRKL were selected for further docking with TLR-2.

Table 4 Prediction of MHC class II epitopes by IEDB server from ompA of A. baumannii
Table 5 Predicted B cell epitopes of protein using IEDB Linear Epitope prediction from ompA of A.baumannii
Fig. 4

B cell epitope antigenicity predictions: a Bepipred Linear Epitope, b Chou &Fasman Beta-TurnPrediction, c.Emini Surface Accessibility Prediction, d Karplus& Schulz Flexibility Prediction, e Kolaskar&Tongaonkar Antigenicity, f Parker Hydrophilicity Prediction. The X-axis and Y-axis denote the sequence position and antigenic propensity respectively. The regions above the threshold value are antigenic, and are depicted in yellow

Protein-peptide interactions

Using the Galaxy-web server, the ability of the epitopes to evoke an immune response is assessed by the docking of the epitopes with the TLR-2 epitopes. E2 was considered to be the best epitope predicted from the ompA protein of A.baumannii with a highest number of hydrogen bonds (n = 16) followed by 13 hydrogen bonds by E-1 (Fig. 5). The possible interactions between E1 and E2 with the TLR-2 receptor was observed and was promising (Table 6). However, both the epitopes showed a similarity score of − 10.0 kcal/mol.

Fig. 5

Docking of the final epitopes from ompA of A.baumannii E1 (13 hydrogen bonds) and E2 (16 hydrogen bonds) with TLR-2. (Key residues forming interaction with epitope 1 and 2 (scaled ball and stick model colored according to atoms—labelled in red color) and amino acid residues in Human TLR-2 (line model in cyan blue color—labelled in black color) (Detailed interactions documented in Table 6)

Table 6 Interactions of the predicted epitopes E1 and E2 with the human TLR-2 receptor with the distance of binding


The multi-drug resistant A. baumannii is considered as a serious threat especially among hospitalized patients. Being a potent nosocomial pathogen, it requires novel treatment strategies to be implemented promptly and the best possible option currently is apt prophylaxis. With the advent of bioinformatics, in-silico experimentation and molecular analysis is integrated into this study to additionally analyze the immuno-dominant epitopes against the ompA of A. baumannii to cease the formation of biofilms. The vital step in the construction of vaccine is the selection of B cell and T cell dominant epitopes which will bind significantly with the immunological receptors. This evokes and elicits the humoral and cell mediated immune responses. With the help of immuno informatics, promising antigenic epitopes have been successfully identified corresponding to the ompA protein. This was possible by utilizing the genomic and proteomic reservoirs available in a single computational platform consisting of various tools and databases.

The specific target under study, the ompA protein and its relevant predictions on the antigenic epitope structures was timely to curb the biofilm formation. The present study, documents the co-occurrence of ompA gene with different groups of drug resistant strains. 48% of the strains showed the presence of ompA gene substantiating its virulence in the establishment of infection among nosocomial patients. The ompA gene is known to be present in both gram positive and gram-negative organisms on the outer membrane, but most predominantly on the latter (Achouak et al. 1998). It accounts for its propensity of multi-drug resistance (Handal et al. 2017), biofilm formation (Gaddy and Actis 2009; Shin and Eom 2020) and the aggressiveness of virulence attributed to ompA based biofilms. The present study aims to assess the same at a molecular level and find the correlation of its occurrence with its property of multidrug resistance which is known to worsen the conditions among nosocomial patients. A similar study conducted in Iran suggested that the multidrug-resistant property of the bacteria will prove to be more virulent and thus causes nosocomial infections. The preventive measure to control such infection contains the inception and spread of the extremely drug-resistant (XDR) A. baumannii (Zeighami et al. 2019). Multiplex PCR assay of the strains was carried out in clonal lineage analysis. This was done to document the role of the ompA gene in A. baumannnii strains resistant to tetracycline. This interpretation of the results from this study documents 33.3% of the tetracycline-resistant strains of the bacteria. Some studies state that the upregulation of the ompAgene is associated with tetracycline mediated RND pumps (Soto, 2013). These pumps are considered to directly or indirectly influence the formation of outer membrane porin which eventually leads to the ejection of the drug, leading to resistance (Fernández and Hancock 2013). The percentage of reduction of ompA occurrence in this study might be due to the effect of the ompA gene on the porin formation. A previous study contradicts this by stating that in other gram-negative bacteria such as E.coli, there is decreased tetracycline susceptibility with increased ompA homologue expression (Smani et al. 2014).

In a similar study conducted in Korea, they proved that the ompA mutant was more susceptible to trimethoprim with > 5.33-fold, but showed a decrease in MIC for colistin, imipenem and tigecycline showed a < twofold decrease for the same mutant ompA gene. They also suggested that there is a reduction of susceptibility to beta-lactam drugs due to the carriage of beta-lactamase. They finally suggested that the ompA gene is responsible for the intrinsic resistance of the bacteria to the antibiotic stressful condition (Kwon et al. 2017).The purified form of ompA in multidrug-resistant strains of A.baumannii is said to be associated and documented to have an influence over apoptosis and internalization of the bacteria which causes disruption to the epithelial cells of the host tissue (Mj 2011). Thus, the present study aims to target the ompA protein for the predictions of antigenic epitopes and thus selected for vaccine peptide construction analysis, and was achieved with the help of immuno-informatics. The first step in predicting vaccine peptides being the selection of specific proteins, it was done based on the non-redundant protein similarity under the human BLASTp. The occurrence of ompA amidst ESBL producers was 100% in the present study.

The study also aims to predict the possible B-cell dominant epitopes from the ompA of A. baumannii. The results suggested that there were many possible epitopes based on in-silico analysis. The recognition of these B-cell dominant epitopes from the partial sequence of ompA gene from the multidrug-resistant A. baumannii has proven to be very reinforcing in identifying the potential and putative vaccine candidates. PSORTb tool was used to identify the location of ompA for filtration and localization of ompA as an outer membrane and cytosolic protein. Thus it can be considered for small molecule drug development which is a variant of antimicrobials. The physico-chemical analysis of ompA protein was highly promising as the results showed a negative GRAVY value which on interpretation signifies a hydrophilic property. The instability index test was used to analyze the stability of the epitope. The results showed − 9.88, 36.69, 29.29 and 33.89 which is < 40 suggesting that the epitopes are stable. The aliphatic index was used to check the thermo-stability of the epitopes and was calculated with a relative volume occupied by the aliphatic side chains.

For an ideal vaccine peptide, it is important that a humoral response is stimulated. Thus, from the B-cell epitope evaluation tests, it is suggested that two epitopes are the final shortlisted epitopes for vaccine prediction from the ompA gene. The protein turns predicted was based on the standard tools that can assess the antigenic epitopes with 80% confidence and 70% of accurate predictions (Pellequer et al. 1993). The propensity scales measured above the set threshold value was used to assess the antigenic epitopes and was interpreted as the yellow peaks. Most of the predictions yielded promising peaks with good scales of measures, however, Kolaskar predictions showed a less score. MHC class-II restrictions with frequent alleles yielded 5 epitope peptides with only 2 peptides restricted to MHC class-I restrictions. Based on the percentile ranks and IC50 values upon ANN, SMM, COMBLib combinatorial ranks, and scores HLA/MHC allele restrictions were interpreted. A final interaction of the selected E1 and E2 epitopes were docked with the TLR-2 receptor and was found to possess an effective docking with multiple interactions with good distance. However further experimental validations related to immunological response and memory of the predicted peptides, have to be initiated and assessed using suitable in-vivo studies.


An attempt on characterizing the ompA gene from the drug resistant strains of A. baumannii and screening of few putative antigenic epitope peptides using immune-informatics approach suggests promising immunodominant peptides in the present study. The study has also documented the correlation of ompA gene occurrence among the drug resistant strains. However, it is the need of the hour to experimentally validate the predicted vaccine peptides with further studies for their suitable immunological response and memory.


  1. (2017) WHO publishes list of bacteria for which new antibiotics are urgently needed [WWW Document], n.d. URL Accessed 15 Dec 2019

  2. Achouak W, Pages J-M, Mot RD, Molle G, Heulin T (1998) A major outer membrane protein of Rahnella aquatilis functions as a porin and root adhesin. J Bacteriol 180:909–913

    CAS  Article  Google Scholar 

  3. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Grüning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46:W537–W544.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. As K, Pc T (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens [WWW Document]. FEBS Lett.

    Article  Google Scholar 

  5. Asif M, Alvi IA, Rehman SU (2018) Insight into Acinetobacter baumannii: pathogenesis, global resistance, mechanisms of resistance, treatment options, and alternative modalities. Infect Drug Resist 11:1249–1260.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Cassin EK, Tseng BS (2019) Pushing beyond the envelope: the potential roles of OprF in Pseudomonas aeruginosa biofilm formation and pathogenicity. J Bacteriol.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Dexter C, Murray GL, Paulsen IT, Peleg AY (2015) Community-acquired Acinetobacter baumannii: clinical characteristics, epidemiology and pathogenesis. Expert Rev Anti Infect Ther 13:567–573.

    CAS  Article  PubMed  Google Scholar 

  8. Emini EA, Hughes JV, Perlow DS, Boger J (1985) Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 55:836–839

    CAS  Article  Google Scholar 

  9. Eze EC, Chenia HY, Zowalaty MEE (2018) Acinetobacter baumannii biofilms: effects of physicochemical factors, virulence, antibiotic resistance determinants, gene regulation, and future antimicrobial treatments. Infect Drug Resist 11:2277–2299.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Fernández L, Hancock REW (2013) Adaptive and mutational resistance: role of porins and efflux pumps in drug resistance. Clin Microbiol Rev 26:163.

    CAS  Article  PubMed Central  Google Scholar 

  11. Flo TH, Halaas O, Torp S, Ryan L, Lien E, Dybdahl B, Sundan A, Espevik T (2001) Differential expression of toll-like receptor 2 in human cells. J. Leukoc. Biol. 69:474–481

    CAS  PubMed  Google Scholar 

  12. Fournier PE, Richet H, Weinstein RA (2006) The epidemiology and control of acinetobacter baumannii in health care facilities. Clin Infect Dis 42:692–699.

    Article  PubMed  Google Scholar 

  13. Gaddy JA, Actis LA (2009) Regulation of Acinetobacter baumannii biofilm formation. Future Microbiol 4:273–278.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Gaddy JA, Tomaras AP, Actis LA (2009) The Acinetobacter baumannii 19606 OmpA protein plays a role in biofilm formation on abiotic surfaces and in the interaction of this pathogen with eukaryotic cells. Infect Immun 77:3150–3160.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Handal R, Qunibi L, Sahouri I, Juhari M, Dawodi R, Marzouqa H, Hindiyeh M (2017) Characterization of carbapenem-resistant Acinetobacter baumannii strains isolated from hospitalized patients in palestine. Int J Microbiol.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jean-Luc Pellequer a'b, Eric Westhof b and Marc H.V. Van Regenmortel aCorrelation between the location of antigenic sites and the prediction of turns in proteins. Immunology Letters, 36 (1993) 83–100

  17. Jm P, D G, Rs H (1986) New Hydrophilicity Scale Derived From High-Performance Liquid Chromatography Peptide Retention Data: Correlation of Predicted Surface Residues With Antigenicity and X-ray-derived Accessible Sites [WWW Document]. Biochemistry (Mosc).

  18. Karplus PA, Schulz GE (1985) Prediction of chain flexibility in proteins. Naturwissenschaften 72:212–213.

    CAS  Article  Google Scholar 

  19. Kwon HI, Kim S, Oh MH, Na SH, Kim YJ, Jeon YH, Lee JC (2017) Outer membrane protein A contributes to antimicrobial resistance of Acinetobacter baumannii through the OmpA-like domain. J Antimicrob Chemother 72:3012–3015.

    CAS  Article  PubMed  Google Scholar 

  20. Larsen JEP, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Magnan CN, Zeller M, Kayala MA, Vigil A, Randall A, Felgner PL, Baldi P (2010) High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 26:2936–2943.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Maltz MA, Weiss BL, O’Neill M, Wu Y, Aksoy S (2012) OmpA-mediated biofilm formation is essential for the commensal bacterium Sodalis glossinidius to colonize the tsetse fly gut. Appl Environ Microbiol 78:7760–7768.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Mj M, J P (2011) Expression, purification, and refolding of biologically active Acinetobacter baumannii OmpA From Escherichia coli Inclusion Bodies [WWW Document]. Protein Expr. Purif.

  24. Orme R, Douglas CWI, Rimmer S, Webb M (2006) Proteomic analysis of Escherichia coli biofilms reveals the overexpression of the outer membrane protein OmpA. Proteomics 6:4269–4277.

    CAS  Article  PubMed  Google Scholar 

  25. Peleg AY, Seifert H, Paterson DL (2008) Acinetobacter baumannii: emergence of a successful pathogen. Clin Microbiol Rev 21:538–582.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Shin D-S, Eom Y-B (2020) Antimicrobial and antibiofilm activities of Clostridium butyricum supernatant against Acinetobacter baumannii. Arch Microbiol 202:1059–1068.

    CAS  Article  PubMed  Google Scholar 

  27. Smani Y, Fàbrega A, Roca I, Sánchez-Encinales V, Vila J, Pachón J (2014) Role of OmpA in the multidrug resistance phenotype of Acinetobacter baumannii. Antimicrob Agents Chemother 58:1806–1808.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Smiline Girija AS, Vijayashree Priyadharsini J, Paramasivam Arumugam (2019) CLSI based antibiogram profile and the detection of MDR and XDR strains of Acinetobacter baumannii isolated from urine samples. Med J Islamic Republic of Iran 33(3): 11–16

  29. Soto SM (2013) Role of efflux pumps in the antibiotic resistance of bacteria embedded in a biofilm. Virulence 4:223–229.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Vijayakumar S, Rajenderan S, Laishram S, Anandan S, Balaji V, Biswas I (2016) Biofilm formation and motility depend on the nature of the Acinetobacter baumannii clinical isolates. Front Public Health.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yang JT (1996) Prediction of protein secondary structure from amino acid sequence. J Protein Chem 15:185–191.

    Article  PubMed  Google Scholar 

  32. Zeighami H, Valadkhani F, Shapouri R, Samadi E, Haghi F (2019) Virulence characteristics of multidrug resistant biofilm forming Acinetobacter baumannii isolated from intensive care unit patients. BMC Infect Dis.

    Article  PubMed  PubMed Central  Google Scholar 

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Sogasu, D., Girija, A.S.S., Gunasekaran, S. et al. Molecular characterization and epitope-based vaccine predictions for ompA gene associated with biofilm formation in multidrug-resistant strains of A.baumannii. In Silico Pharmacol. 9, 15 (2021).

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  • A. baumannii
  • Biofilm
  • ompA
  • Epitope vaccines
  • In-silico