Integrating 16S rRNA identification for a promising epitope-based vaccine strategy against Bacillus licheniformis infections causing foodborne illness
Main Article Content
Keywords
Bacillus licheniformis, epitope-based vaccine, foodborne illness, immunoinformatics, molecular docking, 16S rRNA
Abstract
Background: Bacillus licheniformis is a Gram-positive bacterium associated with foodborne illnesses and opportunistic infections in immunocompromised individuals, resulting in significant economic and health burdens. Despite its significance, no preventive or therapeutic vaccines currently exist against B. licheniformis.
Objective: This study aimed to design a multi-epitope vaccine construct against B. licheniformis using immunoinformatic and bioinformatic approaches, integrating the One Health perspective.
Materials and Methods: Strains of B. licheniformis were isolated from soil and food samples and identified through 16S rRNA gene amplification and sequence analysis. Two antigenic proteins, WP_075876128.1 (hypothetical protein) and WP_009328059.1 (MATE family efflux transporter), were selected as vaccine targets based on antigenicity scores of 0.582 and 0.835, respectively. Immunoinformatics tools were used for epitope prediction, vaccine construct assembly, structural modeling, and immune simulations. Molecular docking was used to assess vaccine-receptor interactions with Toll-like receptors (TLRs) 1, 2, and 5.
Results: The designed vaccine construct exhibited favorable physicochemical properties, including structural stability, thermostability, solubility, and hydrophilicity. Immune simulation predicted a strong immune response, characterized by approximately 225 B-memory cells per mm3 and around 8,500 combined IgM and IgG counts. Docking studies revealed the stable binding of the vaccine construct to TLR1, TLR2, and TLR5, supported by favorable binding free energy values, indicating a robust immunogenic potential.
Conclusion: The immunoinformatically designed multi-epitope vaccine candidate demonstrated high antigenicity, stability, and strong immune-stimulatory capacity against B. licheniformis. These findings support its potential for further in vitro and in vivo validation. This study highlights the effectiveness of immunoinformatic tools in rational vaccine design and reinforces the One Health approach, which links human, animal, and environmental health.
References
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12 Harris CT, Cohen S (2024) Reducing immunogenicity by design: Approaches to minimize immunogenicity of monoclonal antibodies. BioDrugs 38:205–226. 10.1007/s40259-023-00641-2
13 Naveed M, Ali U, Karobari MI, Ahmed N, Mohamed RN, Abullais SS et al (2022) A vaccine construction against COVID-19-associated mucormycosis contrived with immunoinformatics-based scavenging of potential mucoralean epitopes. Vaccines 10(5):664. 10.3390/vaccines10050664
14 Naveed M, Makhdoom SI, Ali U, Jabeen K, Aziz T, Khan AA et al (2022) Immunoinformatics approach to design multi-epitope-based vaccine against machupo virus taking viral nucleocapsid as a potential candidate. Vaccines 10(10):1732. 10.3390/vaccines10101732
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21 Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. 10.1093/bioinformatics/bts565
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28 Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8:e73957. 10.1371/journal.pone.0073957
29 Welsh RM, Fujinami RS (2007) Pathogenic epitopes, heterologous immunity and vaccine design. Nat Rev Microbiol 5:555–563. 10.1038/nrmicro1709
30 Bui H-H, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform 7:153. 10.1186/1471-2105-7-153
31 Chen X, Zaro JL, Shen W-C (2013) Fusion protein linkers: Property, design, and functionality. Adv Drug Deliv Rev 65:1357–1369. 10.1016/j.addr.2012.09.039
32 Naveed M, Yaseen AR, Khalid H, Ali U, Rabaan AA, Garout M et al (2022) Execution and design of an anti-HPIV-1 vaccine with multiple epitopes triggering innate and adaptive immune responses: An immunoinformatic approach. Vaccines 10:869. 10.3390/vaccines10060869
33 Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The Proteomics Protocols Handbook. Humana Press, Totowa, pp 571–607. 10.1385/1-59259-890-0:571
34 Magnan CN, Randall A, Baldi P (2009) SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 25:2200–2207. 10.1093/bioinformatics/btp386
35 Shende G, Haldankar H, Barai RS, Bharmal MH, Shetty V, Idicula-Thomas S (2017) PBIT: Pipeline builder for identification of drug targets for infectious diseases. Bioinformatics 33:929–931. 10.1093/bioinformatics/btw744
36 McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405. 10.1093/bioinformatics/16.4.404
37 Rapin N, Lund O, Castiglione F (2011) Immune system simulation online. Bioinformatics 27:2013–2014. 10.1093/bioinformatics/btr308
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39 Ko J, Park H, Heo L, Seok C (2012) Galaxy WEB server for protein structure prediction and refinement. Nucleic Acids Res 40:W294–W297. 10.1093/nar/gks493
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45 Naz A, Awan FM, Obaid A, Muhammad SA, Paracha RZ, Ahmad J, Ali A (2015) Identification of putative vaccine candidates against Helicobacter pylori exploiting exoproteome and secretome: A reverse vaccinology-based approach. Infect Genet Evol 32:280–291. 10.1016/j.meegid.2015.03.027
46 Raman K (2008) Systems-level modelling and simulation of Mycobacterium tuberculosis: Insights for drug discovery. Dissertation, Indian Institute of Science
47 Arendse LB, Wyllie S, Chibale K, Gilbert IH (2021) Plasmodium kinases as potential drug targets for malaria: Challenges and opportunities. ACS Infect Dis 7:518–534. 10.1021/acsinfecdis.0c00724
48 Chaplin DD (2010) Overview of the immune response. J Allergy Clin Immunol 125:S3–S23. 10.1016/j.jaci.2009.12.980
49 Mukhopadhyay S, Herre J, Brown GD, Gordon S (2004) The potential for toll-like receptors to collaborate with other innate immune receptors. Immunology 112:521–530. 10.1111/j.1365-2567.2004.01941.x
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2 Agerholm JS, Krogh HV, Jensen HE (1995) A retrospective study of bovine abortions associated with Bacillus licheniformis. Zentralblatt Fur Veterinarmedizin. Reihe B. J Vet Med B 42(4): 225–234. 10.1111/j.1439-0450.1995.tb00706.x
3 Banoon S, Ali Z, Salih T (2020) Antibiotic resistance profile of local thermophilic Bacillus licheniformis isolated from Maysan province soil. Comunicata Scientiae. 11:e3921. 10.14295/cs.v11i0.3291
4 Gauvry E, Mathot A-G, Leguérinel I, Couvert O, Postollec F, Broussolle V, Coroller L (2017) Knowledge of the physiology of spore-forming bacteria can explain the origin of spores in the food environment. Res Microbiol 168(4):369–378. 10.1016/j.resmic.2016.10.006
5 Celandroni F, Vecchione A, Cara A, Mazzantini D, Lupetti A, Ghelardi E (2019) Identification of Bacillus species: Implication on the quality of probiotic formulations. PLoS ONE 14(5):e0217021. 10.1371/journal.pone.0217021
6 Environmental Protection Agency (1997) Attachment I—Final risk assessment of Bacillus licheniformis. https://www.epa.gov/sites/default/files/2015-09/documents/fra005.pdf. Accessed 17 June 2025
7 Maxson T, Mitchell DA (2016) Targeted treatment for bacterial infections: Prospects for pathogen-specific antibiotics coupled with rapid diagnostics. Tetrahedron 72(25):3609–3624. 10.1016/j.tet.2015.09.069
8 Ramirez-Olea H, Reyes-Ballesteros B, Chavez-Santoscoy RA (2022) Potential application of the probiotic Bacillus licheniformis as an adjuvant in the treatment of diseases in humans and animals: A systematic review. Front Microbiol 13:993451. 10.3389/fmicb.2022.993451
9 Palkovicsné Pézsa N, Kovács D, Rácz B, Farkas O (2022) Effects of Bacillus licheniformis and Bacillus subtilis on gut barrier function, proinflammatory response, ROS production and pathogen inhibition properties in IPEC-J2—Escherichia coli/Salmonella Typhimurium co-culture. Microorganisms 10(5):936. 10.3390/microorganisms10050936
10 Gopal N, Hill C, Ross PR, Beresford TP, Fenelon MA, Cotter PD (2015) The prevalence and control of bacillus and related spore-forming bacteria in the dairy industry. Front Microbiol 6:167862. 10.3389/fmicb.2015.01418
11 Rani NA, Robin TB, Prome AA et al (2024) Development of multi-epitope subunit vaccines against emerging carp viruses Cyprinid herpesvirus 1 and 3 using immunoinformatics approach. Sci Rep 14:11783. 10.1038/s41598-024-61074-7
12 Harris CT, Cohen S (2024) Reducing immunogenicity by design: Approaches to minimize immunogenicity of monoclonal antibodies. BioDrugs 38:205–226. 10.1007/s40259-023-00641-2
13 Naveed M, Ali U, Karobari MI, Ahmed N, Mohamed RN, Abullais SS et al (2022) A vaccine construction against COVID-19-associated mucormycosis contrived with immunoinformatics-based scavenging of potential mucoralean epitopes. Vaccines 10(5):664. 10.3390/vaccines10050664
14 Naveed M, Makhdoom SI, Ali U, Jabeen K, Aziz T, Khan AA et al (2022) Immunoinformatics approach to design multi-epitope-based vaccine against machupo virus taking viral nucleocapsid as a potential candidate. Vaccines 10(10):1732. 10.3390/vaccines10101732
15 Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y (2020) Epitope-based vaccine design: A comprehensive overview of bioinformatics approaches. Drug Discov Today 25(6):1034–1042. 10.1016/j.drudis.2020.03.006
16 Hessami A, Mogharari Z, Rahim F, Khalesi B, Jamal Nassrullah O, Reza Rahbar M et al (2024) In silico design of a novel hybrid epitope-based antigen harboring highly exposed immunogenic peptides of BamA, OmpA, and Omp34 against Acinetobacter baumannii. Int Immunopharmacol 142:113066. 10.1016/j.intimp.2024.113066
17 Naveed M, Waseem M, Aziz T, Hassan J. ul, Makhdoom SI, Ali U et al (2023) Identification of bacterial strains and development of an mRNA-based vaccine to combat antibiotic resistance in Staphylococcus aureus via in vitro and in silico approaches. Biomedicines 11(4):1039. 10.3390/biomedicines11041039
18 Mahboobi M, Sedighian H, Malekara E, Khalili S, Rahbar MR, Ahmadi Zanoos K et al (2020) Harnessing an integrative in silico approach to engage highly immunogenic peptides in an antigen design against epsilon toxin (ETX) of Clostridium perfringens. Int J Pept Res Ther 27(2):1019–1026. 10.1007/s10989-020-10147-y
19 dos Santos HRM, Argolo CS, Argôlo-Filho RC, Loguercio LL (2019) 16S rDNA PCR-based theoretical to actual delta approach on culturable mock communities revealed severe losses of diversity information. BMC Microbiol 19:74. 10.1186/s12866-019-1446-2
20 Hou Q, Bai X, Li W, Gao X, Zhang F, Sun Z, Zhang H (2018) Design of primers for evaluation of lactic acid bacteria populations in complex biological samples. Front Microbiol 9. 10.3389/fmicb.2018.02045
21 Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. 10.1093/bioinformatics/bts565
22 Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R et al (2010) PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615. 10.1093/bioinformatics/btq249
23 Yu C-S, Cheng C-W, Su W-C, Chang K-C, Huang S-W, Hwang J-K, Lu C-H (2014) CELLO2GO: A web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS ONE 9:e99368. 10.1371/journal.pone.0099368
24 Lee C, Kim JY, Song HS, Kim YB, Choi Y-E, Yoon C et al (2017) Genomic analysis of Bacillus licheniformis CBA7126 isolated from a human fecal sample. Front Pharmacol 8:724. 10.3389/fphar.2017.00724
25 Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR et al (2018) The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47:D339–D343. 10.1093/nar/gky1006
26 Doytchinova IA, Flower DR (2007) VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform 8:4. 10.1186/1471-2105-8-4
27 Dimitrov I, Bangov I, Flower DR, Doytchinova I (2014) AllerTOP v.2—A server for in silico prediction of allergens. J Mol Model 20:2278. 10.1007/s00894-014-2278-5
28 Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8:e73957. 10.1371/journal.pone.0073957
29 Welsh RM, Fujinami RS (2007) Pathogenic epitopes, heterologous immunity and vaccine design. Nat Rev Microbiol 5:555–563. 10.1038/nrmicro1709
30 Bui H-H, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A (2006) Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform 7:153. 10.1186/1471-2105-7-153
31 Chen X, Zaro JL, Shen W-C (2013) Fusion protein linkers: Property, design, and functionality. Adv Drug Deliv Rev 65:1357–1369. 10.1016/j.addr.2012.09.039
32 Naveed M, Yaseen AR, Khalid H, Ali U, Rabaan AA, Garout M et al (2022) Execution and design of an anti-HPIV-1 vaccine with multiple epitopes triggering innate and adaptive immune responses: An immunoinformatic approach. Vaccines 10:869. 10.3390/vaccines10060869
33 Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The Proteomics Protocols Handbook. Humana Press, Totowa, pp 571–607. 10.1385/1-59259-890-0:571
34 Magnan CN, Randall A, Baldi P (2009) SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 25:2200–2207. 10.1093/bioinformatics/btp386
35 Shende G, Haldankar H, Barai RS, Bharmal MH, Shetty V, Idicula-Thomas S (2017) PBIT: Pipeline builder for identification of drug targets for infectious diseases. Bioinformatics 33:929–931. 10.1093/bioinformatics/btw744
36 McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405. 10.1093/bioinformatics/16.4.404
37 Rapin N, Lund O, Castiglione F (2011) Immune system simulation online. Bioinformatics 27:2013–2014. 10.1093/bioinformatics/btr308
38 Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: A protein structure and structural feature prediction server. Nucleic Acids Res 33:W72–W76. 10.1093/nar/gki396
39 Ko J, Park H, Heo L, Seok C (2012) Galaxy WEB server for protein structure prediction and refinement. Nucleic Acids Res 40:W294–W297. 10.1093/nar/gks493
40 Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J et al (2012) Immune epitope database analysis resource. Nucleic Acids Res 40:W525–W530. 10.1093/nar/gks438
41 Remmert M, Biegert A, Hauser A, Söding J (2011) HHblits: Lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9:173–175. 10.1038/nmeth.1818
42 López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacón P (2014) iMODS: Internal coordinates normal mode analysis server. Nucleic Acids Res 42:W271–W276. 10.1093/nar/gku339
43 Clarridge JE (2004) Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 17:840–862. 10.1128/cmr.17.4.840-862.2004
44 Hassan A, Naz A, Obaid A, Paracha RZ, Naz K, Awan FM et al (2016) Pan-genome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets. BMC Genomics 17:732. 10.1186/s12864-016-2951-4
45 Naz A, Awan FM, Obaid A, Muhammad SA, Paracha RZ, Ahmad J, Ali A (2015) Identification of putative vaccine candidates against Helicobacter pylori exploiting exoproteome and secretome: A reverse vaccinology-based approach. Infect Genet Evol 32:280–291. 10.1016/j.meegid.2015.03.027
46 Raman K (2008) Systems-level modelling and simulation of Mycobacterium tuberculosis: Insights for drug discovery. Dissertation, Indian Institute of Science
47 Arendse LB, Wyllie S, Chibale K, Gilbert IH (2021) Plasmodium kinases as potential drug targets for malaria: Challenges and opportunities. ACS Infect Dis 7:518–534. 10.1021/acsinfecdis.0c00724
48 Chaplin DD (2010) Overview of the immune response. J Allergy Clin Immunol 125:S3–S23. 10.1016/j.jaci.2009.12.980
49 Mukhopadhyay S, Herre J, Brown GD, Gordon S (2004) The potential for toll-like receptors to collaborate with other innate immune receptors. Immunology 112:521–530. 10.1111/j.1365-2567.2004.01941.x
50 Aslam M, Shehroz M, Hizbullah Shah M, Khan MA, Afridi SG, Khan A (2020) Potential druggable proteins and chimeric vaccine construct prioritization against Brucella melitensis from species core genome data. Genomics 112:1734–1745. 10.1016/j.ygeno.2019.10.009
51 Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: Strategies, approaches, opportunities, challenges, and directions. J Cheminform 12:46. 10.1186/s13321-020-00450-7
52 Shi S, Zhu H, Xia X, Liang Z, Ma X, Sun B (2019) Vaccine adjuvants: Understanding the structure and mechanism of adjuvanticity. Vaccines 37:3167–3178. 10.1016/j.vaccine.2019.04.055
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