Volume 6, Issue 1 (1-2018)
JoMMID 2018, 6(1): 1-7 |
Back to browse issues page
Department of Biology, Shahed University, Tehran, Iran
Abstract: (5048 Views)
Introduction: Vaccine studies against Pseudomonas aeruginosa have often focused on outer membrane proteins (OPRs) due to their potent stimulation of the immune response. Using major outer membrane proteins of cell walls (mOMPs) of P. aeruginosa and other Gram-negative bacteria actively stimulate the immune system without any toxic side effects. Moreover, these antigens show immunological cross-reactivity with mOMPs of other serotypes belonging to the same species. The main OPRs of P. aeruginosa, OprF, and OprL, have received much attention from biologists as the potential OPR-based vaccine candidates. Methods: Homology modeling of OprF and OprL was done based on the template structures obtained from the BLAST search. The quality of OprF and OprL molecules was assessed using GMQE and QMEAN4 quality assessment tools. The secondary structure of the proteins was predicted as well as the structural topology, subcellular localization, functional analyses, signal peptide and B cell epitopes of proteins. Results: The structures of OprF and OprL proteins were successfully modeled and assessed using 4RLC-A and 4G4V-A as template structures. The regions of the proteins with a high B cell epitope density were identified as candidates for vaccine design. These regions contain functional and exposed amino acids. In these regions, the majority of amino acids were hydrophilic, flexible and accessible. Conclusion: It should be noted that in silico approaches are appealing alternatives for empirical methods. These approaches could pave the way for precise vaccine design efforts with lower cost and time.
Type of Study: Original article |
Subject:
Microbial pathogenesis
Received: 2017/10/9 | Accepted: 2018/07/30 | Published: 2018/08/27
Received: 2017/10/9 | Accepted: 2018/07/30 | Published: 2018/08/27
References
1. -------------
1. Hancock R. Intrinsic antibiotic resistance of Pseudomonas aeruginosa. J Antimicrob Chemother. 1986; 18 (6): 653-6. [DOI:10.1093/jac/18.6.653] [PMID]
2. 2. Von Specht B, Knapp B, Muth G, Bröker M, Hungerer K, Diehl K, et al. Protection of immunocompromised mice against lethal infection with Pseudomonas aeruginosa by active or passive immunization with recombinant P. aeruginosa outer membrane protein F and outer membrane protein I fusion proteins. Infect Immun. 1995; 63 (5): 1855-62. [PMID] [PMCID]
3. 3. Jaffe RI, Lane JD, Bates CW. Real‐time identification of Pseudomonas aeruginosa direct from clinical samples using a rapid extraction method and polymerase chain reaction (PCR). J Clin Lab Anal. 2001; 15 (3): 131-7. [DOI:10.1002/jcla.1016] [PMID]
4. 4. Hancock R, Carey AM. Outer membrane of Pseudomonas aeruginosa: heat-2-mercaptoethanol-modifiable proteins. J Bacteriol. 1979; 140 (3): 902-10. [PMID] [PMCID]
5. 5. Nikaido H. Outer membrane barrier as a mechanism of antimicrobial resistance. Antimicrob Agents Chemother. 1989; 33 (11): 1831-36. [DOI:10.1128/AAC.33.11.1831] [PMID] [PMCID]
6. 6. Nicas TI, Hancock R. Outer membrane protein H1 of Pseudomonas aeruginosa: involvement in adaptive and mutational resistance to ethylenediaminetetraacetate, polymyxin B, and gentamicin. J Bacteriol. 1980; 143 (2): 872-8. [PMID] [PMCID]
7. 7. Priebe GP, Goldberg JB. Vaccines for Pseudomonas aeruginosa: a long and winding road. Expert Rev Vaccines. 2014; 13 (4): 507-19. [DOI:10.1586/14760584.2014.890053] [PMID] [PMCID]
8. 8. Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. J Biomed Inform. 2015; 53: 405-14. [DOI:10.1016/j.jbi.2014.11.003] [PMID]
9. 9. Gasteiger E, Hoogland C, Gattiker A, Duvaud Se, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server: Springer; 2005. [PMCID]
10. 10. Gardy JL, Brinkman FS. Methods for predicting bacterial protein subcellular localization. Nat Rev Microbiol. 2006; 4 (1): 741-51. [DOI:10.1038/nrmicro1494] [PMID]
11. 11. Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol. 2004; 340 (4): 783-95. [DOI:10.1016/j.jmb.2004.05.028] [PMID]
12. 12. Viklund H, Bernsel A, Skwark M, Elofsson A. SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology. Bioinformatics. 2008; 24 (24): 2928-9. [DOI:10.1093/bioinformatics/btn550] [PMID]
13. 13. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics. 2000; 16 (4): 404-5. [DOI:10.1093/bioinformatics/16.4.404] [PMID]
14. 14. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015; 10 (6): 845-58. [DOI:10.1038/nprot.2015.053] [PMID] [PMCID]
15. 15. Guex N, Peitsch MC. SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. electrophoresis. 1997; 18 (15): 2714-23. [DOI:10.1002/elps.1150181505] [PMID]
16. 16. Glaser F, Pupko T, Paz I, Bell RE, Bechor-Shental D, Martz E, et al. ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics. 2003; 19 (1): 163-4. [DOI:10.1093/bioinformatics/19.1.163] [PMID]
17. 17. Roy A, Yang J, Zhang Y. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 2012: gks372. [DOI:10.1093/nar/gks372]
18. 18. Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 2008; 36 (suppl 2): W513-W8. [DOI:10.1093/nar/gkn254] [PMID] [PMCID]
19. 19. Ponomarenko J, Bui H-H, Li W, Fusseder N, Bourne PE, Sette A, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinf. 2008; 9 (1): 1. [DOI:10.1186/1471-2105-9-514] [PMID] [PMCID]
20. 20. Srinivasan A, Wolfenden LL, Song X, Mackie K, Hartsell TL, Jones HD, et al. An outbreak of Pseudomonas aeruginosa infections associated with flexible bronchoscopes. N Engl J Med. 2003; 348 (3): 221-7. [DOI:10.1056/NEJMoa021808] [PMID]
21. 21. Cryz Jr S, Sadoff J, Fürer E. Octavalent Pseudomonas aeruginosa O-polysaccharide-toxin A conjugate vaccine. Microb Pathog. 1989; 6 (1): 75-80. [DOI:10.1016/0882-4010(89)90010-7]
22. 22. Cryz S, Sadoff J, Ohman D, Fürer E. Characterization of the human immune response to a Pseudomonas aeruginosa O-polysaccharide—toxin A conjugate vaccine. J Lab Clin Med. 1988; 111 (6): 701-7. [PMID]
23. 23. Cryz S, Fürer E, Cross A, Wegmann A, Germanier R, Sadoff J. Safety and immunogenicity of a Pseudomonas aeruginosa O-polysaccharide toxin A conjugate vaccine in humans. J Clin Invest. 1987; 80 (1): 51-6. [DOI:10.1172/JCI113062] [PMID] [PMCID]
24. 24. Pier G. Rationale for development of immunotherapies that target mucoid Pseudomonas aeruginosa infection in cystic fibrosis patients. Behring Inst Mitt. 1997 (98): 350-60. [PMID]
25. 25. Price BM, Legutki JB, Galloway DR, von Specht B-U, Gilleland LB, Gilleland Jr HE, et al. Enhancement of the protective efficacy of an oprF DNA vaccine against Pseudomonas aeruginosa. FEMS Immunol Med Microbiol. 2002; 33 (2): 89-99. [DOI:10.1111/j.1574-695X.2002.tb00577.x] [PMID]
26. 26. Baumann U, Mansouri E, Von Specht B-U. Recombinant OprF–OprI as a vaccine against Pseudomonas aeruginosa infections. Vaccine. 2004; 22 (7): 840-7. [DOI:10.1016/j.vaccine.2003.11.029] [PMID]
27. 27. Von Specht B, Knapp B, Muth G, Bröker M, Hungerer K-D, Diehl K-D, et al. Protection of immunocompromised mice against lethal infection with Pseudomonas aeruginosa by active or passive immunization with recombinant P. aeruginosa outer membrane protein F and outer membrane protein I fusion proteins. Infect Immun. 1995; 63 (5): 1855-62. [PMID] [PMCID]
28. 28. Knapp B, Hundt E, Lenz U, Hungerer K-D, Gabelsberger J, Domdey H, et al. A recombinant hybrid outer membrane protein for vaccination against Pseudomonas aeruginosa. Vaccine. 1999; 17 (13-14): 1663-6. [DOI:10.1016/S0264-410X(98)00420-4]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |