Volume 7, Issue 4 (10-2019)
JoMMID 2019, 7(4): 93-106 |
Back to browse issues page
Structure Evaluation of IroN for Designing a Vaccine against Escherichia Coli, an In Silico Approach
Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
Abstract: (7179 Views)
Introduction: Some strains of Escherichia Coli, including intestinal pathogenic strains, commensal strains, and extra intestinal pathogenic E. coli (ExPEC) have a significant impact on human health status. A standard vaccine designed based on conserved epitopes can stimulate a protective immune response against these pathogens. Additionally, enhanced expression at the infection site as a pathogenesis factor in disease is crucial for an ideal vaccine candidate. The IroN protein plays a role in severe infections of E. coli. Hence, this protein will assist in developing the novel and more efficient treatments for E. coli related infections. A better understanding of protein tertiary structure can help to percept their functions and also their interactions with other molecules. There is a growing interest in using bioinformatics tool to make accurate predictions about the functional, immunological, and biochemical features of target antigens. Method: Herein, we aimed to predict the structure of the IroN protein upon its folding and determine their immunological properties. Results: In the present study, using bioinformatics analyses, we identified the highly antigenic regions of IroN protein. Our designed vaccine candidate had the highest immunological properties and folded into a typical beta-barrel structure. Conclusion: The approach of assigning structural and immunological properties of the target antigen to design the vaccine candidate could be deployed as an efficient strategy to circumvent the challenges ahead of empirical methods without dealing with ethical concerns of animal usage and human participants. Although the obtained results are promising, further experimental studies could bring about more insights on the efficiency of the designed vaccine.
Type of Study: Original article |
Subject:
Microbial pathogenesis
Received: 2018/07/18 | Accepted: 2019/09/24 | Published: 2020/03/12
Received: 2018/07/18 | Accepted: 2019/09/24 | Published: 2020/03/12
References
1. Russo TA, Johnson JR. Proposal for a new inclusive designation for extraintestinal pathogenic isolates of Escherichia coli: ExPEC. J Infect Dis. 2000; 181: 1753-4. [DOI:10.1086/315418]
2. Russo TA, Johnson JR. Medical and economic impact of extraintestinal infections due to Escherichia coli: focus on an increasingly important endemic problem. Microb Infect. 2003; 5: 449-56. [DOI:10.1016/S1286-4579(03)00049-2]
3. Gupta K, Hooton TM, Stamm WE. Increasing antimicrobial resistance and the management of uncomplicated community-acquired urinary tract infections. Ann Intern Med. 2001; 135: 41-50. [DOI:10.7326/0003-4819-135-1-200107030-00012]
4. Langermann S, Palaszynski S, Barnhart M, et al. Prevention of mucosal Escherichia coli infection by FimH-adhesin-based systemic vaccination. Science. 1997; 276: 607-11. [DOI:10.1126/science.276.5312.607]
5. Russo TA, McFadden CD, Carlino-MacDonald UB, Beanan JM, Barnard TJ, Johnson JR. IroN functions as a siderophore receptor and is a urovirulence factor in an extraintestinal pathogenic isolate of Escherichia coli. Infect Immun. 2002; 70: 7156-60. [DOI:10.1128/IAI.70.12.7156-7160.2002]
6. Dozois CM, Daigle F, Curtiss R. Identification of pathogen-specific and conserved genes expressed in vivo by an avian pathogenic Escherichia coli strain. Proc Natl Acad Sci Unit States Am. 2003; 100: 247-52. [DOI:10.1073/pnas.232686799]
7. Floudas C, Fung H, McAllister S, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design: A review. Chem Eng Sci. 2006; 61:966-88. [DOI:10.1016/j.ces.2005.04.009]
8. Rahman A, Zomaya AY. An overview of protein-folding techniques: issues and perspectives. Int J Bioinformatics Res Appl. 2005; 1: 121-43. [DOI:10.1504/IJBRA.2005.006911]
9. Gish W, States DJ. Identification of protein coding regions by database similarity search. Nat Genet. 1993; 3: 266. [DOI:10.1038/ng0393-266]
10. Fiser A. Protein structure modeling in the proteomics era. Expet Rev Proteonomics. 2004; 1: 97-110. [DOI:10.1586/14789450.1.1.97]
11. Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook: Springer. 2005: 571-607. [DOI:10.1385/1-59259-890-0:571]
12. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics. 2007; 8: 4. [DOI:10.1186/1471-2105-8-4]
13. Yu CS, Cheng CW, Su WC, et al. CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PloS one. 2014; 9: e99368. [DOI:10.1371/journal.pone.0099368]
14. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995; 11: 681-4. [DOI:10.1093/bioinformatics/11.6.681]
15. Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 2015; 43: W401-W7. [DOI:10.1093/nar/gkv485]
16. Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305: 567-80. [DOI:10.1006/jmbi.2000.4315]
17. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ. PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res. 2004; 32: W400-W4. [DOI:10.1093/nar/gkh417]
18. Petersen TN, Brunak S, Von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Protocol. 2011; 8: 785. [DOI:10.1038/nmeth.1701]
19. Chen CC, Hwang JK, Yang JM. 2-v2: template-based protein structure prediction server. Bmc Bioinformatics. 2009; 10: 366. [DOI:10.1186/1471-2105-10-366]
20. Schwede T, Kopp J, Guex N, Peitsch MC. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res. 2003; 31: 3381-5. [DOI:10.1093/nar/gkg520]
21. Guex N, Peitsch MC. SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. electrophoresis. 1997; 18: 2714-23. [DOI:10.1002/elps.1150181505]
22. Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007; 35: 3375-82. [DOI:10.1093/nar/gkm251]
23. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protocol. 2015; 10: 845. [DOI:10.1038/nprot.2015.053]
24. Carugo O, Djinović-Carugo K. Half a century of Ramachandran plots. Acta Crystallographica Section D: Biological Crystallography. 2013; 69: 1333-41. [DOI:10.1107/S090744491301158X]
25. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011; 101: 2525-34. [DOI:10.1016/j.bpj.2011.10.024]
26. Lomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 2011; 40: D370-D6. [DOI:10.1093/nar/gkr703]
27. Roy A, Yang J, Zhang Y. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 2012; 40: W471-W7. [DOI:10.1093/nar/gks372]
28. Berezin C, Glaser F, Rosenberg J, Paz I, Pupko T, Fariselli P, et al. ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics, 2004; 20 (8): 1322-4. [DOI:10.1093/bioinformatics/bth070]
29. Negi SS, Schein CH, Oezguen N, Power TD, Braun W. InterProSurf: a web server for predicting interacting sites on protein surfaces. Bioinformatics. 2007; 23: 3397-9. [DOI:10.1093/bioinformatics/btm474]
30. Kawabata T. Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins: Structure, Function, and Bioinformatics. 2010; 78: 1195-211. [DOI:10.1002/prot.22639]
31. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res. 2006; 34: W116-W8. [DOI:10.1093/nar/gkl282]
32. Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS. Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins. Nucleic Acids Res. 2013; 41: W314-W21. [DOI:10.1093/nar/gkt503]
33. Vita R, Overton JA, Greenbaum JA, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2014; 43: D405-D12. [DOI:10.1093/nar/gku938]
34. Saha S, Raghava G. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: ICARIS. Springer: 197-204. [DOI:10.1007/978-3-540-30220-9_16]
35. Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017; 45: W24-W9. [DOI:10.1093/nar/gkx346]
36. Yao B, Zhang L, Liang S, Zhang C. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PloS one. 2012; 7: e45152. [DOI:10.1371/journal.pone.0045152]
37. Davies MN, Flower DR. Harnessing bioinformatics to discover new vaccines. Drug Discov Today. 2007; 12: 389-95. [DOI:10.1016/j.drudis.2007.03.010]
38. Ponomarenko J, Bui HH, Li W, et al. ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC bioinformatics. 2008; 9: 514. [DOI:10.1186/1471-2105-9-514]
39. Uehling DT, Hopkins WJ, Elkahwaji JE, Schmidt DM, Leverson GE. Phase 2 clinical trial of a vaginal mucosal vaccine for urinary tract infections. J Urol. 2003; 170: 867-9. [DOI:10.1097/01.ju.0000075094.54767.6e]
40. Bauer HW, Alloussi S, Egger G, et al. A long-term, multicenter, double-blind study of an Escherichia coli extract (OM-89) in female patients with recurrent urinary tract infections. J Urol. 2005; 47: 542-8. [DOI:10.1016/j.eururo.2004.12.009]
41. Goluszko P, Goluszko E, Nowicki B, Nowicki S, Popov V, Wang H-Q. Vaccination with purified Dr Fimbriae reduces mortality associated with chronic urinary tract infection due to Escherichia coli bearing Dr adhesin. Infect Immun. 2005; 73: 627-31. [DOI:10.1128/IAI.73.1.627-631.2005]
42. Jahangiri A, Rasooli I, Gargari SLM, et al. An in silico DNA vaccine against Listeria monocytogenes. Vaccine. 2011; 29: 6948-58. [DOI:10.1016/j.vaccine.2011.07.040]
43. Sefid F, Rasooli I, Jahangiri A. In silico determination and validation of baumannii acinetobactin utilization a structure and ligand binding site. Biomed Res. 2013; 2013. [DOI:10.1155/2013/172784]
44. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH. Ofatumumab monoclonal antibody affinity maturation through in silico modeling. IBJ. 2018; 22: 180.
45. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A. In Silico Analysis for Determination and Validation of Human CD20 Antigen 3D Structure. Int J Pept Res Therapeut. 2019; 25: 123-35. [DOI:10.1007/s10989-017-9654-9]
46. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH, Dastmalchi S. Affinity maturation and characterization of the ofatumumab monoclonal antibody. J Cell Biochem. 2019; 120: 940-50. [DOI:10.1002/jcb.27457]
47. Kleywegt GJ, Jones TA. Databases in protein crystallography. Acta Crystallographica Section D: Biological Crystallography. 1998; 54: 1119-31. [DOI:10.1107/S0907444998007100]
48. Mislin GL, Schalk IJ. Siderophore-dependent iron uptake systems as gates for antibiotic Trojan horse strategies against Pseudomonas aeruginosa. Metallomics. 2014; 6: 408-20. [DOI:10.1039/C3MT00359K]
49. Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. 1994. [DOI:10.1006/jmbi.1994.1017]
50. Bagos PG, Liakopoulos TD, Hamodrakas SJ. Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method. BMC bioinformatics. 2005; 6:7. [DOI:10.1186/1471-2105-6-7]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |