Genomics & Informatics

  • 제14권4호
  • /
  • Pages.255-264
  • /
  • 2016
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지
DOI QR코드

DOI QR Code

Mining the Proteome of Fusobacterium nucleatum subsp. nucleatum ATCC 25586 for Potential Therapeutics Discovery: An In Silico Approach

  • Habib, Abdul Musaweer (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong) ;
  • Islam, Md. Saiful (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong) ;
  • Sohel, Md. (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong) ;
  • Mazumder, Md. Habibul Hasan (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong) ;
  • Sikder, Mohd. Omar Faruk (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong) ;
  • Shahik, Shah Md. (Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong)
  • 투고 : 2016.06.20
  • 심사 : 2016.10.26
  • 발행 : 2016.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1,499 proteins of F. nucleatum, which have no homolog's in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG) that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Automated Annotation Server (KAAS) resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the three dimensional structure of these three proteins. Finally, determination of ligand binding sites of the 2 key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against F. nucleatum.

키워드

참고문헌

  1. Kapatral V, Anderson I, Ivanova N, Reznik G, Los T, Lykidis A, et al. Genome sequence and analysis of the oral bacterium Fusobacterium nucleatum strain ATCC 25586. J Bacteriol 2002;184:2005-2018. https://doi.org/10.1128/JB.184.7.2005-2018.2002
  2. Han YW, Shi W, Huang GT, Kinder Haake S, Park NH, Kuramitsu H, et al. Interactions between periodontal bacteria and human oral epithelial cells: Fusobacterium nucleatum adheres to and invades epithelial cells. Infect Immun 2000; 68:3140-3146. https://doi.org/10.1128/IAI.68.6.3140-3146.2000
  3. Castellarin M, Warren RL, Freeman JD, Dreolini L, Krzywinski M, Strauss J, et al. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome Res 2012;22:299-306. https://doi.org/10.1101/gr.126516.111
  4. Hill GB. Preterm birth: associations with genital and possibly oral microflora. Ann Periodontol 1998;3:222-232. https://doi.org/10.1902/annals.1998.3.1.222
  5. Han YW, Redline RW, Li M, Yin L, Hill GB, McCormick TS. Fusobacterium nucleatum induces premature and term stillbirths in pregnant mice: implication of oral bacteria in preterm birth. Infect Immun 2004;72:2272-2279. https://doi.org/10.1128/IAI.72.4.2272-2279.2004
  6. Kostic AD, Chun E, Robertson L, Glickman JN, Gallini CA, Michaud M, et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 2013;14:207-215. https://doi.org/10.1016/j.chom.2013.07.007
  7. Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F, Earl AM, et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 2012;22: 292-298. https://doi.org/10.1101/gr.126573.111
  8. Nyfors S, Kononen E, Syrjanen R, Komulainen E, Jousimies-Somer H. Emergence of penicillin resistance among Fusobacterium nucleatum populations of commensal oral flora during early childhood. J Antimicrob Chemother 2003;51: 107-112. https://doi.org/10.1093/jac/dkg022
  9. Riordan T. Human infection with Fusobacterium necrophorum (Necrobacillosis), with a focus on Lemierre's syndrome. Clin Microbiol Rev 2007;20:622-659. https://doi.org/10.1128/CMR.00011-07
  10. Kaye D, Kobasa W, Kaye K. Susceptibilities of anaerobic bacteria to cefoperazone and other antibiotics. Antimicrob Agents Chemother 1980;17:957-960. https://doi.org/10.1128/AAC.17.6.957
  11. Jacinto RC, Montagner F, Signoretti FG, Almeida GC, Gomes BP. Frequency, microbial interactions, and antimicrobial susceptibility of Fusobacterium nucleatum and Fusobacterium necrophorum isolated from primary endodontic infections. J Endod 2008;34:1451-1456. https://doi.org/10.1016/j.joen.2008.08.036
  12. Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nat Med 2004;10(12 Suppl): S122-S129. https://doi.org/10.1038/nm1145
  13. Sanders CC, Sanders WE Jr, Goering RV, Werner V. Selection of multiple antibiotic resistance by quinolones, beta-lactams, and aminoglycosides with special reference to cross-resistance between unrelated drug classes. Antimicrob Agents Chemother 1984;26:797-801. https://doi.org/10.1128/AAC.26.6.797
  14. Gleckman RA, Borrego F. Adverse reactions to antibiotics: clues for recognizing, understanding, and avoiding them. Postgrad Med 1997;101:97-98, 101-104, 107-108.
  15. Sakharkar KR, Sakharkar MK, Chow VT. A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa. In Silico Biol 2004;4:355-360.
  16. Nathan C. Antibiotics at the crossroads. Nature 2004;431: 899-902. https://doi.org/10.1038/431899a
  17. Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 2010;26:680-682. https://doi.org/10.1093/bioinformatics/btq003
  18. Sarangi AN, Aggarwal R, Rahman Q, Trivedi N. Subtractive genomics approach for in silico identification and characterization of novel drug targets in Neisseria Meningitides Serogroup B. J Comput Sci Syst Biol 2009;2:255-258.
  19. Li W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 2001;17:282-283. https://doi.org/10.1093/bioinformatics/17.3.282
  20. Li W, Jaroszewski L, Godzik A. Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics 2002;18:77-82. https://doi.org/10.1093/bioinformatics/18.1.77
  21. Rathi B, Sarangi AN, Trivedi N. Genome subtraction for novel target definition in Salmonella typhi. Bioinformation 2009;4: 143-150. https://doi.org/10.6026/97320630004143
  22. Luo H, Lin Y, Gao F, Zhang CT, Zhang R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 2014;42:D574-D580. https://doi.org/10.1093/nar/gkt1131
  23. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007;35:W182-W185. https://doi.org/10.1093/nar/gkm321
  24. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30. https://doi.org/10.1093/nar/28.1.27
  25. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res 2004;32:D277-D280. https://doi.org/10.1093/nar/gkh063
  26. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26: 1608-1615. https://doi.org/10.1093/bioinformatics/btq249
  27. Yu NY, Laird MR, Spencer C, Brinkman FS. PSORTdb: an expanded, auto-updated, user-friendly protein subcellular localization database for Bacteria and Archaea. Nucleic Acids Res 2011;39:D241-D244. https://doi.org/10.1093/nar/gkq1093
  28. 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;101:2525-2534. https://doi.org/10.1016/j.bpj.2011.10.024
  29. Laskowski RA. PDBsum: summaries and analyses of PDB structures. Nucleic Acids Res 2001;29:221-222. https://doi.org/10.1093/nar/29.1.221
  30. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 1993;26:283-291. https://doi.org/10.1107/S0021889892009944
  31. Yang J, Roy A, Zhang Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res 2013;41:D1096-D1103. https://doi.org/10.1093/nar/gks966
  32. Zhang R, Ou HY, Zhang CT. DEG: a database of essential genes. Nucleic Acids Res 2004;32:D271-D272. https://doi.org/10.1093/nar/gkh024
  33. Judson N, Mekalanos JJ. TnAraOut, a transposon-based approach to identify and characterize essential bacterial genes. Nat Biotechnol 2000;18:740-745. https://doi.org/10.1038/77305
  34. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins 1992;12:345-364. https://doi.org/10.1002/prot.340120407
  35. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012;28:3150-3152. https://doi.org/10.1093/bioinformatics/bts565
  36. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006;22:1658-1659. https://doi.org/10.1093/bioinformatics/btl158
  37. Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 2009;37: D455-D458. https://doi.org/10.1093/nar/gkn858
  38. Fidanova S, Lirkov I. 3D protein structure prediction. An Univ Vest Timis Ser Mat-Inform 2009;47:33-46.
  39. Kelley LA, Sternberg MJ. Protein structure prediction on the Web: a case study using the Phyre server. Nat Protoc 2009;4:363-371. https://doi.org/10.1038/nprot.2009.2
  40. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2: 1511-1519. https://doi.org/10.1002/pro.5560020916
  41. Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991;253:164-170. https://doi.org/10.1126/science.1853201
  42. Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992;356:83-85. https://doi.org/10.1038/356083a0
  43. Benkert P, Kunzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res 2009;37:W510-W514. https://doi.org/10.1093/nar/gkp322
  44. Benkert P, Tosatto SC, Schomburg D. QMEAN: a comprehensive scoring function for model quality assessment. Proteins 2008;71:261-277. https://doi.org/10.1002/prot.21715
  45. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 2011;27:343-350. https://doi.org/10.1093/bioinformatics/btq662
  46. Greer J, Erickson JW, Baldwin JJ, Varney MD. Application of the three-dimensional structures of protein target molecules in structure-based drug design. J Med Chem 1994;37:1035-1054. https://doi.org/10.1021/jm00034a001
  47. Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 2013;29: 2588-2595. https://doi.org/10.1093/bioinformatics/btt447
  48. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006;34:D668-D672. https://doi.org/10.1093/nar/gkj067
  49. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-461.

피인용 문헌

  1. : Novel Functions of Cell Division Proteins FtsX and EnvC vol.9, pp.2, 2018, https://doi.org/10.1128/mBio.00360-18