DOI QR코드

DOI QR Code

De-novo Hybrid Protein Design for Biodegradation of Organophosphate Pesticides

  • Awasthi, Garima (Amity Institute of Biotechnology, Amity University Uttar Pradesh) ;
  • Yadav, Ruchi (Amity Institute of Biotechnology, Amity University Uttar Pradesh) ;
  • Srivastava, Prachi (Amity Institute of Biotechnology, Amity University Uttar Pradesh)
  • 투고 : 2019.03.11
  • 심사 : 2019.04.12
  • 발행 : 2019.06.28

초록

In the present investigation, we attempted to design a protocol to develop a hybrid protein with better bioremediation capacity. Using in silico approaches, a Hybrid Open Reading Frame (Hybrid ORF) is developed targeting the genes of microorganisms known for degradation of organophosphates. Out of 21 genes identified through BLAST search, 8 structurally similar genes (opdA, opd, opaA, pte RO, pdeA, parC, mpd and phnE) involved in biodegradation were screened. Gene conservational analysis categorizes these organophosphates degrading 8 genes into 4 super families i.e., Metallo-dependent hydrolases, Lactamase B, MPP and TM_PBP2 superfamily. Hybrid protein structure was modeled using multi-template homology modeling (3S07_A; 99%, 1P9E_A; 98%, 2ZO9_B; 33%, 2DXL_A; 33%) by $Schr{\ddot{o}}dinger$ software suit version 10.4.018. Structural verification of protein models was done using Ramachandran plot, it was showing 96.0% residue in the favored region, which was verified using RAMPAGE. The phosphotriesterase protein was showing the highest structural similarity with hybrid protein having raw score 984. The 5 binding sites of hybrid protein were identified through binding site prediction. The docking study shows that hybrid protein potentially interacts with 10 different organophosphates. The study results indicate that the hybrid protein designed has the capability of degrading a wide range of organophosphate compounds.

키워드

참고문헌

  1. Fritsche W, Hofrichter M. 2005. Aerobic degradation of recalcitrant organic compounds by microorganisms, Environmental Biotechnology: Concepts and Applications (eds H.-J. Jördening and J. Winter), pp. 203-227. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, FRG.
  2. Kumar C, Beliavski M, Tarre S, Green M. 2017. Stability of a mixed microbial population in a biological reactor during long term atrazine degradation under carbon limiting conditions. Int. Biodeterior. Biodegradation 123: 311-319. https://doi.org/10.1016/j.ibiod.2017.07.007
  3. Paul D, Pandey G, Pandey J, Jain RK. 2005. Accessing microbial diversity for bioremediation and environmental restoration. Trends Biotechnol. 23: 135-142. https://doi.org/10.1016/j.tibtech.2005.01.001
  4. Sayler GS, Ripp S. 2000. Field applications of genetically engineered microorganisms for bioremediation processes. Curr. Opin. Biotechnol. 11: 286-289. https://doi.org/10.1016/S0958-1669(00)00097-5
  5. Fujita M, Ike M, Hashimoto S. 1991. Feasibility of wastewater treatment using genetically engineered microorganisms. Water Res. 25: 979-984. https://doi.org/10.1016/0043-1354(91)90147-I
  6. Liu Z, Hong Q, Xu JH, Jun W, Li SP. 2006. Construction of a genetically engineered microorganism for degrading organophosphate and carbamate pesticides. Int. Biodeterior. Biodegradation 58: 65-69. https://doi.org/10.1016/j.ibiod.2006.07.009
  7. Jin R, Yang H, Zhang A, Wang J, Liu G. 2009. Bioaugmentation on decolorization of C.I. Direct Blue 71 by using genetically engineered strain Escherichia coli JM109 (pGEX-AZR). J. Hazard. Mater. 163: 1123-1128. https://doi.org/10.1016/j.jhazmat.2008.07.067
  8. Eskandari V, Yakhchali B, Sadeghi M, Karkhane AA. 2013. In silico design and construction of metal binding hybrid proteins for specific removal of cadmium based on CS3 pili display on the surface of Escherichia coli. Int. J. Appl. Biotechnol. Biochem. 60: 564-572. https://doi.org/10.1002/bab.1132
  9. Skariyachan S, Megha M, Kini MN, Mukund KM, Rizvi A, Vasist K. 2015. Selection and screening of microbial consortia for efficient and ecofriendly degradation of plastic garbage collected from urban and rural areas of Bangalore, India. Environ Monit Assess. 187: 4174. https://doi.org/10.1007/s10661-014-4174-y
  10. Luo Q, He Y, Hou D, Zhang J, Shen X. 2015. GPo1 alkB gene expression for improvement of the degradation of diesel oil by a bacterial consortium. Braz. J. Microbiol. 46: 649-657. https://doi.org/10.1590/S1517-838246320120226
  11. Awasthi G, Kumari A, Path AB, Srivastava P. 2018. In silico identification and construction of microbial gene clusters associated with biodegradation of undesired toxic materials. Microb. Pathog. 114: 340-343. https://doi.org/10.1016/j.micpath.2017.11.059
  12. Umadevi S, Aalfin ES, Ayyasamy PM, Rajakumar S. 2015. Computational approaches in waste management: special emphasis in microbial degradation. research & reviews: J. Ecol. Environ. 38: 22-27.
  13. Finley SD, Broadbelt LJ, Hatzimanikatis V. 2010. In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene. BMC Syst. Biol. 4: 7. https://doi.org/10.1186/1752-0509-4-7
  14. Ellis LB, Roe D, Wackett LP. 2006. Biodegradation Database: the first decade. Nucleic Acids Res. 34: 517-521. https://doi.org/10.1093/nar/gkj441
  15. Srinivasan S, Sadasivam SK. 2018. Exploring docking and aerobic-microaerophilic biodegradation of textile azo dye by bacterial systems. J. Water Process Eng. 22: 180-191. https://doi.org/10.1016/j.jwpe.2018.02.004
  16. Prabhavathi P, Rajendran R, Sundaram SK, Dinesh Kumar S, Santhanam P. 2016. Molecular docking studies on potent adsorbed receptor of Thrh protein: A new target for biodegradation of indigo dye. J. Bioremed. Biodeg. 7: 356.
  17. Bell JA, Cao Y, Gunn JR, Day T, Gallicchio E, Zhou Z, et al. 2012. PrimeX and the Schrodinger computational chemistry suite of programs. pp. 534-538. International Tables for Crystallography.
  18. Andrei A, Ivanov D, Barak A, Jacobson K. 2009. Evaluation of homology modeling of G protein-coupled receptors in light of the $A_{2A}$ adenosine receptor crystallographic structure. J. Med. Chem. 52: 3284-3292. https://doi.org/10.1021/jm801533x
  19. Ferrara P, Edgar J. 2007. Evaluation of the utility of homology models in high throughput docking. J. Mol. Model. 13: 897-905. https://doi.org/10.1007/s00894-007-0207-6
  20. Li Z, Ye Y, Godzik A. 2006. Flexible structural neighborhood - a database of protein structural similarities and alignments. Nucleic Acids Res. 34: D277-280. https://doi.org/10.1093/nar/gkj124
  21. Schrodinger Release 2019-1: Glide, Schrodinger, LLC, New York, NY, 2019.
  22. Halgren TA. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47: 1750-1759. https://doi.org/10.1021/jm030644s
  23. Friesner RA. 2006. Extra precision glide docking and scoring incorporating a model of hydrophobic enclosure for proteinligand complexes. J. Med. Chem. 49: 6177-6196. https://doi.org/10.1021/jm051256o
  24. Friesner RA. 2004. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47: 1739-1749. https://doi.org/10.1021/jm0306430
  25. Schulz-Gasch T, Stahl M. 2003. Binding site characteristics in structure-based virtual screening: evaluation of current docking tools. J. Mol. Model. 9: 47-57. https://doi.org/10.1007/s00894-002-0112-y
  26. Horne I, Sutherl TD, Harcourt RL, Russell RJ, Oakeshott JG. 2002. Identification of an opd (Organophosphate Degradation Gene) in an agrobacterium isolate. Appl. Environ. Microbiol. 68: 3371-3376. https://doi.org/10.1128/AEM.68.7.3371-3376.2002
  27. Mulbry WW, Karns JS. 1989. Parathion hydrolase specified by the Flavobacterium opd gene: relationship between the gene and protein. J. Bacteriol. 171: 6740-6746. https://doi.org/10.1128/jb.171.12.6740-6746.1989
  28. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. 2011. GenBank. Nucleic Acids Res. 39: D32-37. https://doi.org/10.1093/nar/gkq1079
  29. Cheng TC, Harvey SP, Chen GL. 1996. Cloning and expression of a gene encoding a bacterial enzyme for decontamination of organophosphorus nerve agents and nucleotide sequence of the enzyme. Appl. Environ. Microbiol. 62: 1636-1641. https://doi.org/10.1128/AEM.62.5.1636-1641.1996
  30. Zhang R, Cui Z, Jiang J, He J, Gu X, Li S. 2005. Diversity of organophosphorus pesticide-degrading bacteria in a polluted soil and conservation of their organophosphorus hydrolase genes. Can. J. Microbiol. 51: 337-343. https://doi.org/10.1139/w05-010
  31. Tehara SK, Keasling JD. 2003. Gene cloning, purification and characterization of a phosphodiesterase from Delftia acidovorans. Appl. Environ. Microbiol. 69: 504-508. https://doi.org/10.1128/AEM.69.1.504-508.2003
  32. Bi D, Xie Y, Tai C, Jiang X, Zhang J, Harrison EM, et al. 2016. A sitespecific integrative plasmid found in Pseudomonas eruginosa clinical isolate HS87 along with a plasmid carrying an aminoglycoside-resistant gene. PLoS One. 11: e0148367. https://doi.org/10.1371/journal.pone.0148367
  33. Elashvili I, Defrank JJ, Culotta VC. 1996. phnE and glpT genes enhance utilization of organophosphates in Escherichia coli K-12. Appl. Environ. Microbiol. 64: 2601-2608. https://doi.org/10.1128/aem.64.7.2601-2608.1998
  34. Ramachandran GN, Ramakrishnan C, Sasisekharan V. 1963. Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7: 95-99. https://doi.org/10.1016/S0022-2836(63)80023-6
  35. Kleywegt GJ, Jones TA. 1996. Phi/psi-chology: Ramachandran revisited. Structure 4: 1395-1400. https://doi.org/10.1016/S0969-2126(96)00147-5
  36. Lovell SC, Davis IW, Arendall III WB, de Bakker PIW, Word JM, et al. 2002. Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50: 437-450. https://doi.org/10.1002/prot.10286
  37. Ho BK, Brasseur R. 2005. The Ramachandran plots of glycine and pre-proline. BMC Struct. Biol. 5: 14. https://doi.org/10.1186/1472-6807-5-14
  38. RAMPAGE Available from http://mordred.bioc.cam.ac.uk/-rapper/rampage.php Assessed June 11, 2019.
  39. Cragg GM, Newman DJ. 2013. Natural products: a continuing source of novel drug leads. Biochim. Biophys. Acta 1830: 3670-3695. https://doi.org/10.1016/j.bbagen.2013.02.008
  40. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. 2009. Pub-Chem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 37: 623-633.
  41. Li Q, Cheng T, Wang Y, Bryant SH. 2010. Pub chem as a public resource for drug discovery. Drug Discov. Today. 15: 1052-1057. https://doi.org/10.1016/j.drudis.2010.10.003
  42. Bitetti-Putzer R. 2001. Functional group placement in protein binding sites: a comparison of GRID and MCSS. J. Comput. Aided Mol. Des. 15: 935-960. https://doi.org/10.1023/A:1014309222984
  43. Finley SD, Broadbelt LJ, Hatzimanikatis V. 2009. Computational framework for predictive biodegradation. Biotechnol. Bioeng. 104: 1080-1097.