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In-silico functional annotation of a hypothetical protein from Edwardsiella tarda revealed Proline metabolism and apoptosis in fish

Year 2022, Volume: 5 Issue: 1, 78 - 96, 15.04.2022
https://doi.org/10.38001/ijlsb.1032171

Abstract

Edwardsiella tarda is one of the most widespread pathogens in aquatic species. A wide variety of diseases can be caused by this microbe, including Edwardsiella septicaemia but clinical signs of infection differ between species of fish. The fact that the bacteria is resistant to a wide range of antimicrobials is extremely important. Furthermore, several proteins in its genome are classified as hypothetical proteins (HPs). As a result, the current work sought to elucidate the roles of a HP found in the genome of E.tarda. To determine the structure and function of this protein, many bioinformatics methods were used. To locate the homologous protein, the sequence similarity was searched across the available bioinformatics databases. Quality evaluation methods were used to predict and confirm the secondary and tertiary structure. Additionally, the active site and interacting proteins were examined using CASTp and the STRING server. An important biological activity of the HP is that it contains single functional domains that may be responsible for host-cell invasion and autolysis. Further, protein-protein interactions within selected HP revealed several functional partners that are essential for bacterial survival. One such partner is the proline dehydrogenase/delta-1-pyrroline-5-carboxylate dehydrogenase (putA) of E. tarda. In addition, molecular docking and simulation results showed stable bonding between HP and Proline metabolism protein. Finally, the current work shows that the annotated HP is associated with possible mitochondrial metabolism and autolysis formation activities, as well as having a stable binding with the putA protein, which might be of significant relevance to future bacterial genetics research.

References

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Year 2022, Volume: 5 Issue: 1, 78 - 96, 15.04.2022
https://doi.org/10.38001/ijlsb.1032171

Abstract

References

  • 1. Hassan, H.A., et al., Fish borne Edwardsiella tarda eha involved in the bacterial biofilm formation, hemolytic activity, adhesion capability and pathogenicity. Arch Microbiol, 2020. 202(4): p. 835-842.
  • 2. Mohanty, B.R. and P.K. Sahoo, Edwardsiellosis in fish: a brief review. Journal of Biosciences, 2007. 32(3): p. 1331-1344.
  • 3. Lima, L., et al., Isolation and characterizaton of Edwardsiella tarda from pacu Myleus micans. Arquivo Brasileiro De Medicina Veterinaria E Zootecnia - ARQ BRAS MED VET ZOOTEC, 2008. 60.
  • 4. Damme, L. and J. Vandepitte, Frequent isolation of Edwardsiella tarda and Pleisiomonas shigelloides from healthy Zairese freshwater fish: A possible source of sporadic diarrhea in the tropics. Applied and environmental microbiology, 1980. 39: p. 475-9.
  • 5. Zhang, M., J. Sun, and L. Sun, Regulation of autoinducer 2 production and luxS expression in a pathogenic Edwardsiella tarda strain. Microbiology (Reading, England), 2008. 154: p. 2060-9.
  • 6. Sun, J., et al., Genetic Mechanisms of Multi-Antimicrobial Resistance in a Pathogenic Edwardsiella tarda Strain. Aquaculture, 2009. 289: p. 134-139.
  • 7. Wang, Y., X.-H. Zhang, and B. Austin, Comparative analysis of the phenotypic characteristics of high- and low-virulent strains of Edwardsiella tarda. Journal of fish diseases, 2010. 33: p. 985-94.
  • 8. Done, H.Y., A.K. Venkatesan, and R.U. Halden, Does the Recent Growth of Aquaculture Create Antibiotic Resistance Threats Different from those Associated with Land Animal Production in Agriculture? The AAPS Journal, 2015. 17(3): p. 513-524.
  • 9. Watts, J.E.M., et al., The Rising Tide of Antimicrobial Resistance in Aquaculture: Sources, Sinks and Solutions. Marine Drugs, 2017. 15(6): p. 158.
  • 10. Yu, J., et al., Large antibiotic-resistance plasmid of Edwardsiella tarda contributes to virulence in fish. Microbial pathogenesis, 2012. 52: p. 259-66.
  • 11. Roberts, M.C., Update on acquired tetracycline resistance genes. FEMS Microbiology Letters, 2005. 245(2): p. 195-203.
  • 12. Sakai, T., et al., Identification of a 19.3-kDa protein in MRHA-positive Edwardsiella tarda: Putative fimbrial major subunit. FEMS microbiology letters, 2003. 226: p. 127-33.
  • 13. Boeckmann, B., et al., The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research, 2003. 31(1): p. 365-370.
  • 14. Johnson, M., et al., NCBI BLAST: a better web interface. Nucleic Acids Research, 2008. 36(suppl_2): p. W5-W9.
  • 15. Alzohairy, A., BioEdit: An important software for molecular biology. GERF Bulletin of Biosciences, 2011. 2: p. 60-61.
  • 16. Gasteiger, E., et al., ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Research, 2003. 31(13): p. 3784-3788.
  • 17. Yu, C. and J. Hwang. Prediction of Protein Subcellular Localizations. in 2008 Eighth International Conference on Intelligent Systems Design and Applications. 2008.
  • 18. Yu, N.Y., et al., PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics, 2010. 26(13): p. 1608-1615.
  • 19. Bhasin, M., A. Garg, and G.P.S. Raghava, PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics, 2005. 21(10): p. 2522-2524.
  • 20. Möller, S., M.D.R. Croning, and R. Apweiler, Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics, 2001. 17(7): p. 646-653.
  • 21. Tusnády, G.E. and I. Simon, The HMMTOP transmembrane topology prediction server. Bioinformatics, 2001. 17(9): p. 849-850.
  • 22. Dobson, L., I. Reményi, and G.E. Tusnády, CCTOP: a Consensus Constrained TOPology prediction web server. Nucleic Acids Research, 2015. 43(W1): p. W408-W412.
  • 23. Marchler-Bauer, A., et al., CDD: a Conserved Domain Database for protein classification. Nucleic Acids Research, 2005. 33(suppl_1): p. D192-D196.
  • 24. Kanehisa, M., et al., The KEGG databases at GenomeNet. Nucleic Acids Research, 2002. 30(1): p. 42-46.
  • 25. Finn, R.D., Pfam: the protein families database, in Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 2005.
  • 26. Wilson, D., et al., The SUPERFAMILY database in 2007: families and functions. Nucleic Acids Research, 2006. 35(suppl_1): p. D308-D313.
  • 27. Hunter, S., et al., InterPro: the integrative protein signature database. Nucleic Acids Research, 2008. 37(suppl_1): p. D211-D215.
  • 28. Shen, H.-B. and K.-C. Chou, Predicting protein fold pattern with functional domain and sequential evolution information. Journal of Theoretical Biology, 2009. 256(3): p. 441-446.
  • 29. McGuffin, L.J., K. Bryson, and D.T. Jones, The PSIPRED protein structure prediction server. Bioinformatics, 2000. 16(4): p. 404-405.
  • 30. Xu, J., M. McPartlon, and J. Li, Improved protein structure prediction by deep learning irrespective of co-evolution information. Nat Mach Intell, 2021. 3: p. 601-609.
  • 31. Szklarczyk, D., et al., STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res, 2015. 43(Database issue): p. D447-52.
  • 32. Grützner, A., et al., Modulation of titin-based stiffness by disulfide bonding in the cardiac titin N2-B unique sequence. Biophysical journal, 2009. 97(3): p. 825-834.
  • 33. Ferrè, F. and P. Clote, DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res, 2005. 33(Web Server issue): p. W230-2.
  • 34. Heo, L., et al., GalaxySite: ligand-binding-site prediction by using molecular docking. Nucleic Acids Res, 2014. 42(Web Server issue): p. W210-4.
  • 35. Dundas, J., et al., CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Research, 2006. 34(suppl_2): p. W116-W118.
  • 36. Kozakov, D., et al., The ClusPro web server for protein-protein docking. Nature protocols, 2017. 12(2): p. 255-278.
  • 37. Laskowski, R.A., et al., PDBsum: Structural summaries of PDB entries. Protein Sci, 2018. 27(1): p. 129-134.
  • 38. Weng, G., et al., HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res, 2019. 47(W1): p. W322-w330.
  • 39. Källberg, M., et al., RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol, 2014. 1137: p. 17-27.
  • 40. Tian, W., et al., CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, 2018. 46(W1): p. W363-W367.
There are 40 citations in total.

Details

Primary Language English
Subjects Microbiology
Journal Section Research Articles
Authors

Sk Injamamul Islam 0000-0002-0888-6075

Saloa Sanjida This is me 0000-0003-0982-0876

Moslema Jahan Mou This is me 0000-0002-2203-4207

Md. Sarower-e-mahfuj 0000-0002-1014-8565

Saad Nasir This is me 0000-0003-4953-2131

Early Pub Date January 1, 2022
Publication Date April 15, 2022
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

EndNote Islam SI, Sanjida S, Mou MJ, Sarower-e-mahfuj M, Nasir S (April 1, 2022) In-silico functional annotation of a hypothetical protein from Edwardsiella tarda revealed Proline metabolism and apoptosis in fish. International Journal of Life Sciences and Biotechnology 5 1 78–96.



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