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Molecular phylogenetics and comparative modeling of MnSOD, an enzyme involved during environmental stress conditions in Oryza sativa

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Abstract

Superoxide dismutases are a class of enzymes that catalyze the dismutation of superoxide into oxygen and hydrogen peroxide. As such, they are an important antioxidant defense in nearly all cells exposed to oxygen. Superoxide dismutase (SOD) acts as first line of defense against oxidative and genetic stress. Manganese superoxide dismutase (MnSOD), found in mitochondria or peroxisomes, contains Mn (III) at the active site. The three dimensional structure of MnSOD of Oryza sativa is not yet available in protein data bank so we have predicted the structure model of O. sativa MnSOD using homology modeling. The predicted model can further be explored for identification of ligand binding sites which may be useful for understanding specific role in functional site residues during catalysis. This study also demonstrated that the phylogenetic analysis of O. sativa MnSOD protein with distinct dicot and monocot plant species. The MnSOD protein of O. sativa has shown similarity with both monocot and as well as dicot plant species.

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References

  1. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res 25: 3389–3402.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Arnold, K., Bordoli, L., Kopp, J., Schwede, T. 2006. The SWISS-MODEL workspace: a web-based environ ment for protein structure homology modeling. Bioinformatics 22: 195–201.

    Article  CAS  PubMed  Google Scholar 

  3. Brady, G., and Stouten, P. 2000. Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14: 383–401.

    Article  CAS  PubMed  Google Scholar 

  4. Bowler, C., Alliotte, T., Van Den Bulcke, M., Bauw, G., Vandekerckhove, J., Van Montagu, M., Inze, D. 1989. A plant manganese superoxide dismutase is efficiently imported and correctly processed by yeast mitochondria. Proc. Natl. Acad. Sci. U.S.A. 86: 3237–3241.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Bowler, C., Slooten, L., Vandenbranden, S., De Rycke, R., Botterman, J., Sybesma, C., Van Montagu, M., Inze, D. 1991. Manganese superoxide dismutase can reduce cellular damage mediated by oxygen radicals in transgenic plants. EMBO J, 10(7): 1723–1732.

    CAS  PubMed Central  PubMed  Google Scholar 

  6. Bowler, C., Van Montagu, M., Inze, D. 1992. Superoxide dismutase and stress tolerance. Annu. Rev. Plant Physiol. Plant. Mol. Biol. 43: 83–116.

    Article  CAS  Google Scholar 

  7. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I. N., Bourne, P. E. 2000. The Protein Data Bank. Nucleic Acids Research 28: 235–242.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. DeLano, W.L. 2002. The PyMOL Molecular Graphics System. DeLano Scientific, San Carlos, CA, USA.

    Google Scholar 

  9. Felsenstein, J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39: 783–791.

    Article  Google Scholar 

  10. Henikoff, S., Henikoff, J.G. 1992. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89: 10915–10919.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E. 2008. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory. Comput. 4: 435–447.

    Article  CAS  Google Scholar 

  12. Laskowski, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.M. 1993. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 26: 283–291.

    Article  CAS  Google Scholar 

  13. Laskowski, R.A., Watson, J.D., Thornton, J.M. 2005. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res. 33: 89–93.

    Article  Google Scholar 

  14. Laurie, A. and Jackson, R. 2005. Q-SiteFinder: an energy-based method for the prediction of proteinligand binding sites. Bioinformatics 21: 1908–1916.

    Article  CAS  PubMed  Google Scholar 

  15. Lüthy, R., Bowie, J.U., Eisenberg, D. 1992. Assessment of protein models with three-dimensional profiles. Nature 356: 83–85.

    Article  PubMed  Google Scholar 

  16. Lippi, M., Passerini, A., Punta, M., Rost B., Frasconi, P. 2008. MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence. Bioinformatics 24(18): 2094–2095.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Martí-Renom, M.A., Stuart, A.C., Fiser, A., Sánchez, R., Melo, F., Sali, A. 2000. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29: 291–325

    Article  PubMed  Google Scholar 

  18. Matthias Heinig, Dmitrij, Frishman. 2004 STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res. 32: 500–502.

    Article  Google Scholar 

  19. Moller, I.M. 2001. Annu. Rev. Plant. Physiol. Plant. Mol. Biol. 52: 561.

    Article  CAS  PubMed  Google Scholar 

  20. Michener, C.D., Sokal, R.R. 1957. A quantitative approach to a problem of classification. Evolution. 11: 490–499.

    Article  Google Scholar 

  21. Ouyang, S., Zhu W., Hamilton, J., Lin H., Campbell, M., Childs, K., Thibaud-Nissen, F., Malek, R. L., Lee, Y., Zheng, L., Orvis, J., Haas, B., Wortman, J., and Buell, C. R. 2007. The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Research. 35: 883–887.

    Article  Google Scholar 

  22. Sakamoto, A., Nosaka, Y. and Tanaka, K. 1993. Cloning and Sequencing Analysis of a Complementary DNA for Manganese-Superoxide Dismutase from Rice (Oryza sativa). Plant Physiol. 103: 1477–1478.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Sali, A., Blundell, T.L. 1993. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234: 779–815.

    Article  CAS  PubMed  Google Scholar 

  24. Tamura, K., Dudley, J., Nei, M., Kumar, S. 2007. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24: 1596–9.

    Article  CAS  PubMed  Google Scholar 

  25. Thompson, J.D., Higgins, D.G., Gibson, T.J. 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res. 22(22): 4673–80.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Trinh, C.H., Hunter, T., Stewart, E.E., Phillips, S.E., Hunter, G.J. 2008. Purification, crystallization and Xray structures of the two manganese superoxide dismutases from Caenorhabditis elegans. Acta Crystallogr., Sect. F 64: 1110–1114.

    Article  CAS  Google Scholar 

  27. Wiberg, K.B. 1965. A scheme for strain energy minimization. J. Am. Chem. Soc. 87: 1070–1078.

    Article  CAS  Google Scholar 

  28. Wiederstein, M., Sippl, M.J. 2007. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucl. Acids. Res. 35: 407–410.

    Article  Google Scholar 

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Correspondence to Pooja Tripathi.

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Tripathi, V., Tripathi, P. Molecular phylogenetics and comparative modeling of MnSOD, an enzyme involved during environmental stress conditions in Oryza sativa . Interdiscip Sci Comput Life Sci 6, 251–258 (2014). https://doi.org/10.1007/s12539-011-0050-4

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  • DOI: https://doi.org/10.1007/s12539-011-0050-4

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