DOI QR코드

DOI QR Code

Protein Phosphatase 1D (PPM1D) Structure Prediction Using Homology Modeling

  • 투고 : 2016.01.31
  • 심사 : 2016.03.25
  • 발행 : 2016.03.30

초록

Protein phosphatase manganese dependent 1D (PPM1D) is one of the Ser/Thr protein phosphatases belongs to the PP2C family. They play an important role in cancer tumorigenesis of various tumors including neuroblastoma, pancreatic adenocarcinoma, medulloblastoma, breast cancer, prostate cancer and ovarian cancer. Even though PPM1D is involved in the pathophysiology of various tumors, the three dimensional protein structure is still unknown. Hence in the present study, homology modelling of PPM1D was performed. 20 different models were modelled using single- and multiple-template based homology modelling and validated using different techniques. Best models were selected based on the validation. Three models were selected and found to have similar structures. The predicted models may be useful as a tool in studying the pathophysiological role of PPM1D.

키워드

참고문헌

  1. X. Lu, T. A. Nguyen, S. H. Moon, Y. Darlington, M. Sommer, and L. A. Donehower, "The type 2C phosphatase Wip1: an oncogenic regulator of tumor suppressor and DNA damage response pathways", Cancer Metast. Rev., Vol. 27, pp. 123-135, 2008. https://doi.org/10.1007/s10555-008-9127-x
  2. H. Yang, X. Y. Gao, P. Li, and T. S. Jiang, "PPM1D overexpression predicts poor prognosis in non-small cell lung cancer", Tumor Biol., Vol. 36, pp. 2179-2184, 2015. https://doi.org/10.1007/s13277-014-2828-6
  3. F. Saito-Ohara, I. Imoto, J. Inoue, H. Hosoi, A. Nakagawara, T. Sugimoto, J. Inazawa, "PPM1D is a potential target for 17q gain in neuroblastoma", Cancer Res., Vol. 63, pp. 1876-1883, 2003.
  4. P. Loukopoulos, T. Shibata, H. Katoh, A. Kokubu, M. Sakamoto, K. Yamazaki, T. Kosuge, Y. Kanai, F. Hosoda, I. Imoto, M. Ohki, J. Inazawa, and S. Hirohashi, "Genome-wide array-based comparative genomic hybridization analysis of pancreatic adenocarcinoma: identification of genetic indicators that predict patient outcome", Cancer Sci., vol. 98, pp. 392-400, 2007. https://doi.org/10.1111/j.1349-7006.2007.00395.x
  5. R. C. Castellino, M. D. Bortoli, X. Lu, S. H. Moon, T. A. Nguyen, M. A. Shepard, P. H. Rao, L. A. Donehower, and J. Y. Kim, "Medulloblastomas overexpress the p53-inactivating oncogene WIP1/PPM1D", J. Neuro-oncol., Vol. 86, pp. 245-256, 2008. https://doi.org/10.1007/s11060-007-9470-8
  6. M. B. Lambros, R. Natrajan, F. C. Geyer, M. A. Lopez-Garcia, K. J. Dedes, K. Savage, M. Lacroix-Triki, R. L. Jones, C. J. Lord, S. Linardopoulos, A. Ashworth, and J. S. Reis-Filho, "PPM1D gene amplification and overexpression in breast cancer: a qRT-PCR and chromogenic in situ hybridization study", Modern Pathol., Vol. 23, pp. 1334-1345, 2010. https://doi.org/10.1038/modpathol.2010.121
  7. J. Parssinen, E. L. Alarmo, S. Khan, R. Karhu, M. Vihinen, and A. Kallioniemi, "Identification of differentially expressed genes after PPM1D silencing in breast cancer", Cancer Lett., Vol. 259, pp. 61-70, 2008. https://doi.org/10.1016/j.canlet.2007.09.019
  8. S. Shreeram, O. N. Demidov, W. K. Hee, H. Yamaguchi, N. Onishi, C. Kek, O. N. Timofeev, C. Dudgeon, A. J. Fornace, C. W. Anderson, Y. Minami, E. Appella, and D. V. Bulavin, "Wip1 phosphatase modulates ATM-dependent signaling pathways", Mol. Cell, Vol. 23, pp. 757-764, 2006. https://doi.org/10.1016/j.molcel.2006.07.010
  9. L. Jiao, D. Shen, G. Liu, J. Jia, J. Geng, H. Wang, and Y. Sun, "PPM1D as a novel biomarker for prostate cancer after radical prostatectomy", Anticancer Res., Vol. 34, No. 2919-2925, 2014.
  10. G. C. Baker, J. J. Smith, and D. A. Cowan. "Review and re-analysis of domain-specific 16S primers", J. Microbiol. Meth., Vol. 55, pp. 541-555, 2003. https://doi.org/10.1016/j.mimet.2003.08.009
  11. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, "Basic local alignment search tool", J. Mol. Biol., Vol. 215, pp. 403-410, 1990. https://doi.org/10.1016/S0022-2836(05)80360-2
  12. H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne, "The protein data bank", Nucleic Acids Res., Vol. 28, pp. 235-242, 2000. https://doi.org/10.1093/nar/28.1.235
  13. M. Shalini and T. Madhavan, "Homology modeling of CCR 4: Novel therapeutic target and preferential maker for Th2 cells", J. Chosun Natural Sci., Vol. 7, pp. 234-240, 2014. https://doi.org/10.13160/ricns.2014.7.4.234
  14. B. Sathya and T. Madhavan, "Homology modeling of cysteinyl leukotriene1 receptor", J. Chosun Natural Sci., Vol. 8, pp. 13-18, 2015. https://doi.org/10.13160/ricns.2015.8.1.13
  15. S. K. Nagarajan, and T. Madhavan, "3D structure prediction of thromboxane A2 receptor by homology modeling", J. Chosun Natural Sci., Vol. 8, pp. 75-79, 2015. https://doi.org/10.13160/ricns.2015.8.1.75
  16. B. K Kuntal, P. Aparoy, and P. Reddanna, "Easy-Modeller: A graphical interface to MODELLER", BMC Research Notes, Vol. 3, pp. 226, 2010. https://doi.org/10.1186/1756-0500-3-226
  17. N. Eswar, M. A. Marti-Renom, B. Webb, M. S. Madhusudhan, D. Eramian, M. Shen, U. Pieper, and A. Sali, "Comparative protein structure modeling With MODELLER", Current Protocols in Bioinformatics, New York: John Wiley & Sons, Inc., pp. 5.6.1-5.6.30, 2006.
  18. S. C. Lovell, I. W. Davis, W. B. Arendall III, P. I. W. de Bakker, J. M. Word, M. G. Prisant, J. S. Richardson, and D. C. Richardson, "Structure validation by $C{\alpha}$ geometry: ${\phi}$, ${\psi}$ and $C{\beta}$ deviation", Proteins., Vol. 50, pp. 437-450, 2002.
  19. J. U. Bowie, R. Luthy, D. Eisenberg, "A method to identify protein sequences that fold into a known three-dimensional structure", Science, Vol. 253, pp. 164-170, 1991. https://doi.org/10.1126/science.1853201
  20. J. D. Thompson, D. G. Higgins, and T. J. Gibson, "CLUSTAL W: improving the sensitivity of progressive equence weighting, position-specific gap penalties and weight matrix choice", Nucleic Acids Res., Vol. 22, pp. 4673-4680, 1994. https://doi.org/10.1093/nar/22.22.4673

피인용 문헌

  1. Theoretical Structure Prediction of Bradykinin Receptor B2 Using Comparative Modeling vol.9, pp.4, 2016, https://doi.org/10.13160/ricns.2016.9.4.234
  2. Theoretical Protein Structure Prediction of Glucagon-like Peptide 2 Receptor Using Homology Modelling vol.10, pp.3, 2016, https://doi.org/10.13160/ricns.2017.10.3.119
  3. Molecular Docking Analysis of Protein Phosphatase 1D (PPM1D) Receptor with SL-175, SL-176 and CDC5L vol.11, pp.1, 2016, https://doi.org/10.13160/ricns.2018.11.1.25
  4. Structural approaches for the DNA binding motifs prediction in Bacillus thuringiensis sigma-E transcription factor (σETF) vol.25, pp.10, 2019, https://doi.org/10.1007/s00894-019-4192-3