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Integrated In Silico–In Vitro Identification and Characterization of the SH3-Mediated Interaction between Human PTTG and its Cognate Partners in Medulloblastoma

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Abstract

The human pituitary tumor-transforming gene is an oncogenic protein which serves as a central hub in the cellular signaling network of medulloblastoma. The protein contains two vicinal PxxP motifs at its C terminus that are potential binding sites of peptide-recognition SH3 domains. Here, a synthetic protocol that integrated in silico analysis and in vitro assay was described to identify the SH3-binding partners of pituitary tumor-transforming gene in the gene expression profile of medulloblastoma. In the procedure, a variety of structurally diverse, non-redundant SH3 domains with high gene expression in medulloblastoma were compiled, and their three-dimensional structures were either manually retrieved from the protein data bank database or computationally modeled through bioinformatics technique. The binding capability of these domains towards the two PxxP-containing peptides m1p: 161LGPPSPVK168 and m2p: 168KMPSPPWE175 of pituitary tumor-transforming gene were ranked by structure-based scoring and fluorescence-based assay. Consequently, a number of SH3 domains, including MAP3K and PI3K, were found to have moderate or high affinity for m1p and/or m2p. Interestingly, the two overlapping peptides exhibits a distinct binding profile to these identified domain partners, suggesting that the binding selectivity of m1p and m2p is optimized across the medulloblastoma expression spectrum by competing for domain candidates. In addition, two redesigned versions of m1p peptide ware obtained via a structure-based rational mutation approach, which exhibited an increased affinity for the domain as compared to native peptide.

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References

  1. Xiao, J. Q., Liu, X. H., Hou, B., Yao, Y., Deng, K., Feng, M., Xing, B., Lian, W., Wang, R. Z., & Feng, F. (2014). Correlations of pituitary tumor transforming gene expression with human pituitary adenomas: A meta-analysis. PLoS One, 9(3), e90396.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pei, L., & Melmed, S. (1997). Isolation and characterization of a pituitary tumor-transforming gene (PTTG). Molecular Endocrinology, 11(4), 433–441.

    Article  CAS  PubMed  Google Scholar 

  3. Zhang, X., Horwitz, G. A., Heaney, A. P., Nakashima, M., Prezant, T. R., Bronstein, M. D., & Melmed, S. (1999). Pituitary tumor transforming gene (PTTG) expression in pituitary adenomas. Journal of Clinical Endocrinology and Metabolism, 84(2), 761–767.

    Article  CAS  PubMed  Google Scholar 

  4. Salehi, F., Kovacs, K., Scheithauer, B. W., Lloyd, R. V., & Cusimano, M. (2008). Pituitary tumor-transforming gene in endocrine and other neoplasms: A review and update. Endocrine Related Cancer, 15(3), 721–743.

    Article  CAS  PubMed  Google Scholar 

  5. Liu, J., Wang, Y., He, H., Jin, W., & Zheng, R. (2015). Overexpression of the pituitary tumor transforming gene upregulates metastasis in malignant neoplasms of the human salivary glands. Experimental and Therapeutic Medicine, 10(2), 763–768.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cui, L., Xu, S., Song, Z., Zhao, G., Liu, X., & Song, Y. (2015). Pituitary tumor transforming gene: A novel therapeutic target for glioma treatment. Acta Biochimica et Biophysica Sinica, 47(6), 414–421.

    Article  CAS  PubMed  Google Scholar 

  7. Yu, R., Heaney, A. P., Lu, W., Chen, J., & Melmed, S. (2000). Pituitary tumor transforming gene causes aneuploidy and p53-dependent and p53-independent apoptosis. Journal of Biological Chemistry, 275(47), 36502–36505.

    Article  CAS  PubMed  Google Scholar 

  8. McCabe, C. J., Khaira, J. S., Boelaert, K., Heaney, A. P., Tannahill, L. A., Hussain, S., Mitchell, R., Olliff, J., Sheppard, M. C., Franklyn, J. A., & Gittoes, N. J. (2003). Expression of pituitary tumour transforming gene (PTTG) and fibroblast growth factor-2 (FGF-2) in human pituitary adenomas: Relationships to clinical tumour behaviour. Clinical Endocrinology, 58(2), 141–150.

    Article  CAS  PubMed  Google Scholar 

  9. Boelaert, K., Yu, R., Tannahill, L. A., Stratford, A. L., Khanim, F. L., Eggo, M. C., Moore, J. S., Young, L. S., Gittoes, N. J., Franklyn, J. A., Melmed, S., & McCabe, C. J. (2004). PTTG’s C-terminal PXXP motifs modulate critical cellular processes in vitro. Journal of Molecular Endocrinology, 33(3), 663–677.

    Article  CAS  PubMed  Google Scholar 

  10. Domínguez, A., Ramos-Morales, F., Romero, F., Rios, R. M., Dreyfus, F., Tortolero, M., & Pintor-Toro, J. A. (1998). hpttg, a human homologue of rat pttg, is overexpressed in hematopoietic neoplasms. Evidence for a transcriptional activation function of hPTTG. Oncogene, 17(17), 2187–2193.

    Article  PubMed  Google Scholar 

  11. Zhang., X., Horwitz, G. A., Prezant, T. R., Valentini, A., Nakashima, M., Bronstein, M. D., & Melmed, S. (1999). Structure, expression, and function of human pituitary tumor-transforming gene (PTTG). Molecular Endocrinology, 13(1), 156–166.

    Article  CAS  PubMed  Google Scholar 

  12. Pei, L. (2000). Activation of mitogen-activated protein kinase cascade regulates pituitary tumor-transforming gene transactivation function. Journal of Biological Chemistry, 275(40), 31191–31198.

    Article  CAS  PubMed  Google Scholar 

  13. Vlotides, G., Cruz-Soto, M., Rubinek, T., Eigler, T., Auernhammer, C. J., & Melmed, S. (2006). Mechanisms for growth factor-induced pituitary tumor transforming gene-1 expression in pituitary folliculostellate TtT/GF cells. Molecular Endocrinology, 20(12), 3321–3335.

    Article  CAS  PubMed  Google Scholar 

  14. Boon, K., Edwards, J. B., Siu, I. M., Olschner, D., Eberhart, C. G., Marra, M. A., Strausberg, R. L., & Riggins, G. J. (2003). Comparison of medulloblastoma and normal neural transcriptomes identifies a restricted set of activated genes. Oncogene, 22(48), 7687–7694.

    Article  CAS  PubMed  Google Scholar 

  15. Kärkkäinen, S., Hiipakka, M., Wang, J. H., Kleino, I., Vähä-Jaakkola, M., Renkema, G. H., Liss, M., Wagner, R., & Saksela, K. (2006). Identification of preferred protein interactions by phage-display of the human Src homology-3 proteome. EMBO Reports, 7(2), 186–191.

    Article  PubMed  Google Scholar 

  16. Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., Lopez, R., Thompson, J. D., Gibson, T. J., & Higgins, D. G. (2007). ClustalW and ClustalX version 2. Bioinformatics, 23(21), 2947–2948.

  17. 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(1), 235–242.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Canutescu, A. A., & Dunbrack, Jr., R. L. (2005). MollDE: A homology modeling framework you can click with. Bioinformatics, 21(12), 2914–2916.

    Article  CAS  PubMed  Google Scholar 

  19. Bordoli, L., Kiefer, F., Arnold, K., Benkert, P., Battey, J., & Schwede, T. (2009). Protein structure homology modeling using SWISS-MODEL workspace. Nature Protocols, 4(1), 1–13.

    Article  CAS  PubMed  Google Scholar 

  20. Canutescu, A. A., Shelenkov, A. A., & Dunbrack, Jr., R. L. (2003). A graph-theory algorithm for rapid protein side-chain prediction. Protein Science, 12(9), 2001–2014.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Krivov, G. G., Shapovalov, M. V., & Dunbrack, Jr., R. L. (2009). Improved prediction of protein side-chain conformations with SCWRL4. Proteins, 77(4), 778–795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Krieger, E., Koraimann, G., & Vriend, G. (2002). Increasing the precision of comparative models with YASARA NOVA — A self-parameterizing force field. Proteins, 47(3), 393–402.

    Article  CAS  PubMed  Google Scholar 

  23. Li, Y., Li, M., Min, W., Han, G., Wang, L., Chen, C., Li, Z., Zhang, Y., Li, J., & Yue, Z. (2017). Characterization, identification and disruption of the intermolecular interaction between protein kinases and human pituitary tumor-transforming gene 1. General Physiology and Biophysics, 36(1), 23–30.

    Article  Google Scholar 

  24. Donsky, E., & Wolfson, H. J. (2011). PepCrawler: A fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors. Bioinformatics, 27(20), 2836–2842.

    Article  CAS  PubMed  Google Scholar 

  25. London, N., Raveh, B., Cohen, E., Fathi, G., & Schueler-Furman, O. (2011). Rosetta FlexPepDock web server--high resolution modeling of peptide-protein interactions. Nucleic Acids Research, 39(Web Server Issue), W249–W253.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T., Caldwell, J., Wang, J., & Kollman, P. (2003). A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. Journal of Computational Chemistry, 24(16), 1999–2012.

    Article  CAS  PubMed  Google Scholar 

  27. Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26(16), 1668–1688.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics, 79(2), 926–935.

    Article  CAS  Google Scholar 

  29. Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. C. (1977). Numerical integration of the Cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Chemical Physics, 23(3), 327–341.

    CAS  Google Scholar 

  30. Darden, T., York, D., & Pedersen, L. (1993). Particle mesh Ewald: An N.log(N) method for Ewald sums in large systems. Chemical Physics, 98(12), 10089–10092.

    CAS  Google Scholar 

  31. Gangwal, R. P., Dhoke, G. V., Damre, M. V., Khandelwal, K., & Sangamwar, A. T. (2013). Structure-based virtual screening and molecular dynamic simulation studies to identify novel cytochrome bc1 inhibitors as antimalarial agents. Journal of Computational Medicine, 2013(1), 637901.

    Google Scholar 

  32. Homeyer, N., & Gohlke, H. (2012). Free energy calculations by the molecular mechanics Poisson-Boltzmann surface area method. Molecular Informatics, 31(2), 114–122.

    Article  CAS  PubMed  Google Scholar 

  33. Yang, C., Zhang, S., He, P., Wang, C., Huang, J., & Zhou, P. (2015). Self-binding peptides: Folding or binding? Journal of Chemical Information and Modeling, 55(2), 329–342.

    Article  CAS  PubMed  Google Scholar 

  34. Pisabarro, M. T., & Serrano, L. (1996). Rational design of specific high-affinity peptide ligands for the Abl-SH3 domain. Biochemistry, 35(33), 10634–10640.

    Article  CAS  PubMed  Google Scholar 

  35. He, P., Tan, D. L., Liu, H. X., Lv, F. L., & Wu, W. (2015). The auto-inhibitory state of Rho guanine nucleotide exchange factor ARHGEF5/TIM can be relieved by targeting its SH3 domain with rationally designed peptide aptamers. Biochimie, 111(4), 10–18.

    Article  CAS  PubMed  Google Scholar 

  36. Schweimer, K., Hoffmann, S., Bauer, F., Friedrich, U., Kardinal, C., Feller, S. M., Biesinger, B., & Sticht, H. (2002). Structural investigation of the binding of a herpesviral protein to the SH3 domain of tyrosine kinase Lck. Biochemistry, 41(16), 5120–5130.

    Article  CAS  PubMed  Google Scholar 

  37. Han, K., Wu, G., & Lv, F. (2013). Development of QSAR-improved statistical potential for the structure-based analysis of protein-peptide binding affinities. Molecular Informatics, 32(9), 783–792.

    Article  CAS  PubMed  Google Scholar 

  38. Tong, Y., & Eigler, T. (2009). Transcriptional targets for pituitary tumor-transforming gene-1. Journal of Molecular Endocrinology, 43(5), 179–185.

    Article  CAS  PubMed  Google Scholar 

  39. London, N., Movshovitz-Attias, D., & Schueler-Furman, O. (2010). The structural basis of peptide-protein binding strategies. Structure, 18(2), 188–199.

    Article  CAS  PubMed  Google Scholar 

  40. Adzhubei, A. A., Sternberg, M. J., & Makarov, A. (2013). A. Polyproline-II helix in proteins: Structure and function. Journal of Molecular Biology, 425(12), 2100–2132.

    Article  CAS  PubMed  Google Scholar 

  41. Liu, P., Cheng, H., Roberts, T. M., & Zhao, J. J. (2009). Targeting the phosphoinositide 3-kinase pathway in cancer. Nature Reviews Drug Discovery, 8(8), 627–644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by the Health and Family Planning Commission of Jiangsu Province Youth Research Subject (No. Q201606), the Six Talent Peaks Project in Jiangsu Province (No. 2014-wsw-021), and the Suzhou Applied Basic Research (No. Sys201535).

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    1. Department of Neurosurgery, the First Affiliated Hospital of Soochow University, Suzhou, 215006, China

      Jiangang Liu, Dapeng Wang, Yanyan Li, Hui Yao, Nan Zhang, Xuewen Zhang, Fangping Zhong & Yulun Huang

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    1. Jiangang Liu
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    Jiangang Liu and Dapeng Wang contributed equally to this work.

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    Liu, J., Wang, D., Li, Y. et al. Integrated In Silico–In Vitro Identification and Characterization of the SH3-Mediated Interaction between Human PTTG and its Cognate Partners in Medulloblastoma. Cell Biochem Biophys 76, 83–90 (2018). https://doi.org/10.1007/s12013-017-0810-9

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