Skip to main content
Log in

Probing the Dynamic Mechanism of Uncommon Allosteric Inhibitors Optimized to Enhance Drug Selectivity of SHP2 with Therapeutic Potential for Cancer Treatment

  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

There is currently considerable interest in SHP2 as a potential target for treatment of cancer. Mutation in SHP2, particularly the E76A mutation, has been found to seriously confer the phosphatase high activity. Recently, two compounds, 1 and 23, have been reported as potent allosteric inhibitors of both SHP2 wild type (SHP2WT) and the E76A mutant (SHP2E76A), with higher activity than other inhibitors. However, the structural and dynamic implications of their inhibitory mechanisms are yet unexplored which deserve further attention. Herein, the MD simulation applies to gain insight into the atomistic nature of each binding mode of inhibitors 1 and 23 in both SHP2WT and SHP2E76A. The comparative analysis reveals inhibitor 1 can freeze SHP2WT and SHP2E76A in their auto-inhibited conformation better than 23, in agreement with experimental data. GLU250 in both SHP2WT and SHP2E76A and ARG111 and ARG229 in SHP2E76A play a crucial role in the higher activity of 1 compared to 23. The mutation E76A increases the binding affinity of 1 and 23 compared to the wild type, implying that the two inhibitors have been well adopted by the E76A mutant. The findings here can substantially shed light on new strategies for developing novel classes of SHP2 inhibitors with increased potency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Scheme 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Tonks, N. K. (2006). Protein tyrosine phosphatases: from genes, to function, to disease. Nature Reviews Molecular Cell Biology, 7(11), 833–846.

    Article  CAS  PubMed  Google Scholar 

  2. Van Huijsduijnen, R. H., Bombrun, A., & Swinnen, D. (2002). Selecting protein tyrosine phosphatases as drug targets. Drug Discovery Today, 7(19), 1013–1019.

    Article  Google Scholar 

  3. Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., & Wang, W. (2000). Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Accounts of Chemical Research, 33(12), 889–897.

    Article  CAS  PubMed  Google Scholar 

  4. Zhang, Z.-Y. (2001). Protein tyrosine phosphatases: prospects for therapeutics. Current Opinion in Chemical Biology, 5(4), 416–423.

    Article  CAS  PubMed  Google Scholar 

  5. Bialy, L., & Waldmann, H. (2005). Inhibitors of protein tyrosine phosphatases: next-generation drugs? Angewandte Chemie International Edition, 44(25), 3814–3839.

    Article  CAS  PubMed  Google Scholar 

  6. Mohi, M. G., & Neel, B. G. (2007). The role of Shp2 (PTPN11) in cancer. Current Opinion in Genetics & Development, 17(1), 23–30.

    Article  CAS  Google Scholar 

  7. Chan, R. J., & Feng, G.-S. (2007). PTPN11 is the first identified proto-oncogene that encodes a tyrosine phosphatase. Blood, 109(3), 862–867.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Matozaki, T., Murata, Y., Saito, Y., Okazawa, H., & Ohnishi, H. (2009). Protein tyrosine phosphatase SHP-2: a proto-oncogene product that promotes Ras activation. Cancer Science, 100(10), 1786–1793.

    Article  CAS  PubMed  Google Scholar 

  9. Gavrieli, M., Watanabe, N., Loftin, S. K., Murphy, T. L., & Murphy, K. M. (2003). Characterization of phosphotyrosine binding motifs in the cytoplasmic domain of B and T lymphocyte attenuator required for association with protein tyrosine phosphatases SHP-1 and SHP-2. Biochemical and Biophysical Research Communications, 312(4), 1236–1243.

    Article  CAS  PubMed  Google Scholar 

  10. Yokosuka, T., Takamatsu, M., Kobayashi-Imanishi, W., Hashimoto-Tane, A., Azuma, M., & Saito, T. (2012). Programmed cell death 1 forms negative costimulatory microclusters that directly inhibit T cell receptor signaling by recruiting phosphatase SHP2. Journal of Experimental Medicine, 209(6), 1201–1217.

    Article  CAS  PubMed  Google Scholar 

  11. Chemnitz, J. M., Parry, R. V., Nichols, K. E., June, C. H., & Riley, J. L. (2004). SHP-1 and SHP-2 associate with immunoreceptor tyrosine-based switch motif of programmed death 1 upon primary human T cell stimulation, but only receptor ligation prevents T cell activation. The Journal of Immunology, 173(2), 945–954.

    Article  CAS  PubMed  Google Scholar 

  12. Darian, E., Guvench, O., Yu, B., Qu, C. K., & MacKerell, A. D. (2011). Structural mechanism associated with domain opening in gain-of-function mutations in SHP2 phosphatase. Proteins: Structure, Function, and Bioinformatics, 79(5), 1573–1588.

    Article  CAS  Google Scholar 

  13. Hof, P., Pluskey, S., Dhe-Paganon, S., Eck, M. J., & Shoelson, S. E. (1998). Crystal structure of the tyrosine phosphatase SHP-2. Cell, 92(4), 441–450.

    Article  CAS  PubMed  Google Scholar 

  14. Xie, J., Si, X., Gu, S., Wang, M., Shen, J., Li, H., Shen, J., Li, D., Fang, Y., & Liu, C. (2017). Allosteric inhibitors of SHP2 with therapeutic potential for cancer treatment. Journal of Medicinal Chemistry, 60(24), 10205–10219.

    Article  CAS  PubMed  Google Scholar 

  15. Garcia Fortanet, J., Chen, C. H.-T., Chen, Y.-N. P., Chen, Z., Deng, Z., Firestone, B., Fekkes, P., Fodor, M., Fortin, P. D., & Fridrich, C. (2016). Allosteric inhibition of SHP2: identification of a potent, selective, and orally efficacious phosphatase inhibitor. Journal of Medicinal Chemistry, 59(17), 7773–7782.

    Article  CAS  PubMed  Google Scholar 

  16. Butterworth, S., Overduin, M., & Barr, A. J. (2014). Targeting protein tyrosine phosphatase SHP2 for therapeutic intervention. Future Medicinal Chemistry, 6(12), 1423–1437.

    Article  CAS  PubMed  Google Scholar 

  17. M Scott, L., R Lawrence, H., M Sebti, S., J Lawrence, N., & Wu, J. (2010). Targeting protein tyrosine phosphatases for anticancer drug discovery. Current Pharmaceutical Design, 16(16), 1843–1862.

    Article  Google Scholar 

  18. Song, K., Liu, X., Huang, W., Lu, S., Shen, Q., Zhang, L., & Zhang, J. (2017). Improved method for the identification and validation of allosteric sites. Journal of Chemical Information and Modeling, 57(9), 2358–2363.

    Article  CAS  PubMed  Google Scholar 

  19. Boycott, K. M., Rath, A., Chong, J. X., Hartley, T., Alkuraya, F. S., Baynam, G., Brookes, A. J., Brudno, M., Carracedo, A., & den Dunnen, J. T. (2017). International cooperation to enable the diagnosis of all rare genetic diseases. The American Journal of Human Genetics, 100(5), 695–705.

    Article  CAS  PubMed  Google Scholar 

  20. Huang, W., Wang, G., Shen, Q., Liu, X., Lu, S., Geng, L., Huang, Z., & Zhang, J. (2015). ASBench: benchmarking sets for allosteric discovery. Bioinformatics, 31(15), 2598–2600.

    Article  CAS  PubMed  Google Scholar 

  21. De Vivo, M., Masetti, M., Bottegoni, G., & Cavalli, A. (2016). Role of molecular dynamics and related methods in drug discovery. Journal of Medicinal Chemistry, 59(9), 4035–4061.

    Article  CAS  PubMed  Google Scholar 

  22. Ma, X., Meng, H., & Lai, L. (2016). Motions of allosteric and orthosteric ligand-binding sites in proteins are highly correlated. Journal of Chemical Information and Modeling, 56(9), 1725–1733.

    Article  CAS  PubMed  Google Scholar 

  23. Salamoun, J. M., & Wipf, P. (2016). Allosteric modulation of phosphatase activity may redefine therapeutic value. Washington, DC: ACS Publications.

    Book  Google Scholar 

  24. Chio, C. M., Lim, C. S., & Bishop, A. C. (2015). Targeting a cryptic allosteric site for selective inhibition of the oncogenic protein tyrosine phosphatase Shp2. Biochemistry, 54(2), 497–504.

    Article  CAS  PubMed  Google Scholar 

  25. Ahmed-Belkacem, A., Guichou, J.-F., Brillet, R., Ahnou, N., Hernandez, E., Pallier, C., & Pawlotsky, J.-M. (2014). Inhibition of RNA binding to hepatitis C virus RNA-dependent RNA polymerase: a new mechanism for antiviral intervention. Nucleic Acids Research, 42(14), 9399–9409.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Miyamoto, D., Miyamoto, M., Takahashi, A., Yomogita, Y., Higashi, H., Kondo, S., & Hatakeyama, M. (2008). Isolation of a distinct class of gain-of-function SHP-2 mutants with oncogenic RAS-like transforming activity from solid tumors. Oncogene, 27(25), 3508–3515.

    Article  CAS  PubMed  Google Scholar 

  27. Chan, G., Kalaitzidis, D., & Neel, B. G. (2008). The tyrosine phosphatase Shp2 (PTPN11) in cancer. Cancer and Metastasis Reviews, 27(2), 179–192.

    Article  CAS  PubMed  Google Scholar 

  28. Tartaglia, M., Mehler, E., Goldberg, R., Zampino, G., Brunner, H., Kremer, H., van der Burgt, I., Crosby, A., Ion, A., & Jeffery, S. (2001). Mutations in the protein tyrosine kinase gene, PTPN11, cause Noonan syndrome. Nature Genetics, 29(4), 491.

    Article  CAS  Google Scholar 

  29. Bentires-Alj, M., Paez, J. G., David, F. S., Keilhack, H., Halmos, B., Naoki, K., Maris, J. M., Richardson, A., Bardelli, A., & Sugarbaker, D. J. (2004). Activating mutations of the Noonan syndrome-associated SHP2/PTPN11 gene in human solid tumors and adult acute myelogenous leukemia. Cancer Research, 64(24), 8816–8820.

    Article  CAS  PubMed  Google Scholar 

  30. Che, X., Du, X.-X., Cai, X., Zhang, J., Xie, W. J., Long, Z., Ye, Z.-Y., Zhang, H., Yang, L., & Su, X.-D. (2017). Single mutations reshape the structural correlation network of the DMXAA–human STING complex. The Journal of Physical Chemistry B, 121(9), 2073–2082.

    Article  CAS  PubMed  Google Scholar 

  31. Aier, I., Varadwaj, P. K., & Raj, U. (2016). Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Scientific Reports, 6(1), 34984.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bai, Q., Pérez-Sánchez, H., Zhang, Y., Shao, Y., Shi, D., Liu, H., & Yao, X. (2014). Ligand induced change of β 2 adrenergic receptor from active to inactive conformation and its implication for the closed/open state of the water channel: insight from molecular dynamics simulation, free energy calculation and Markov state model analysis. Physical Chemistry Chemical Physics, 16(30), 15874–15885.

    Article  CAS  PubMed  Google Scholar 

  33. Bhakat, S., Martin, A. J., & Soliman, M. E. (2014). An integrated molecular dynamics, principal component analysis and residue interaction network approach reveals the impact of M184V mutation on HIV reverse transcriptase resistance to lamivudine. Molecular BioSystems, 10(8), 2215–2228.

    Article  CAS  PubMed  Google Scholar 

  34. Eswar, N., Webb, B., Marti-Renom, M. A., Madhusudhan, M., Eramian, D., Shen, M. y., Pieper, U., & Sali, A. (2006). Comparative protein structure modeling using Modeller. Current Protocols in Bioinformatics, 15(1), 5.6. 1–5.6. 30.

    Article  Google Scholar 

  35. Becke, A. D. (1993). Density-functional thermochemistry. III. The role of exact exchange. The Journal of Chemical Physics, 98(7), 5648–5652.

    Article  CAS  Google Scholar 

  36. Frisch, M., Trucks, G., Schlegel, H., Scuseria, G., Robb, M., Cheeseman, J., Scalmani, G., Barone, V., Petersson, G., Nakatsuji, H. (2016). Gaussian Inc. 16, revision A. 03; Gaussian Inc. Wallingford, CT.

  37. Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612.

    Article  CAS  PubMed  Google Scholar 

  38. Ramharack, P., Oguntade, S., & Soliman, M. E. (2017). Delving into Zika virus structural dynamics—a closer look at NS3 helicase loop flexibility and its role in drug discovery. RSC Advances, 7(36), 22133–22144.

    Article  CAS  Google Scholar 

  39. Olotu, F. A., Agoni, C., Adeniji, E., Abdullahi, M., & Soliman, M. E. (2018). Probing Gallate-mediated selectivity and high-affinity binding of epigallocatechin Gallate: a way-forward in the design of selective inhibitors for anti-apoptotic Bcl-2 proteins. Applied Biochemistry and Biotechnology, 1–20.

  40. Ncub, B. N., Ramharack, P., & Soliman, M. E. (2017). An “all-in-one” pharmacophoric architecture for the discovery of potential broad-spectrum anti-flavivirus drugs. Applied Biochemistry and Biotechnology, 1–16.

  41. Ndagi, U., Mhlongo, N. N., & Soliman, M. E. (2017). Emergence of a promising lead compound in the treatment of triple negative breast cancer: an insight into conformational features and ligand binding landscape of c-Src protein with UM-164. Applied Biochemistry and Biotechnology, 185, 655–675.

    Article  CAS  PubMed  Google Scholar 

  42. Srivastava, H. K., & Sastry, G. N. (2012). Molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches. Journal of Chemical Information and Modeling, 52(11), 3088–3098.

    Article  CAS  PubMed  Google Scholar 

  43. Mathew, B., Adeniyi, A. A., Dev, S., Joy, M., Ucar, G. l., Mathew, G. E., Singh-Pillay, A., & Soliman, M. E. (2017). Pharmacophore-based 3D-QSAR analysis of thienyl chalcones as a new class of human MAO-B inhibitors: investigation of combined quantum chemical and molecular dynamics approach. The Journal of Physical Chemistry B, 121(6), 1186–1203.

    Article  CAS  PubMed  Google Scholar 

  44. 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 

  45. Berendsen, H. J., Postma, J. v., van Gunsteren, W. F., DiNola, A., & Haak, J. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8), 3684–3690.

    Article  CAS  Google Scholar 

  46. Roe, D. R., & Cheatham III, T. E. (2013). PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. Journal of Chemical Theory and Computation, 9(7), 3084–3095.

    Article  CAS  PubMed  Google Scholar 

  47. Seifert, E. (2014). OriginPro 9.1: scientific data analysis and graphing software—software review. ACS Publications

  48. Tsui, V., & Case, D. A. (2000). Theory and applications of the generalized Born solvation model in macromolecular simulations. Biopolymers, 56(4), 275–291.

    Article  CAS  PubMed  Google Scholar 

  49. Massova, I., & Kollman, P. A. (2000). Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspectives in Drug Discovery and Design, 18(1), 113–135.

    Article  CAS  Google Scholar 

  50. Barreca, M. L., Lee, K. W., Chimirri, A., & Briggs, J. M. (2003). Molecular dynamics studies of the wild-type and double mutant HIV-1 integrase complexed with the 5CITEP inhibitor: mechanism for inhibition and drug resistance. Biophysical Journal, 84(3), 1450–1463.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Harvey, M., & De Fabritiis, G. (2009). An implementation of the smooth particle mesh Ewald method on GPU hardware. Journal of Chemical Theory and Computation, 5(9), 2371–2377.

    Article  CAS  PubMed  Google Scholar 

  52. Word, J. M., Lovell, S. C., LaBean, T. H., Taylor, H. C., Zalis, M. E., Presley, B. K., Richardson, J. S., & Richardson, D. C. (1999). Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms1. Journal of Molecular Biology, 285(4), 1711–1733.

    Article  CAS  PubMed  Google Scholar 

  53. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Doncheva, N. T., Klein, K., Domingues, F. S., & Albrecht, M. (2011). Analyzing and visualizing residue networks of protein structures. Trends in Biochemical Sciences, 36(4), 179–182.

    Article  CAS  PubMed  Google Scholar 

  55. Yu, Z.-H., Zhang, R.-Y., Walls, C. D., Chen, L., Zhang, S., Wu, L., Liu, S., & Zhang, Z.-Y. (2014). Molecular basis of gain-of-function LEOPARD syndrome-associated SHP2 mutations. Biochemistry, 53(25), 4136–4151.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the School of Health Sciences, the University of KwaZulu-Natal, Westville Campus, for financial assistance. They also acknowledge the Centre for High Performance Computing (CHPC, www.chpc.ac.za), Cape Town, South Africa, for computational resources. The editorial support of Ms. Carrin Martin, editor for the School of Health Sciences at UKZN, is also acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Farideh Badichi Akher or Mahmoud E. S. Soliman.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

ESM 1

(DOCX 25 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farrokhzadeh, A., Akher, F.B. & Soliman, M.E.S. Probing the Dynamic Mechanism of Uncommon Allosteric Inhibitors Optimized to Enhance Drug Selectivity of SHP2 with Therapeutic Potential for Cancer Treatment. Appl Biochem Biotechnol 188, 260–281 (2019). https://doi.org/10.1007/s12010-018-2914-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-018-2914-0

Keywords

Navigation