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Structure based design of selective SHP2 inhibitors by De novo design, synthesis and biological evaluation

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Abstract

SHP2 phosphatase, encoded by the PTPN11 gene, is a non-receptor PTP, which plays an important role in growth factor, cytokine, integrin, hormone signaling pathways, and regulates cellular responses, such as proliferation, differentiation, adhesion migration and apoptosis. Many studies have reported that upregulation of SHP2 expression is closely related to human cancer, such as breast cancer, liver cancer and gastric cancer. Hence, SHP2 has become a promising target for cancer immunotherapy. In this paper, we reported the identification of compound 1 as SHP2 inhibitor. Fragment-based ligand design, De novo design, ADMET and Molecular docking were performed to explore potential selective SHP2 allosteric inhibitors based on SHP836. The results of docking studies indicated that the selected compounds had higher selective SHP2 inhibition than existing inhibitors. Compound 1 was found to have a novel selectivity against SHP2 with an in vitro enzyme activity IC50 value of 9.97 μM. Fluorescence titration experiment confirmed that compound 1 directly bound to SHP2. Furthermore, the results of binding free energies demonstrated that electrostatic energy was the primary factor in elucidating the mechanism of SHP2 inhibition. Dynamic cross correlation studies also supported the results of docking and molecular dynamics simulation. This series of analyses provided important structural features for designing new selective SHP2 inhibitors as potential drugs and promising candidates for pre-clinical pharmacological investigations.

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Abbreviations

JMML:

Juvenile myelomonocytic leukemia

MS:

Myelodysplastic syndrome

AML:

Acute myeloid leukemia

TCPTP:

T cell protein-tyrosine phosphatase

PTP1B:

Protein tyrosine phosphatase 1B

SHP1:

SH2 domain-containing phosphatase 1

H bond:

Hydrogen bond

ADMET:

Absorption, distribution, metabolism, excretion, and toxicity

MCSS:

Multi-copy simultaneous search

FBDD:

Fragment based drug design

HIA:

Human intestinal absorption

BBB:

Blood–brain barrier

PPB:

Aqueous solubility plasma protein binding

PME:

Particle mesh Ewald

MM-PBSA:

Molecular mechanics Poisson Boltzmann surface area

LCPO:

Linear combination of pairwise overlaps

DCC:

Dynamic cross correlation

MD:

Molecular dynamics

HTVS:

High throughput virtual screening

RMSD:

Root mean square deviation

RMSF:

Root mean square fluctuation

References

  1. Hof P, Pluskey S, Dhe-Paganon S, Eck MJ, Shoelson SE (1998) Crystal structure of the tyrosine phosphatase SHP-2. Cell 92(4):441–450

    Article  CAS  Google Scholar 

  2. Chen C, Cao M, Zhu S, Wang C, Liang F, Yan L, Luo D (2015) Discovery of a novel inhibitor of the protein tyrosine phosphatase Shp2. Sci Rep 5:17626. https://doi.org/10.1038/srep17626

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Zhang X, He Y, Liu S, Yu Z, Jiang ZX, Yang Z, Dong Y, Nabinger SC, Wu L, Gunawan AM, Wang L, Chan RJ, Zhang ZY (2010) Salicylic acid based small molecule inhibitor for the oncogenic Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2). J Med Chem 53(6):2482–2493. https://doi.org/10.1021/jm901645u

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Yang W, Wang J, Moore DC, Liang H, Dooner M, Wu Q, Terek R, Chen Q, Ehrlich MG, Quesenberry PJ, Neel BG (2013) Ptpn11 deletion in a novel progenitor causes metachondromatosis by inducing hedgehog signalling. Nature 499(7459):491–495. https://doi.org/10.1038/nature12396

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Lawrence HR, Pireddu R, Chen L, Luo Y, Sung SS, Szymanski AM, Yip ML, Guida WC, Sebti SM, Wu J, Lawrence NJ (2008) Inhibitors of Src homology-2 domain containing protein tyrosine phosphatase-2 (Shp2) based on oxindole scaffolds. J Med Chem 51(16):4948–4956. https://doi.org/10.1021/jm8002526

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Chan RJ, Feng GS (2007) PTPN11 is the first identified proto-oncogene that encodes a tyrosine phosphatase. Blood 109(3):862–867. https://doi.org/10.1182/blood-2006-07-028829

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Bentires-Alj M, Paez JG, David FS, Keilhack H, Halmos B, Naoki K, Maris JM, Richardson A, Bardelli A, Sugarbaker DJ, Richards WG, Du J, Girard L, Minna JD, Loh ML, Fisher DE, Velculescu VE, Vogelstein B, Meyerson M, Sellers WR, Neel BG (2004) Activating mutations of the noonan syndrome-associated SHP2/PTPN11 gene in human solid tumors and adult acute myelogenous leukemia. Cancer Res 64(24):8816–8820. https://doi.org/10.1158/0008-5472.CAN-04-1923

    Article  PubMed  CAS  Google Scholar 

  8. Chen L, Sung SS, Yip ML, Lawrence HR, Ren Y, Guida WC, Sebti SM, Lawrence NJ, Wu J (2006) Discovery of a novel shp2 protein tyrosine phosphatase inhibitor. Mol Pharmacol 70(2):562–570. https://doi.org/10.1124/mol.106.025536

    Article  PubMed  CAS  Google Scholar 

  9. He R, Yu ZH, Zhang RY, Wu L, Gunawan AM, Lane BS, Shim JS, Zeng LF, He Y, Chen L, Wells CD, Liu JO, Zhang ZY (2015) Exploring the existing drug space for novel pTyr mimetic and SHP2 inhibitors. ACS Med Chem Lett 6(7):782–786. https://doi.org/10.1021/acsmedchemlett.5b00118

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Fortanet JG, Chen CHT, Chen YNP, Chen ZL, Deng Z, Firestone B, Fekkes P, Fodor M, Fortin PD, Fridrich C, Grunenfelder D, Ho S, Kang ZB, Karki R, Kato M, Keen N, LaBonte LR, Larrow J, Lenoir F, Liu G, Liu SM, Lombardo F, Majumdar D, Meyer MJ, Palermo M, Perez L, Pu MY, Ramsey T, Sellers WR, Shultz MD, Stams T, Towler C, Wang P, Williams SL, Zhang JH, LaMarche MJ (2016) Allosteric inhibition of SHP2: identification of a potent, selective, and orally efficacious phosphatase inhibitor. J Med Chem 59(17):7773–7782. https://doi.org/10.1021/acs.jmedchem.6b00680

    Article  CAS  Google Scholar 

  11. Chen C, Liang F, Chen B, Sun Z, Xue T, Yang R, Luo D (2017) Identification of demethylincisterol A3 as a selective inhibitor of protein tyrosine phosphatase Shp2. Eur J Pharmacol 795:124–133. https://doi.org/10.1016/j.ejphar.2016.12.012

    Article  PubMed  CAS  Google Scholar 

  12. Barr AJ (2010) Protein tyrosine phosphatases as drug targets: strategies and challenges of inhibitor development. Future Med Chem 2(10):1563–1576. https://doi.org/10.4155/Fmc.10.241

    Article  PubMed  CAS  Google Scholar 

  13. Chen YN, LaMarche MJ, Chan HM, Fekkes P, Garcia-Fortanet J, Acker MG, Antonakos B, Chen CH, Chen Z, Cooke VG, Dobson JR, Deng Z, Fei F, Firestone B, Fodor M, Fridrich C, Gao H, Grunenfelder D, Hao HX, Jacob J, Ho S, Hsiao K, Kang ZB, Karki R, Kato M, Larrow J, La Bonte LR, Lenoir F, Liu G, Liu S, Majumdar D, Meyer MJ, Palermo M, Perez L, Pu M, Price E, Quinn C, Shakya S, Shultz MD, Slisz J, Venkatesan K, Wang P, Warmuth M, Williams S, Yang G, Yuan J, Zhang JH, Zhu P, Ramsey T, Keen NJ, Sellers WR, Stams T, Fortin PD (2016) Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases. Nature 535(7610):148–152. https://doi.org/10.1038/nature18621

    Article  PubMed  CAS  Google Scholar 

  14. Veselovsky AV, Ivanov AS (2003) Strategy of computer-aided drug design. Curr Drug Targets Infect Disord 3(1):33–40

    Article  CAS  Google Scholar 

  15. Wang W, Liu LJ, Song X, Mo Y, Komma C, Bellamy HD, Zhao ZJ, Zhou GW (2011) Crystal structure of human protein tyrosine phosphatase SHP-1 in the open conformation. J Cell Biochem 112(8):2062–2071. https://doi.org/10.1002/jcb.23125

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Prlic A, Kalro T, Bhattacharya R, Christie C, Burley SK, Rose PW (2016) Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank. Bioinformatics 32(24):3833–3835. https://doi.org/10.1093/bioinformatics/btw547

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Ma Y, Jin YY, Wang YL, Wang RL, Lu XH, Kong DX, Xu WR (2014) The discovery of a novel and selective inhibitor of PTP1B over TCPTP: 3D QSAR pharmacophore modeling, virtual screening, synthesis, and biological evaluation. Chem Biol Drug Des 83(6):697–709. https://doi.org/10.1111/cbdd.12283

    Article  PubMed  CAS  Google Scholar 

  18. Schubert CR, Stultz CM (2009) The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design. J Comput Aided Mol Des 23(8):475–489. https://doi.org/10.1007/s10822-009-9287-y

    Article  PubMed  CAS  Google Scholar 

  19. Bohm HJ (1992) The computer program LUDI: a new method for the De novo design of enzyme inhibitors. J Comput Aided Mol Des 6(1):61–78

    Article  CAS  Google Scholar 

  20. Schneider G, Fechner U (2005) Computer-based De novo design of drug-like molecules. Nat Rev Drug Discov 4(8):649–663. https://doi.org/10.1038/nrd1799

    Article  PubMed  CAS  Google Scholar 

  21. Egan WJ, Merz KM, Baldwin JJ (2000) Prediction of drug absorption using multivariate statistics. J Med Chem 43(21):3867–3877. https://doi.org/10.1021/jm000292e

    Article  PubMed  CAS  Google Scholar 

  22. Egan WJ, Walters WP, Murcko MA (2002) Guiding molecules towards drug-likeness. Curr Opin Drug Discov Dev 5(4):540–549

    CAS  Google Scholar 

  23. Cheng A, Merz KM Jr (2003) Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J Med Chem 46(17):3572–3580. https://doi.org/10.1021/jm020266b

    Article  PubMed  CAS  Google Scholar 

  24. Wesson L, Eisenberg D (1992) Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Sci 1(2):227–235. https://doi.org/10.1002/pro.5560010204

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Xia XY, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47(18):4463–4470. https://doi.org/10.1021/jm0303195

    Article  PubMed  CAS  Google Scholar 

  26. Koska J, Spassov VZ, Maynard AJ, Yan L, Austin N, Flook PK, Venkatachalam CM (2008) Fully automated molecular mechanics based induced fit protein-ligand docking method. J Chem Inf Model 48(10):1965–1973. https://doi.org/10.1021/ci800081s

    Article  PubMed  CAS  Google Scholar 

  27. Spassov VZ, Yan L, Flook PK (2007) The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactions. Protein Sci 16(3):494–506. https://doi.org/10.1110/ps.062447107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Wu G, Robertson DH, Brooks CL 3rd, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem 24(13):1549–1562. https://doi.org/10.1002/jcc.10306

    Article  PubMed  CAS  Google Scholar 

  29. Abdolmaleki A, Ghasemi F, Ghasemi JB (2017) Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications. Chem Biol Drug Des 89(2):257–268. https://doi.org/10.1111/cbdd.12825

    Article  PubMed  CAS  Google Scholar 

  30. Fernandes CL, Sachett LG, Pol-Fachin L, Verli H (2010) GROMOS96 43a1 performance in predicting oligosaccharide conformational ensembles within glycoproteins. Carbohydr Res 345(5):663–671. https://doi.org/10.1016/j.carres.2009.12.018

    Article  PubMed  CAS  Google Scholar 

  31. Zang LL, Wang XJ, Li XB, Wang SQ, Xu WR, Xie XB, Cheng XC, Ma H, Wang RL (2014) SAHA-based novel HDAC inhibitor design by core hopping method. J Mol Gr Model 54:10–18. https://doi.org/10.1016/j.jmgm.2014.08.005

    Article  CAS  Google Scholar 

  32. Hess B (2008) P-LINCS: a parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4(1):116–122. https://doi.org/10.1021/ct700200b

    Article  PubMed  CAS  Google Scholar 

  33. Kumari R, Kumar R, Lynn A, Consort OSDD (2014) g_mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54(7):1951–1962. https://doi.org/10.1021/ci500020m

    Article  PubMed  CAS  Google Scholar 

  34. Hou TJ, Wang JM, Li YY, Wang W (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32(5):866–877. https://doi.org/10.1002/jcc.21666

    Article  PubMed  CAS  Google Scholar 

  35. Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20(2):217–230. https://doi.org/10.1002/(Sici)1096-987x(19990130)20:2%3c217:Aid-Jcc4%3e3.0.Co;2-A

    Article  CAS  Google Scholar 

  36. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins Struct Funct Bioinform 55(2):383–394. https://doi.org/10.1002/prot.20033

    Article  CAS  Google Scholar 

  37. Tama F, Sanejouand YH (2001) Conformational change of proteins arising from normal mode calculations. Protein Eng 14(1):1–6. https://doi.org/10.1093/Protein/14.1.1

    Article  PubMed  CAS  Google Scholar 

  38. Tang BW, Li BQ, Qian YQ, Ao MT, Guo KQ, Fang MJ, Wu Z (2017) The molecular mechanism of hPPAR alpha activation. RSC Adv 7(28):17193–17201. https://doi.org/10.1039/c6ra27740c

    Article  CAS  Google Scholar 

  39. Kasahara K, Fukuda I, Nakamura H (2014) A novel approach of dynamic cross correlation analysis on molecular dynamics simulations and its application to Ets1 dimer-DNA complex. PLoS ONE 9(11):e112419. https://doi.org/10.1371/journal.pone.0112419

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Cole JC, Murray CW, Nissink JWM, Taylor RD, Taylor R (2005) Comparing protein-ligand docking programs is difficult. Proteins Struct Funct Bioinform 60(3):325–332. https://doi.org/10.1002/prot.20497

    Article  CAS  Google Scholar 

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Acknowledgements

This study was supported by the Natural Science Foundations of China (Grant No. 81273361), the Natural Science Foundation of Tianjin (Grant No. 16JCZDJC32500) and the International (Regional) Cooperation and Exchange Project of the National Natural Science Foundation of China (Grant No. 81611130090). The Science & Technology Development Fund of Tianjin Education Commission for Higher Education (Grant No. 2017KJ229).

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  1. Wen-Shan Liu and Wen-Yan Jin contributed equally to this work.

Authors and Affiliations

  1. Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, No. 22 Qixiangtai Rd, Heping Dist, Tianjin, 300070, People’s Republic of China

    Wen-Shan Liu, Wen-Yan Jin, Liang Zhou, Xing-Hua Lu, Wei-Ya Li, Ying Ma & Run-Ling Wang

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Liu, WS., Jin, WY., Zhou, L. et al. Structure based design of selective SHP2 inhibitors by De novo design, synthesis and biological evaluation. J Comput Aided Mol Des 33, 759–774 (2019). https://doi.org/10.1007/s10822-019-00213-z

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