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Characterization of interactions and pharmacophore development for DFG-out inhibitors to RET tyrosine kinase

  • Chunxia Gao
  • Morten Grøtli
  • Leif A. Eriksson
Original Paper

Abstract

RET (rearranged during transfection) tyrosine kinase is a promising target for several human cancers. Abt-348, Birb-796, Motesanib and Sorafenib are DFG-out multi-kinase inhibitors that have been reported to inhibit RET activity with good IC50 values. Although the DFG-out conformation has attracted great interest in the design of type II inhibitors, the structural requirements for binding to the RET DFG-out conformation remains unclear. Herein, the DFG-out conformation of RET was determined by homology modelling, the four inhibitors were docked, and the binding modes investigated by molecular dynamics simulation. Binding free energies were calculated using the molecular mechanics/Poisson-Bolzmann surface area (MM/PBSA) method. The trends in predicted binding free affinities correlated well with experimental data and were used to explain the activity difference of the studied inhibitors. Per-residue energy decomposition analyses provided further information on specific interaction properties. Finally, we also conducted a detailed e-pharmacophore modelling of the different RET-inhibitor complexes, explaining the common and specific pharmacophore features of the different complexes. The results reported herein will be useful in future rational design of novel DFG-out RET inhibitors.

Graphical Abstract

Left Ribbon representation of DFG-out RET tyrosine kinase structure showing key residues of RET interacting with inhibitors. Right e-Pharmacophore hypothesis for RET-Abt-348 generated from the complex structure

Keywords

RET DFG-out inhibitors Molecular dynamics simulation MM-PB(GB)SA e-pharmacophore 

Notes

Acknowledgments

L.A.E. gratefully acknowledges financial support from the Swedish Research Council (VR) and the Faculty of Science at the University of Gothenburg. Grants of computing time at the Chalmers computing center C3SE, within the SNIC framework, are gratefully acknowledged.

Supplementary material

894_2015_2708_MOESM1_ESM.docx (702 kb)
ESM 1 (DOCX 702 kb)

References

  1. 1.
    Mulligan LM (2014) RET revisited: expanding the oncogenic portfolio. Nat Rev Cancer 14:173–186CrossRefGoogle Scholar
  2. 2.
    Hubner RA, Houlston RS (2006) Molecular advances in medullary thyroid cancer diagnostics. Clin Chim Acta 370(1–2):2–8CrossRefGoogle Scholar
  3. 3.
    Plaza-Menacho I, Morandi A, Robertson D, Pancholi S, Drury S, Dowsett M, Martin LA, Isacke CM (2010) Targeting the receptor tyrosine kinase RET sensitizes breast cancer cells to tamoxifen treatment and reveals a role for RET in endocrine resistance. Oncogene 29(33):4648–4657Google Scholar
  4. 4.
    Mologni L (2011) Development of RET kinase inhibitors for targeted cancer therapy. Curr Med Chem 18(2):162–175CrossRefGoogle Scholar
  5. 5.
    Carlomagno F, Vitagliano D, Guida T, Napolitano M, Vecchio G, Fusco A, Gazit A, Levitzki A, Santoro M (2002) The kinase inhibitor PP1 blocks tumorigenesis induced by RET oncogenes. Cancer Res 62(4):1077–1082Google Scholar
  6. 6.
    Mologni L, Sala E, Riva B, Cesaro L, Cazzaniga S, Redaelli S, Marin O, Pasquato N, Donella-Deana A, Gambacorti-Passerini C (2005) Expression, purification, and inhibition of human RET tyrosine kinase. Protein Expr Purif 41(1):177–185Google Scholar
  7. 7.
    Liu Y, Gray NS (2006) Rational design of inhibitors that bind to inactive kinase conformations. Nat Chem Biol 2(7):358–364CrossRefGoogle Scholar
  8. 8.
    Zhang J, Adrián FJ, Jahnke W et al (2010) Targeting Bcr-Abl by combining allosteric with ATP-binding-site inhibitors. Nature 463(7280):501–506Google Scholar
  9. 9.
    García-Echeverría C (2010) Allosteric and ATP-competitive kinase inhibitors of mTOR for cancer treatment. Bioorg Med Chem Lett 20(15):4308–4312CrossRefGoogle Scholar
  10. 10.
    Dietrich J, Hulme C, Hurley LH (2010) The design, synthesis, and evaluation of 8 hybrid DFG-out allosteric kinase inhibitors: a structural analysis of the binding interactions of Gleevec®, Nexavar®, and BIRB-796. Bioorg Med Chem 18(15):5738–5748CrossRefGoogle Scholar
  11. 11.
    Simard JR, Grütter C, Pawar V, Aust B, Wolf A, Rabiller M, Wulfert S, Robubi A, Klüter S, Ottmann C, Rauh D (2009) High-throughput screening to identify inhibitors which stabilize inactive kinase conformations in p38α. J Am Chem Soc 131(51):18478–18488Google Scholar
  12. 12.
    Jacobs MD, Caron PR, Hare BJ (2008) Classifying protein kinase structures guides use of ligand-selectivity profiles to predict inactive conformations: structure of lck/imatinib complex. Proteins 70(4):1451–1460CrossRefGoogle Scholar
  13. 13.
    Pargellis C, Tong L, Churchill L, Cirillo PF, Gilmore T, Graham AG, Grob PM, Hickey ER, Moss N, Pav S, Regan J (2002) Inhibition of p38 MAP kinase by utilizing a novel allosteric binding site. Nat Struct Mol Biol 9(4):268–272Google Scholar
  14. 14.
    Kufareva I, Abagyan R (2008) Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states. J Med Chem 51(24):7921–7932CrossRefGoogle Scholar
  15. 15.
    Zhao Z, Wu H, Wang L, Liu Y, Knapp S, Liu Q, Gray NS (2014) Exploration of type II binding mode: a privileged approach for kinase inhibitor focused drug discovery? ACS Chem Biol 9(6):1230–1241Google Scholar
  16. 16.
    Plaza-Menacho I, Mologni L, Sala E, Gambacorti-Passerini C, Magee AI, Links TP, Hofstra RM, Barford D, Isacke CM (2007) Sorafenib functions to potently suppress RET tyrosine kinase activity by direct enzymatic inhibition and promoting RET lysosomal degradation independent of proteasomal targeting. J Biol Chem 282(40):29230–29240Google Scholar
  17. 17.
    Schlumberger MJ, Elisei R, Bastholt et al (2009) Phase II study of safety and efficacy of Motesanib in patients with progressive or symptomatic, advanced or metastatic medullary thyroid cancer. J Clin Oncol 27(23):3794–3801Google Scholar
  18. 18.
    Phay JE, Shah MH (2010) Targeting RET receptor tyrosine kinase activation in cancer. Clin Cancer Res 16(24):5936–5941CrossRefGoogle Scholar
  19. 19.
    Moffett K, Konteatis Z, Nguyen D (2011) Discovery of a novel class of non-ATP site DFG-out state p38 inhibitors utilizing computationally assisted virtual fragment-based drug design (vFBDD). Bioorg Med Chem Lett 21(23):7155–7165Google Scholar
  20. 20.
    Curtin ML, Frey RR, Heyman HR (2012) Thienopyridine ureas as dual inhibitors of the VEGF and Aurora kinase families. Bioorg Med Chem Lett 22(9):3208–3212Google Scholar
  21. 21.
    McTigue M, Murray BW, Chen JH, Deng YL, Solowiej J, Kania RS (2012) Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proc Natl Acad Sci USA 109(45):18281–18289Google Scholar
  22. 22.
    Kollman PA, Massova I, Reyes C (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897Google Scholar
  23. 23.
    Gohlke H, Kiel C, Case DA (2003) Insights into protein–protein binding by binding free energy calculation and free energy decomposition for the Ras–Raf and Ras–RalGDS complexes. J Mol Biol 330(4):891–913CrossRefGoogle Scholar
  24. 24.
    Tuccinardi T, Manetti F, Schenone S, Martinelli A, Botta M (2007) Construction and validation of a RET TK catalytic domain by homology modeling†. J Chem Inf Model 47(2):644–655CrossRefGoogle Scholar
  25. 25.
    Labute P (2008) The generalized Born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 29(10):1693–1698CrossRefGoogle Scholar
  26. 26.
    Molecular Operating Environment (MOE) (2013) Chemical Computing Group, MontréalGoogle Scholar
  27. 27.
    Jorgensen WL, Tirado-Rives J (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110(6):1657–1666CrossRefGoogle Scholar
  28. 28.
    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47(7):1750–1759Google Scholar
  29. 29.
    Namboodiri HV, Bukhtiyarova M, Ramcharan J, Karpusas M, Lee Y, Springman EB (2010) Analysis of Imatinib and Sorafenib binding to p38α compared with c-Abl and b-Raf provides structural insights for understanding the selectivity of inhibitors targeting the DFG-out form of protein kinases. Biochemistry 49(17):3611–3618Google Scholar
  30. 30.
    Simard JR, Getlik M, Grütter C, Pawar V, Wulfert S, Rabiller M, Rauh D (2009) Development of a fluorescent-tagged kinase assay system for the detection and characterization of allosteric kinase inhibitors. J Am Chem Soc 131(37):13286–13296Google Scholar
  31. 31.
    Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J Am Chem Soc 107(13):3902–3909CrossRefGoogle Scholar
  32. 32.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174CrossRefGoogle Scholar
  33. 33.
    Case DA, Darden TA, Cheatham TE et al (2008) AMBER, version 10 University of California, San Francisco, CAGoogle Scholar
  34. 34.
    Wang J, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21(12):1049–1074CrossRefGoogle Scholar
  35. 35.
    Duan Y, Wu C, Chowdhury S, Lee MC, 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. J Comput Chem 24(16):1999–2012Google Scholar
  36. 36.
    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(3):435–447CrossRefGoogle Scholar
  37. 37.
    Essmann U, Perera l, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995) A smooth particle mesh Ewald method. J Chem Phys 103(19):8577–8593Google Scholar
  38. 38.
    Darden T, York D, Pedersson L (1993) Particle mesh Ewald: an N. log(N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092CrossRefGoogle Scholar
  39. 39.
    Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690CrossRefGoogle Scholar
  40. 40.
    Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18(12):1463–1472CrossRefGoogle Scholar
  41. 41.
    Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31(3):1695–1697CrossRefGoogle Scholar
  42. 42.
    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Physics 52(12):7182–7190CrossRefGoogle Scholar
  43. 43.
    Rocchia W, Alexov E, Honig B (2001) Extending the applicability of the nonlinear Poisson − Boltzmann equation: multiple dielectric constants and multivalent ions†. J Phys Chem B 105(28):6507–6514CrossRefGoogle Scholar
  44. 44.
    Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55(2):383–394CrossRefGoogle Scholar
  45. 45.
    Rastelli G, Rio AD, Degliesposti G, Sgobba M (2010) Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. J Comput Chem 31(4):797–810Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Chunxia Gao
    • 1
  • Morten Grøtli
    • 1
  • Leif A. Eriksson
    • 1
  1. 1.Department of Chemistry and Molecular BiologyUniversity of GothenburgGöteborgSweden

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