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Theoretical studies on the selectivity mechanisms of PI3Kδ inhibition with marketed idelalisib and its derivatives by 3D-QSAR, molecular docking, and molecular dynamics simulation

  • Jingyu ZhuEmail author
  • Ke Ke
  • Lei Xu
  • Jian JinEmail author
Original Paper
  • 87 Downloads

Abstract

Phosphoinositide 3-kinases (PI3Ks) are crucial for cell proliferation, metabolism, motility, and cancer progression. Since the selective PI3Kδ inhibitor, idelalisib, was firstly approved by the FDA in 2014, large numbers of selective PI3Kδ inhibitors have been reported, but the detailed mechanisms of selective inhibition to PI3Kδ for idelalisib or its derivatives have not been well addressed. In this study, 3D-QSAR with COMFA, molecular docking, and molecular dynamic (MD) simulations was used to explore the binding modes between PI3Kδ and idelalisib derivatives. Firstly, a reliable COMFA model (q2 = 0.59, ONC = 8, r2 = 0.966) was built and the contour maps showed that the electrostatic field had more significant contribution to the bioactivities of inhibitors. Secondly, two molecular docking methods including rigid receptor docking (RRD) and induced fit docking (IFD) were employed to predict the docking poses of all the studied inhibitors and revealed the selective binding mechanisms. And then, the results of the MD simulation and the binding free energy decomposition verified that the binding of PI3Kδ/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interactions and some key residues for selective binding were highlighted. Finally, based on the models developed, 14 novel inhibitors were optimized and some showed satisfactory predicted bioactivity. Taken together, the results provided by this study may facilitate the rational design of novel and selective PI3Kδ inhibitors.

Graphical abstract

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Keywords

PI3Kδ inhibitors Idelalisib 3D-QSAR COMFA Molecular docking Molecular dynamics simulations 

Notes

Funding information

The study was financially supported by the National Natural Science Foundation of China (No. 21807049, 81803430) and the Fundamental Research Funds for the Central Universities (JUSRP11892).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

894_2019_4129_MOESM1_ESM.docx (2.3 mb)
ESM 1 (DOCX 2327 kb)

References

  1. 1.
    Fruman DA, Rommel C (2014) PI3K and cancer: lessons, challenges and opportunities. Nat. Rev. Drug Discov. 13(2):140–156PubMedPubMedCentralGoogle Scholar
  2. 2.
    Cantley LC (2002) The phosphoinositide 3-kinase pathway. Science 296(5573):1655–1657PubMedGoogle Scholar
  3. 3.
    Carracedo A, Pandolfi PP (2008) The PTEN-PI3K pathway: of feedbacks and cross-talks. Oncogene 27(41):5527–5541PubMedGoogle Scholar
  4. 4.
    Liu P, Cheng H, Roberts TM, Zhao JJ (2009) Targeting the phosphoinositide 3-kinase pathway in cancer. Nat. Rev. Drug Discov. 8(8):627–644PubMedPubMedCentralGoogle Scholar
  5. 5.
    Courtney KD, Corcoran RB, Engelman JA (2010) The PI3K pathway as drug target in human cancer. J. Clin. Oncol. 28(6):1075–1083PubMedPubMedCentralGoogle Scholar
  6. 6.
    Jiang BH, Liu LZ (2009) PI3K/PTEN signaling in angiogenesis and tumorigenesis. Adv. Cancer Res. 102:19–65PubMedPubMedCentralGoogle Scholar
  7. 7.
    Hennessy BT, Smith D, Ram P, Lu Y (2005) Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat. Rev. Drug Discov. 4(12):988–1004PubMedGoogle Scholar
  8. 8.
    Zhu J, Wang M, Cao B, Hou T, Mao X (2014) Targeting the phosphatidylinositol 3-kinase/AKT pathway for the treatment of multiple myeloma. Curr. Med. Chem. 21(27):3173–3187PubMedGoogle Scholar
  9. 9.
    Setti A, Kumar MJ, Babu KR, Rasagna A (2015) Potency and pharmacokinetics of broad spectrum and isoform-specific p110γ and δ inhibitors in cancers. J. Recept. Signal Transduct. Res. 36(1):26–36PubMedGoogle Scholar
  10. 10.
    Zhu J, Pan P, Li Y, Wang M, Li D, Cao B, Mao X, Hou T (2014) Theoretical studies on beta and delta isoform-specific binding mechanisms of phosphoinositide 3-kinase inhibitors. Mol. BioSyst. 10(3):454–466PubMedGoogle Scholar
  11. 11.
    Yuan TL, Cantley LC (2008) PI3K pathway alterations in cancer: variations on a theme. Oncogene 27(41):5497–5510PubMedPubMedCentralGoogle Scholar
  12. 12.
    Jabbour E, Ottmann OG, Deininger M, Hochhaus A (2014) Targeting the phosphoinositide 3-kinase pathway in hematologic malignancies. Haematologica 99(1):7–18PubMedPubMedCentralGoogle Scholar
  13. 13.
    Zhu J, Hou T, Mao X (2015) Discovery of selective phosphatidylinositol 3-kinase inhibitors to treat hematological malignancies. Drug Discov. Today 20(8):988–994PubMedGoogle Scholar
  14. 14.
    Li T, Wang G (2014) Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities. Int. J. Mol. Sci. 15(10):18856–18891PubMedPubMedCentralGoogle Scholar
  15. 15.
    Li M, Sala V (2018) Phosphoinositide 3-kinase gamma inhibition protects from anthracycline cardiotoxicity and reduces tumor growth. Circulation 138(7):696–711PubMedGoogle Scholar
  16. 16.
    Vyas P, Vohora D (2016) Phosphoinositide-3-kinases as the novel therapeutic targets for the inflammatory diseases: current and future perspectives. Curr. Drug Targets 18(14):1622–1640Google Scholar
  17. 17.
    Vangapandu HV, Jain N, Gandhi V (2017) Duvelisib: a phosphoinositide-3 kinase δ/γ inhibitor for chronic lymphocytic leukemia. Expert Opin. Investig. Drugs 26(5):625–632PubMedPubMedCentralGoogle Scholar
  18. 18.
    Lannutti BJ, Meadows SA, Herman SE, Kashishian A (2011) CAL-101, a p110δ selective phosphatidylinositol-3-kinase inhibitor for the treatment of B-cell malignancies, inhibits PI3K signaling and cellular viability. Blood 117(2):591–594PubMedPubMedCentralGoogle Scholar
  19. 19.
    Sabbah DA, Vennerstrom JL, Zhong HA (2012) Binding selectivity studies of phosphoinositide 3-kinases using free energy calculations. J. Chem. Inf. Model. 52(12):3213–3224PubMedGoogle Scholar
  20. 20.
    Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3(11):935–949PubMedGoogle Scholar
  21. 21.
    Sabbah DA, Vennerstrom JL, Zhong HZ (2010) Docking studies on isoform-specific inhibition of phosphoinositide-3-kinases. J. Chem. Inf. Model. 50(10):1887–1898PubMedPubMedCentralGoogle Scholar
  22. 22.
    Shao S, Yu R, Yu Y, Li Y (2014) Dual-inhibitors of STAT5 and STAT3: studies from molecular docking and molecular dynamics simulations. J. Mol. Model. 20(8):2399PubMedGoogle Scholar
  23. 23.
    Zhang C, Du C, Feng Z, Zhu J, Li Y (2015) Hologram quantitative structure activity relationship, docking, and molecular dynamics studies of inhibitors for CXCR4. Chem. Biol. Drug Des. 85(2):119–136PubMedGoogle Scholar
  24. 24.
    Iqbal S, Krishnan DA, Gunasekaran K (2018) Identification of potential PKC inhibitors through pharmacophore designing, 3D–QSAR and molecular dynamics simulations targeting Alzheimer’s disease. J Biomol Struct Dyn 36(15):4029–4044PubMedGoogle Scholar
  25. 25.
    Katari SK, Natarajan P, Swargam S, Kanipakam H, Pasala C (2016) Inhibitor design against JNK1 through e-pharmacophore modeling docking and molecular dynamics simulations. J. Recept. Signal Transduct. Res. 36(6):558–571PubMedGoogle Scholar
  26. 26.
    Rajamanikandan S, Jeyakanthan J, Srinivasan P (2017) Molecular docking, molecular dynamics simulations, computational screening to design quorum sensing inhibitors targeting LuxP of Vibrio harveyi and its biological evaluation. Appl. Biochem. Biotechnol. 181(1):192–218PubMedGoogle Scholar
  27. 27.
    Zondagh J, Balakrishnan V, Achilonu I, Dirr HW, Sayed Y (2018) Molecular dynamics and ligand docking of a hinge region variant of South African HIV-1 subtype C protease. J Mol Graph Model 82:1–11PubMedGoogle Scholar
  28. 28.
    Xu C, Ren Y (2015) Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations. Bioorg. Med. Chem. Lett. 25(20):4522–4528PubMedGoogle Scholar
  29. 29.
    Tang HJ, Yang L, Li JH, Chen J (2016) Molecular modelling studies of 3,5-dipyridyl-1,2,4-triazole derivatives as xanthine oxidoreductase inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamic simulations. J Taiwan Inst Chem E 68:64–73Google Scholar
  30. 30.
    Aksoydan B, Kantarcioglu I, Erol I, Salmas RE, Durdagi S (2018) Structure-based design of hERG-neutral antihypertensive oxazalone and imidazolone derivatives. J Mol Graph Model 79:103–117PubMedGoogle Scholar
  31. 31.
    Zhao S, Zhu J, Xu L, Jin J (2017) Theoretical studies on the selective mechanisms of GSK3beta and CDK2 by molecular dynamics simulations and free energy calculations. Chem. Biol. Drug Des. 89(6):846–855PubMedGoogle Scholar
  32. 32.
    Shen M, Zhou S, Li Y, Li D, Hou T (2013) Theoretical study on the interaction of pyrrolopyrimidine derivatives as LIMK2 inhibitors: insight into structure-based inhibitor design. Mol. BioSyst. 9(10):2435–2446PubMedGoogle Scholar
  33. 33.
    Xu L, Li Y, Li L, Zhou S, Hou T (2012) Understanding microscopic binding of macrophage migration inhibitory factor with phenolic hydrazones by molecular docking, molecular dynamics simulations and free energy calculations. Mol. BioSyst. 8(9):2260–2273PubMedGoogle Scholar
  34. 34.
    Ekhteiari Salmas R, Unlu A, Bektas M, Yurtsever M, Mestanoglu M, Durdagi S (2017) Virtual screening of small molecules databases for discovery of novel PARP-1 inhibitors: combination of in silico and in vitro studies. J. Biomol. Struct. Dyn. 35(9):1899–1915PubMedGoogle Scholar
  35. 35.
    Patel L, Chandrasekhar J, Evarts J (2016) 2,4,6-Triaminopyrimidine as a novel hinge binder in a series of PI3Kδ selective inhibitors. J. Med. Chem. 59(7):3532–3548PubMedGoogle Scholar
  36. 36.
    Sherman W, Beard H, Farid R (2010) Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 67(1):83–84Google Scholar
  37. 37.
    Somoza JR, David K (2015) Structural, biochemical, and biophysical characterization of idelalisib binding to phosphoinositide 3-kinase δ. J Bio Chem 290(13):8439–8446Google Scholar
  38. 38.
    Case DA, Cheatham TE, Darden T, Gohlke H (2005) The Amber biomolecular simulation programs. J. Comput. Chem. 26(16):1668–1688PubMedPubMedCentralGoogle Scholar
  39. 39.
    Duan Y, Wu C, Chowdhury S, Lee MC, Xiong GM (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J. Comput. Chem. 24(16):1999–2012PubMedGoogle Scholar
  40. 40.
    Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J. Comput. Chem. 25(9):1157–1174PubMedGoogle Scholar
  41. 41.
    Stewart JPJ (2010) Optimization of parameters for semiempirical methods I. Method. J Comput Chem 10(2):221–264Google Scholar
  42. 42.
    Bayly CI, Cieplak P, Cornell WD, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the resp model. J. Phys. Chem. 97(40):10269–10280Google Scholar
  43. 43.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J. Phys. Chem. 98(12):10089–10092Google Scholar
  44. 44.
    Vincent K, Van Gunsteren W, Hünenberger P (2001) A fast SHAKE algorithm to solve distance constraint equations for small molecules in molecular dynamics simulations. J. Comput. Chem. 22(5):501–508Google Scholar
  45. 45.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Cheminform 32(10):889–897Google Scholar
  46. 46.
    Hou T, Li Y, Wang W (2011) Prediction of peptides binding to the PKA RIIα subunit using a hierarchical strategy. Bioinformatics 27(13):1814–1821PubMedPubMedCentralGoogle Scholar
  47. 47.
    Sun H, Li Y, Shen M, Tian S, Xu L, Pan P, Guan Y, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys. Chem. Chem. Phys. 16(40):22035–22045PubMedGoogle Scholar
  48. 48.
    Chen F, Liu H, Sun H, Pan P, Li Y, Li D, Hou T (2016) Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking. Phys. Chem. Chem. Phys. 18(18):22129–22139PubMedGoogle Scholar
  49. 49.
    Pan P, Yu H, Liu Q, Kong X, Chen H, Chen J, Liu Q, Li D, Kang Y, Sun H (2017) Combating drug-resistant mutants of anaplastic lymphoma kinase with potent and selective type-I1/2 inhibitors by stabilizing unique DFG-shifted loop conformation. Acs Cent Sci 3(11):1208–1220PubMedPubMedCentralGoogle Scholar
  50. 50.
    Hou T, Li Y, Wang W (2011) Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy. Bioinformatics 27(13):1814–1821PubMedPubMedCentralGoogle Scholar
  51. 51.
    Sun H, Li Y, Li D, Hou T (2013) Insight into crizotinib resistance mechanisms caused by three mutations in ALK tyrosine kinase using free energy calculation approaches. J. Chem. Inf. Model. 53(9):2376–2389PubMedGoogle Scholar
  52. 52.
    Sun H, Li Y, Tian S, Xu L, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys. 16(31):16719–16729PubMedGoogle Scholar
  53. 53.
    Sun H, Duan L, Chen F, Liu H, Wang Z, Pan P, Zhu F (2018) Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches. Phys. Chem. Chem. Phys. 20(21):14450–14460PubMedGoogle Scholar
  54. 54.
    Chen F, Liu H, Sun H, Pan P, Li Y, Li D, Hou T (2016) Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking. Phys. Chem. Chem. Phys. 18(32):22129–22139PubMedGoogle Scholar
  55. 55.
    Chen F, Sun H, Wang J, Zhu F, Liu H (2018) Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes. RNA 24(9):1183–1194PubMedGoogle Scholar
  56. 56.
    Onufriev A, Donald Bashford A, Case DA (2000) Modification of the generalized born model suitable for macromolecules. J. Phys. Chem. B 104(15):3712–3720Google Scholar
  57. 57.
    Weiser J, Shenkin P, Still W (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J. Comput. Chem. 20(2):217–230Google Scholar
  58. 58.
    Berndt A, Miller S, Williams O, Le D (2010) The p110δ crystal structure uncovers mechanisms for selectivity and potency of novel PI3K inhibitors. Nat. Chem. Biol. 6(2):117–124PubMedPubMedCentralGoogle Scholar
  59. 59.
    Williams R, Berndt A, Miller S, Hon W, Zhang X (2009) Form and flexibility in phosphoinositide 3-kinases. Biochem. Soc. Trans. 37(4):615–626PubMedGoogle Scholar
  60. 60.
    Safina BS, Sweeney ZK, Li J, Chan BK (2013) Identification of GNE-293, a potent and selective PI3Kδ inhibitor: navigating in vitro genotoxicity while improving potency and selectivity. Bioorg. Med. Chem. Lett. 23(17):4953–4959PubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Pharmaceutical SciencesJiangnan UniversityWuxiChina
  2. 2.Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina

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