Journal of Molecular Modeling

, 24:337 | Cite as

In silico assessment of new progesterone receptor inhibitors using molecular dynamics: a new insight into breast cancer treatment

  • Vahid Zarezade
  • Marzie Abolghasemi
  • Fakher Rahim
  • Ali Veisi
  • Mohammad Behbahani
Original Paper


Nowadays, breast cancer is one of the most widespread malignancies in women, and the second leading cause of cancer death among women. The progesterone receptor (PR) is one of the treatment targets in breast cancer, and can be blocked with selective progesterone receptor modulators (SPRMs). Since administration of chemical drugs can cause serious side effects, and patients, especially those undergoing long-term treatment, can suffer harmful consequences, there is an urgent need to discover novel potent drugs. Large-scale structural diversity is a feature of natural compounds. Accordingly, in the present study, we selected a library of 20,000 natural compounds from the ZINC database, and screened them against the PR for binding affinity and efficacy. In addition, we evaluated the pharmacodynamics and ADMET properties of the compounds and performed molecular docking. Moreover, molecular dynamics (MD) simulation was carried out in order to examine the stability of the protein. In addition, principal component analysis (PCA) was performed to study the motions of the protein. Finally, the MMPBSA method was applied in order to estimate the binding free energy. Our docking results reveal that compounds ZINC00936598, ZINC00869973 and ZINC01020370 have the highest binding energy into the PR binding site, comparable with that of Levonorgestrel (positive control). Moreover, RMSD, RMSF, Rg and H-bond analysis demonstrate that the lead compounds preserve stability in complex with PR during simulation. Our PCA analysis results were in accordance with MD results and the binding free energies support the docking results. This study paves the way for discovery of novel drugs from natural sources and with optimal efficacy, targeting the PR.

Graphical Abstract

The binding mode of new progesterone receptor inhibitors.


Molecular dynamics simulation Docking Progesterone receptor Inhibitor Breast cancer Virtual screening 



We thank Behbahan Faculty of Medical Sciences for financial support (grant number: 9523).

Compliance with ethical standards

Conflict of interest

None Declared.

Supplementary material

894_2018_3858_MOESM1_ESM.pdf (5.6 mb)
ESM 1 (PDF 5746 kb)


  1. 1.
    DeSantis C, Siegel R, Bandi P, Jemal A (2011) Breast cancer statistics, 2011. CA Cancer J Clin. 61(6):408–418. CrossRefGoogle Scholar
  2. 2.
    Toss A, Cristofanilli M (2015) Molecular characterization and targeted therapeutic approaches in breast cancer. Breast Cancer Res 17(1):60. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Gatza ML, Lucas JE, Barry WT, Kim JW, Wang Q, D. Crawford M, B. Datto M, Kelley M, Mathey-Prevot B, Potti A, Nevins JR (2010) A pathway-based classification of human breast cancer. Proc Natl Acad Sci USA 107(15):6994–6999. CrossRefPubMedGoogle Scholar
  4. 4.
    Grimm SL, Hartig SM, Edwards DP (2016) Progesterone receptor signaling mechanisms. J Mol Biol 428(19):3831–3849. CrossRefPubMedGoogle Scholar
  5. 5.
    Abdel-Hafiz HA, Horwitz KB (2012) Control of progesterone receptor transcriptional synergy by SUMOylation and deSUMOylation. BMC Mol Biol 13(1):10. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Anderson E (2002) Progesterone receptors - animal models and cell signaling in breast cancer: the role of oestrogen and progesterone receptors in human mammary development and tumorigenesis. Breast Cancer Res 4(5):197. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Bain DL, Franden MA, McManaman JL, Takimoto GS, Horwitz KB (2000) The N-terminal region of the human progesterone A-receptor: structural analysis and the influence of the dna binding domain. J Biol Chem 275(10):7313–7320. CrossRefPubMedGoogle Scholar
  8. 8.
    Wetendorf M, Demayo FJ (2014) Progesterone receptor signaling in the initiation of pregnancy and preservation of a healthy uterus. Int J Dev Biol 58(0):95–106. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Li H, Fidler ML, Lim CS (2005) Effect of initial subcellular localization of progesterone receptor on import kinetics and transcriptional activity. Mol Pharm 2(6):509–518. CrossRefPubMedGoogle Scholar
  10. 10.
    Wagenfeld A, Saunders PTK, Whitaker L, Critchley HOD (2016) Selective progesterone receptor modulators (SPRMs): progesterone receptor action, mode of action on the endometrium and treatment options in gynecological therapies. Expert Opin Ther Targets 20(9):1045–1054. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Girish C, Jayanthi M, Sivaraman G (2005) Asoprisnil: A selective progesterone receptor modulator. Indian J Pharmacol 37(4):266CrossRefGoogle Scholar
  12. 12.
    Robbins A, Spitz IM (1996) Mifepristone: clinical pharmacology. Clin Obstet Gynecol 39(2):436–450CrossRefGoogle Scholar
  13. 13.
    Spitz IM (2003) Progesterone antagonists and progesterone receptor modulators: an overview. Steroids 68(10–13):981–993. CrossRefPubMedGoogle Scholar
  14. 14.
    Buss A (2010) Chiral centers. In: Natural product chemistry for drug discovery. RSC, London, p 37Google Scholar
  15. 15.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Sanchez R, Sali A (1997) Evaluation of comparative protein structure modeling by MODELLER-3. Proteins Suppl 1:50–58CrossRefGoogle Scholar
  17. 17.
    Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1):43–56. CrossRefGoogle Scholar
  18. 18.
    O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open babel: an open chemical toolbox. Journal of Cheminformatics 3(1):33. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comb Chem 31(2):455–461. CrossRefGoogle Scholar
  20. 20.
    Miteva MA, Violas S, Montes M, Gomez D, Tuffery P, Villoutreix BO (2006) FAF-drugs: free ADME/tox filtering of compound collections. Nucleic Acids Res 34(suppl_2):W738–W744. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260. CrossRefPubMedGoogle Scholar
  23. 23.
    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):1157–1174. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    David CC, Jacobs DJ (2014) Principal component analysis: a method for determining the essential dynamics of proteins. Methods Mol Biol 1084:193–226. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2013) Molecular dynamic simulation reveals damaging impact of RAC1 F28L mutation in the switch I region. PLoS One 8(10):e77453. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    C GPD, B R, Chakraborty C, N N, Ali SK, Zhu H (2014) Structural signature of the G719S-T790M double mutation in the EGFR kinase domain and its response to inhibitors. Sci Rep 4:5868. CrossRefPubMedCentralGoogle Scholar
  28. 28.
    The PyMOL Molecular Graphics System, Version 2.0, Schrödinger, LLCGoogle Scholar
  29. 29.
    Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discovery 10(5):449–461. CrossRefGoogle Scholar
  30. 30.
    Kumari R, Kumar R, Lynn A (2014) g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54(7):1951–1962. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA 98(18):10037–10041. CrossRefPubMedGoogle Scholar
  32. 32.
    Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Bowie J, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170. CrossRefPubMedGoogle Scholar
  34. 34.
    Nobili S, Lippi D, Witort E, Donnini M, Bausi L, Mini E, Capaccioli S (2009) Natural compounds for cancer treatment and prevention. Pharmacol Res 59(6):365–378. CrossRefPubMedGoogle Scholar
  35. 35.
    Karami M, Jalali C, Mirzaie S (2017) Combined virtual screening, MMPBSA, molecular docking and dynamics studies against deadly anthrax: an in silico effort to inhibit bacillus anthracis nucleoside hydrolase. J Theor Biol 420(Supplement C):180–189. CrossRefPubMedGoogle Scholar
  36. 36.
    Park H, Park SY, Ryu SE (2013) Homology modeling and virtual screening approaches to identify potent inhibitors of slingshot phosphatase 1. J Mol Graph Model 39(Supplement C):65–70. CrossRefPubMedGoogle Scholar
  37. 37.
    Manivannan P, Muralitharan G (2014) Molecular modeling of abc transporter system—permease proteins from Microcoleus chthonoplastes PCC 7420 for effective binding against secreted aspartyl proteinases in Candida albicans—a therapeutic intervention. Interdisciplinary Sciences: Computational Life Sciences 6(1):63–70. CrossRefGoogle Scholar
  38. 38.
    Sheikh IA (2016) Stereoselectivity and the potential endocrine disrupting activity of di-(2-ethylhexyl)phthalate (DEHP) against human progesterone receptor: a computational perspective. J Appl Toxicol 36(5):741–747. CrossRefPubMedGoogle Scholar
  39. 39.
    Sarath Josh MK, Pradeep S, Vijayalekshmy Amma KS, Sudha Devi R, Balachandran S, Sreejith MN, Benjamin S (2016) Human ketosteroid receptors interact with hazardous phthalate plasticizers and their metabolites: an in silico study. J Appl Toxicol 36(6):836–843. CrossRefPubMedGoogle Scholar
  40. 40.
    Jadhav A, Dash R, Hirwani R, Abdin M (2017) Sequence and structure insights of kazal type thrombin inhibitor protein: studied with phylogeny, homology modeling and dynamic MM/GBSA studies. Int J Biol Macromol.
  41. 41.
    Zheng L, Lin VC, Mu Y (2016) Exploring flexibility of progesterone receptor ligand binding domain using molecular dynamics. PLoS One 11(11):e0165824. CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    John A, Sivashanmugam M, Umashankar V, Natarajan SK (2016) Virtual screening, molecular dynamics, and binding free energy calculations on human carbonic anhydrase IX catalytic domain for deciphering potential leads. J Biomol Struct Dyn 1–14.
  43. 43.
    Shahlaei M, Madadkar-Sobhani A, Mahnam K, Fassihi A, Saghaie L, Mansourian M (2011) Homology modeling of human CCR5 and analysis of its binding properties through molecular docking and molecular dynamics simulation. Biochim Biophys Acta Biomembr 1808(3):802–817. CrossRefGoogle Scholar
  44. 44.
    Sepehri S, Saghaie L, Fassihi A (2017) Anti-HIV-1 activity prediction of novel Gp41 inhibitors using structure-based virtual screening and molecular dynamics simulation. Molecular Informatics 36(3):1600060. CrossRefGoogle Scholar
  45. 45.
    Fakhar Z, Naiker S, Alves CN, Govender T, Maguire GEM, Lameira J, Lamichhane G, Kruger HG, Honarparvar B (2016) A comparative modeling and molecular docking study on Mycobacterium tuberculosis targets involved in peptidoglycan biosynthesis. J Biomol Struct Dyn 34(11):2399–2417. CrossRefPubMedGoogle Scholar
  46. 46.
    Yang X, Lu J, Ying M, Mu J, Li P, Liu Y (2017) Docking and molecular dynamics studies on triclosan derivatives binding to FabI. J Mol Model 23(1):25. CrossRefPubMedGoogle Scholar
  47. 47.
    Verma S, Singh A, Kumari A, Tyagi C, Goyal S, Jamal S, Grover A (2017) Natural polyphenolic inhibitors against the antiapoptotic BCL-2. J Recept Signal Transduct Res 37(4):391–400. CrossRefPubMedGoogle Scholar
  48. 48.
    Singh SP, Gupta D (2017) Discovery of potential inhibitor against human acetylcholinesterase: a molecular docking and molecular dynamics investigation. Comput Biol Chem 68(Supplement C):224–230. CrossRefPubMedGoogle Scholar
  49. 49.
    Zobnina V, Lambreva MD, Rea G, Campi G, Antonacci A, Scognamiglio V, Giardi MT, Polticelli F (2017) The plastoquinol–plastoquinone exchange mechanism in photosystem II: insight from molecular dynamics simulations. Photosynth Res 131(1):15–30. CrossRefPubMedGoogle Scholar
  50. 50.
    Aguayo-Ortiz R, Chavez-Garcia C, Straub JE, Dominguez L (2017) Characterizing the structural ensemble of [gamma]-secretase using a multiscale molecular dynamics approach. Chem Sci 8(8):5576–5584. CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Saadhali SA, Hassan S, Hanna LE, Ranganathan UD, Kumar V (2016) Homology modeling, substrate docking, and molecular simulation studies of mycobacteriophage Che12 lysin A. J Mol Model 22(8):180. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Behbahan Faculty of Medical SciencesBehbahanIran
  2. 2.Genetic Division, Department of Biology, Faculty of Basic ScienceShahrekord UniversityShahrekordIran
  3. 3.Thalassemia and Hemoglobinopathy Research Centre, Health Research InstituteAhvaz Jundishapur University of Medical SciencesAhvazIran
  4. 4.Faculty of EngineeringShohadaye Hoveizeh University of TechnologySusangerdIran

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