Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

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.

The binding mode of new progesterone receptor inhibitors.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2a,b
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17a–d

Similar content being viewed by others

References

  1. DeSantis C, Siegel R, Bandi P, Jemal A (2011) Breast cancer statistics, 2011. CA Cancer J Clin. 61(6):408–418. https://doi.org/10.3322/caac.20134

    Article  Google Scholar 

  2. Toss A, Cristofanilli M (2015) Molecular characterization and targeted therapeutic approaches in breast cancer. Breast Cancer Res 17(1):60. https://doi.org/10.1186/s13058-015-0560-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1073/pnas.0912708107

    Article  PubMed  PubMed Central  Google Scholar 

  4. Grimm SL, Hartig SM, Edwards DP (2016) Progesterone receptor signaling mechanisms. J Mol Biol 428(19):3831–3849. https://doi.org/10.1016/j.jmb.2016.06.020

    Article  CAS  PubMed  Google Scholar 

  5. Abdel-Hafiz HA, Horwitz KB (2012) Control of progesterone receptor transcriptional synergy by SUMOylation and deSUMOylation. BMC Mol Biol 13(1):10. https://doi.org/10.1186/1471-2199-13-10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1186/bcr452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1074/jbc.275.10.7313

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1387/ijdb.140069mw

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1021/mp0500418

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1080/14728222.2016.1180368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Girish C, Jayanthi M, Sivaraman G (2005) Asoprisnil: A selective progesterone receptor modulator. Indian J Pharmacol 37(4):266

    Article  CAS  Google Scholar 

  12. Robbins A, Spitz IM (1996) Mifepristone: clinical pharmacology. Clin Obstet Gynecol 39(2):436–450

    Article  CAS  PubMed  Google Scholar 

  13. Spitz IM (2003) Progesterone antagonists and progesterone receptor modulators: an overview. Steroids 68(10–13):981–993. https://doi.org/10.1016/j.steroids.2003.08.007

    Article  CAS  PubMed  Google Scholar 

  14. Buss A (2010) Chiral centers. In: Natural product chemistry for drug discovery. RSC, London, p 37

  15. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  CAS  PubMed  Google Scholar 

  16. Sanchez R, Sali A (1997) Evaluation of comparative protein structure modeling by MODELLER-3. Proteins Suppl 1:50–58

    Article  Google Scholar 

  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. https://doi.org/10.1016/0010-4655(95)00042-E

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1002/jcc.21334

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1093/nar/gkl065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1016/j.jmgm.2005.12.005

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1002/jcc.20035

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1002/jcc.10128

    Article  CAS  PubMed  Google Scholar 

  25. David CC, Jacobs DJ (2014) Principal component analysis: a method for determining the essential dynamics of proteins. Methods Mol Biol 1084:193–226. https://doi.org/10.1007/978-1-62703-658-0_11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1371/journal.pone.0077453

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1038/srep05868

    Article  CAS  PubMed Central  Google Scholar 

  28. The PyMOL Molecular Graphics System, Version 2.0, Schrödinger, LLC

  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. https://doi.org/10.1517/17460441.2015.1032936

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1021/ci500020m

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1073/pnas.181342398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1126/science.1853201

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.phrs.2009.01.017

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.jtbi.2017.03.010

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.jmgm.2012.10.008

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1007/s12539-014-0189-x

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1002/jat.3302

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1002/jat.3221

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.ijbiomac.2017.11.020

  41. Zheng L, Lin VC, Mu Y (2016) Exploring flexibility of progesterone receptor ligand binding domain using molecular dynamics. PLoS One 11(11):e0165824. https://doi.org/10.1371/journal.pone.0165824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1080/07391102.2016.1207565

  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. https://doi.org/10.1016/j.bbamem.2010.12.004

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1002/minf.201600060

    Article  CAS  Google Scholar 

  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. https://doi.org/10.1080/07391102.2015.1117397

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1007/s00894-016-3192-9

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1080/10799893.2017.1298129

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.compbiolchem.2017.04.002

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1007/s11120-016-0292-4

    Article  CAS  PubMed  Google Scholar 

  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. https://doi.org/10.1039/C7SC00980A

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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. https://doi.org/10.1007/s00894-016-3056-3

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgment

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

Author information

Authors and Affiliations

  1. Behbahan Faculty of Medical Sciences, Behbahan, Iran

    Vahid Zarezade & Ali Veisi

  2. Genetic Division, Department of Biology, Faculty of Basic Science, Shahrekord University, Shahrekord, Iran

    Marzie Abolghasemi

  3. Thalassemia and Hemoglobinopathy Research Centre, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Fakher Rahim

  4. Faculty of Engineering, Shohadaye Hoveizeh University of Technology, Susangerd, Dasht-e Azadegan, Iran

    Mohammad Behbahani

Authors
  1. Vahid Zarezade
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzie Abolghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fakher Rahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Veisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Behbahani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahid Zarezade.

Ethics declarations

Conflict of interest

None Declared.

Electronic supplementary material

ESM 1

(PDF 5746 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarezade, V., Abolghasemi, M., Rahim, F. et al. In silico assessment of new progesterone receptor inhibitors using molecular dynamics: a new insight into breast cancer treatment. J Mol Model 24, 337 (2018). https://doi.org/10.1007/s00894-018-3858-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-018-3858-6

Keywords

Search

Navigation