Journal of Computer-Aided Molecular Design

, Volume 32, Issue 1, pp 273–286 | Cite as

Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations

  • Edithe Selwa
  • Eddy Elisée
  • Agustin Zavala
  • Bogdan I. IorgaEmail author


Our participation to the D3R Grand Challenge 2 involved a protocol in two steps, with an initial analysis of the available structural data from the PDB allowing the selection of the most appropriate combination of docking software and scoring function. Subsequent docking calculations showed that the pose prediction can be carried out with a certain precision, but this is dependent on the specific nature of the ligands. The correct ranking of docking poses is still a problem and cannot be successful in the absence of good pose predictions. Our free energy calculations on two different subsets provided contrasted results, which might have the origin in non-optimal force field parameters associated with the sulfonamide chemical moiety.


Docking Scoring function Gold Vina Autodock Farnesoid X receptor FXR D3R Drug design data resource Grand Challenge 2 



We thank Prof. Bert de Groot for helpful discussions. The comments and suggestions of the reviewers are also acknowledged, as they greatly contributed to improve the manuscript. This work was supported by the Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT) [Grant No. ANR-10-LABX-33], by the JPIAMR transnational project DesInMBL [Grant No. ANR-14-JAMR-0002] and by the Région Ile-de-France (DIM Malinf).

Supplementary material

10822_2017_54_MOESM1_ESM.pdf (6.2 mb)
Supplementary material 1 (PDF 6324 KB)


  1. 1.
    Bishop-Bailey D (2004) FXR as a novel therapeutic target for vascular disease. Drug News Perspect 17(8):499–504CrossRefGoogle Scholar
  2. 2.
    Claudel T, Sturm E, Kuipers F, Staels B (2004) The farnesoid X receptor: a novel drug target? Expert Opin Investig Drugs 13(9):1135–1148. doi: 10.1517/13543784.13.9.1135 CrossRefGoogle Scholar
  3. 3.
    Pellicciari R, Costantino G, Fiorucci S (2005) Farnesoid X receptor: from structure to potential clinical applications. J Med Chem 48(17):5383–5403. doi: 10.1021/jm0582221 CrossRefGoogle Scholar
  4. 4.
    Westin S, Heyman RA, Martin R (2005) FXR, a therapeutic target for bile acid and lipid disorders. Mini Rev Med Chem 5(8):719–727. doi: 10.2174/1389557054553802 CrossRefGoogle Scholar
  5. 5.
    Cai SY, Boyer JL (2006) FXR: a target for cholestatic syndromes? Expert Opin Ther Targets 10(3):409–421. doi: 10.1517/14728222.10.3.409 CrossRefGoogle Scholar
  6. 6.
    Lee FY, Lee H, Hubbert ML, Edwards PA, Zhang Y (2006) FXR, a multipurpose nuclear receptor. Trends Biochem Sci 31(10):572–580. doi: 10.1016/j.tibs.2006.08.002 CrossRefGoogle Scholar
  7. 7.
    Cariou B, Staels B (2007) FXR: a promising target for the metabolic syndrome?. Trends Pharmacol Sci 28(5):236–243. doi: 10.1016/ CrossRefGoogle Scholar
  8. 8.
    Wang YD, Chen WD, Huang W (2008) FXR, a target for different diseases. Histol Histopathol 23(5):621–627. doi: 10.14670/hh-23.621 Google Scholar
  9. 9.
    Zimber A, Gespach C (2008) Bile acids and derivatives, their nuclear receptors FXR, PXR and ligands: role in health and disease and their therapeutic potential. Anticancer Agents Med Chem 8(5):540–563. doi: 10.2174/187152008784533008 CrossRefGoogle Scholar
  10. 10.
    Crawley ML (2010) Farnesoid X receptor modulators: a patent review. Expert Opin Ther Pat 20(8):1047–1057. doi: 10.1517/13543776.2010.496777 CrossRefGoogle Scholar
  11. 11.
    Fiorucci S, Mencarelli A, Distrutti E, Palladino G, Cipriani S (2010) Targetting farnesoid-X-receptor: from medicinal chemistry to disease treatment. Curr Med Chem 17(2):139–159. doi: 10.2174/092986710790112666 CrossRefGoogle Scholar
  12. 12.
    Mencarelli A, Fiorucci S (2010) FXR an emerging therapeutic target for the treatment of atherosclerosis. J Cell Mol Med 14(1–2):79–92. doi: 10.1111/j.1582-4934.2009.00997.x CrossRefGoogle Scholar
  13. 13.
    Teodoro JS, Rolo AP, Palmeira CM (2011) Hepatic FXR: key regulator of whole-body energy metabolism. Trends Endocrinol Metab 22(11):458–466. doi: 10.1016/j.tem.2011.07.002 CrossRefGoogle Scholar
  14. 14.
    Adorini L, Pruzanski M, Shapiro D (2012) Farnesoid X receptor targeting to treat nonalcoholic steatohepatitis. Drug Discov Today 17(17–18):988–997. doi: 10.1016/j.drudis.2012.05.012 CrossRefGoogle Scholar
  15. 15.
    Fiorucci S, Mencarelli A, Distrutti E, Zampella A (2012) Farnesoid X receptor: from medicinal chemistry to clinical applications. Future Med Chem 4(7):877–891. doi: 10.4155/fmc.12.41 CrossRefGoogle Scholar
  16. 16.
    Fiorucci S, Zampella A, Distrutti E (2012) Development of FXR, PXR and CAR agonists and antagonists for treatment of liver disorders. Curr Top Med Chem 12(6):605–624. doi: 10.2174/156802612799436678 CrossRefGoogle Scholar
  17. 17.
    Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7):845–854. doi: 10.1093/bioinformatics/btt055 CrossRefGoogle Scholar
  18. 18.
    Carotti A, Marinozzi M, Custodi C, Cerra B, Pellicciari R, Gioiello A, Macchiarulo A (2014) Beyond bile acids: targeting Farnesoid X Receptor (FXR) with natural and synthetic ligands. Curr Top Med Chem 14(19):2129–2142. doi: 10.2174/1568026614666141112094058 CrossRefGoogle Scholar
  19. 19.
    Fiorucci S, Distrutti E, Ricci P, Giuliano V, Donini A, Baldelli F (2014) Targeting FXR in cholestasis: hype or hope. Expert Opin Ther Targets 18(12):1449–1459. doi: 10.1517/14728222.2014.956087 Google Scholar
  20. 20.
    Gege C, Kinzel O, Steeneck C, Schulz A, Kremoser C (2014) Knocking on FXR’s door: the “hammerhead"-structure series of FXR agonists: amphiphilic isoxazoles with potent in vitro and in vivo activities. Curr Top Med Chem 14(19):2143–2158. doi: 10.2174/1568026614666141112094430 CrossRefGoogle Scholar
  21. 21.
    Huang H, Xu Y, Zhu J, Li J (2014) Recent advances in non-steroidal FXR antagonists development for therapeutic applications. Curr Top Med Chem 14(19):2175–2187. doi: 10.2174/1568026614666141112101840 CrossRefGoogle Scholar
  22. 22.
    Lamers C, Schubert-Zsilavecz M, Merk D (2014) Medicinal chemistry and pharmacological effects of Farnesoid X Receptor (FXR) antagonists. Curr Top Med Chem 14(19):2188–2205. doi: 10.2174/1568026614666141112103516 CrossRefGoogle Scholar
  23. 23.
    Ali AH, Carey EJ, Lindor KD (2015) Recent advances in the development of farnesoid X receptor agonists. Ann Transl Med 3(1):5. doi: 10.3978/j.issn.2305-5839.2014.12.06 Google Scholar
  24. 24.
    Carr RM, Reid AE (2015) FXR agonists as therapeutic agents for non-alcoholic fatty liver disease. Curr Atheroscler Rep 17(4):500. doi: 10.1007/s11883-015-0500-2 CrossRefGoogle Scholar
  25. 25.
    Koutsounas I, Theocharis S, Delladetsima I, Patsouris E, Giaginis C (2015) Farnesoid X receptor in human metabolism and disease: the interplay between gene polymorphisms, clinical phenotypes and disease susceptibility. Expert Opin Drug Metab Toxicol 11(4):523–532. doi: 10.1517/17425255.2014.999664 CrossRefGoogle Scholar
  26. 26.
    Sanyal AJ (2015) Use of farnesoid X receptor agonists to treat nonalcoholic fatty liver disease. Dig Dis 33(3):426–432. doi: 10.1159/000371698 CrossRefGoogle Scholar
  27. 27.
    Sepe V, Distrutti E, Fiorucci S, Zampella A (2015) Farnesoid X receptor modulators (2011–2014): a patent review. Expert Opin Ther Pat 25(8):885–896. doi: 10.1517/13543776.2015.1045413 CrossRefGoogle Scholar
  28. 28.
    Sepe V, Distrutti E, Limongelli V, Fiorucci S, Zampella A (2015) Steroidal scaffolds as FXR and GPBAR1 ligands: from chemistry to therapeutical application. Future Med Chem 7(9):1109–1135. doi: 10.4155/fmc.15.54 CrossRefGoogle Scholar
  29. 29.
    Alawad AS, Levy C (2016) FXR agonists: from bench to bedside, a guide for clinicians. Dig Dis Sci 61(12):3395–3404. doi: 10.1007/s10620-016-4334-8 CrossRefGoogle Scholar
  30. 30.
    De Magalhaes Filho CD, Downes M, Evans RM (2017) Farnesoid X Receptor an emerging target to combat obesity. Dig Dis 35(3):185–190. doi: 10.1159/000450909 CrossRefGoogle Scholar
  31. 31.
    Feng S, Yang M, Zhang Z, Wang Z, Hong D, Richter H, Benson GM, Bleicher K, Grether U, Martin RE, Plancher JM, Kuhn B, Rudolph MG, Chen L (2009) Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist. Bioorg Med Chem Lett 19(9):2595–2598. doi: 10.1016/j.bmcl.2009.03.008 CrossRefGoogle Scholar
  32. 32.
    Richter HG, Benson GM, Bleicher KH, Blum D, Chaput E, Clemann N, Feng S, Gardes C, Grether U, Hartman P, Kuhn B, Martin RE, Plancher JM, Rudolph MG, Schuler F, Taylor S (2011) Optimization of a novel class of benzimidazole-based farnesoid X receptor (FXR) agonists to improve physicochemical and ADME properties. Bioorg Med Chem Lett 21(4):1134–1140. doi: 10.1016/j.bmcl.2010.12.123 CrossRefGoogle Scholar
  33. 33.
    Richter HG, Benson GM, Blum D, Chaput E, Feng S, Gardes C, Grether U, Hartman P, Kuhn B, Martin RE, Plancher JM, Rudolph MG, Schuler F, Taylor S, Bleicher KH (2011) Discovery of novel and orally active FXR agonists for the potential treatment of dyslipidemia & diabetes. Bioorg Med Chem Lett 21(1):191–194. doi: 10.1016/j.bmcl.2010.11.039 CrossRefGoogle Scholar
  34. 34.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242. doi: 10.1093/nar/28.1.235 CrossRefGoogle Scholar
  35. 35.
    Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. doi: 10.1006/jmbi.1993.1626 CrossRefGoogle Scholar
  36. 36.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein–ligand docking using GOLD. Proteins Struct Funct Bioinf 52(4):609–623. doi: 10.1002/prot.10465 CrossRefGoogle Scholar
  37. 37.
    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. doi: 10.1002/jcc.21256 CrossRefGoogle Scholar
  38. 38.
    Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera: a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. doi: 10.1002/jcc.20084 CrossRefGoogle Scholar
  39. 39.
    Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. doi: 10.1002/jcc.21334 Google Scholar
  40. 40.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105(28):6474–6487. doi: 10.1021/jp003919d CrossRefGoogle Scholar
  41. 41.
    Robertson MJ, Tirado-Rives J, Jorgensen WL (2015) Improved peptide and protein torsional energetics with the OPLS-AA force field. J Chem Theory Comput 11(7):3499–3509. doi: 10.1021/acs.jctc.5b00356 CrossRefGoogle Scholar
  42. 42.
    Gapsys V, Michielssens S, Seeliger D, de Groot BL (2015) pmx: Automated protein structure and topology generation for alchemical perturbations. J Comput Chem 36(5):348–354. doi: 10.1002/jcc.23804 CrossRefGoogle Scholar
  43. 43.
    Gapsys V, Michielssens S, Peters JH, de Groot BL, Leonov H (2015) Calculation of binding free energies. Methods Mol Biol 1215:173–209. doi: 10.1007/978-1-4939-1465-4_9 CrossRefGoogle Scholar
  44. 44.
    Gapsys V, Michielssens S, Seeliger D, de Groot BL (2016) Accurate and rigorous prediction of the changes in protein free energies in a large-scale mutation scan. Angew Chem 55(26):7364–7368. doi: 10.1002/anie.201510054 CrossRefGoogle Scholar
  45. 45.
    Surpateanu G, Iorga BI (2012) Evaluation of docking performance in a blinded virtual screening of fragment-like trypsin inhibitors. J Comput Aided Mol Des 26(5):595–601. doi: 10.1007/s10822-011-9526-x CrossRefGoogle Scholar
  46. 46.
    Colas C, Iorga BI (2014) Virtual screening of the SAMPL4 blinded HIV integrase inhibitors dataset. J Comput Aided Mol Des 28(4):455–462. doi: 10.1007/s10822-014-9707-5 CrossRefGoogle Scholar
  47. 47.
    Martiny VY, Martz F, Selwa E, Iorga BI (2016) Blind pose prediction, scoring, and affinity ranking of the CSAR 2014 dataset. J Chem Inf Model 56(6):996–1003. doi: 10.1021/acs.jcim.5b00337 CrossRefGoogle Scholar
  48. 48.
    Selwa E, Martiny VY, Iorga BI (2016) Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets. J Comput Aided Mol Des 30(9):829–839. doi: 10.1007/s10822-016-9983-3 CrossRefGoogle Scholar
  49. 49.
    Grunenberg J, Licari G (2016) Effective in silico prediction of new oxazolidinone antibiotics: force field simulations of the antibiotic-ribosome complex supervised by experiment and electronic structure methods. Beilstein J Org Chem 12:415–428. doi: 10.3762/bjoc.12.45 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut de Chimie des Substances Naturelles, CNRS UPR 2301, LabEx LERMITGif-sur-YvetteFrance

Personalised recommendations