Skip to main content
SpringerLink
Log in

Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

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. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN et al (2016) D3R Grand Challenge 2015: evaluation of protein–ligand pose and affinity predictions. J Comput Aided Mol Des 30:651–668

    Article  CAS  Google Scholar 

  2. Gaieb Z, Liu S, Chui M, Yang H, Shao C, Gathiaka S et al (2016) Drug Design Data Resource Grand Challenge 2 Dataset: Farnesoid X Receptor. Drug Design Data Resource. University of California, San Diego

    Google Scholar 

  3. Jin L, Feng X, Rong H, Pan Z, Inaba Y, Qiu L et al (2013) The antiparasitic drug ivermectin is a novel FXR ligand that regulates metabolism. Nat Commun 4:1937

    Google Scholar 

  4. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  Google Scholar 

  5. Schneider N, Lange G, Hindle S, Klein R, Rarey M (2013) A consistent description of HYdrogen bond and DEhydration energies in protein–ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des 27:15–29

    Article  CAS  Google Scholar 

  6. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminf 3:33

    Article  Google Scholar 

  7. SeeSAR v5.5, BioSolveIT GmbH, St.Augustin, Germany. http://www.biosolveit.de/SeeSAR

  8. Bietz S, Urbaczek S, Schulz B, Rarey M (2014) Protoss: a holistic approach to predict tautomers and protonation states in protein–ligand complexes. J Cheminf 6:12

    Article  Google Scholar 

  9. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  10. De Lano W (2002) The PyMOL molecular graphics system. DeLano Scientific, Palo Alto. http://www.pymol.org

  11. Dessau RB, Pipper CB (2008) [‘‘R"--project for statistical computing]. Ugeskr Laeger 170:328–330

    Google Scholar 

  12. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  Google Scholar 

  13. Olsson MH, Sondergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: Consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537

    Article  CAS  Google Scholar 

  14. RDKit, Open-source cheminformatics. http://www.rdkit.org

  15. Reulecke I, Lange G, Albrecht J, Klein R, Rarey M (2008) Towards an integrated description of hydrogen bonding and dehydration: decreasing false positives in virtual screening with the HYDE scoring function. ChemMedChem 3:885–897

    Article  CAS  Google Scholar 

  16. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  Google Scholar 

  17. Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25:247–260

    Article  Google Scholar 

  18. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  CAS  Google Scholar 

  19. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    Article  CAS  Google Scholar 

  20. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725

    Article  CAS  Google Scholar 

  21. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55:383–394

    Article  CAS  Google Scholar 

  22. Weiser J, Shenkin P, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20:217–230

    Article  CAS  Google Scholar 

  23. 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 31:455–461

    CAS  Google Scholar 

Download references

Acknowledgements

MR is recipient of a MNESR fellowship. We thank Dr. Marcus Gastreich and BioSolveIT GmBH for providing SeeSAR.

Author information

Author notes

    Authors and Affiliations

    1. Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, 2 rue Conté, 75003, Paris, France

      Manon Réau, Florent Langenfeld, Jean-François Zagury & Matthieu Montes

    Authors
    1. Manon Réau
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Florent Langenfeld
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Jean-François Zagury
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Matthieu Montes
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Matthieu Montes.

    Additional information

    Manon Réau and Florent Langenfeld have contributed equally to this study.

    Electronic supplementary material

    Below is the link to the electronic supplementary material.

    Supplementary material 1 (DOCX 50 KB)

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Réau, M., Langenfeld, F., Zagury, JF. et al. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study. J Comput Aided Mol Des 32, 231–238 (2018). https://doi.org/10.1007/s10822-017-0063-0

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s10822-017-0063-0

    Keywords

    Search

    Navigation