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Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

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Abstract

The 2016 D3R Grand Challenge 2 provided an opportunity to test multiple protein–ligand docking protocols on a set of ligands bound to farnesoid X receptor that has many available experimental structures. We participated in the Stage 1 of the Challenge devoted to the docking pose predictions, with the mean RMSD value of our submission poses of 2.9 Å. Here we present a thorough analysis of our docking predictions made with AutoDock Vina and the Convex-PL rescoring potential by reproducing our submission protocol and running a series of additional molecular docking experiments. We conclude that a correct receptor structure, or more precisely, the structure of the binding pocket, plays the crucial role in the success of our docking studies. We have also noticed the important role of a local ligand geometry, which seems to be not well discussed in literature. We succeed to improve our results up to the mean RMSD value of 2.15–2.33 Å  dependent on the models of the ligands, if docking these to all available homologous receptors. Overall, for docking of ligands of diverse chemical series we suggest to perform docking of each of the ligands to a set of multiple receptors that are homologous to the target.

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Notes

  1. Residue numbers are given with respect to the 3rvf receptor and may differ slightly for other proteins. The corresponding apoprotein residues are Ala346, Ala354, Ala459.

References

  1. Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA (2013) CSAR benchmark exercise 2011–2012: evaluation of results from docking and relative ranking of blinded congeneric series. J Chem Inf Model 53(8):1853–1870

    Article  CAS  Google Scholar 

  2. Smith RD, Dunbar JB Jr, Ung PMU, Esposito EX, Yang CY, Wang S, Carlson HA (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115–2131

    Article  CAS  Google Scholar 

  3. Grudinin S, Kadukova M, Eisenbarth A, Marillet S, Cazals F (2016) Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R grand challenge using a physical model with a statistical parameter estimation. J Comput-Aided Mol Des 30(9):791–804

    Article  CAS  Google Scholar 

  4. Grudinin S, Popov P, Neveu E, Cheremovskiy G (2015) Predicting binding poses and affinities in the CSAR 2013–2014 docking exercises using the knowledge-based Convex-PL potential. J Chem Inf Model 56(6):1053–1062

    Article  Google Scholar 

  5. Smith RD, Damm-Ganamet KL, Dunbar JB Jr, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J et al (2015) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model 56(6):1022–1031

    Article  Google Scholar 

  6. Carlson HA, Smith RD, Damm-Ganamet KL, Stuckey JA, Ahmed A, Convery MA, Somers DO, Kranz M, Elkins PA, Cui G, Peishoff CE, Lambert MH, Dunbar JB Jr (2016) CSAR 2014: a benchmark exercise using unpublished data from pharma. J Chem Inf Model 56(6):1063–1077

  7. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB, Carlson HA et al (2016) D3R grand challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput-Aided Mol Des 30(9):651–668

    Article  CAS  Google Scholar 

  8. Hogues H, Sulea T, Purisima EO (2015) Evaluation of the Wilma-SIE virtual screening method in community structure-activity resource 2013 and 2014 blind challenges. J Chem Inf Model 56(6):955–964

    Article  Google Scholar 

  9. Huang SY, Li M, Wang J, Pan Y (2015) Hybriddock: a hybrid protein-ligand docking protocol integrating protein-and ligand-based approaches. J Chem Inf Model 56(6):1078–1087

    Article  Google Scholar 

  10. Kumar A, Zhang KY (2015) Application of shape similarity in pose selection and virtual screening in CSARdock2014 exercise. J Chem Inf Model 56(6):965–973

    Article  Google Scholar 

  11. Kumar A, Zhang KY (2016) Prospective evaluation of shape similarity based pose prediction method in D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):685–693

    Article  CAS  Google Scholar 

  12. Martiny VY, Martz F, Selwa E, Iorga BI (2015) Blind pose prediction, scoring, and affinity ranking of the CSAR 2014 dataset. J Chem Inf Model 56(6):996–1003

    Article  Google Scholar 

  13. Piotto S, Di Biasi L, Fino R, Parisi R, Sessa L et al (2016) Yada: a novel tool for molecular docking calculations. J Comput-Aided Mol Des 30(9):753–759

    Article  CAS  Google Scholar 

  14. Prathipati P, Mizuguchi K (2015) Integration of ligand and structure based approaches for CSAR-2014. J Chem Inf Model 56(6):974–987

    Article  Google Scholar 

  15. Prathipati P, Nagao C, Ahmad S, Mizuguchi K (2016) Improved pose and affinity predictions using different protocols tailored on the basis of data availability. J Comput-Aided Mol Des 30(9):817–828

    Article  CAS  Google Scholar 

  16. Salmaso V, Sturlese M, Cuzzolin A, Moro S (2016) Dockbench as docking selector tool: the lesson learned from D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):773–789

    Article  CAS  Google Scholar 

  17. Shin WH, Lee GR, Seok C (2015) Evaluation of galaxydock based on the community structure-activity resource 2013 and 2014 benchmark studies. J Chem Inf Model 56(6):988–995

    Article  Google Scholar 

  18. Slynko I, Da Silva F, Bret G, Rognan D (2016) Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):669–683

    Article  CAS  Google Scholar 

  19. Sunseri J, Ragoza M, Collins J, Koes DR (2016) A D3R prospective evaluation of machine learning for protein-ligand scoring. J Comput-Aided Mol Des 30(9):761–771

    Article  CAS  Google Scholar 

  20. Yan C, Grinter SZ, Merideth BR, Ma Z, Zou X (2015) Iterative knowledge-based scoring functions derived from rigid and flexible decoy structures: evaluation with the 2013 and 2014 CSAR benchmarks. J Chem Inf Model 56(6):1013–1021

    Article  Google Scholar 

  21. Zhu X, Shin WH, Kim H, Kihara D (2015) Combined approach of patch-surfer and PL-patchsurfer for protein-ligand binding prediction in CSAR 2013 and 2014. J Chem Inf Model 56(6):1088–1099

    Article  Google Scholar 

  22. Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A (2013) Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. J Chem Inf Model 53(8):1915–1922

    Article  CAS  Google Scholar 

  23. Politi R, Convertino M, Popov K, Dokholyan NV, Tropsha A (2016) Docking and scoring with target-specific pose classifier succeeds in native-like pose identification but not binding affinity prediction in the CSAR 2014 benchmark exercise. J Chem Inf Model 56(6):1032–1041

    Article  CAS  Google Scholar 

  24. Fourches D, Politi R, Tropsha A (2014) Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR 2013 benchmark. J Chem Inf Model 55(1):63–71

    Article  Google Scholar 

  25. Santos-Martins D (2016) Interaction with specific HSP90 residues as a scoring function: validation in the D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):731–742

    Article  CAS  Google Scholar 

  26. Deng N, Flynn WF, Xia J, Vijayan R, Zhang B, He P, Mentes A, Gallicchio E, Levy RM (2016) Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):743–751

    Article  CAS  Google Scholar 

  27. Ignjatović MM, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F, Ryde U (2016) Binding-affinity predictions of HSP90 in the D3R grand challenge 2015 with docking, mm/gbsa, qm/mm, and free-energy simulations. J Comput-Aided Mol Des 30(9):707–730

    Article  Google Scholar 

  28. Mey AS, Juárez-Jiménez J, Hennessy A, Michel J (2016) Blinded predictions of binding modes and energies of HSP90-\(\alpha\) ligands for the 2015 D3R grand challenge. Bioorg Med Chem 24(20):4890–4899

    Article  Google Scholar 

  29. Ruiz-Carmona S, Barril X (2016) Docking-undocking combination applied to the D3R grand challenge 2015. J Comput-Aided Mol Des 30(9):805–815

    Article  CAS  Google Scholar 

  30. Baumgartner MP, Camacho CJ (2015) Choosing the optimal rigid receptor for docking and scoring in the CSAR 2013/2014 experiment. J Chem Inf Model 56(6):1004–1012

    Article  Google Scholar 

  31. 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

    Article  CAS  Google Scholar 

  32. Ye Z, Baumgartner MP, Wingert BM, Camacho CJ (2016) Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R grand challenge. J Comput-Aided Mol Des 30(9):695–706

    Article  CAS  Google Scholar 

  33. Nedumpully-Govindan P, Jemec DB, Ding F (2015) CSAR benchmark of flexible medusadock in affinity prediction and nativelike binding pose selection. J Chem Inf Model 56(6):1042–1052

    Article  Google Scholar 

  34. Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH, Di Costanzo L, Duarte JM, Dutta S, Feng Z et al (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45(D1):D271–D281

    CAS  Google Scholar 

  35. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) OpenBabel: an open chemical toolbox. J Cheminform 3:33

    Article  Google Scholar 

  36. 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

    CAS  Google Scholar 

  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

    Article  CAS  Google Scholar 

  38. Kadukova M, Grudinin S (2017) Convex-PL: a novel knowledge-based potential for protein–ligand interactions deduced from structural databases using convex optimization. J Comput-Aided Mol Des. doi:10.1007/s10822-017-0068-8

  39. Kadukova M, Grudinin S (2016) Knodle: a support vector machines-based automatic perception of organic molecules from 3D coordinates. J Chem Inf Model 56(8):1410–1419

    Article  CAS  Google Scholar 

  40. Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17(5–6):490–519

    Article  CAS  Google Scholar 

  41. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR (2012) Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform 4(1):17

    Article  CAS  Google Scholar 

  42. Rappé AK, Casewit CJ, Colwell K, Goddard Iii W, Skiff W (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114(25):10024–10035

    Article  Google Scholar 

  43. Boratyn GM, Schäffer AA, Agarwala R, Altschul SF, Lipman DJ, Madden TL (2012) Domain enhanced lookup time accelerated blast. Biol Direct 7(1):12

    Article  CAS  Google Scholar 

  44. Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77(4):778–795

    Article  CAS  Google Scholar 

  45. Schrödinger, LLC: The PyMOL molecular graphics system, version 1.3 (2011)

  46. Sigma-Aldrich Amino Acids Reference Chart. http://www.sigmaaldrich.com/life-science/metabolomics/learning-center/amino-acid-reference-chart.html

  47. Landrum G Rdkit: open-source cheminformatics. http://www.rdkit.org

  48. Richter HG, Benson G, Bleicher K, Blum D, Chaput E, Clemann N, Feng S, Gardes C, Grether U, Hartman P et al (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

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank Vladimir Chupin from MIPT Moscow for helpful discussions during the development of the Convex-PL potential. The authors also thank Andreas Eisenbarth from the University of Kaiserslautern for the development of docking protocols. This work was partially supported by the Ministry of Education and Science of the Russian Federation (No. 6.3157.2017/PP).

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Authors and Affiliations

  1. Univ. Grenoble Alpes, LJK, 38000, Grenoble, France

    Maria Kadukova & Sergei Grudinin

  2. CNRS, LJK, 38000, Grenoble, France

    Maria Kadukova & Sergei Grudinin

  3. Inria, 38000, Grenoble, France

    Maria Kadukova & Sergei Grudinin

  4. Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700

    Maria Kadukova

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  1. Maria Kadukova
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Correspondence to Sergei Grudinin.

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Kadukova, M., Grudinin, S. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2. J Comput Aided Mol Des 32, 151–162 (2018). https://doi.org/10.1007/s10822-017-0062-1

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