Advertisement

Journal of Computer-Aided Molecular Design

, Volume 32, Issue 1, pp 103–111 | Cite as

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

  • Rui Duan
  • Xianjin Xu
  • Xiaoqin ZouEmail author
Article

Abstract

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.

Keywords

D3R Drug Design Data Resource Molecular docking Scoring function Ligand similarity Template-based Binding mode prediction Binding affinity prediction Drug discovery 

Notes

Acknowledgements

Support to XZ from OpenEye Scientific Software Inc. (Santa Fe, NM, http://www.eyesopen.com) is gratefully acknowledged. This work was supported by the NSF CAREER Award DBI-0953839, NIH R01GM109980, and American Heart Association (Midwest Affiliate) 13GRNT16990076 to XZ. The computations were performed on the high performance computing infrastructure supported by NSF CNS-1429294 (PI: Chi-Ren Shyu) and the HPC resources supported by the University of Missouri Bioinformatics Consortium (UMBC).

Supplementary material

10822_2017_82_MOESM1_ESM.docx (21 kb)
Supplementary material 1 (DOCX 21 KB)

References

  1. 1.
    Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB Jr, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK (2016) D3R Grand Challenge 2015: evaluation of protein-ligand pose and affinity predictions. J Comput Aided Mol Des 30:651–668CrossRefGoogle Scholar
  2. 2.
    Forman BM, Ode E, Chen J, Oro AE, Bradley DJ, Perlmann T, Noonan DJ, Burka LT, McMorris T, Lamph WW, Evans RM, Weinberger C (1995) Identification of a nuclear receptor that is activated by farnesol metabolites. Cell 81:687–693CrossRefGoogle Scholar
  3. 3.
    Parks DJ, Blanchard SG, Bledsoe RK, Chandra G, Consler TG, Kliewer SA, Stimmel JB, Willson TM, Zavacki AM, Moore DD, Lehmann JM (1999) Bile acids: natural ligands for an orphan nuclear receptor. Science 284:1365–1368CrossRefGoogle Scholar
  4. 4.
    Lambert G, Amar MJ, Guo G, Brewer HB Jr, Gonzalez FJ, Sinal CJ (2003) The farnesoid X-receptor is an essential regulator of cholesterol homeostasis. J Biol Chem 278:2563–2570CrossRefGoogle Scholar
  5. 5.
    Ma K, Saha PK, Chan L, Moore DD (2006) Farnesoid X receptor is essential for normal glucose homeostasis. J Clin Invest 116:1102–1109CrossRefGoogle Scholar
  6. 6.
    Sinal CJ, Tohkin M, Miyata M, Ward JM, Lambert G, Gonzalez FJ (2000) Targeted disruption of the nuclear receptor FXR/BAR impairs bile acid and lipid homeostasis. Cell 102:731–744CrossRefGoogle Scholar
  7. 7.
    Zhang Y, Ge X, Heemstra LA, Chen WD, Xu J, Smith JL, Ma H, Kasim N, Edwards PA, Novak CM (2012) Loss of FXR protects against diet-induced obesity and accelerates liver carcinogenesis in ob/ob mice. Mol Endocrinol 26:272–280CrossRefGoogle Scholar
  8. 8.
    Cariou B, van Harmelen K, Duran-Sandoval D, van Dijk TH, Grefhorst A, Abdelkarim M, Caron S, Torpier G, Fruchart J, Gonzalez FJ, Kuipers F, Staels B (2006) The farnesoid X receptor modulates adiposity and peripheral insulin sensitivity in mice. J Biol Chem 281:11039–11049CrossRefGoogle Scholar
  9. 9.
    Xu X, Yan C, Zou X (2017) Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R GRAND challenge 2015. J Comput Aided Mol Des 31:689–699CrossRefGoogle Scholar
  10. 10.
    Liu X, Jiang H, Li H (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. J Chem Inf Model 51:2372–2385CrossRefGoogle Scholar
  11. 11.
    Lu W, Liu X, Cao X, Xue M, Liu K, Zhao Z, Shen X, Jiang H, Xu Y, Huang J, Li H (2011) SHAFTS: a hybrid approach for 3D molecular similarity calculation. 2. Prospective case study in the discovery of diverse P90 ribosomal S6 protein kinase 2 inhibitors to suppress cell migration. J Med Chem 54:3564–3574CrossRefGoogle Scholar
  12. 12.
    Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Model 38:983–996Google Scholar
  13. 13.
    Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204–3218CrossRefGoogle Scholar
  14. 14.
    Cheng T, Li X, Li Y, Liu Z, Wang R (2009) Comparative assessment of scoring functions on a diverse test set. J Chem Inf Model 49:1079–1093CrossRefGoogle Scholar
  15. 15.
    Wang R, Fang X, Lu Y, Yang CY, Wang S (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111–4119CrossRefGoogle Scholar
  16. 16.
    Huang S, Zou X (2006) An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials. J Comput Chem 27:1866–1875CrossRefGoogle Scholar
  17. 17.
    Huang S, Zou X (2006) An iterative knowledge-based scoring function to predict protein–ligand interactions: II. Validation of the scoring function. J Comput Chem 27:1876–1882CrossRefGoogle Scholar
  18. 18.
    Yan C, Grinter SZ, Merideth BR, Ma Z, Zou X (2016) Iterative knowledge-based scoring functions derived from rigid and flexible decoy structures: evaluation with the 2013 and 2014 CSAR benchmarks. J Chem Inf Model 56:1013–1021CrossRefGoogle Scholar
  19. 19.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33:889–897CrossRefGoogle Scholar
  20. 20.
    Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate–DNA helices. J Am Chem Soc 120:9401–9409CrossRefGoogle Scholar
  21. 21.
    Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with omega: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50:572–584CrossRefGoogle Scholar
  22. 22.
    Hawkins PC, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52:2919–2936CrossRefGoogle Scholar
  23. 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 31:455–461Google Scholar
  24. 24.
    Case DA, Darden TA, Cheatham TE III, Simmerling CL, Wang J, Duke RE, Luo R, Walker RC, Zhang W, Merz KM, Roberts B, Wang B, Hayik S, Roitberg A, Seabra G, Kolossváry I, Wong KF, Paesani F, Vanicek J, Liu J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh MJ, Cui G, Roe DR, Mathews DH, Seetin MG, Sagui C, Babin V, Luchko T, Gusarov S, Kovalenko A, Kollman PA (2010) AMBER 11. University of California, San FranciscoGoogle Scholar
  25. 25.
    Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J Am Chem Soc 107:3902–3909CrossRefGoogle Scholar
  26. 26.
    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas Ö, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ (2009) Gaussian 09, revision D.01. Gaussian, Inc., WallingfordGoogle Scholar
  27. 27.
    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 23:1623–1641CrossRefGoogle Scholar
  28. 28.
    Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24:1999–2012CrossRefGoogle Scholar
  29. 29.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174CrossRefGoogle Scholar
  30. 30.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  31. 31.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  32. 32.
    Pastor RW, Brooks BR, Szabo A (1988) An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol Phys 65:1409–1419CrossRefGoogle Scholar
  33. 33.
    Ryckaert JP, Ciccotti G, Berendsen HJ (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  34. 34.
    Luo R, David L, Gilson MK (2002) Accelerated Poisson–Boltzmann calculations for static and dynamic systems. J Comput Chem 23:1244–1253CrossRefGoogle Scholar
  35. 35.
    Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Phys Chem 98:1978–1988CrossRefGoogle Scholar
  36. 36.
    Nguyen DT, Case DA (1985) On finding stationary states of large molecule potential energy surfaces. J Phys Chem 89:4020–4026CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaUSA
  2. 2.Department of Physics and AstronomyUniversity of MissouriColumbiaUSA
  3. 3.Department of BiochemistryUniversity of MissouriColumbiaUSA
  4. 4.Informatics InstituteUniversity of MissouriColumbiaUSA

Personalised recommendations