Skip to main content
SpringerLink
Log in

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

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

Abstract

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

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
Fig. 7

Similar content being viewed by others

References

  1. Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  Google Scholar 

  2. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  4. Smith RD, Dunbar JB Jr, Ung PM et al (2011) CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51:2115–2131

    Article  CAS  Google Scholar 

  5. 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 

  6. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  Google Scholar 

  7. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  Google Scholar 

  8. Oshiro CM, Kuntz ID, Dixon JS (1995) Flexible ligand docking using a genetic algorithm. J Comput Aided Mol Des 9:113–130

    Article  CAS  Google Scholar 

  9. Jones G, Willett P (1995) Docking small-molecule ligands into active-sites. Curr Opin Biotechnol 6:652–656

    Article  CAS  Google Scholar 

  10. Wang R, Lu Y, Wang S (2003) Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 46:2287–2303

    Article  CAS  Google Scholar 

  11. Najmanovich R, Kuttner J, Sobolev V, Edelman M (2000) Side-chain flexibility in proteins upon ligand binding. Proteins Struct Funct Genet 39:261–268

    Article  CAS  Google Scholar 

  12. Zavodszky MI (2005) Side-chain flexibility in protein-ligand binding: the minimal rotation hypothesis. Protein Sci 14:1104–1114

    Article  CAS  Google Scholar 

  13. Brylinski M, Skolnick J (2008) What is the relationship between the global structures of apo and holo proteins? Proteins Struct Funct Genet 70:363–377

    Article  CAS  Google Scholar 

  14. Boström J, Hogner A, Schmitt S (2006) Do structurally similar ligands bind in a similar fashion? J Med Chem 49:6716–6725

    Article  Google Scholar 

  15. Sutherland JJ, Nandigam RK, Erickson JA, Vieth M (2007) Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. J Chem Inf Model 47:2293–2302

    Article  CAS  Google Scholar 

  16. Lin JH, Perryman AL, Schames JR, McCammon JA (2002) Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J Am Chem Soc 124:5632–5633

    Article  CAS  Google Scholar 

  17. Koes DR, Baumgartner MP, Camacho CJ (2013) Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J Chem Inf Model 53:1893–1904

    Article  CAS  Google Scholar 

  18. Clark AJ, Tiwary P, Borrelli K et al (2016) Prediction of protein–ligand binding poses via a combination of induced fit docking and metadynamics simulations. J Chem Theory Comput 12:2990–2998

    Article  CAS  Google Scholar 

  19. Sherman W, Day T, Jacobson MP et al (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553

    Article  CAS  Google Scholar 

  20. Fletcher CM, Wagner G (1998) The interaction of eIF4E with 4E-BP1 is an induced fit to a completely disordered protein. Protein Sci 7:1639–1642

    Article  CAS  Google Scholar 

  21. Prasad JC, Goldstone JV, Camacho CJ et al (2007) Ensemble modeling of substrate binding to cytochromes P450: analysis of catalytic differences between CYP1A orthologs. Biochemistry 46:2640–2654

    Article  CAS  Google Scholar 

  22. Knegtel RM, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266:424–440

    Article  CAS  Google Scholar 

  23. Huang SY, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins Struct Funct Genet 66:399–421

    Article  CAS  Google Scholar 

  24. Luitz MP, Zacharias M (2014) Protein-ligand docking using Hamiltonian replica exchange simulations with soft core potentials. J Chem Inf Model 54:1669–1675

    Article  CAS  Google Scholar 

  25. Colizzi F, Perozzo R, Scapozza L et al (2010) Single-molecule pulling simulations can discern active from inactive enzyme inhibitors. J Am Chem Soc 132:7361–7371

    Article  CAS  Google Scholar 

  26. Di Nola A, Roccatano D, Berendsen HJ (1994) Molecular dynamics simulation of the docking of substrates to proteins. Proteins 19:174–182

    Article  Google Scholar 

  27. Osguthorpe DJ, Sherman W, Hagler AT (2012) Exploring protein flexibility: incorporating structural ensembles from crystal structures and simulation into virtual screening protocols. J Phys Chem B 116:6952–6959

    Article  CAS  Google Scholar 

  28. Wang K, Chodera JD, Yang Y, Shirts MR (2013) Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics. J Comput Aided Mol Des 27:989–1007

    Article  CAS  Google Scholar 

  29. Laio A, Gervasio FL, et al (2008) Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep Prog Phys 71:126601

    Article  Google Scholar 

  30. Gervasio FL, Laio A, Parrinello M (2005) Flexible docking in solution using metadynamics. J Am Chem Soc 127:2600–2607

    Article  CAS  Google Scholar 

  31. Sandak B, Wolfson HJ, Nussinov R (1998) Flexible docking allowing induced fit in proteins: insights from an open to closed conformational isomers. Proteins Struct Funct Genet 32:159–174

    Article  CAS  Google Scholar 

  32. Claußen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308:377–395

    Article  Google Scholar 

  33. Jorgensen WL (2004) The many roles of computation in drug discovery. Science 303:1813–1818

    Article  CAS  Google Scholar 

  34. Jorgensen WL (2009) Efficient drug lead discovery and optimization. Acc Chem Res 42:724–733

    Article  CAS  Google Scholar 

  35. Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    Article  CAS  Google Scholar 

  36. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  Google Scholar 

  37. Woo HJ, Roux B (2005) Calculation of absolute protein-ligand binding free energy from computer simulations. Proc Natl Acad Sci USA 102:6825–6830

    Article  CAS  Google Scholar 

  38. Masetti M, Cavalli A, Recanatini M, Gervasio FL (2009) Exploring complex protein–ligand recognition mechanisms with coarse metadynamics. J Phys Chem B 113:4807–4816

    Article  CAS  Google Scholar 

  39. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:27-28-38

    Article  Google Scholar 

  40. De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061

    Article  Google Scholar 

  41. Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9:71

    Article  CAS  Google Scholar 

  42. Cavalli A, Spitaleri A, Saladino G, Gervasio FL (2015) Investigating drug-target association and dissociation mechanisms using metadynamics-based algorithms. Acc Chem Res 48:277–285

    Article  CAS  Google Scholar 

  43. Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169

    Article  CAS  Google Scholar 

  44. Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51:1307–1314

    Article  CAS  Google Scholar 

  45. Wolber G, Seidel T, Bendix F, Langer T (2008) Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today 13:23–29

    Article  CAS  Google Scholar 

  46. Cherkasov A, Muratov EN, Fourches D et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  Google Scholar 

  47. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488

    Article  CAS  Google Scholar 

  48. Gathiaka S, Liu S, Chiu M 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 

  49. Forman BM, Goode E, Chen J et al (1995) Identification of a nuclear receptor that is activated by farnesol metabolites. Cell 81:687–693

    Article  CAS  Google Scholar 

  50. Jiao Y, Lu Y, Li X (2015) Farnesoid X receptor: a master regulator of hepatic triglyceride and glucose homeostasis. Acta Pharmacol Sin 36:44–50

    Article  CAS  Google Scholar 

  51. 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 30:695–706

    Article  CAS  Google Scholar 

  52. Baumgartner MP, Camacho CJ (2016) Choosing the optimal rigid receptor for docking and scoring in the CSAR 2013/2014 experiment. J Chem Inf Model 56:1004–1012

    Article  CAS  Google Scholar 

  53. Berman HM (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  54. Rose PW, Prlić A, Altunkaya A et al (2016) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45(D1):D271–D281

    Google Scholar 

  55. Small-Molecule Drug Discovery Suite 2016-3 (2016) Schrödinger, LLC, New York, NY

  56. Madhavi Sastry G, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234

    Article  CAS  Google Scholar 

  57. Jacobson MP, Pincus DL, Rapp CS et al (2004) A hierarchical approach to all-atom protein loop prediction. Proteins Struct Funct Genet 55:351–367

    Article  CAS  Google Scholar 

  58. Jacobson MP, Friesner RA, Xiang Z, Honig B (2002) On the role of the crystal environment in determining protein side-chain conformations. J Mol Biol 320:597–608

    Article  CAS  Google Scholar 

  59. Harder E, Damm W, Maple J et al (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12:281–296

    Article  CAS  Google Scholar 

  60. Chemical Computing Group ULC (2017) Molecular operating environment (MOE), 2013.08. Montreal, QC, Canada

  61. Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273–1280

    Article  CAS  Google Scholar 

  62. Gao C, Thorsteinson N, Watson I et al (2015) Knowledge-based strategy to improve ligand pose prediction accuracy for lead optimization. J Chem Inf Model 55:1460–1468

    Article  CAS  Google Scholar 

  63. POSIT 3.1.0.5: OpenEye Scientific Software, Santa Fe, NM. http://www.eyesopen.com

  64. Watts KS, Dalal P, Murphy RB et al (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50:534–546

    Article  CAS  Google Scholar 

  65. Forge, 10.4.0, Cresset, Litlington, Cambridgeshire, UK. http://www.cresset-group.com/forge/

  66. Richter HGF, Benson GM, Blum D et al (2011) Discovery of novel and orally active FXR agonists for the potential treatment of dyslipidemia & diabetes. Bioorg Med Chem Lett 21:191–194

    Article  CAS  Google Scholar 

  67. Richter HGF, Benson GM, Bleicher KH 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 21:1134–1140

    Article  CAS  Google Scholar 

  68. Akwabi-Ameyaw A, Bass JY, Caldwell RD et al (2008) Conformationally constrained farnesoid X receptor (FXR) agonists: naphthoic acid-based analogs of GW 4064. Bioorg Med Chem Lett 18:4339–4343

    Article  CAS  Google Scholar 

  69. Sherman W, Beard HS, Farid R (2006) Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 67:83–84

    Article  CAS  Google Scholar 

  70. Farid R, Day T, Friesner RA, Pearlstein RA (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 14:3160–3173

    Article  CAS  Google Scholar 

  71. Dolgikh E, Watson IA, Desai PV et al (2016) QSAR model of unbound brain-to-plasma partition coefficient, Kp,uu,brain: incorporating P-glycoprotein efflux as a variable. J Chem Inf Model 56:2225–2233

    Article  CAS  Google Scholar 

  72. PyMOL: The PyMOL Molecular Graphics System, Version 1.8.6.0 Schrödinger, LLC

Download references

Acknowledgements

The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 612347. The authors wish to acknowledge Schrödinger, LLC for providing temporary software licenses for this work. The authors would like to thank Davide Branduardi (Schrödinger) for helpful discussion and advice. The authors thank Ian Watson (Eli Lilly) for his assistance and advice with the ligand-based scoring methods and Lewis Vidler (Eli Lilly) for his critical feedback and discussion.

Author information

Authors and Affiliations

  1. Computational Chemistry and Cheminformatics, Eli Lilly and Company Ltd., Windlesham, UK

    Matthew P. Baumgartner & David A. Evans

Authors
  1. Matthew P. Baumgartner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David A. Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David A. Evans.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 519 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baumgartner, M.P., Evans, D.A. Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2. J Comput Aided Mol Des 32, 45–58 (2018). https://doi.org/10.1007/s10822-017-0081-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-017-0081-y

Keywords

Search

Navigation