Journal of Computer-Aided Molecular Design

, Volume 26, Issue 6, pp 675–686 | Cite as

Docking and scoring with ICM: the benchmarking results and strategies for improvement

Article

Abstract

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC = 82.2 and ROC(2%) = 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.

Keywords

Docking Scoring Virtual ligand screening Structure-based drug design ICM Internal coordinate mechanics 

Supplementary material

10822_2012_9547_MOESM1_ESM.docx (965 kb)
Supplementary material 1 (DOCX 965 kb)

References

  1. 1.
    Andricopulo AD, Salum LB, Abraham DJ (2009) Structure-based drug design strategies in medicinal chemistry. Curr Top Med Chem 9:771–790CrossRefGoogle Scholar
  2. 2.
    Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 153:S7–S26CrossRefGoogle Scholar
  3. 3.
    Kroemer RT (2007) Structure-based drug design: docking and scoring. Curr Protein Peptide Sci 8:312–328CrossRefGoogle Scholar
  4. 4.
    Morra G, Genoni A, Neves MAC, Merz KM, Colombo G (2010) Molecular recognition and drug-lead identification: what can molecular simulations tell us? Curr Med Chem 17:25–41CrossRefGoogle Scholar
  5. 5.
    Zou XQ, Sun YX, Kuntz ID (1999) Inclusion of solvation in ligand binding free energy calculations using the generalized-born model. J Am Chem Soc 121:8033–8043CrossRefGoogle Scholar
  6. 6.
    Ruvinsky AM (2007) Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions. J Comput Chem 28:1364–1372CrossRefGoogle Scholar
  7. 7.
    Scharer K, Morgenthaler M, Paulini R, Obst-Sander U, Banner DW, Schlatter D, Benz J, Stihle M, Diederich F (2005) Quantification of cation-π interactions in protein-ligand complexes: crystal-structure analysis of factor Xa bound to a quaternary ammonium ion ligand. Angew Chem Int Edit 44:4400–4404CrossRefGoogle Scholar
  8. 8.
    Bartlett GJ, Choudhary A, Raines RT, Woolfson DN (2010) n- > π* interactions in proteins. Nat Chem Biol 6:615–620CrossRefGoogle Scholar
  9. 9.
    Takahashi O, Kohno Y, Nishio M (2010) Relevance of weak hydrogen bonds in the conformation of organic compounds and bioconjugates: evidence from recent experimental data and high-level ab initio MO calculations. Chem Rev 110:6049–6076CrossRefGoogle Scholar
  10. 10.
    Milletti F, Vulpetti A (2010) Tautomer preference in PDB complexes and its impact on structure-based drug discovery. J Chem Inf Model 50:1062–1074CrossRefGoogle Scholar
  11. 11.
    Robeits BC, Mancera RL (2008) Ligand-protein docking with water molecules. J Chem Inf Model 48:397–408CrossRefGoogle Scholar
  12. 12.
    Kirton SB, Murray CW, Verdonk ML, Taylor RD (2005) Prediction of binding modes for ligands in the cytochromes p450 and other heme-containing proteins. Proteins 58:836–844CrossRefGoogle Scholar
  13. 13.
    Irwin JJ, Raushel FM, Shoichet BK (2005) Virtual screening against metalloenzymes for inhibitors and substrates. Biochemistry 44:12316–12328CrossRefGoogle Scholar
  14. 14.
    ten Brink T, Exner TE (2010) pKa based protonation states and microspecies for protein-ligand docking. J Comput Aided Mol Des 24:935–942CrossRefGoogle Scholar
  15. 15.
    Rose PW, Beran B, Bi CX, Bluhm WF, Dimitropoulos D, Goodsell DS, Prlic A, Quesada M, Quinn GB, Westbrook JD, Young J, Yukich B, Zardecki C, Berman HM, Bourne PE (2011) The RCSB protein data bank: redesigned web site and web services. Nucleic Acids Res 39:D392–D401CrossRefGoogle Scholar
  16. 16.
    Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model 47:488–508CrossRefGoogle Scholar
  17. 17.
    Kufareva I, Abagyan R (2008) Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states. J Med Chem 51:7921–7932CrossRefGoogle Scholar
  18. 18.
    Monceaux CJ, Hirata-Fukae C, Lam PCH, Totrov MM, Matsuoka Y, Carlier PR (2011) Triazole-linked reduced amide isosteres: an approach for the fragment-based drug discovery of anti-Alzheimer’s BACE1 inhibitors. Bioorg Med Chem Lett 21:3992–3996CrossRefGoogle Scholar
  19. 19.
    Bowers EM, Yan G, Mukherjee C, Orry A, Wang L, Holbert MA, Crump NT, Hazzalin CA, Liszczak G, Yuan H, Larocca C, Saldanha SA, Abagyan R, Sun Y, Meyers DJ, Marmorstein R, Mahadevan LC, Alani RM, Cole PA (2010) Virtual ligand screening of the p300/CBP histone acetyltransferase: identification of a selective small molecule inhibitor. Chem Biol 17:471–482CrossRefGoogle Scholar
  20. 20.
    Endo S, Matsunaga T, Kuwata K, Zhao HT, El-Kabbani O, Kitade Y, Hara A (2010) Chromene-3-carboxamide derivatives discovered from virtual screening as potent inhibitors of the tumour maker, AKR1B10. Bioorg Med Chem 18:2485–2490CrossRefGoogle Scholar
  21. 21.
    Odell LR, Howan D, Gordon CP, Robertson MJ, Chau N, Mariana A, Whiting AE, Abagyan R, Daniel JA, Gorgani NN, Robinson PJ, McCluskey A (2010) The pthaladyns: GTP competitive inhibitors of dynamin I and II GTPase derived from virtual screening. J Med Chem 53:5267–5280CrossRefGoogle Scholar
  22. 22.
    Khan MTH, Fuskevag OM, Sylte I (2009) Discovery of potent thermolysin inhibitors using structure based virtual screening and binding assays. J Med Chem 52:48–61CrossRefGoogle Scholar
  23. 23.
    Wu SD, Bottini M, Rickert RC, Mustelin T, Tautz L (2009) In silico screening for PTPN22 inhibitors: active hits from an inactive phosphatase conformation. Chemmedchem 4:440–444CrossRefGoogle Scholar
  24. 24.
    An JH, Lee DCW, Law AHY, Yang CLH, Poon LLM, Lau ASY, Jones SJM (2009) A novel small-molecule inhibitor of the avian influenza H5N1 virus determined through computational screening against the neuraminidase. J Med Chem 52:2667–2672CrossRefGoogle Scholar
  25. 25.
    Bisson WH, Cheltsov AV, Bruey-Sedano N, Lin B, Chen J, Goldberger N, May LT, Christopoulos A, Dalton JT, Sexton PM, Zhang XK, Abagyan R (2007) Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs. Proc Natl Acad Sci USA 104:11927–11932CrossRefGoogle Scholar
  26. 26.
    Cavasotto CN, Orry AJ W, Murgolo NJ, Czarniecki MF, Kocsi SA, Hawes BE, O’Neill KA, Hine H, Burton MS, Voigt JH, Abagyan RA, Bayne ML, Monsma FJ (2008) Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 51:581–588CrossRefGoogle Scholar
  27. 27.
    Katritch V, Jaakola VP, Lane JR, Lin J, IJzerman AP, Yeager M, Kufareva I, Stevens RC, Abagyan R (2010) Structure-based discovery of novel chemotypes for adenosine A2A receptor antagonists. J Med Chem 53:1799–1809CrossRefGoogle Scholar
  28. 28.
    Schapira M, Abagyan R, Totrov M (2003) Nuclear hormone receptor targeted virtual screening. J Med Chem 46:3045–3059CrossRefGoogle Scholar
  29. 29.
    Schapira M, Raaka BM, Das S, Fan L, Totrov M, Zhou ZG, Wilson S, Abagyan R, Samuels HH (2003) Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking. Proc Natl Acad Sci USA 100:7354–7359CrossRefGoogle Scholar
  30. 30.
    Schapira M, Raaka BM, Samuels HH, Abagyan R (2001) In silico discovery of novel Retinoic Acid Receptor agonist structures. BMC Struct Biol 1:1–7CrossRefGoogle Scholar
  31. 31.
    Dey R, Chen L (2011) In search of allosteric modulators of alpha 7-nAChR by solvent density guided virtual screening. J Biomol Struct Dyn 28:695–715CrossRefGoogle Scholar
  32. 32.
    Schapira M, Abagyan R, Totrov M (2002) Structural model of nicotinic acetylcholine receptor isotypes bound to acetylcholine and nicotine. BMC Struct Biol 2:1–8CrossRefGoogle Scholar
  33. 33.
    Ravna AW, Sylte I, Sager G (2009) Binding site of ABC transporter homology models confirmed by ABCB1 crystal structure. Theor Biol Med Model 6Google Scholar
  34. 34.
    Ravna AW, Sylte I, Dahl SG (2003) Molecular mechanism of citalopram and cocaine interactions with neurotransmitter transporters. J Pharmacol Exp Ther 307:34–41CrossRefGoogle Scholar
  35. 35.
    Ravna AW, Sylte I, Dahl SG (2003) Molecular model of the neural dopamine transporter. J Comput Aided Mol Des 17:367–382CrossRefGoogle Scholar
  36. 36.
    Hartshorn MJ, Verdonk ML, Chessari G, Brewerton SC, Mooij WTM, Mortenson PN, Murray CW (2007) Diverse, high-quality test set for the validation of protein-ligand docking performance. J Med Chem 50:726–741CrossRefGoogle Scholar
  37. 37.
    Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801CrossRefGoogle Scholar
  38. 38.
    Good AC, Oprea TI (2008) Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J Comput Aided Mol Des 22:169–178CrossRefGoogle Scholar
  39. 39.
    Abagyan R, Totrov M, Kuznetsov D (1994) ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15:488–506CrossRefGoogle Scholar
  40. 40.
    Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092CrossRefGoogle Scholar
  41. 41.
    Schapira M, Totrov M, Abagyan R (1999) Prediction of the binding energy for small molecules, peptides and proteins. J Mol Recognit 12:177–190CrossRefGoogle Scholar
  42. 42.
    Totrov M, Abagyan R (1997) Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins 29 (suppl 1):215–220Google Scholar
  43. 43.
    Halgren TA (1996) Merck molecular force field.1. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519CrossRefGoogle Scholar
  44. 44.
    Kufareva I, Rueda M, Katritch V, Stevens RC, Abagyan R (2011) Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19:1108–1126CrossRefGoogle Scholar
  45. 45.
    Rueda M, Katritch V, Raush E, Abagyan R (2010) SimiCon: a web tool for protein-ligand model comparison through calculation of equivalent atomic contacts. Bioinformatics 26:2784–2785CrossRefGoogle Scholar
  46. 46.
    McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51:578–596CrossRefGoogle Scholar
  47. 47.
    Davis IW, Baker D (2009) ROSETTALIGAND docking with full ligand and receptor flexibility. J Mol Biol 385:381–392CrossRefGoogle Scholar
  48. 48.
    Olsen L, Pettersson I, Hemmingsen L, Adolph HW, Jorgensen FS (2004) Docking and scoring of metallo-β-lactamases inhibitors. J Comput Aided Mol Des 18:287–302CrossRefGoogle Scholar
  49. 49.
    Korb O, Stutzle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49:84–96CrossRefGoogle Scholar
  50. 50.
    Donnecke D, Schweinitz A, Sturzebecher A, Steinmetzer P, Schuster M, Sturzebecher U, Nicklisch S, Sturzebecher J, Steinmetzer T (2007) From selective substrate analogue factor Xa inhibitors to dual inhibitors of thrombin and factor Xa. Part 3. Bioorga Medicinal Chem Lett 17:3322–3329CrossRefGoogle Scholar
  51. 51.
    Nar H, Bauer M, Schmid A, Stassen JM, Wienen W, Priepke HW M, Kauffmann IK, Ries UJ, Hauel NH (2001) Structural basis for inhibition promiscuity of dual specific thrombin and factor Xa blood coagulation inhibitors. Structure 9:29–37CrossRefGoogle Scholar
  52. 52.
    Kufareva I, Ilatovskiy AV, Abagyan R (2011) Pocketome: an encyclopedia of small-molecule binding sites in 4D. Nucleic Acids ResGoogle Scholar
  53. 53.
    Bottegoni G, Kufareva I, Totrov M, Abagyan R (2009) Four-dimensional docking: A fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52:397–406CrossRefGoogle Scholar
  54. 54.
    Neves MAC, Simoes S, Melo MLSE (2010) Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach. J Comput Aided Mol Des 24:1023–1033CrossRefGoogle Scholar
  55. 55.
    Park SJ, Kufareva I, Abagyan R (2010) Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J Comput Aided Mol Des 24:459–471CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Marco A. C. Neves
    • 1
    • 2
  • Maxim Totrov
    • 3
  • Ruben Abagyan
    • 1
    • 3
  1. 1.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California, San DiegoLa JollaUSA
  2. 2.Lab. Química Farmacêutica, Centro de Neurociências, Faculdade de FarmáciaUniversidade de Coimbra, Pólo das Ciências da SaúdeCoimbraPortugal
  3. 3.Molsoft L.L.CSan DiegoUSA

Personalised recommendations