On Setting Up and Assessing Docking Simulations for Virtual Screening

  • Jacek Biesiada
  • Aleksey Porollo
  • Jaroslaw MellerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 928)


Small molecule docking and virtual screening of candidate compounds have become an integral part of drug discovery pipelines, complementing and streamlining experimental efforts in that regard. In this chapter, we describe specific software packages and protocols that can be used to efficiently set up a computational screening using a library of compounds and a docking program. We also discuss consensus- and clustering-based approaches that can be used to assess the results, and potentially re-rank the hits. While docking programs share many common features, they may require tailored implementation of virtual screening pipelines for specific computing platforms. Here, we primarily focus on solutions for several public domain packages that are widely used in the context of drug development.

Key words

Drug discovery Virtual screening Small molecule docking Flexible docking AutoDock Polyview-MM Visualization Clustering Re-ranking 



This work was supported in part by NIH grants A1055649, UL1RR026314, and P01HD013021. Computational resources were made available by Cincinnati Childrens Hospital Research Foundation and University of Cincinnati College of Medicine.


  1. 1.
    Huang SY, Zou X (2010) Advances and challenges in protein-ligand docking. Int J Mol Sci 11:3016–3034PubMedCrossRefGoogle Scholar
  2. 2.
    Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16(9–10):372–376PubMedCrossRefGoogle Scholar
  3. 3.
    Morris GM, Lim-Wilby M (2008) Molecular docking. Methods Mol Biol 443:365–382PubMedCrossRefGoogle Scholar
  4. 4.
    Petrenko R, Meller J (2009) Molecular dynamics. In: Encyclopedia of life sciences. WileyGoogle Scholar
  5. 5.
    Kellenberger E, Rodrigo J, Muller P, Rognan D (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57:225–242PubMedCrossRefGoogle Scholar
  6. 6.
    Rajamani R, Good AC (2007) Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Discov Devel 10:308–315PubMedGoogle Scholar
  7. 7.
    Duch W, Swaminathan K, Meller J (2007) Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des 13:1497–1508PubMedCrossRefGoogle Scholar
  8. 8.
    Warren GL, Andrews CW, Capelli AM, Clarke B et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931PubMedCrossRefGoogle Scholar
  9. 9.
    Kolb P, Ferreira RS, Irwin JJ, Shoichet BK (2009) Docking and chemoinformatic screens for new ligands and targets. Curr Opin Biotechnol 20:429–436PubMedCrossRefGoogle Scholar
  10. 10.
    Goodsell DS, Morris GM, Olson AJ (1996) Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 9:1–5PubMedCrossRefGoogle Scholar
  11. 11.
    Morris GM, Huey R, Lindstrom W, Sanner MF et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791PubMedCrossRefGoogle Scholar
  12. 12.
    Lang PT, Brozell SR, Mukherjee S, Pettersen EF et al (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15:1219–1230PubMedCrossRefGoogle Scholar
  13. 13.
    Shoichet BK, Bodian DL, Kuntz ID (1992) Molecular Docking Using Shape Descriptors. J Comput Chem 13:380–397CrossRefGoogle Scholar
  14. 14.
    Meng EC, Shoichet BK, Kuntz ID (1992) Automated Docking with Grid-Based Energy Evaluation. J Comput Chem 13:505–524CrossRefGoogle Scholar
  15. 15.
    Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308:377–395PubMedCrossRefGoogle Scholar
  16. 16.
    Friesner RA, Banks JL, Murphy RB, Halgren TA et al (2004) Glide: a new approach for rapid, accurate docking and scoring 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749PubMedCrossRefGoogle Scholar
  17. 17.
    Verdonk ML, Cole JC, Hartshorn MJ, Murray CW et al (2003) Improved protein-ligand docking using GOLD. Proteins 52:609–623PubMedCrossRefGoogle Scholar
  18. 18.
    Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385:381–392PubMedCrossRefGoogle Scholar
  19. 19.
    Zavodszky MI, Sanschagrin PC, Korde RS, Kuhn LA (2002) Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening. J Comput Aided Mol Des 16: 883–902PubMedCrossRefGoogle Scholar
  20. 20.
    Zavodszky MI, Rohatgi A, Van Voorst JR, Yan H et al (2009) Scoring ligand similarity in structure-based virtual screening. J Mol Recognit 22:280–292PubMedCrossRefGoogle Scholar
  21. 21.
    Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46: 499–511PubMedCrossRefGoogle Scholar
  22. 22.
    Huang SY, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 66:399–421PubMedCrossRefGoogle Scholar
  23. 23.
    Morris GM, Huey R, Olson AJ (2008) Using AutoDock for ligand-receptor docking. Curr Protoc Bioinformatics 24:8.14.1–8.14.40Google Scholar
  24. 24.
    Yang JM, Chen YF, Shen TW, Kristal BS et al (2005) Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model 45:1134–1146PubMedCrossRefGoogle Scholar
  25. 25.
    Biesiada J, Porollo A, Velayutham P, Kouril M, Meller J (2011) Survey of public domain software for docking simulations and virtual screening. Hum Genomics 5(5):497–505PubMedGoogle Scholar
  26. 26.
    Kirchmair J, Markt P, Distinto S, Wolber G et al (2008) Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection–what can we learn from earlier mistakes?, J Comput Aided Mol Des 22:213–228PubMedCrossRefGoogle Scholar
  27. 27.
    Kim R, Skolnick J (2008) Assessment of programs for ligand binding affinity prediction. J Comput Chem 29:1316–1331PubMedCrossRefGoogle Scholar
  28. 28.
    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 31:455–461PubMedGoogle Scholar
  29. 29.
    Khodade P, Prabhu R, Chandra N, Raha S et al (2007) Parallel implementation of AutoDock. J Appl Crystallogr 40:598–599CrossRefGoogle Scholar
  30. 30.
    Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45: 177–182PubMedCrossRefGoogle Scholar
  31. 31.
    Jmol: an open-source Java viewer for chemical structures in 3D.
  32. 32.
  33. 33.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38, 27–38PubMedCrossRefGoogle Scholar
  34. 34.
    Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 8:127–134PubMedCrossRefGoogle Scholar
  35. 35.
    Seco J, Luque FJ, Barril X (2009) Binding site detection and druggability index from first principles. J Med Chem 52(8):2363–2371PubMedCrossRefGoogle Scholar
  36. 36.
    Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53(15):5858–5867PubMedCrossRefGoogle Scholar
  37. 37.
    Cui Q, Bahar I (2006) Normal mode analysis: theory and applications to biological and chemical systems. Chapman & Hall, Boca RatonGoogle Scholar
  38. 38.
    Chennubhotla C, Rader AJ, Yang LW, Bahar I (2005) Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies. Phys Biol 2(4): S173–S180PubMedCrossRefGoogle Scholar
  39. 39.
    Porollo A, Meller J (2007) Versatile annotation and publication quality visualization of protein complexes using POLYVIEW-3D. BMC Bioinformatics 8(316)Google Scholar
  40. 40.
    Landau M, Mayrose I, Rosenberg Y, Glaser F, Martz E, Pupko T, Ben-Tal N (2005) ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res 33:W299–W302PubMedCrossRefGoogle Scholar
  41. 41.
    Dundas J et al (2006) CASTp: computed atas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucl Acids Res 34:W116–W118PubMedCrossRefGoogle Scholar
  42. 42.
    Porollo A, Meller J (2007) Prediction-based fingerprints of protein-protein interactions. Proteins 66:630–645PubMedCrossRefGoogle Scholar
  43. 43.
    Cerqueira NMFSA, Ribeiro J, Fernandes PA, Ramos MJ (2011) vsLab—An implementation for virtual high-throughput screening using AutoDock and VMD. Int J Quantum Chem 111:1208–1212CrossRefGoogle Scholar
  44. 44.
    Wolf LK (2009) New software and Websites for the Chemical Enterprise. Chem Eng News 87:31Google Scholar
  45. 45.
    Forli S Raccoon|AutoDock VS: an automated tool for preparing AutoDock virtual screenings.
  46. 46.
  47. 47.
    Pettersen EF, Goddard TD, Huang CC, Couch GS et al (2004) UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612PubMedCrossRefGoogle Scholar
  48. 48.
    Porollo A, Meller J (2010) POLYVIEW-MM: web-based platform for animation and analysis of molecular simulations. Nucleic Acids Res 38(Suppl):W662–W666PubMedCrossRefGoogle Scholar
  49. 49.
    Fang H, Tong W, Shi LM, Blair R et al (2001) Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chem Res Toxicol 14:280–294PubMedCrossRefGoogle Scholar
  50. 50.
    Barrett I, Meegan MJ, Hughes RB, Carr M et al (2008) Synthesis, biological evaluation, structural-activity relationship, and docking study for a series of benzoxepin-derived estrogen receptor modulators. Bioorg Med Chem 16:9554–9573PubMedCrossRefGoogle Scholar
  51. 51.
    AutoDock Software in Parallel with GPUs.
  52. 52.
    Lindahl E, Azuara C, Koehl P, Delarue M (2006) NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Res 36:W52–W56CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Jacek Biesiada
    • 1
    • 2
  • Aleksey Porollo
    • 3
  • Jaroslaw Meller
    • 1
    • 3
    • 4
    Email author
  1. 1.Biomedical InformaticsChildren’s Hospital Research FoundationCincinnatiUSA
  2. 2.Division of Management and InformaticsTechnical University of SilesiaKatowicePoland
  3. 3.Department of Environmental HealthUniversity of Cincinnati College of MedicineCincinnatiUSA
  4. 4.Department of InformaticsNicholas Copernicus UniversityTorunPoland

Personalised recommendations