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Virtual Screening in Drug Design

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 993))

Abstract

Virtual screening has become a standard tool in drug discovery to identify novel lead compounds that target a biomolecule of interest. I present several concepts in ligand-based and structure-based virtual screening and discuss some of the current shortcomings and new developments. I also highlight approaches that combine concepts from structure- and ligand-based design.

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References

  1. Macarron R, Banks MN, Bojanic D et al (2011) Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10(3):188–195

    Article  PubMed  CAS  Google Scholar 

  2. NIH Center for Translational Therapeutics Web site (2012) http://nctt.nih.gov. Accessed

  3. Academic Screening Facilities Directory. Society for Laboratory Automation and Screening Web site (2012) http://www.slas.org/screeningFacilities/facilityList.cfm. Accessed

  4. 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(11):935–949

    Article  PubMed  CAS  Google Scholar 

  5. Villoutreix BO, Eudes R, Miteva MA (2009) Structure-based virtual ligand screening: recent success stories. Comb Chem High Throughput Screen 12(10):1000–1016

    Article  PubMed  CAS  Google Scholar 

  6. Waszkowycz B, Clark DE, Gancia E (2011) Outstanding challenges in protein-ligand docking and structure-based virtual screening. Wiley Interdiscip Rev Comput Mol Sci 1(2):229–259

    Article  CAS  Google Scholar 

  7. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11(5):494–502

    Article  PubMed  CAS  Google Scholar 

  8. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11(13–14):580–594

    Article  PubMed  CAS  Google Scholar 

  9. Halperin I, Ma BY, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47(4):409–443

    Article  PubMed  CAS  Google Scholar 

  10. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161(2):269–288

    Article  PubMed  CAS  Google Scholar 

  11. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489

    Article  PubMed  CAS  Google Scholar 

  12. 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(7):1739–1749

    Article  PubMed  CAS  Google Scholar 

  13. 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(7):1750–1759

    Article  PubMed  CAS  Google Scholar 

  14. Welch W, Ruppert J, Jain AN (1996) Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 3(6):449–462

    Article  PubMed  CAS  Google Scholar 

  15. Goodsell DS, Morris GM, Olson AJ (1996) Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 9(1):1–5

    Article  PubMed  CAS  Google Scholar 

  16. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748

    Article  PubMed  CAS  Google Scholar 

  17. Totrov M, Abagyan R (1997) Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins 1(Suppl 1):215–220

    Article  PubMed  Google Scholar 

  18. Hamelberg D, Mongan J, McCammon JA (2004) Enhanced sampling of conformational transitions in proteins using full atomistic accelerated molecular dynamics simulations. Protein Sci 13:76–76

    Google Scholar 

  19. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929

    Article  PubMed  CAS  Google Scholar 

  20. Gervasio FL, Laio A, Parrinello M (2005) Flexible docking in solution using metadynamics. J Am Chem Soc 127(8):2600–2607

    Article  PubMed  CAS  Google Scholar 

  21. Laio A, Parrinello M (2006) Computing free energies and accelerating rare events with metadynamics. In: Ferrario M, Ciccotti G, Binder K (eds) Computer simulations in condensed matter: from materials to chemical biology, vol 1, Springer. Berlin, Heidelberg, New York, pp 315–347

    Chapter  Google Scholar 

  22. McGovern SL, Shoichet BK (2003) Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J Med Chem 46(14):2895–2907

    Article  PubMed  CAS  Google Scholar 

  23. Xu M, Lill MA (2011) Significant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inf Model 51:693–706

    Article  PubMed  CAS  Google Scholar 

  24. Kua J, Zhang Y, McCammon JA (2002) Studying enzyme binding specificity in acetylcholinesterase using a combined molecular dynamics and multiple docking approach. J Am Chem Soc 124(28):8260–8267

    Article  PubMed  CAS  Google Scholar 

  25. Murray CW, Baxter CA, Frenkel AD (1999) The sensitivity of the results of molecular docking to induced fit effects: application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des 13(6):547–562

    Article  PubMed  CAS  Google Scholar 

  26. Hoffmann D, Kramer B, Washio T et al (1999) Two-stage method for protein-ligand docking. J Med Chem 42(21):4422–4433

    Article  PubMed  CAS  Google Scholar 

  27. Carlson HA (2002) Protein flexibility and drug design: how to hit a moving target. Curr Opin Chem Biol 6(4):447–452

    Article  PubMed  CAS  Google Scholar 

  28. Teodoro ML, Kavraki LE (2003) Conformational flexibility models for the receptor in structure based drug design. Curr Pharm Des 9(20):1635–1648

    Article  PubMed  CAS  Google Scholar 

  29. Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18(2):178–184

    Article  PubMed  CAS  Google Scholar 

  30. Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 5(4):347–359

    Article  PubMed  CAS  Google Scholar 

  31. Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7–8):394–400

    Article  Google Scholar 

  32. Sotriffer CA (2011) Accounting for induced-fit effects in docking: what is possible and what is not? Curr Top Med Chem 11(2):179–191

    Article  PubMed  CAS  Google Scholar 

  33. Lin JH (2011) Accommodating protein flexibility for structure-based drug design. Curr Top Med Chem 11(2):171–178

    Article  PubMed  CAS  Google Scholar 

  34. Lill MA (2011) Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 50(28):6157–6169

    Article  PubMed  CAS  Google Scholar 

  35. Atilgan AR, Durell SR, Jernigan RL et al (2001) Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys J 80(1):505–515

    Article  PubMed  CAS  Google Scholar 

  36. Bahar I, Atilgan AR, Erman B (1997) Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold Des 2(3):173–181

    Article  PubMed  CAS  Google Scholar 

  37. Armen RS, Chen J, Brooks CL (2009) An evaluation of explicit receptor flexibility in molecular docking using molecular dynamics and torsion angle molecular dynamics. J Chem Theory Comput 5(10):2909–2923

    Article  PubMed  CAS  Google Scholar 

  38. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443

    Article  PubMed  CAS  Google Scholar 

  39. Amaro RE, Baron R, McCammon JA (2008) An improved relaxed complex scheme for receptor flexibility in computer-aided drug design. J Comput Aided Mol Des 22(9):693–705

    Article  PubMed  CAS  Google Scholar 

  40. Bolstad ES, Anderson AC (2009) In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking. Proteins 75(1):62–74

    Article  PubMed  CAS  Google Scholar 

  41. Xu M, Lill MA (2012) Utilizing experimental data for reducing ensemble size in flexible-protein docking. J Chem Inf Model 52(1):187–198

    Article  PubMed  CAS  Google Scholar 

  42. Ferrara P, Gohlke H, Price DJ et al (2004) Assessing scoring functions for protein-ligand interactions. J Med Chem 47(12):3032–3047

    Article  PubMed  CAS  Google Scholar 

  43. Huang SY, Grinter SZ, Zou X (2010) Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 12(40):12899–12908

    Article  PubMed  CAS  Google Scholar 

  44. Bohm HJ (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6(6):593–606

    Article  PubMed  CAS  Google Scholar 

  45. Eldridge MD, Murray CW, Auton TR et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11(5):425–445

    Article  PubMed  CAS  Google Scholar 

  46. Wang RX, Lai LH, Wang SM (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16(1):11–26

    Article  PubMed  CAS  Google Scholar 

  47. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295(2):337–356

    Article  PubMed  CAS  Google Scholar 

  48. Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42(5):791–804

    Article  PubMed  CAS  Google Scholar 

  49. DeWitte RS, Shakhnovich EI (1996) SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11733–11744

    Article  CAS  Google Scholar 

  50. Warren GL, Andrews CW, Capelli AM et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931

    Article  PubMed  CAS  Google Scholar 

  51. Li L, Wang B, Meroueh SO (2011) Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Model 51(9):2132–2138

    Article  PubMed  CAS  Google Scholar 

  52. Li LW, Khanna M, Jo IH et al (2011) Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model 51(4):755–759

    Article  PubMed  CAS  Google Scholar 

  53. Seifert MHJ (2009) Robust optimization of scoring functions for a target class. J Comput Aided Mol Des 23(9):633–644

    Article  PubMed  CAS  Google Scholar 

  54. Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42(25):5100–5109

    Article  PubMed  CAS  Google Scholar 

  55. Brown SP, Muchmore SW (2007) Rapid estimation of relative protein-ligand binding affinities using a high-throughput version of MM-PBSA. J Chem Inf Model 47(4):1493–1503

    Article  PubMed  CAS  Google Scholar 

  56. Brown SP, Muchmore SW (2006) High-throughput calculation of protein-ligand binding affinities: Modification and adaptation of the MM-PBSA protocol to enterprise grid computing. J Chem Inf Model 46(3):999–1005

    Article  PubMed  CAS  Google Scholar 

  57. Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16(9–10):372–376

    Article  PubMed  CAS  Google Scholar 

  58. Brown RD, Martin YC (1996) Use of structure–activity data to compare structure-based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci 36:572–584

    Article  CAS  Google Scholar 

  59. Brown RD, Martin YC (1997) The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci 37:1–9

    Article  CAS  Google Scholar 

  60. Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280

    Article  PubMed  CAS  Google Scholar 

  61. Melville JL, Burke EK, Hirst JD (2009) Machine learning in virtual screening. Comb Chem High Throughput Screen 12(4):332–343

    Article  PubMed  CAS  Google Scholar 

  62. Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50(2):205–216

    Article  PubMed  CAS  Google Scholar 

  63. Nicholls A, McGaughey GB, Sheridan RP et al (2010) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53(10):3862–3886

    Article  PubMed  CAS  Google Scholar 

  64. Rush TS 3rd, Grant JA, Mosyak L, Nicholls A (2005) A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. J Med Chem 48(5):1489–1495

    Article  PubMed  CAS  Google Scholar 

  65. Martin Y (1995) Distance comparisons (DISCO): a new strategy for examining 3D structure-activity relationships. American Chemical Society, Washington, DC

    Google Scholar 

  66. Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36(3):563–571

    Article  PubMed  CAS  Google Scholar 

  67. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20(10):647–671

    Article  PubMed  CAS  Google Scholar 

  68. Richmond NJ, Abrams CA, Wolohan PRN et al (2006) GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20(9):567–587

    Article  PubMed  CAS  Google Scholar 

  69. Chen X, Rusinko A III, Tropsha A, Young SS (1999) Automated pharmacophore identifica-tion for large chemical data sets 1. J Chem Inf Comput Sci 39(5):887–896

    Article  PubMed  CAS  Google Scholar 

  70. 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(1):160–169

    Article  PubMed  CAS  Google Scholar 

  71. Kirchhoff PD, Brown R, Kahn S et al (2001) Application of structure-based focusing to the estrogen receptor. J Comput Chem 22(10):993–1003

    Article  CAS  Google Scholar 

  72. Hu B, Lill MA (2012) Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model 52(4):1046–1060

    Google Scholar 

  73. Bollt EM, ben-Avraham D (2005) What is special about diffusion on scale-free nets? New J Phys 7:26

    Article  Google Scholar 

  74. Hawkins PC, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82

    Article  PubMed  CAS  Google Scholar 

  75. McGaughey GB, Sheridan RP, Bayly CI et al (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47(4):1504–1519

    Article  PubMed  CAS  Google Scholar 

  76. Tan L, Batista J, Bajorath J (2010) Computational methodologies for compound database searching that utilize experimental protein-ligand interaction information. Chem Biol Drug Des 76(3):191–200

    PubMed  CAS  Google Scholar 

  77. Wilson GL, Lill MA (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3(6):735–750

    Article  PubMed  CAS  Google Scholar 

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Lill, M. (2013). Virtual Screening in Drug Design. In: Kortagere, S. (eds) In Silico Models for Drug Discovery. Methods in Molecular Biology, vol 993. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-342-8_1

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  • DOI: https://doi.org/10.1007/978-1-62703-342-8_1

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