Virtual screening in drug design

Methods in Molecular Biology

Volumn 993, Issue , 2013, Pages 1-12

  Lill, Markus ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Docking and scoring; Drug design; Ligand based methods; Machine learning; Protein flexibility; Shape matching; Similarity; Structure based methods; Virtual screening

Indexed keywords

LIGAND; RALOXIFENE; PROTEIN;

ALGORITHM; ARTICLE; BINDING AFFINITY; DRUG DESIGN; DRUG PROTEIN BINDING; HIGH THROUGHPUT SCREENING; HYDROGEN BOND; LIGAND BINDING; MACHINE LEARNING; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MONTE CARLO METHOD; PHARMACOPHORE; PRIORITY JOURNAL; PROTEIN CONFORMATION; VIRTUAL SCREENING; CHEMISTRY; COMPUTER INTERFACE; DRUG SCREENING; METABOLISM; METHODOLOGY; REVIEW;

DRUG DESIGN; DRUG EVALUATION, PRECLINICAL; LIGANDS; PROTEINS; USER-COMPUTER INTERFACE;

EID: 84884254531     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-342-8_1     Document Type: Article

References (77)

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
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
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
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
7. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11(5):494-502
8. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11(13-14):580-594
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
10. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161(2):269-288
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
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
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
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
15. Goodsell DS, Morris GM, Olson AJ (1996) Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 9(1): 1-5
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
17. Totrov M, Abagyan R (1997) Flexible proteinligand docking by global energy optimization in internal coordinates. Proteins 1(Suppl 1): 215-220
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
19. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and ef ficient simulation method for biomolecules. J Chem Phys 120(24): 11919-11929
20. Gervasio FL, Laio A, Parrinello M (2005) Flexible docking in solution using metadynamics. J Am Chem Soc 127(8):2600-2607
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
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
23. Xu M, Lill MA (2011) Signi ficant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inf Model 51: 693-706
24. Kua J, Zhang Y, McCammon JA (2002) Studying enzyme binding speci ficity in acetylcholinesterase using a combined molecular dynamics and multiple docking approach. J Am Chem Soc 124(28):8260-8267
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
26. Hoffmann D, Kramer B, Washio T et al (1999) Two-stage method for protein-ligand docking. J Med Chem 42(21):4422-4433
27. Carlson HA (2002) Protein flexibility and drug design: how to hit a moving target. Curr Opin Chem Biol 6(4):447-452
28. Teodoro ML, Kavraki LE (2003) Conformational flexibility models for the receptor in structure based drug design. Curr Pharm Des 9(20):1635-1648
29. Totrov M, Abagyan R (2008) Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr Opin Struct Biol 18(2):178-184
30. Beier C, Zacharias M (2010) Tackling the challenges posed by target flexibility in drug design. Expert Opin Drug Discov 5(4): 347-359
31. Rao C, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7-8): 394-400
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
33. Lin JH (2011) Accommodating protein flexibility for structure-based drug design. Curr Top Med Chem 11(2):171-178
34. Lill MA (2011) Ef ficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 50(28): 6157-6169
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
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
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
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
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
40. Bolstad ES, Anderson AC (2009) In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased ef ficiency and accuracy during docking. Proteins 75(1):62-74
41. Xu M, Lill MA (2012) Utilizing experimental data for reducing ensemble size in flexibleprotein docking. J Chem Inf Model 52(1): 187-198
42. Ferrara P, Gohlke H, Price DJ et al (2004) Assessing scoring functions for protein-ligand interactions. J Med Chem 47(12):3032-3047
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
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
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 af finity of ligands in receptor complexes. J Comput Aided Mol Des 11(5):425-445
46. Wang RX, Lai LH, Wang SM (2002) Further development and validation of empirical scoring functions for structure-based binding af finity prediction. J Comput Aided Mol Des 16(1):11-26
47. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295(2):337-356
48. Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simpli fied potential approach. J Med Chem 42(5):791-804
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
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
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
52. Li LW, Khanna M, Jo IH et al (2011) Targetspeci fic 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
53. Seifert MHJ (2009) Robust optimization of scoring functions for a target class. J Comput Aided Mol Des 23(9):633-644
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
55. Brown SP, Muchmore SW (2007) Rapid estimation of relative protein-ligand binding af finities using a high-throughput version of MM-PBSA. J Chem Inf Model 47(4): 1493-1503
56. Brown SP, Muchmore SW (2006) Highthroughput calculation of protein-ligand binding af finities: Modi fication and adaptation of the MM-PBSA protocol to enterprise grid computing. J Chem Inf Model 46(3): 999-1005
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
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
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
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
61. Melville JL, Burke EK, Hirst JD (2009) Machine learning in virtual screening. Comb Chem High Throughput Screen 12(4): 332-343
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
63. Nicholls A, McGaughey GB, Sheridan RP et al (2010) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53(10): 3862-3886
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
65. Martin Y (1995) Distance comparisons (DISCO): a new strategy for examining 3D structure-activity relationships. American Chemical Society, Washington, DC
66. Barnum D, Greene J, Smellie A, Sprague P (1996) Identi fication of common functional con figurations among molecules. J Chem Inf Comput Sci 36(3):563-571
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
68. Richmond NJ, Abrams CA, Wolohan PRN et al (2006) GALAHAD: 1. Pharmacophore identi fication by hypermolecular alignment of ligands in 3D. J Comput Aided Mol Des 20(9):567-587
69. Chen X, Rusinko A III, Tropsha A, Young SS (1999) Automated pharmacophore identi fication for large chemical data sets 1. J Chem Inf Comput Sci 39(5):887-896
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
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
72. Hu B, Lill MA (2012) Protein pharmacophore selection using hydration-site analysis. J Chem Inf Model 52(4):1046-1060
73. Bollt EM, ben-Avraham D (2005) What is special about diffusion on scale-free nets? New J Phys 7:26
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
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
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
77. Wilson GL, Lill MA (2011) Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 3(6):735-750