Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 - A public library of challenging docking benchmark sets

Journal of Chemical Information and Modeling

Volumn 53, Issue 6, 2013, Pages 1447-1462

  Bauer, Matthias R ^a   Ibrahim, Tamer M ^a   Vogel, Simon M ^a   Boeckler, Frank M ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL LIBRARIES; LIBRARIES;

AUTODOCK VINAS; MOLECULAR CHARGE; PUBLIC LIBRARY; SELECTION AND OPTIMIZATIONS; STRUCTURE-BASED; TARGET CLASS; VIRTUAL SCREENING; WORK-FLOWS;

BENCHMARKING;

PROTEIN; PROTEIN BINDING;

ARTICLE; CHEMISTRY; DRUG DESIGN; HUMAN; METABOLISM; MOLECULAR DOCKING; PROTEIN BINDING; PROTEIN DATABASE; WORKFLOW;

DATABASES, PROTEIN; DRUG DESIGN; HUMANS; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEINS; WORKFLOW;

EID: 84879583689     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400115b     Document Type: Article

References (95)

1. Ripphausen, P.; Nisius, B.; Peltason, L.; Bajorath, J. Quo vadis, virtual screening? A comprehensive survey of prospective applications J. Med. Chem. 2010, 53, 8461-8467
2. McInnes, C. Virtual screening strategies in drug discovery Curr. Opin. Chem. Biol. 2007, 11, 494-502
3. Boeckler, F. M.; Joerger, A. C.; Jaggi, G.; Rutherford, T. J.; Veprintsev, D. B.; Fersht, A. R. Targeted rescue of a destabilized mutant of p53 by an in silico screened drug Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 10360-10365
4. Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S. H. Structure-based virtual screening for drug discovery: a problem-centric review AAPS J. 2012, 14, 133-141
5. Villoutreix, B. O.; Eudes, R.; Miteva, M. A. Structure-based virtual ligand screening: recent success stories Comb. Chem. High Throughput Screening 2009, 12, 1000-1016
6. Vogel, S. M.; Bauer, M. R.; Joerger, A. C.; Wilcken, R.; Brandt, T.; Veprintsev, D. B.; Rutherford, T. J.; Fersht, A. R.; Boeckler, F. M. Lithocholic acid is an endogenous inhibitor of MDM4 and MDM2 Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 16906-16910
7. Tuccinardi, T. Docking-based virtual screening: recent developments Comb. Chem. High Throughput Screening 2009, 12, 303-314
8. Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discovery 2010, 9, 273-276
9. Vogel, S. M.; Bauer, M. R.; Boeckler, F. M. DEKOIS: demanding evaluation kits for objective in silico screening - a versatile tool for benchmarking docking programs and scoring functions J. Chem. Inf. Model. 2011, 51, 2650-2665
10. Jain, A. N.; Nicholls, A. Recommendations for evaluation of computational methods J. Comput.-Aided Mol. Des. 2008, 22, 133-139
11. Jain, A. N. Bias, reporting, and sharing: computational evaluations of docking methods J. Comput.-Aided Mol. Des. 2008, 22, 201-212
12. Nicholls, A. What do we know and when do we know it? J. Comput.-Aided Mol. Des. 2008, 22, 239-255
13. Clark, R. D.; Webster-Clark, D. J. Managing bias in ROC curves J. Comput.-Aided Mol. Des. 2008, 22, 141-146
14. Bissantz, C.; Folkers, G.; Rognan, D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations J. Med. Chem. 2000, 43, 4759-4767
15. Pham, T. A.; Jain, A. N. Parameter estimation for scoring protein-ligand interactions using negative training data J. Med. Chem. 2006, 49, 5856-5868
16. McGovern, S. L.; Shoichet, B. K. Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes J. Med. Chem. 2003, 46, 2895-2907
17. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J. C.; Kreatsoulas, C.; Lindsley, S.; Maiorov, V.; Truchon, J. F.; Cornell, W. D. Comparison of topological, shape, and docking methods in virtual screening J. Chem. Inf. Model. 2007, 47, 1504-1519
18. Verdonk, M. L.; Berdini, V.; Hartshorn, M. J.; Mooij, W. T.; Murray, C. W.; Taylor, R. D.; Watson, P. Virtual screening using protein-ligand docking: avoiding artificial enrichment J. Chem. Inf. Comput. Sci. 2004, 44, 793-806
19. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking J. Med. Chem. 2006, 49, 6789-6801
20. Rohrer, S. G.; Baumann, K. Impact of benchmark data set topology on the validation of virtual screening methods: exploration and quantification by spatial statistics J. Chem. Inf. Model. 2008, 48, 704-718
21. Rohrer, S. G.; Baumann, K. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data J. Chem. Inf. Model. 2009, 49, 169-184
22. Wallach, I.; Lilien, R. Virtual decoy sets for molecular docking benchmarks J. Chem. Inf. Model. 2011, 51, 196-202
23. Gatica, E. A.; Cavasotto, C. N. Ligand and decoy sets for docking to G protein-coupled receptors J. Chem. Inf. Model. 2012, 52, 1-6
24. Mysinger, M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys, Enhanced (DUD-E)-better ligands and decoys for better benchmarking J. Med. Chem. 2012, 55, 6582-6594
25. Pham, T. A.; Jain, A. N. Customizing scoring functions for docking J. Comput.-Aided Mol. Des. 2008, 22, 269-286
26. Meganathan, C.; Sakkiah, S.; Lee, Y.; Narayanan, J. V.; Lee, K. W. Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches J. Mol. Model. 2013, 19, 715-726
27. Spitzer, R.; Jain, A. N. Surflex-Dock: docking benchmarks and real-world application J. Comput.-Aided Mol. Des. 2012, 26, 687-699
28. Neves, M. A.; Totrov, M.; Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement J. Comput.-Aided Mol. Des. 2012, 26, 675-686
29. Katritch, V.; Rueda, M.; Abagyan, R. Ligand-guided receptor optimization Methods Mol. Biol. 2012, 857, 189-205
30. Wassermann, A. M.; Bajorath, J. BindingDB and ChEMBL: online compound databases for drug discovery Expert Opin. Drug Discovery 2011, 6, 683-687
31. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 2012, 40, D1100-1107
32. Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Bryant, S. H. PubChem: a public information system for analyzing bioactivities of small molecules Nucleic Acids Res. 2009, 37, W623-633
33. Liu, T.; Lin, Y.; Wen, X.; Jorissen, R. N.; Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities Nucleic Acids Res. 2007, 35, D198-201
34. Chen, X.; Liu, M.; Gilson, M. K. BindingDB: a web-accessible molecular recognition database Comb. Chem. High Throughput Screening 2001, 4, 719-725
35. Chen, X.; Lin, Y.; Liu, M.; Gilson, M. K. The Binding Database: data management and interface design Bioinformatics 2002, 18, 130-139
36. Irwin, J. J. Community benchmarks for virtual screening J. Comput.-Aided Mol. Des. 2008, 22, 193-199
37. Good, A.; Oprea, T. Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? J. Comput.-Aided Mol. Des. 2008, 22, 169-178
38. Mackey, M. D.; Melville, J. L. Better than random? The chemotype enrichment problem J. Chem. Inf. Model. 2009, 49, 1154-1162
39. Hopkins, A. L.; Groom, C. R. The druggable genome Nat. Rev. Drug Discovery 2002, 1, 727-730
40. Jubb, H.; Higueruelo, A. P.; Winter, A.; Blundell, T. L. Structural biology and drug discovery for protein-protein interactions Trends Pharmacol. Sci. 2012, 33, 241-248
41. Keller, T. H.; Pichota, A.; Yin, Z. A practical view of 'druggability' Curr. Opin. Chem. Biol. 2006, 10, 357-361
42. Wells, J. A.; McClendon, C. L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces Nature 2007, 450, 1001-1009
43. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank Nucleic Acids Res. 2000, 28, 235-242
44. Csermely, P.; Palotai, R.; Nussinov, R. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events Trends Biochem. Sci. 2010, 35, 539-546
45. Erickson, J. A.; Jalaie, M.; Robertson, D. H.; Lewis, R. A.; Vieth, M. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy J. Med. Chem. 2004, 47, 45-55
46. Pipeline Pilot, 6.1.5.0 student ed.; Accelrys: San Diego, 2007.
47. Verdonk, M. L.; Giangreco, I.; Hall, R. J.; Korb, O.; Mortenson, P. N.; Murray, C. W. Docking performance of fragments and druglike compounds J. Med. Chem. 2011, 54, 5422-5431
48. Baell, J. B.; Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays J. Med. Chem. 2010, 53, 2719-2740
49. Rath, V. L.; Ammirati, M.; Danley, D. E.; Ekstrom, J. L.; Gibbs, E. M.; Hynes, T. R.; Mathiowetz, A. M.; McPherson, R. K.; Olson, T. V.; Treadway, J. L.; Hoover, D. J. Human liver glycogen phosphorylase inhibitors bind at a new allosteric site Chem. Biol. 2000, 7, 677-682
50. Lawandi, J.; Toumieux, S.; Seyer, V.; Campbell, P.; Thielges, S.; Juillerat-Jeanneret, L.; Moitessier, N. Constrained peptidomimetics reveal detailed geometric requirements of covalent prolyl oligopeptidase inhibitors J. Med. Chem. 2009, 52, 6672-6684
51. Ouyang, X.; Zhou, S.; Su, C. T.; Ge, Z.; Li, R.; Kwoh, C. K. CovalentDock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints J. Comput. Chem. 2013, 34, 326-336
52. Powers, J. C.; Asgian, J. L.; Ekici, O. D.; James, K. E. Irreversible inhibitors of serine, cysteine, and threonine proteases Chem. Rev. 2002, 102, 4639-4750
53. Costanzo, M. J.; Almond, H. R., Jr.; Hecker, L. R.; Schott, M. R.; Yabut, S. C.; Zhang, H. C.; Andrade-Gordon, P.; Corcoran, T. W.; Giardino, E. C.; Kauffman, J. A.; Lewis, J. M.; de Garavilla, L.; Haertlein, B. J.; Maryanoff, B. E. In-depth study of tripeptide-based alpha-ketoheterocycles as inhibitors of thrombin. Effective utilization of the S1' subsite and its implications to structure-based drug design J. Med. Chem. 2005, 48, 1984-2008
54. Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J. L.; Martinez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D. K. Recognizing pitfalls in virtual screening: a critical review J. Chem. Inf. Model. 2012, 52, 867-881
55. Suite 2012: Protein Preparation Wizard; Schrödinger, LLC: New York, NY, 2012.
56. Ligprep, 2.4; Schrödinger, LLC: New York, NY, 2010.
57. Hanks, S. K.; Hunter, T. Protein kinases 6. The eukaryotic protein kinase superfamily: kinase (catalytic) domain structure and classification FASEB J. 1995, 9, 576-596
58. Ma, B.; Shatsky, M.; Wolfson, H. J.; Nussinov, R. Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations Protein Sci. 2002, 11, 184-197
59. Biamonte, M. A.; Van de Water, R.; Arndt, J. W.; Scannevin, R. H.; Perret, D.; Lee, W. C. Heat shock protein 90: inhibitors in clinical trials J. Med. Chem. 2010, 53, 3-17
60. Ivetac, A.; McCammon, J. A. Molecular recognition in the case of flexible targets Curr. Pharm. Des. 2011, 17, 1663-1671
61. Luthi-Carter, R.; Taylor, D. M.; Pallos, J.; Lambert, E.; Amore, A.; Parker, A.; Moffitt, H.; Smith, D. L.; Runne, H.; Gokce, O.; Kuhn, A.; Xiang, Z.; Maxwell, M. M.; Reeves, S. A.; Bates, G. P.; Neri, C.; Thompson, L. M.; Marsh, J. L.; Kazantsev, A. G. SIRT2 inhibition achieves neuroprotection by decreasing sterol biosynthesis Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 7927-7932
62. Narayan, N.; Lee, I. H.; Borenstein, R.; Sun, J.; Wong, R.; Tong, G.; Fergusson, M. M.; Liu, J.; Rovira, I. I.; Cheng, H. L.; Wang, G.; Gucek, M.; Lombard, D.; Alt, F. W.; Sack, M. N.; Murphy, E.; Cao, L.; Finkel, T. The NAD-dependent deacetylase SIRT2 is required for programmed necrosis Nature 2012, 492, 199-204
63. Huhtiniemi, T.; Salo, H. S.; Suuronen, T.; Poso, A.; Salminen, A.; Leppanen, J.; Jarho, E.; Lahtela-Kakkonen, M. Structure-based design of pseudopeptidic inhibitors for SIRT1 and SIRT2 J. Med. Chem. 2011, 54, 6456-6468
64. Kuntz, I. D.; Chen, K.; Sharp, K. A.; Kollman, P. A. The maximal affinity of ligands Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 9997-10002
65. Buchholz, M.; Heiser, U.; Schilling, S.; Niestroj, A. J.; Zunkel, K.; Demuth, H. U. The first potent inhibitors for human glutaminyl cyclase: synthesis and structure-activity relationship J. Med. Chem. 2006, 49, 664-677
66. Brickmann, J.; Exner, T. E.; Gimmler, J.; Lautenschläger, P.; Heiden, W.; Moeckel, G.; Zahn, D. MOLCAD II; MOLCAD GmbH: Darmstadt, 2000.
67. Brickmann, J.; Exner, T. E.; Keil, M.; Marhofer, R. J. Molecular graphics-Trends and perspectives J. Mol. Model. 2000, 6, 328-340
68. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes J. Med. Chem. 2006, 49, 6177-6196
69. Prime, 2.2; Schrödinger, LLC: New York, NY, 2010.
70. Korb, O.; Stutzle, T.; Exner, T. E. Empirical scoring functions for advanced protein-ligand docking with PLANTS J. Chem. Inf. Model. 2009, 49, 84-96
71. Korb, O.; Ten Brink, T.; Victor Paul Raj, F. R.; Keil, M.; Exner, T. E. Are predefined decoy sets of ligand poses able to quantify scoring function accuracy? J. Comput.-Aided Mol. Des. 2012, 26, 185-197
72. Trott, O.; Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J. Comput. Chem. 2010, 31, 455-461
73. Wilcken, R.; Liu, X.; Zimmermann, M. O.; Rutherford, T. J.; Fersht, A. R.; Joerger, A. C.; Boeckler, F. M. Halogen-enriched fragment libraries as leads for drug rescue of mutant p53 J. Am. Chem. Soc. 2012, 134, 6810-6818
74. Wilcken, R.; Wang, G.; Boeckler, F. M.; Fersht, A. R. Kinetic mechanism of p53 oncogenic mutant aggregation and its inhibition Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 13584-13589
75. Taldone, T.; Sun, W.; Chiosis, G. Discovery and development of heat shock protein 90 inhibitors Bioorg. Med. Chem. 2009, 17, 2225-2235
76. Chang, D. J.; An, H.; Kim, K. S.; Kim, H. H.; Jung, J.; Lee, J. M.; Kim, N. J.; Han, Y. T.; Yun, H.; Lee, S.; Lee, G.; Lee, J. S.; Cha, J. H.; Park, J. H.; Park, J. W.; Lee, S. C.; Kim, S. G.; Kim, J. H.; Lee, H. Y.; Kim, K. W.; Suh, Y. G. Design, synthesis, and biological evaluation of novel deguelin-based heat shock protein 90 (HSP90) inhibitors targeting proliferation and angiogenesis J. Med. Chem. 2012, 55, 10863-10884
77. Mihasan, M. What in silico molecular docking can do for the 'bench-working biologists' J. Biosci. 2012, 37, 1089-1095
78. ten Brink, T.; Exner, T. E. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results J. Chem. Inf. Model. 2009, 49, 1535-1546
79. ten Brink, T.; Exner, T. E. pK(a) based protonation states and microspecies for protein-ligand docking J. Comput.-Aided Mol. Des. 2010, 24, 935-942
80. Yuriev, E.; Agostino, M.; Ramsland, P. A. Challenges and advances in computational docking: 2009 in review J. Mol. Recognit. 2011, 24, 149-164
81. Kuhn, B.; Fuchs, J. E.; Reutlinger, M.; Stahl, M.; Taylor, N. R. Rationalizing tight ligand binding through cooperative interaction networks J. Chem. Inf. Model. 2011, 51, 3180-3198
82. Wilcken, R.; Zimmermann, M. O.; Lange, A.; Joerger, A. C.; Boeckler, F. M. Principles and applications of halogen bonding in medicinal chemistry and chemical biology J. Med. Chem. 2013, 56, 1363-1388
83. Wilcken, R.; Zimmermann, M. O.; Lange, A.; Zahn, S.; Boeckler, F. M. Using halogen bonds to address the protein backbone: a systematic evaluation J. Comput.-Aided Mol. Des. 2012, 26, 935-945
84. Team, R. D. C. R: A language and environment for statistical computing; R Foundation for Statistical Computing: Vienna, Austria, 2008.
85. Zhang, Z.; Burch, P. E.; Cooney, A. J.; Lanz, R. B.; Pereira, F. A.; Wu, J.; Gibbs, R. A.; Weinstock, G.; Wheeler, D. A. Genomic analysis of the nuclear receptor family: new insights into structure, regulation, and evolution from the rat genome Genome Res. 2004, 14, 580-590
86. Chemaxon pipeline pilot components, 5.4.1.1; Chemaxon: Budapest, 2011.
87. Epik, 2.1; Schrödinger, LLC: New York, NY, 2010.
88. Shelley, J. C.; Cholleti, A.; Frye, L. L.; Greenwood, J. R.; Timlin, M. R.; Uchimaya, M. Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules J. Comput.-Aided Mol. Des. 2007, 21, 681-691
89. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 2004, 47, 1750-1759
90. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 2004, 47, 1739-1749
91. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking J. Mol. Biol. 1997, 267, 727-748
92. Jones, G.; Willett, P.; Glen, R. C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation J. Mol. Biol. 1995, 245, 43-53
93. Jones, G.; Willett, P.; Glen, R. C. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation J. Comput.-Aided Mol. Des. 1995, 9, 532-549
94. Hartshorn, M. J.; Verdonk, M. L.; Chessari, G.; Brewerton, S. C.; Mooij, W. T.; Mortenson, P. N.; Murray, C. W. Diverse, high-quality test set for the validation of protein-ligand docking performance J. Med. Chem. 2007, 50, 726-741
95. O'Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; Hutchison, G. R. Open Babel: An open chemical toolbox J. Cheminf. 2011, 3, 33