Scoring functions for protein-ligand interactions: A critical perspective

Drug Discovery Today: Technologies

Volumn 1, Issue 3, 2004, Pages 231-239

  Schulz Gasch, Tanja ^a   Stahl, Martin ^a  

^a F HOFFMANN LA ROCHE LTD   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BETA LACTAMASE AMPC; CARBONATE DEHYDRATASE; CASEIN KINASE II; CHECKPOINT KINASE 1; CYCLIN DEPENDENT KINASE 2; CYCLIN DEPENDENT KINASE INHIBITOR; CYCLOOXYGENASE 2 INHIBITOR; DIHYDROFOLATE REDUCTASE; GLYCOSYLTRANSFERASE; INOSINATE DEHYDROGENASE; LIGAND; PROTEIN TYROSINE PHOSPHATASE 1B;

ANALYTICAL ERROR; BINDING AFFINITY; BINDING SITE; DRUG IDENTIFICATION; DRUG SCREENING; DRUG TARGETING; MATHEMATICAL COMPUTING; PREDICTION; PROBABILITY; PROTEIN INTERACTION; RECEPTOR BINDING; REVIEW; SCORING SYSTEM; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; VIRTUAL REALITY;

EID: 13444292556     PISSN: 17406749     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ddtec.2004.08.004     Document Type: Review

References (89)

1. A. Good Structure-based virtual screening protocols Curr. Opin. Drug Discov. Dev. 4 2001 301-307
2. B. Waszkowycz Structure-based approaches to drug design and virtual screening Curr. Opin. Drug Discov. Dev. 5 2002 407-413
3. P.D. Lyne Structure-based virtual screening: An overview Drug Discov. Today 7 2002 1047-1055
4. J.F. Blake E.R. Laird Recent advances in virtual ligand screening Annu. Rep. Med. Chem. 38 2003 305-314
5. I. Muegge M. Rarey Small molecule docking and scoring. In: K.B. Lipkowitz D.B. Boyd (Eds.) Reviews in Computational Chemistry Vol. 17 2001 VCH 1-60
6. I. Halperin et al. Principles of docking: An overview of search algorithms and a guide to scoring functions Proteins 47 2002 409-443
7. R.D. Taylor et al. A review of protein-small molecule docking methods J. Comput. Aided Mol. Des. 16 2002 151-166
8. N. Brooijmans I.D. Kuntz Molecular recognition and docking algorithms Annu. Rev. Biophys. Biomol. Struct. 32 2003 335-373
9. H.-J. Boehm M. Stahl The use of scoring functions in drug discovery applications K.B. Lipkowitz D.B. Boyd Reviews in Computational Chemistry Vol. 18 2002 VCH 41-88
10. H. Gohlke G. Klebe Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors Angew. Chem. Int. Ed. 41 2002 2645-2676
11. R.D. Taylor et al. FDS: Flexible ligand and receptor docking with a continuum solvent model and soft-core energy function J. Comput. Chem. 24 2002 1637-1656
12. R.A. Friesner et al. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 47 2004 1739-1749
13. B.K. Shoichet et al. Ligand solvation in molecular docking Proteins 34 1999 4-16
14. B.Q. Wei et al. A model binding site for testing scoring functions in molecular docking J. Mol. Biol. 322 2002 339-355
15. T.I. Oprea G.R. Marshall Receptor-based prediction of binding activities Perspect. Drug Discov. Des. 9-11 1998 3-17
16. O. Roche et al. Ligand-protein database: Linking protein-ligand complex structures to binding data J. Med. Chem. 44 2001 3592-3598
17. R. Wang et al. The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures J. Med. Chem. 2004 47 (in press)
18. M.D. Eldridge et al. 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 1997 425-445
19. C.A. Baxter et al. Flexible docking using Tabu search and an empirical estimate of binding affinity Proteins 33 1998 367-382
20. H. Gohlke G. Klebe Statistical potentials and scoring functions applied to protein-ligand binding Curr. Opin. Struct. Biol. 11 2001 231-235
21. A.E. Muryshev et al. A novel scoring function for molecular docking J. Comput. Aided Mol. Des. 17 2003 597-605
22. E.C. Meng et al. Automated docking with grid-based energy evaluation J. Comput. Chem. 13 1992 505-524
23. T.A. Ewing et al. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases J. Comput. Aided Mol. Des. 15 2001 411-428
24. M. Rarey et al. Multiple automatic base selection: Protein-ligand docking based on incremental construction without manual intervention J. Comput. Aided Mol. Des. 11 1997 369-384
25. G. Jones et al. Development and validation of a genetic algorithm for flexible docking J. Mol. Biol. 267 1997 727-748
26. R.D. Clark et al. Consensus scoring for ligand/protein interactions J. Mol. Graphics Mod. 20 2002 281-295
27. B. Kramer et al. Evaluation of the FlexX incremental construction algorithm for protein-ligand docking Proteins 37 1999 228-241
28. H. Gohlke et al. Knowledge-based scoring function to predict protein-ligand interactions J. Mol. Biol. 295 2000 337-356
29. C.A. Sotriffer et al. Docking into knowledge-based potential fields: A comparative evaluation of DrugScore J. Med. Chem. 45 2002 1967-1970
30. J.W.M. Nissink et al. A new test set for validating predictions of protein-ligand interactions Proteins 49 2002 457-471
31. R. Wang et al. Comparative evaluation of 11 scoring functions for molecular docking J. Med. Chem. 46 2003 2287-2303
32. R. Smith et al. Analysis and optimization of structure-based virtual screening protocols (3). New methods and old problems in scoring function design J. Mol. Graphics Mod. 22 2003 41-53
33. M. Kontoyanni et al. Evaluation of docking performance: Comparative data on docking algorithms J. Med. Chem. 47 2004 558-565
34. P. Ferrara et al. Assessing scoring functions for protein-ligand interactions J. Med. Chem. 47 2004 3032-3047
35. C. Bissantz et al. Protein-based virtual screening of chemical databases 1. Evaluation of different docking/scoring combinations J. Med. Chem. 43 2000 4759-4767
36. M. Stahl M. Rarey Detailed analysis of scoring functions for virtual screening J. Med. Chem. 44 2001 1035-1042
37. T. Schulz-Gasch M. Stahl Binding site characteristics in structure-based virtual screening: Evaluation of current docking tools J. Mol. Model 9 2003 47-57
38. T.A. Halgren et al. Glide: A new approach for rapid, accurate docking and scoring 2. Enrichment factors in database screening J. Med. Chem. 47 2004 1750-1759
39. J.L. Jenkins et al. Virtual screening to enrich hit lists from high-throughput screening: A case study on small-molecule inhibitors of angiogenin Proteins 50 2003 81-93
40. A.E. Klon et al. Finding more needles in the haystack: A simple and efficient method for improving high-throughput screening J. Med. Chem. 47 2004 2743-2749
41. Y. Pan et al. Consideration of molecular weight during compound selection in virtual target-based database screening J. Chem. Inf. Comput. Sci. 43 2003 267-272
42. M.L. Verdonket al. Virtual screening using protein-ligand docking: avoiding artificial enrichment J. Chem. Inf. Comput. Sci. 44 2004 793-806
43. D. Fattori Molecular recognition: The fragment approach to lead generation Drug Discov. Today 9 2004 229-238
44. M.K. Holloway et al. A priori prediction of activity for HIV-protease inhibitors employing energy minimization in the active site J. Med. Chem. 38 1995 305-317
45. D.A. Pearlman P.S. Charifson Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system J. Med. Chem. 44 2001 3417-3423
46. D.K. Gehlhaar et al. Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: Conformationally flexible docking by evolutionary programming Chem. Biol. 2 1995 317-324
47. P.S. Charifson et al. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins J. Med. Chem. 42 1999 5100-5109
48. R. Wang S. Wang How does consensus scoring work for virtual library screening? An idealized computer experiment J. Chem. Inf. Comput. Sci. 41 2001 1422-1426
49. M. Feher et al. BHB: A simple knowledge-based scoring function to improve the eficiency of database screening J. Chem. Inf. Comput. Sci. 43 2003 1316-1327
50. G.E. Terp et al. A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand complexes J. Med. Chem. 41 2001 2333-2343
51. M. Jacobsson et al. Improving structure-based virtual screening by multivariate analysis of scoring data J. Med. Chem. 46 2003 5781-5789
52. G.P.A. Vigers J.P. Rizzi Multiple active site corrections for docking and virtual screening J. Med. Chem. 47 2004 80-89
53. A. Laederach P.J. Reilly Specific empirical free energy function for automated docking of carbohydrates to proteins J. Comput. Chem. 24 2002 1748-1757
54. H. Gohlke G. Klebe DrugScore meets CoMFA: Adaptation of fields for molecular comparison (AFMoC) or how to taylor knowledge-based pair potentials to a particular protein J. Med. Chem. 45 2002 4153-4170
55. W. Deng et al. Predicting protein-ligand binding affinities using novel geometric descriptors and machine-learning methods J. Chem. Inf. Comput. Sci. 2004 699-703
56. X. Fradera et al. Similarity-driven flexible ligand docking Proteins 40 2000 623-636
57. G. Wu M. Vieth SDOCKER: A method utilizing existing x-ray structures to improve docking accuracy J. Med. Chem. 47 2004 3142-3148
58. S.A. Hindle et al. Flexible docking under pharmacophore constraints J. Comput. Aided Mol. Des. 16 2002 129-149
59. P.S. Charifson P.W. Walters Filtering databases and chemical libraries J. Comput. Aided Mol. Des. 16 2002 311-323
60. P.D. Lyne et al. Identification of compounds with nanomolar binding activity for checkpoint kinase-1 using knowledge-based virtual screening J. Med. Chem. 47 1968 1962-1968
61. S.D. Pickett et al. Discovery of novel low molecular weight inhibitors of IMPDH via virtual needle screening Bioorg. Med. Chem. Lett. 13 2003 1691-1694
62. M. Schapira et al. Nuclear hormone receptor targeted virtual screening J. Med. Chem. 46 2003 3045-3059
63. M. Schapira et al. Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking Proc. Natl. Acad. Sci. USA 100 2003 7354-7359
64. M. Otyepka et al. Docking-based development of purine-like inhibitors of cyclin-depentent kinase-2 J. Med. Chem. 43 2000 2506-2513
65. E. Vangrevelinghe et al. Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking J. Med. Chem. 46 2003 2656-2662
66. I. Muegge I.J. Enyedy Virtual screening for kinase targets Curr. Med. Chem. 11 2004 693-707
67. S.L. McGovern B.K. Shoichet Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes J. Med. Chem. 46 2003 2895-2907
68. J.A. Erickson et al. Lessons in molecular recognition: The effects of ligand and protein flexibility on molecular docking accuracy J. Med. Chem. 47 2004 45-55
69. C. Oshiro et al. Performance of 3D-database molecular docking studies into homology models J. Med. Chem. 47 2004 764-767
70. B.Q. Wei et al. Testing a flexible-receptor docking algorithm in a model binding site J. Mol. Biol. 337 2004 1161-1182
71. M. Stahl H.-J. Boehm Development of filter functions for protein-ligand docking J. Mol. Graphics Mod. 16 1998 121-132
72. D.H. Williams et al. Ligand binding energy and catalytic efficiency from improved packing within receptors and enzymes J. Mol. Biol. 329 2003 389-399
73. I.J. Bruno et al. IsoStar: A library of information about nonbonded interactions J. Comput. Aided Mol. Des. 11 1997 525-537
74. J.W.M. Nissink et al. Simple knowledge-based descriptors to predict protein-ligand interactions. Methodology and validation J. Comput. Aided Mol. Des. 14 2000 787-803
75. A. Bergner et al. Use of Relibase for retrieving complex three-dimensional interaction patterns including crystallographic packing effects Biopolymers 61 2002 99-110
76. T.N. Doman et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B J. Med. Chem. 45 2002 2213-2221
77. B.A. Grzybowski et al. Combinatorial computational method gives new picomolar ligands for a known enzyme Proc. Natl. Acad. Sci. USA 99 2002 1270-1273
78. R.A. Powers et al. Structure-based discovery of a novel, noncovalent inhibitor of AmpC beta-lactamase Structure 10 2002 1013-1023
79. R. Brenk et al. Virtual Screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis J. Med. Chem. 46 2003 1133-1143
80. P.C. Wyss et al. Novel dihydrofolate reductase inhibitors. Structure-based vs. diversity-based library design and high-throughput synthesis and screening J. Med. Chem. 46 2003 2304-2312
81. Keserü, G.M., and Kolossváry, I. (1999) Molecular Mechanics and Conformational Analysis in Drug Design, Blackwell Science
82. I. Muegge Y.C. Martin A general and fast scoring function for protein-ligand interactions: A simplified potential approach J. Med. Chem. 42 1999 791-804
83. M.R. McGann et al. Gaussian docking functions Biopolymers 2002 76-90
84. D.M. Lorber B.K. Shoichet Flexible ligand docking using conformational ensembles Protein Sci. 7 1998 938-950
85. M. Rarey et al. Docking of hydrophobic ligands with interaction-based matching algorithms Bioinformatics 15 1999 243-250
86. M.I. Zavodszky et al. Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening J. Comput. Aided Mol. Des. 16 2002 883-902
87. G.M. Morris et al. Distributed automated docking of flexible ligands to proteins: Parallel applications of Autodock 2.4 J. Comput. Aided Mol. Des. 10 1996 293-304
88. C. McMartin R.S. Bohacek QXP: Powerful, rapid computer algorithms for structure-based drug design J. Comput. Aided Mol. Des. 11 1997 333-344
89. R. Abagyan et al. ICM - a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation J. Comput. Chem. 15 1994 488-506