Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction

Journal of Chemical Information and Modeling

Volumn 53, Issue 11, 2013, Pages 3097-3112

  Brylinski, Michal ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; BINDING SITES; COMPLEXATION; CORRELATION METHODS; DIGITAL LIBRARIES; FORECASTING; PROTEINS;

ACADEMIC COMMUNITY; BINDING AFFINITIES; INTERMOLECULAR INTERACTIONS; PEARSON CORRELATION COEFFICIENTS; PROTEIN RECEPTORS; PROTEIN-LIGAND COMPLEXES; PROTEIN-LIGAND INTERACTIONS; STRUCTURAL IMPERFECTIONS;

LIGANDS;

BLOOD CLOTTING FACTOR 10A; CDK2 PROTEIN, HUMAN; CYCLIN DEPENDENT KINASE 2; LIGAND; PROTEIN KINASE INHIBITOR; PROTEINASE INHIBITOR;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BINDING SITE; CHEMISTRY; COMPUTER PROGRAM; DRUG DESIGN; HUMAN; METHODOLOGY; MOLECULAR DOCKING; PROTEIN BINDING; PROTEIN DATABASE; PROTEIN DOMAIN; PROTEIN SECONDARY STRUCTURE; QUALITY CONTROL; RECEIVER OPERATING CHARACTERISTIC; THERMODYNAMICS; X RAY CRYSTALLOGRAPHY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BENCHMARKING; BINDING SITES; CRYSTALLOGRAPHY, X-RAY; CYCLIN-DEPENDENT KINASE 2; DATABASES, PROTEIN; DRUG DESIGN; FACTOR XA; HUMANS; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEASE INHIBITORS; PROTEIN BINDING; PROTEIN INTERACTION DOMAINS AND MOTIFS; PROTEIN KINASE INHIBITORS; PROTEIN STRUCTURE, SECONDARY; RESEARCH DESIGN; ROC CURVE; SOFTWARE; THERMODYNAMICS;

EID: 84888602591     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400510e     Document Type: Article

References (83)

1. Irwin, J. J.; Shoichet, B. K. ZINC - a free database of commercially available compounds for virtual screening J. Chem. Inf. Model. 2005, 45, 177-82
2. Reddy, A. S.; Pati, S. P.; Kumar, P. P.; Pradeep, H. N.; Sastry, G. N. Virtual screening in drug discovery - a computational perspective Curr. Protein Pept. Sci. 2007, 8, 329-51
3. Jain, A. N. Virtual screening in lead discovery and optimization Curr. Opin. Drug Discovery Dev. 2004, 7, 396-403
4. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation J. Chem. Inf. Model. 2010, 50, 205-16
5. Ripphausen, P.; Nisius, B.; Bajorath, J. State-of-the-art in ligand-based virtual screening Drug Discovery Today 2011, 16, 372-6
6. Xue, L.; Bajorath, J. Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening Comb. Chem. High Throughput Screening 2000, 3, 363-72
7. Durant, J. L.; Leland, B. A.; Henry, D. R.; Nourse, J. G. Reoptimization of MDL keys for use in drug discovery J. Chem. Inf. Comput. Sci. 2002, 42, 1273-80
8. Willett, P. Similarity-based virtual screening using 2D fingerprints Drug Discovery Today 2006, 11, 1046-53
9. Seal, A.; Yogeeswari, P.; Sriram, D.; Consortium, O.; Wild, D. J. Enhanced ranking of PknB Inhibitors using data fusion methods J. Cheminf. 2013, 5, 2
10. Plewczynski, D.; Spieser, S. A.; Koch, U. Performance of machine learning methods for ligand-based virtual screening Comb. Chem. High Throughput Screening 2009, 12, 358-68
11. Xue, L.; Godden, J. W.; Stahura, F. L.; Bajorath, J. Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys J. Chem. Inf. Comput. Sci. 2003, 43, 1218-25
12. Verma, J.; Khedkar, V. M.; Coutinho, E. C. 3D-QSAR in drug design - a review Curr. Top. Med. Chem. 2010, 10, 95-115
13. Free, S. M., Jr.; Wilson, J. W. A mathematical contribution to structure-activity studies J. Med. Chem. 1964, 7, 395-9
14. Martins, J. P.; Barbosa, E. G.; Pasqualoto, K. F.; Ferreira, M. M. LQTA-QSAR: a new 4D-QSAR methodology J. Chem. Inf. Model. 2009, 49, 1428-36
15. Vedani, A.; Dobler, M. 5D-QSAR: the key for simulating induced fit? J. Med. Chem. 2002, 45, 2139-49
16. Vedani, A.; Dobler, M.; Lill, M. A. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor J. Med. Chem. 2005, 48, 3700-3
17. Ebalunode, J. O.; Zheng, W. Molecular shape technologies in drug discovery: methods and applications Curr. Top. Med. Chem. 2010, 10, 669-79
18. Brylinski, M.; Skolnick, J. FINDSITE(LHM): a threading-based approach to ligand homology modeling PLoS Comput. Biol. 2009, 5, e1000405
19. Gschwend, D. A.; Good, A. C.; Kuntz, I. D. Molecular docking towards drug discovery J. Mol. Recognit. 1996, 9, 175-86
20. Dror, R. O.; Pan, A. C.; Arlow, D. H.; Borhani, D. W.; Maragakis, P.; Shan, Y.; Xu, H.; Shaw, D. E. Pathway and mechanism of drug binding to G-protein-coupled receptors Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 13118-23
21. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM-A new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation J. Comput. Chem. 1994, 15, 488-506
22. Brylinski, M.; Skolnick, J. Q-Dock: low-resolution flexible ligand docking with pocket-specific threading restraints J. Comput. Chem. 2008, 29, 1574-1588
23. Goodsell, D. S.; Morris, G. M.; Olson, A. J. Automated docking of flexible ligands: applications of AutoDock J. Mol. Recognit. 1996, 9, 1-5
24. 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, 727-48
25. Ewing, T. J. A.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases J. Comput.-Aided Mol. Des. 2001, 15, 411-428
26. 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, 1739-49
27. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function J. Comput. Chem. 1998, 19, 1639-1662
28. Bohm, H. J. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs J. Comput.-Aided Mol. Des. 1998, 12, 309-23
29. Neudert, G.; Klebe, G. DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes J. Chem. Inf. Model. 2011, 51, 2731-45
30. Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach J. Med. Chem. 1999, 42, 791-804
31. 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, 235-42
32. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking J. Med. Chem. 2003, 46, 2287-303
33. 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
34. Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L., 3rd Assessing scoring functions for protein-ligand interactions J. Med. Chem. 2004, 47, 3032-47
35. 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, 2895-907
36. 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
37. Bordogna, A.; Pandini, A.; Bonati, L. Predicting the accuracy of protein-ligand docking on homology models J. Comput. Chem. 2011, 32, 81-98
38. Kairys, V.; Fernandes, M. X.; Gilson, M. K. Screening drug-like compounds by docking to homology models: a systematic study J. Chem. Inf. Model. 2006, 46, 365-79
39. Oshiro, C.; Bradley, E. K.; Eksterowicz, J.; Evensen, E.; Lamb, M. L.; Lanctot, J. K.; Putta, S.; Stanton, R.; Grootenhuis, P. D. Performance of 3D-database molecular docking studies into homology models J. Med. Chem. 2004, 47, 764-7
40. Bissantz, C.; Bernard, P.; Hibert, M.; Rognan, D. Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets? Proteins 2003, 50, 5-25
41. Sherman, W.; Day, T.; Jacobson, M. P.; Friesner, R. A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects J. Med. Chem. 2006, 49, 534-53
42. Davis, I. W.; Baker, D. RosettaLigand docking with full ligand and receptor flexibility J. Mol. Biol. 2009, 385, 381-92
43. Nissink, J. W.; Murray, C.; Hartshorn, M.; Verdonk, M. L.; Cole, J. C.; Taylor, R. A new test set for validating predictions of protein-ligand interaction Proteins 2002, 49, 457-71
44. Mancera, R. L.; Kallblad, P.; Todorov, N. P. Ligand-protein docking using a quantum stochastic tunneling optimization method J. Comput. Chem. 2004, 25, 858-64
45. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved protein-ligand docking using GOLD Proteins 2003, 52, 609-23
46. 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
47. Kawabata, T. Build-up algorithm for atomic correspondence between chemical structures J. Chem. Inf. Model. 2011, 51, 1775-87
48. Bindewald, E.; Skolnick, J. A scoring function for docking ligands to low-resolution protein structures J. Comput. Chem. 2005, 26, 374-83
49. Daylight Chemical Information Systems, I. Daylight Theory Manual. http://www.daylight.com/dayhtml/doc/theory/index.html (accessed November 5, 2013).
50. Vainio, M. J.; Johnson, M. S. Generating conformer ensembles using a multiobjective genetic algorithm J. Chem. Inf. Model. 2007, 47, 2462-74
51. Rasmussen, M. D.; Deshpande, M. S.; Karypis, G.; Johnson, J.; Crow, J. A.; Retzel, E. F. wCLUTO: a Web-enabled clustering toolkit Plant Physiol. 2003, 133, 510-6
52. Brylinski, M.; Feinstein, W. P. eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands J. Comput.-Aided Mol. Des. 2013, 27, 551-67
53. Brylinski, M.; Skolnick, J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 129-34
54. Brylinski, M.; Lingam, D. eThread: a highly optimized machine learning-based approach to meta-threading and the modeling of protein tertiary structures PLoS One 2012, 7, e50200
55. 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, 455-61
56. 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
57. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility J. Comput. Chem. 2009, 30, 2785-91
58. Kinnings, S. L.; Liu, N.; Tonge, P. J.; Jackson, R. M.; Xie, L.; Bourne, P. E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing J. Chem. Inf. Model. 2011, 51, 408-19
59. Sobolev, V.; Sorokine, A.; Prilusky, J.; Abola, E. E.; Edelman, M. Automated analysis of interatomic contacts in proteins Bioinformatics 1999, 15, 327-32
60. Zhang, C.; Liu, S.; Zhu, Q.; Zhou, Y. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes J. Med. Chem. 2005, 48, 2325-35
61. Sud, M. MayaChemTools. http://www.MayaChemTools.org (accessed November 5, 2013).
62. Chang, C. C.; Lin, C. J. LIBSVM: a library for support vector machines ACM Trans. Intell. Syst. Technol. 2011, 2, 27
63. Fontaine, F.; Pastor, M.; Zamora, I.; Sanz, F. Anchor-GRIND: filling the gap between standard 3D QSAR and the GRid-INdependent descriptors J. Med. Chem. 2005, 48, 2687-94
64. Yoshikawa, K.; Kobayashi, S.; Nakamoto, Y.; Haginoya, N.; Komoriya, S.; Yoshino, T.; Nagata, T.; Mochizuki, A.; Watanabe, K.; Suzuki, M.; Kanno, H.; Ohta, T. Design, synthesis, and SAR of cis-1,2-diaminocyclohexane derivatives as potent factor Xa inhibitors. Part II: exploration of 6-6 fused rings as alternative S1 moieties Bioorg. Med. Chem. 2009, 17, 8221-33
65. Voigt, J. H.; Bienfait, B.; Wang, S.; Nicklaus, M. C. Comparison of the NCI open database with seven large chemical structural databases J. Chem. Inf. Comput. Sci. 2001, 41, 702-12
66. Keselj, V. Naive Bayes Classification. http://search.cpan.org/∼vlado/ (accessed November 5, 2013).
67. Brown, N. R.; Noble, M. E.; Lawrie, A. M.; Morris, M. C.; Tunnah, P.; Divita, G.; Johnson, L. N.; Endicott, J. A. Effects of phosphorylation of threonine 160 on cyclin-dependent kinase 2 structure and activity J. Biol. Chem. 1999, 274, 8746-56
68. Pandit, S. B.; Skolnick, J. Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score BMC Bioinf. 2008, 9, 531
69. Zhang, Y.; Skolnick, J. Scoring function for automated assessment of protein structure template quality Proteins 2004, 57, 702-10
70. Lorber, D. M.; Shoichet, B. K. Flexible ligand docking using conformational ensembles Protein Sci. 1998, 7, 938-50
71. Griewel, A.; Kayser, O.; Schlosser, J.; Rarey, M. Conformational sampling for large-scale virtual screening: accuracy versus ensemble size J. Chem. Inf. Model. 2009, 49, 2303-11
72. Miteva, M. A.; Guyon, F.; Tuffery, P. Frog2: efficient 3D conformation ensemble generator for small compounds Nucleic Acids Res. 2010, 38, W622-7
73. Brozell, S. R.; Mukherjee, S.; Balius, T. E.; Roe, D. R.; Case, D. A.; Rizzo, R. C. Evaluation of DOCK 6 as a pose generation and database enrichment tool J. Comput.-Aided Mol. Des. 2012, 26, 749-73
74. Gobbi, S.; Cavalli, A.; Rampa, A.; Belluti, F.; Piazzi, L.; Paluszcak, A.; Hartmann, R. W.; Recanatini, M.; Bisi, A. Lead optimization providing a series of flavone derivatives as potent nonsteroidal inhibitors of the cytochrome P450 aromatase enzyme J. Med. Chem. 2006, 49, 4777-80
75. Pencheva, T.; Lagorce, D.; Pajeva, I.; Villoutreix, B. O.; Miteva, M. A. AMMOS: automated molecular mechanics optimization tool for in silico screening BMC Bioinf. 2008, 9, 438
76. Kabsch, W. A solution for the best rotation to relate two sets of vectors Acta Crystallogr., Sect. A: Cryst. Phys., Diffr., Theor. Gen. Crystallogr. 1976, 32, 922-3
77. Sali, A.; Potterton, L.; Yuan, F.; van Vlijmen, H.; Karplus, M. Evaluation of comparative protein modeling by MODELLER Proteins 1995, 23, 318-26
78. Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M. C. SWISS-MODEL: an automated protein homology-modeling server Nucleic Acids Res. 2003, 31, 3381-5
79. Jaroszewski, L.; Rychlewski, L.; Zhang, B.; Godzik, A. Fold prediction by a hierarchy of sequence, threading, and modeling methods Protein Sci. 1998, 7, 1431-40
80. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking J. Med. Chem. 2006, 49, 6789-6801
81. Mysinger, M. 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
82. Truchon, J. F.; Bayly, C. I. Evaluating virtual screening methods: good and bad metrics for the ″early recognition″ problem J. Chem. Inf. Model. 2007, 47, 488-508
83. Chung, M. K. Correlation Coefficient. In Encyclopedia of Measurement and Statistics; Salkin, N. J., Ed.; Sage Publications: London, 2007; pp 189-201.