Use of experimental design to optimize docking performance: The case of LiGenDock, the docking module of ligen, a new de novo design program

Journal of Chemical Information and Modeling

Volumn 53, Issue 6, 2013, Pages 1503-1517

  Beato, Claudia ^a   Beccari, Andrea R ^a,b   Cavazzoni, Carlo ^c   Lorenzi, Simone ^b   Costantino, Gabriele ^a  

^a UNIVERSITY OF PARMA   (Italy)

^b Chemistry Section   (Italy)

^c CINECA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING SITES; MOLECULAR MODELING; PHARMACODYNAMICS;

ADJUSTABLE PARAMETERS; DOCKING ALGORITHMS; DOCKING PROGRAMS; OPTIMIZATION PROCEDURES; PHARMACOPHORE MODELS; POSE PREDICTIONS; STRUCTURE-BASED; VIRTUAL SCREENING;

DESIGN OF EXPERIMENTS;

LIGAND; PROTEIN; PROTEIN BINDING;

ALGORITHM; ANIMAL; BINDING SITE; CHEMISTRY; COMPUTER PROGRAM; DRUG DESIGN; HUMAN; METABOLISM; MOLECULAR DOCKING; PROTEIN DATABASE; ARTICLE; PROTEIN BINDING;

ALGORITHMS; ANIMALS; BINDING SITES; DATABASES, PROTEIN; DRUG DESIGN; HUMANS; LIGANDS; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEINS; SOFTWARE;

EID: 84879596822     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400079k     Document Type: Article

References (56)

1. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications Nat. Rev. Drug Discovery 2004, 3, 935-949
2. Jain, A. N. Bias, reporting, and sharing: Computational evaluations of docking methods J. Comput.-Aided Mol. Des. 2008, 22
3. Brooijmans, N.; Kuntz, I. D. Molecular recognition and docking algorithms Annu. Rev. Biophys. Biomol. Struct. 2003, 32, 335-373
4. Dias, R.; de Azevedo, W. F. Molecular docking algorithms Curr. Drug Targets 2008, 9, 1040-1047
5. Halperin, I.; Ma, B. Y.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions Proteins: Struct., Funct., Genet. 2002, 47, 409-443
6. McGann, M. R.; Almond, H. R.; Nicholls, A.; Grant, J. A.; Brown, F. K. Gaussian docking functions Biopolymers 2003, 68, 76-90
7. Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A geometric approach to macromolecule-ligand interactions J. Mol. Biol. 1982, 161
8. Shoichet, B. K.; Kuntz, I. D. Matching chemistry and shape in molecular docking Protein Eng. 1993, 6, 732-733
9. Sousa, S. F.; Fernandes, P. A.; Ramos, M. J. Protein-ligand docking: Current status and future challenges Proteins: Struct., Funct., Bioinf. 2006, 65, 15-26
10. McMartin, C.; Bohacek, R. S. QXP: Powerful, rapid computer algorithms for structure-based drug design J. Comput.-Aided Mol. Des. 1997, 11, 333-344
11. Stouten, P. F. W.; Kroemer, R. T. Core Concepts and Methods: Target Structure-Based Docking and Scoring. In Comprehensive Medicinal Chemistry II; Taylor, J. B.; Triggle, D. J., Eds.; Elsevier: Oxford, UK, 2006; Vol. 4, pp 255-281.
12. Bursulaya, B. D.; Totrov, M.; Abagyan, R.; Brooks, C. L. Comparative study of several algorithms for flexible ligand docking J. Comput.-Aided Mol. Des. 2003, 17, 755-763
13. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm J. Mol. Biol. 1996, 261, 470-489
14. Jain, A. N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine J. Med. Chem. 2003, 46, 499-511
15. Zsoldos, Z.; Reid, D.; Simon, A.; Sadjad, B. S.; Johnson, A. P. eHITS: An innovative approach to the docking and scoring function problems Curr. Protein Pept. Sci. 2006, 7, 421-435
16. 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
17. 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
18. Antes, I.; Merkwirth, C.; Lengauer, T. POEM: Parameter optimization using ensemble methods: Application to target specific scoring functions J. Chem. Inf. Model. 2005, 45, 1291-1302
19. Andersson, C. D.; Thysell, E.; Lindstrom, A.; Bylesjo, M.; Raubacher, F.; Linusson, A. A multivariate approach to investigate docking parameters' effects on docking performance J. Chem. Inf. Model. 2007, 47, 1673-1687
20. Beccari, A.; Cavazzoni, C.; Beato, C.; Costantino, G. LiGen: High performance workflow for chemistry driven de novo design J. Chem. Inf. Model 2013, 10.1021/ci400078g
21. Wang, R.; Gao, Y.; Lai, L. H. LigBuilder: A multi-purpose program for structure-based drug design J. Mol. Model. 2000, 6, 498-516
22. Wang, R.; Lai, L. H.; Wang, S. M. Further development and validation of empirical scoring functions for structure-based binding affinity prediction J. Comput. Aided Mol. Des. 2002, 16, 11-26
23. Nissink, J. W. M.; Murray, C.; Hartshorn, M.; Verdonk, M. L.; Cole, J. C.; Taylor, R. A new test set for validating predictions of protein-ligand interaction Proteins: Struct., Funct., Bioinf. 2002, 49, 457-471
24. Protein Data Bank (PDB). http://www.rcsb.org/pdb/home/home.do (accessed April 20, 2013).
25. Wang, R.; Fang, X. L., Y.; Yang, C.-Y.; Wang, S. The PDBbind database: Methodologies and updates J. Med. Chem. 2005, 48, 4111-4119
26. Wang, R.; Fang, X. L.; Wang, Y.; PDBbind, S. Database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures J. Med. Chem. 2004, 47, 2977-2980
27. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 1997, 23, 3-26
28. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking J. Med. Chem. 2006, 49, 6789-6801
29. Protein Preparation Wizard; Epik version 2.0; Impact version 5.5; Prime version 2.1; Schrödinger, LLC: New York, 2009.
30. LigPrep, version 2.3; Schrödinger, LLC: New York, 2009.
31. ConfGen, version 2.1: Schrödinger, LLC: New York, 2009.
32. Onodera, K.; Satou, K.; Hirota, H. Evaluations of molecular docking programs for virtual screening J. Chem. Inf. Model. 2007, 47, 1609-1618
33. Box, G. E. P.; Hunter, J. S.; Hunter, W. G. Basics: Probability, Parameters and Statistics. In Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2005; pp 17-65.
34. MATLAB and Statistics Toolbox, Release 2010; MathWorks: Natick, MA, 2010.
35. Process Improvement. In NIST/SEMATECH e-Handbook of Statistical Methods; National Institute of Standards and Technology (NIST): Washington, DC, 2012.
36. Lundstedt, T.; Seifert, E.; Abramo, L.; Thelin, B.; Nystrom, A.; Pettersen, J.; Bergman, R. Experimental design and optimization Chemom. Intell. Lab. Syst. 1998, 42, 3-40
37. Box, G. E. P.; Hunter, J. S.; Hunter, W. G. Factorial Designs at Two Levels: Advantages of Experimental Design. Fraction Factorial Designs: Economy in Experimentation. In Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2005; pp 173-279.
38. Bezerra, M. A.; Santelli, R. E.; Oliveira, E. P.; Villar, L. S.; Escaleira, L. A. Response surface methodology (RSM) as a tool for optimization in analytical chemistry Talanta 2008, 76, 965-977
39. Box, G. E. P.; Hunter, J. S.; Hunter, W. G. Modelling Relationships, Sequential Assembly: Basics for Response Surface Methods. In Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2005; pp 437-487.
40. Box, G. E. P.; Wilson, K. B. On the experimental attainment of optimum J. R. Stat. Soc. 1951, 13, 1-45
41. 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
42. Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation of docking performance: Comparative data on docking algorithms J. Med. Chem. 2004, 47, 558-565
43. Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes J. Comput. Chem. 2010, 31, 2109-2125
44. Plewczynski, D.; Lazniewski, M.; Augustyniak, R.; Ginalski, K. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database J. Comput. Chem. 2011, 32, 742-755
45. Jain, A. N.; Nicholls, A. Recommendations for evaluation of computational methods J. Comput. Aided Mol. Des. 2008, 22, 133-139
46. Repasky, M. P.; Murphy, R. B.; Banks, J. L.; Greenwood, J. R.; Tubert-Brohman, I.; Bhat, S.; Friesner, R. A. Docking performance of the Glide program as evaluated on the Astex and DUD datasets: A complete set of Glide SP results and selected results for a new scoring function integrating WaterMap and Glide J. Comput. Aided Mol. Des. 2012, 26, 787-799
47. Triballeau, N.; Acher, F.; Brabet, I.; Pin, J. P.; Bertrand, H. O. Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4 J. Med. Chem. 2005, 48
48. 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
49. Kirkman, T. W. Statistics To Use. http://www.physics.csbsju.edu/stats/ (accessed January 2012).
50. Massey, F. J. The Kolmogorov-Smirnov test for goodness of fit J. Am. Stat. Assoc. 1951, 46, 68-78
51. Trott, O.; Olson, A. J. Software news and update 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
52. 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
53. 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, 749-773
54. Cross, J. B.; Thompson, D. C.; Rai, B. K.; Baber, J. C.; Fan, K. Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: Pose prediction and virtual screening accuracy J. Chem. Inf. Model. 2009, 49, 1455-1474
55. Loewer, M.; Geppert, T.; Schneider, P.; Hoy, B.; Wessler, S.; Schneider, G. Inhibitors of Helicobacter pylori protease HtrA found by 'virtual ligand' screening combat bacterial invasion of epithelia PLoS One 2011, 6, e17986
56. DeLano, W. L. PyMol Molecular Graphic System, version1.2r1; DeLano Scientific: South San Francisco, CA, 2009.