Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies

Journal of Chemical Information and Modeling

Volumn 54, Issue 10, 2014, Pages 2980-2986

  Tuccinardi, Tiziano ^a   Poli, Giulio ^a   Romboli, Veronica ^a   Giordano, Antonio ^b   Martinelli, Adriano ^a  

^a UNIVERSITY OF PISA   (Italy)

^b TEMPLE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; LIGANDS; MOLECULAR MODELING;

BINDING MODES; CROSSDOCKING; DOCKING PROCEDURES; DOCKING PROGRAMS; LIGAND BINDING; MOLECULAR DOCKING; POSE PREDICTIONS; VIRTUAL SCREENING;

FORECASTING;

ASPARTIC PROTEINASE; BACE1 PROTEIN, HUMAN; CDK2 PROTEIN, HUMAN; CYCLIN DEPENDENT KINASE 2; ENZYME INHIBITOR; ESTROGEN RECEPTOR ALPHA; LIGAND; MOLECULAR LIBRARY; PROTEIN BINDING; SECRETASE;

ANTAGONISTS AND INHIBITORS; BINDING SITE; CHEMISTRY; COMPUTER INTERFACE; CONFORMATION; DRUG DEVELOPMENT; HUMAN; HYDROGEN BOND; MOLECULAR DOCKING; MOLECULAR LIBRARY; PROCEDURES; STRUCTURE ACTIVITY RELATION; THERMODYNAMICS;

AMYLOID PRECURSOR PROTEIN SECRETASES; ASPARTIC ACID ENDOPEPTIDASES; BINDING SITES; CYCLIN-DEPENDENT KINASE 2; DRUG DISCOVERY; ENZYME INHIBITORS; ESTROGEN RECEPTOR ALPHA; HUMANS; HYDROGEN BONDING; LIGANDS; MOLECULAR CONFORMATION; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; SMALL MOLECULE LIBRARIES; STRUCTURE-ACTIVITY RELATIONSHIP; THERMODYNAMICS; USER-COMPUTER INTERFACE;

EID: 84908231422     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci500424n     Document Type: Article

References (24)

1. Tuccinardi, T. Docking-based virtual screening: recent developments Comb. Chem. High Throughput Screen 2009, 12, 303-314
2. Tuccinardi, T.; Botta, M.; Giordano, A.; Martinelli, A. Protein kinases: docking and homology modeling reliability J. Chem. Inf. Model. 2010, 50, 1432-1441
3. Liebeschuetz, J. W.; Cole, J. C.; Korb, O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test J. Comput. Aided Mol. Des. 2012, 26, 737-748
4. Mukherjee, S.; Balius, T. E.; Rizzo, R. C. Docking validation resources: protein family and ligand flexibility experiments J. Chem. Inf. Model. 2010, 50, 1986-2000
5. Houston, D. R.; Walkinshaw, M. D. Consensus docking: improving the reliability of docking in a virtual screening context J. Chem. Inf. Model. 2013, 53, 384-390
6. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking J. Med. Chem. 2006, 49, 6789-6801
7. 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
8. Babel, version 2.2; OpenEye Scientific Software: Santa Fe, NM, USA, 2010.
9. Szybki, version 1.3; OpenEye Scientific Software: Santa Fe, NM, USA, 2010.
10. Macromodel, version 9.7; Schrdinger Inc: Portland, OR, 2009.
11. Sanner, M. F. Python: a programming language for software integration and development J. Mol. Graph. Model. 1999, 17, 57-61
12. 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, 2785-2791
13. DOCK, version 6.5; Molecular Design Institute: University of California: San Francisco, CA, 1998.
14. Case, D. A.; Darden, T. A.; Cheatam, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B.; Wang, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang; J.; Hsieh, M.-J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A. AMBER, version 11; University of California: San Francisco, CA, 2010.
15. FRED, version 3.0.0; OpenEye Scientific Software: Santa Fe, NM, USA, 2013.
16. OMEGA, version 2.4.6; OpenEye Scientific Software: Santa Fe, NM, USA, 2013.
17. Glide, version 5.0; Schrdinger Inc: Portland, OR, 2009.
18. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved protein-ligand docking using GOLD Proteins 2003, 52, 609-623
19. 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
20. Poli, G.; Tuccinardi, T.; Rizzolio, F.; Caligiuri, I.; Botta, L.; Granchi, C.; Ortore, G.; Minutolo, F.; Schenone, S.; Martinelli, A. Identification of New Fyn Kinase Inhibitors Using a FLAP-Based Approach J. Chem. Inf. Model. 2013, 53, 2538-2547
21. Cross, S.; Baroni, M.; Goracci, L.; Cruciani, G. GRID-based three-dimensional pharmacophores I: FLAPpharm, a novel approach for pharmacophore elucidation J. Chem. Inf. Model. 2012, 52, 2587-2598
22. Baroni, M.; Cruciani, G.; Sciabola, S.; Perruccio, F.; Mason, J. S. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and application J. Chem. Inf. Model. 2007, 47, 279-294
23. Arciniega, M.; Lange, O. F. Improvement of virtual screening results by docking data feature analysis J. Chem. Inf. Model. 2014, 54, 1401-1411
24. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules J. Med. Chem. 1985, 28, 849-857-857