Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy

Journal of Medicinal Chemistry

Volumn 47, Issue 7, 2004, Pages 1739-1749

  Friesner, Richard A ^a   Banks, Jay L ^b   Murphy, Robert B ^b   Halgren, Thomas A ^b   Klicic, Jasna J ^b,d   Mainz, Daniel T ^b   Repasky, Matthew P ^b   Knoll, Eric H ^a   Shelley, Mee ^b   Perry, Jason K ^b   Shaw, David E ^b,c   Francis, Perry ^b   Shenkin, Peter S ^b  

^a Columbia University ^*  (United States)

^b SCHRÖDINGER INC   (United States)

^c D E SHAW RESEARCH   (United States)

^d Serono International SA   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

LIGAND; PROTEIN;

ACCURACY; ANALYTIC METHOD; ARTICLE; COMPUTER PROGRAM; ENERGY; MONTE CARLO METHOD; PROTEIN CONFORMATION;

BINDING SITES; DRUG DESIGN; LIGANDS; MODELS, MOLECULAR; MOLECULAR CONFORMATION; MOLECULAR STRUCTURE; MONTE CARLO METHOD; PROTEIN CONFORMATION; PROTEINS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; THERMODYNAMICS; THYMIDINE KINASE;

EID: 12144289984     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm0306430     Document Type: Article

References (37)

1. Jones, G.; Wilett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a generic algorithm and an empirical binding free energy function. J. Mol. Biol. 1997, 267, 727-748.
2. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A. A fast flexible docking method using an incremental construction algorithm. Chem. Biol. 1996, 261, 470-489.
3. Ewing, T. J. A.; Kuntz, I. D. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem. 1997, 18, 1175-1189. Ewing, T. J. A.; Makino, S.; Skillman, 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.
4. Ewing, T. J. A.; Kuntz, I. D. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem. 1997, 18, 1175-1189. Ewing, T. J. A.; Makino, S.; Skillman, 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.
5. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM - a new method for protein modeling and design. Application to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 1994, 15, 488-506. Abagyan, R.; Totrov, M. High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 2001, 5, 375-382.
6. Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM - a new method for protein modeling and design. Application to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 1994, 15, 488-506. Abagyan, R.; Totrov, M. High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 2001, 5, 375-382.
7. Welch, W.; Ruppert, J.; Jain, A. N. Hammerhead: fast, fully automated docking of ligands to protein binding sites. Chem. Biol. 1996, 3, 449-462.
8. McMartin, C.; Bohacek, R. QXP: powerful, rapid computer algorithms for structure-based drug design. J. Comput.-Aided Mol. Des. 1997, 11, 333-344.
9. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. Automated docking using a Lamarkian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 1998, 19, 1639-1662.
10. Murray, C. W.; Baxter, C. A. Frenkel, A. D. The sensitivity of the results of molecular docking to induced fit effects: Application to thrombin, thermolysin, and neuraminidase. J. Comput.-Aided. Mol. Des. 1999, 13, 547-562.
11. McGann, M.; Almond, H.; Nicholls, A.; Grant, J. A.; Brown, F. Gaussian docking functions. Biopolymers 2003, 68, 76-90.
12. Venkatachalam, C. M.; Jiang, X.; Oldfield, T.; Waldman, M. J. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graphics Modell. 2003, 21, 289-307.
13. Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus scoring: A method of obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 1999, 42, 5100-5109.
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. Stahl, M.; Rarey, M. Detailed analysis of scoring functions for virtual screening. J. Med. Chem. 2001, 44, 1035-1042.
16. Schrödinger, L.L.C., New York. The Glide 2.5 calculations used FirstDiscovery, version 2.5021, which was released in June 2003.
17. The results for the GOLD test set are available at http://www.ccdc.cam.ac.uk/prods/gold/rms_tab.html.
18. 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.
19. Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996, 118, 11225-11236.
20. Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee, R. P. 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. 1997, 11, 425-445.
21. Murphy, R. B.; Friesner, R. A.; Halgren, T. A. Unpublished research.
22. Babine, R. E.; Bender, S. L. Molecular recognition of protein-ligand complexes: Applications to drug design. Chem. Rev. 1997, 97, 1359-1472.
23. Whittaker, M.; Floyd, C. D.; Brown, P.; Gearing, A. J. H. Design and therapeutic application of matrix metalloproteinase inhibitors. Chem. Rev. 1999, 99, 2735-2776.
24. Bell, I. M.; Gallicchio, S. N.; Abrams, M.; Beese, L. S.; Beshore, D. C. ; Bhimnathwala, H.; Bogusky, M. J.; Buser, C. A.; Culberson, J. C.; Davide, J.; Ellis-Hutchings, M.; Fernandes, C.; Gibbs, J. B.; Graham, S. L.; Hamilton, K. A.; Hartman, G. D.; Heimbrook, D. C.; Homnick, C. F.; Huber, H. E.; Huff, J. R. ; Kassahun, K.; et al. 3-Aminopyrrolidinone farnesyltransferase inhibitors: design of macrocyclic compounds with improved pharmacokinetics and excellent cell potency. J. Med. Chem. 2002, 45, 2388-2409.
25. The reduction in the vdW radii is needed to roughly preserve hydrogen-bonding distances, which reflect a balance between attractive electrostatic and repulsive vdW forces.
26. Research Collaboratory for Structural Bioinformatics, Rutgers University, New Brunswick, NJ. http://www.rcsb.org.
27. Halgren, T. A. MMFF VI. MMFF94s option for energy minimization studies. J. Comput. Chem. 1999, 20, 720-729.
28. For 1cps, we used the native ligand structure because the ligand contains functionality that cannot be treated by MMFF94s. The remaining four cases have larger rings that Glide's conformation generator cannot handle and for which the conformational search yielded a different ring conformation. For 1hbv and 1pro, the MMFF94s-optimized native ligand geometry was used instead, while for 1d8f and 2hct, conformational search was employed but the ring conformation was not sampled.
29. Gasteiger, J.; Rudolph, C.; Sadowski, J. Automated generation of 3D atomic coordinates for organic molecules. Tetrahedron Comput. Methodol. 1990, 3, 537-547.
30. Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FlexX incremental construction algorithm for protein-ligand docking. Proteins 1999, 37, 228-241.
31. The results for the FlexX test set are available at http://cartan.gmd.de/flexx/html/flexx-eval.html.
32. Jain, A. N. Surflex: fully automatic flexible molecular docking using a molecular-similarity-based search engine. J. Med. Chem. 2003, 46, 499-511.
33. CMC database is available from MDL Information Systems, Inc., San Leanrdo, CA.
34. Oprea, T. I. Property distribution of drug-related chemical databases. J. Comput.-Aided Mol. Des. 1999, 14, 251-264. See Figure 2 in this reference.
35. This distance is defined as the distance from a heteroatom that bears or shares a positive or negative formal charge in the ionized protein residue to the nearest ligand atom.
36. Impact is the computational engine of Schrödinger's FirstDiscovery suite. Impact was developed in the laboratories of Prof. Ronald Levy (Rutgers University). It and the FirstDiscovery suite are available from Schrödinger, L.L.C., New York.
37. MacroModel (formerly known as BatchMin) is a general-purpose molecular mechanics program available from Schrödinger, L.L.C., New York. MacroModel was developed in the laboratories of Prof. Clark Still (Columbia University).