An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes

Journal of Chemical Information and Computer Sciences

Volumn 44, Issue 6, 2004, Pages 2114-2125

  Wang, Renxiao ^a   Lu, Yipin ^a   Fang, Xueliang ^a   Wang, Shaomeng ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMATIONS; CORRELATION METHODS; CRYSTAL STRUCTURE; DATABASE SYSTEMS; FUNCTIONS; SET THEORY;

CONFORMATIONAL SAMPLING; DECOYS; LIGAND COMPLEXES; MOLECULAR DOCKING;

PROTEINS;

LIGAND; PROTEIN;

ALGORITHM; ARTICLE; CHEMISTRY; COMPUTER SIMULATION; DRUG DESIGN; PROTEIN BINDING; PROTEIN CONFORMATION; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; COMPUTER SIMULATION; DRUG DESIGN; LIGANDS; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEINS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 10044294023     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci049733j     Document Type: Article

References (47)

1. Böhm, H. J.; Stahl, M. The use of scoring functions in drug discovery applications. In Reviews in Computational Chemistry; Lipkowitz, K. B., Boyd, D. B., Eds.; Wiley-VCH Inc.: New Jersey, 2002; Vol. 18, pp 41-88.
2. Böhm, H. J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput.-Aided Mol. Des. 1994, 8, 243-256.
3. Jain, A. N. Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities. J. Comput.-Aided Mol. Des. 1996, 10, 427-440.
4. Head, R. D.; Smythe, M. L.; Oprea, T. I.; Waller, C. L.; Green, S. M.; Marshall, G. R. VALIDATE: A new method for the receptor-based prediction of binding affinities of novel ligands J. Am. Chem. Soc. 1996, 118, 3959-3969.
5. 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.
6. Murray, C. W.; Auton, T. R.; Eldridge, M. D. Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. J. Comput.-Aided Mol. Des. 1998, 12, 503-519.
7. Böhm, 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, 309-323.
8. Wang, R.; Gao, Y.; Lai, L. SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex. J. Mol. Model. 1998, 4, 379-394.
9. 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.
10. Muegge, I. A knowledge-based scoring function for protein-ligand interactions: Probing the reference state. Perspect. Drug Discovery Des. 2000, 20, 99-114.
11. Muegge, I. Effect of ligand volume correction on PMF scoring. J. Comput. Chem. 2001, 22, 418-425.
12. Mitchell, J. B. O.; Laskowski, R. A.; Alex, A.; Thornton, J. M. BLEEP: potential of mean force describing protein-ligand interactions. I. Generating potential. J. Comput. Chem. 1999, 20, 1165-1176.
13. Mitchell, J. B. O.; Laskowski, R. A.; Alex, A.; Forster, M. J.; Thornton, J. M. BLEEP: potential of mean force describing protein-ligand interactions. II. Calculation of binding energies and comparison with experimental data. J. Comput. Chem. 1999, 20, 1177-1185.
14. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol. 2000, 295, 337-356.
15. Gohlke, H.; Hendlich, M.; Klebe, G. Predicting binding modes, binding affinities and "hot spots" for Protein-ligand complexes using a knowledge-based scoring function. Perspect. Drug Discovery Des. 2000, 20, 115-144.
16. Ishchenko, A. V.; Shakhnovich, E. I. Small molecule growth 2001 (SMoG2001): An improved knowledge-based scoring function for protein-ligand interactions. J. Med. Chem. 2002, 45, 2770-2780.
17. Cozzini, P.; Fornabaio, M.; Marabotti, A.; Abraham, D. J.; Kellogg, G. E.; Mozzarelli, A. Simple, Intuitive Calculations of Free Energy of Binding for Protein-Ligand Complexes. 1. Models without Explicit Constrained Water. J. Med. Chem. 2002, 45, 2469-2483.
18. Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput.-Aided Mol. Des. 2002, 16, 11-26.
19. Verkhivker, G.; Appell, K.; Freer, S. T.; Villafranca, J. E. Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. Protein Eng. 1995, 8, 677-691.
20. Verkhivker, G. M.; Bouzida, D.; Gehlhaar, D. K.; Rejto, P. A.; Arthurs, S.; Rose, P. W. Deciphering common failures in molecular docking of ligand-protein complexes. J. Comput.-Aided Mol. Des. 2000, 14, 731-751.
21. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 267, 470-489.
22. 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, 727-748.
23. Jones, G.; Willett, P.; Glen, R. C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 1995, 245, 43-53.
24. 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.
25. 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.
26. Stahl, M.; Rarey, M. Detailed analysis of scoring functions for virtual screening. J. Med. Chem. 2001, 44, 1035-1042.
27. Terp, G. E.; Johansen, B. N.; Christensen, I. T.; Jorgensen, F. S. A new concept for multidimensional selection of ligand conformations (MultiSelect) and Multidimensional scoring (MultiScore) of protein-ligand binding affinities. J. Med. Chem. 2001, 44, 2333-2343.
28. Perez, C.; Ortiz, A. R. Evaluation of Docking Functions for Protein-Ligand Docking. J. Med. Chem. 2001, 44, 3768-3785.
29. Paul, N.; Rognan, D. ConsDock: A new program for the consensus analysis of protein-ligand interactions. Proteins: Struct., Funct., Genet. 2002, 47, 521-533.
30. Wilton, D.; Willett, P. Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs. J. Chem. Inf. Comput. Sci. 2003, 43, 469-474.
31. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46, 2287-2303.
32. Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L. Assessing scoring functions for protein-ligand interactions. J. Med. Chem. 2004, 47, 3032-3047.
33. Röche, O.; Kiyama, R.; Brooks, C. L., Ill Ligand-Protein DataBase: Linking Protein-Ligand Complex Structures to Binding Data. J. Med. Chem. 2001, 44, 3592-3598. http://lpdb.scripps.edu/.
34. Puvanendrampillai, D.; Mitchell, J. B. O. Protein Ligand Database (PLD): additional understanding of the nature and specificity of protein-ligand complexes. Bioinformatics 2003, 19, 1856-1857. http://www-mitchell.ch.cam.ac.uk/ pld/.
35. Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures. J. Med. Chem. 2004, 47, 2977-2980. http://www.pdbbind.org/.
36. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, I. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235-242. http://www.rcsb.org/pdb/.
37. The SYBYL software, version 6.9, Tripos Inc., http://www.tripos.com/.
38. The Cerius2 software, version 4.6, Accelrys Inc., http://www.accelrys. com/.
39. Huang, C. C.; Lesburg, C. A.; Kiefer, L. L.; Fierke, C. A.; Christianson, D. W. Reversal of the Hydrogen Bond to Zinc Ligand Histidine-119 Dramatically Diminishes Catalysis and Enhances Metal Equilibration Kinetics in Carbonic Anhydrase II. Biochemistry 1996, 35, 3439-3446.
40. Babine, R. E.; Bender, S. L. Molecular Recognition of Protein-Ligand Complexes: applications to Drug Design. Chem. Rev. 1997, 97, 1359-1472, and the references therein.
41. Kollman, P. Free energy calculations: Application to chemical and biological phenomena. Chem. Rev. 1993, 7, 2395-2417, and the references therein.
42. Aqvist, J.; Medina, C.; Samuelsson, J. E. New method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994, 7, 385-391.
43. Carlson, H. A.; Jorgensen, W. L. Extended linear response method for determining free energies of hydration. J. Phys. Chem. 1995, 99, 10667-10673.
44. Massova, I.; Kollman, P. A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discovery Des. 2000, 18, 113-135.
45. Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, W. P. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem. 1999, 42, 5100-5109.
46. Wang, R.; Wang, S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J. Chem. Inf. Comput. Sci. 2001, 41, 1422-1426.
47. 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, 75, 411-428.