M-score: A knowledge-based potential scoring function accounting for protein atom mobility

Journal of Medicinal Chemistry

Volumn 49, Issue 20, 2006, Pages 5903-5911

  Yang, Chao Yie ^a   Wang, Renxiao ^a   Wang, Shaomeng ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LIGAND; PROTEIN;

ARTICLE; BINDING AFFINITY; COMPLEX FORMATION; CORRELATION ANALYSIS; CRYSTAL STRUCTURE; LIGAND BINDING; M SCORE; NORMAL DISTRIBUTION; PROTEIN ATOM MOBILITY; PROTEIN BINDING; PROTEIN DETERMINATION; PROTEIN FAMILY; PROTEIN STRUCTURE; SCORING SYSTEM; VALIDATION PROCESS;

ALGORITHMS; KNOWLEDGE BASES; LIGANDS; MOLECULAR STRUCTURE; PROBABILITY; PROTEIN BINDING; PROTEINS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 33749239216     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm050043w     Document Type: Article

References (24)

1. Kitchen, D. B.; Decernez, H.; Furr, J. R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Rev. Drug Discovery 2004, 3, 935-949.
2. Tame, J. R. H. Scoring Functions: A view from the bench. J. Comput.-Aided Mol. Des. 1999, 13, 99-108.
3. 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. 2004, 15, 411-428.
4. 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.
5. 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.
6. 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.
7. Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: a simplied potential approach. J. Med. Chem. 1999, 42, 791-804.
8. 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.
9. 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, 1165-1176.
10. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactgions. J. Mol. Biol. 2000, 295, 337-356.
11. Ishchenko, A. V.; Shakhnovich, E. I. Small Molecule Growth 2001 (SMoG2001): An improved knowledge-based scoring function for protein-ligand interactions. J. Med. Chem. 2000, 45, 2770-2780.
12. DeWitte, R. S.; Shakhnovich, E. I. SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J. Am. Chem. Soc. 1996, 118, 11733-11744.
13. Zhou, H.; Zhou, Y. Distance-sealed, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Prot. Sci. 2002, 11, 2714-2726.
14. 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.
15. Wang, R. Fang, X.; Lu, Y.; Yang, C.-Y.; Wang, S. The PDBbind Database: Methodologies and Updates. J. Med. Chem. 2005, 48, 4111-4119.
16. 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.
17. Rodes, G. Crystallography made crystal clear, 2nd ed.; Academic Press: New York, 2000.
18. Coppens, P. X-ray charge densities and chemical bonding; Oxford University Press: New York, 1997; pp 28-29.
19. Tripos, Inc., St. Louis, MO 63144.
20. Abramowitz, M.; Stegun, I. A., Eds. Handbook of mathematical functions; National Bureau of Standards; Dover: New York, 1974.
21. Acton, F. S.; Watkins, W. Numerical methods that work; The Mathematical Association of America: Washington, DC, 1997.
22. Ferrari, A. M.; Wei, B. Q.; Costantino, L.; Shoichet, B. K. Soft docking and multiple receptor conformations in virtual screening. J. Med. Chem. 2004, 47, 5076-5084.
23. Stahl, M.; Rarey, M. Detailed analysis of scoring functions for virtual screening. J. Med. Chem. 2001, 44, 1035-1042.
24. Wang, R.; Lu, Y.; Fang, X.; Wang, S. An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J. Chem. Inf. Comput. Sci. 2004, 44, 2114-2125.