Optimizing the signal-to-noise ratio of scoring functions for protein-ligand docking

Journal of Chemical Information and Modeling

Volumn 48, Issue 3, 2008, Pages 602-612

  Seifert, Markus H J ^a  

^a 4SC AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DOCKING; FREE ENERGY; LIGANDS; SIGNAL TO NOISE RATIO;

DOCKING ALGORITHMS;

PROTEINS;

LIGAND; PROTEIN;

ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; METABOLISM;

LIGANDS; MODELS, MOLECULAR; PROTEINS;

EID: 42149183130     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci700345n     Document Type: Article

References (49)

1. Seifert, M. H.; Kraus, J.; Kramer, B. Virtual high-throughput screening of molecular databases. Curr. Opin. Drug Discovery Dev. 2007, 10, 298.
2. Michel, J.; Verdonk, M. L.; Essex, J. W. Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization. J. Med. Chem. 2006, 49, 7427-7439.
3. Foloppe, N.; Hubbard, R. Towards predictive ligand design with freeenergy based computational methods. Curr. Med. Chem. 2006, 13, 3583-3608.
4. Kuhn, B.; Gerber, P.; Schulz-Gasch, T.; Stahl, M. Validation and use of the MM-PBSA approach for drug discovery. J. Med. Chem. 2005, 48, 4040-4048.
5. Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem., Int. Ed. Engl. 2002, 41, 2644-2676.
6. Hansen, C. On the structure of medicinal chemistry. J. Med. Chem. 1976, 19, 1-6.
7. Crippen, G. M. Distance geometry approach to rationalizing binding data. J. Med Chem. 1979, 22, 988-997.
8. Topliss, J. G.; Edwards, R. P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem. 1979, 22, 1238-1244.
9. Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A Critical Assessment of Docking Programs and Scoring Functions. J. Med. Chem. 2006, 49, 5912-5931.
10. Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. L., III. Assessing Scoring Functions for Protein-Ligand Interactions. J. Med. Chem. 2004, 47, 3032-3047.
11. Hetenyi, C.; Paragi, G.; Maran, U.; Timar, Z.; Karelson, M.; Penke, B. Combination of a modified scoring function with two-dimensional descriptors for calculation of binding affinities of bulky, flexible ligands to proteins. J. Am. Chem. Soc. 2006, 128, 1233-1239.
12. 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.
13. Salo, J.-K.; Yliniemelä, A.; Taskinen, J. Parameter refinement for molecular docking. J. Chem. Inf. Comput. Sci. 1998, 38, 832-839.
14. Andersson, C. D.; Thysell, E.; Lindström, A.; Bylesjö, M.; Raubacher, F.; Linusson, A. A multivariate approach to investigate docking parameters' effects on docking performance. J. Chem. Inf. Model. 2007, 47, 1673-1687.
15. Pham, T. A.; Jain, A. N. Parameter estimation for scoring proteinligand interactions using negative training data. J. Med. Chem. 2006, 49, 5856-5868.
16. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking. J. Med. Chem. 2006, 49, 6789-6801.
17. Wang, R.; Fang, X.; Lu, Y.; Yang, C. Y.; Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem. 2005, 48, 4111-4119.
18. Kashem, M. A.; Nelson, R. M.; Yingling, J. D.; Pullen, S. S.; Prokopowicz, A. S., III; Jones, J. W.; Wolak, J. P.; Rogers, G. R.; Morelock, M. M.; Snow, R. J.; Homon, C. A.; Jakes, S. Three mechanistically distinct kinase assays compared: Measurement of intrinsic ATPase activity identified the most comprehensive set of ITK inhibitors. J. Biomol. Screening 2007, 12, 70-83.
19. Seifert, M. H. Assessing the discriminatory power of scoring functions for virtual screening. J. Chem. Inf. Model. 2006, 46, 1456-1465.
20. Fisher, R. A. The use of multiple measurements in taxonomic problems. Eugenics 1936, 7, 179-188.
21. Hastie, T.; Tibshirani, R.; Friedman, J. H. The Elements of Statistical Learning: Data mining, inference, and prediction; Springer-Verlag: Heidelberg, Germany, 2001.
22. 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.
23. 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.
24. Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinburgh 1918, 52, 399-433.
25. Bortz, J. Statistik, 6th ed.; Springer Medizin Verlag: Heidelberg, Germany, 2005.
26. Octave, version 2.1.40; Eaton, J. W. University of Wisconsin, Department of Chemical Engineering: Madison, WI, 2002. http://www.gnu.org/ software/octave/ (accessed Dec 6, 2007).
27. Kruskal, W. H.; Wallis, A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 1952, 47, 583-621.
28. Wilkie, A. D. Measures for comparing scoring systems. In Credit Scoring and Credit Control; Thomas, L. C., Crook, J. N., Edelman, D. B., Eds.; Clarendon Press: Oxford, 1992; pp 123-138.
29. Kolmogoroff, A. Grundbegriffe der Wahrscheinlichkeitsrechnung; Springer: Berlin, 1933 (Reprint: Springer, Berlin, 1973).
30. Matthews, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 1975, 405, 442-451.
31. 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.
32. Kullback, S.; Leibler, R. A. On information and sufficiency. Ann. Math. Stat. 1951, 22, 79-86.
33. E-mail: Kurt.Homik@ci.tuwien.ac.at.
34. 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, 2534-2547.
35. Seifert, M. H.; Schmitt, F.; Herz, T.; Kramer, B. ProPose: a docking engine based on a fully configurable protein-ligand interaction model. J. Mol. Model. 2004, 10, 342-357.
36. Seifert, M. H. ProPose: steered virtual screening by simultaneous protein-ligand docking and ligand-ligand alignment. J. Chem. Inf. Model. 2005, 45, 449-460.
37. Nissink, J. W.; Murray, C.; Hartshorn, M.; Verdonk, M. L.; Cole, J. C.; Taylor, R. A new test set for validating predictions of protein-ligand interaction, Proteins 2002, 49, 457-471.
38. Gray, N. S.; Wodicka, L.; Thunnissen, A. M.; Norman, T. C.; Kwon, S.; Espinoza, F. H.; Morgan, D. O.; Barnes, G.; LeClerc, S.; Meijer, L.; Kim, S. H.; Lockhart, D. J.; Schultz, P. G. Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors, Science 1998, 281, 533-538.
39. Zweig, M. H.; Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 1993, 39, 561-577.
40. ROCKIT, version 1.1b; Kurt Rossmann Laboratories for Radiological Image Research, University of Chicago: Chicago, IL, 2007. http://www-radiology. uchicago.edu/krl/roc_soft6.htm (accessed Dec 6, 2007).
41. Bradley, E. K.; Miller, J. L.; Saiah, E.; Grootenhuis, P. D. Informative library design as an efficient strategy to identify and optimize leads: application to cyclin-dependent kinase 2 antagonists. J. Med. Chem. 2003, 46, 4360-4364.
42. Sybyl, version 7.0; Tripos Inc.: St. Louis, MO, 2006.
43. Corina, version 2.4; Molecular Networks GmbH: Erlangen, Germany, 1999.
44. Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C. A.; Nielsen, H. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics 2000, 16, 412-424
45. 2 as the ratio of signal variance to noise variance, F is basically the square of the signal-to-noise ratio. The square root of F is related to the area under the ROC curve (AURC), which itself is linked to other measures of distributional differences (Bamber, D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psychol. 1975, 12, 387-415.).
46. 2 denote the sample means and variances for ligand and decoy scores (Reiser, B.; Guttman, I. Statistical Inference for P(Y
47. D = n results in AURC = φ(√F/n).
48. Hanley, J. A.; McNeil, B. J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148, 839-843.
49. Metz, C. E.; Herman, B. A.; Shen, J.-H. Maximum-likelihood estimation of ROC curves from continuously-distributed data. Stat. Med. 1998, 17, 1033.