AutoShim: Empirically corrected scoring functions for quantitative docking with a crystal structure and IC50 training data

Journal of Chemical Information and Modeling

Volumn 48, Issue 4, 2008, Pages 861-872

  Martin, Eric J ^a   Sullivan, David C ^a  

^a NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEAST SQUARES APPROXIMATIONS; NUCLEAR MAGNETIC RESONANCE; PARAMETERIZATION; PROTEINS;

AUTOSHIM; PROTEIN TARGET; QUANTITATIVE DOCKING;

CRYSTAL STRUCTURE;

PROTEIN;

ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; CRYSTALLOGRAPHY; HYDROGEN BOND;

CRYSTALLOGRAPHY; HYDROGEN BONDING; MOLECULAR STRUCTURE; PROTEINS;

EID: 44449091584     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci7004548     Document Type: Article

References (37)

1. Warren, G. L.; Andrews, C. W.; Capelli, A. M.; Clarke, B.; Lalonde, J.; et al. A Critical Assessment of Docking Programs and Scoring Functions. J. Med. Chem. 2006, 49, 5912-5931.
2. Aqvist, J.; Medina, C.; Samuelsson, J. E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994, 7, 385-391.
3. Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C. Prediction of Drug Binding Affinities by Comparative Binding Energy Analysis. J. Med. Chem. 1995, 38, 2681-2691.
4. Pan, D.; Tseng, Y.; Hopfinger, A. J. Quantitative structure-based design: formalism and application of receptor-dependent RD-4DQSAR analysis to a set of glucose analogue inhibitors of glycogen Phosphorylase. J. Chem. Inf. Comput. Sci. 2003, 43, 1591-1607.
5. Deng, Z.; Chuaqui, C.; Singh, J. Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein-Ligand Binding Interactions. J. Med. Chem. 2004, 47, 337-344.
6. Claussen, H.; Gastreich, M.; Apelt, V.; Greene, J.; Hindle, S. A.; et al. The FlexX Database Docking Environment - Rational Extraction of Receptor Based Pharmacophores. Curr. Drug Discov. Technol. 2004, 1, 49-60.
7. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. D. Improved Protein-Ligand Docking Using GOLD. Proteins: Struct., Funct., Bioinf. 2003, 52, 609-623.
8. Jansen, J. M.; Martin, E. J. Target-biased scoring approaches and expert systems in structure-based virtual screening. Curr. Opin. Chem. Biol. 2004, 8, 359-364.
9. Magnet. htto://www.metophorics.com/products/magnet/mdex.html. (Accessed March 19, 2008).
10. SYBYL. http://www.tripos.com. (Accessed March 19, 2008).
11. Wold, H. Estimation of principal components and related models by iterative least squares. Multivariate Analysis; Academic Press: New York, 1966; pp 391-420.
12. Chen, H.; Lyne, P. D.; Giordanetto, F.; Lovell, T.; Li, J. On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors. J. Chem. Inf. Model. 2006, 46, 401-415.
13. Perola, E.; Walters, W. P.; Charifson, P. Comments on the Article "On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors". J. Chem. Inf. Model. 2807, 47, 251-253.
14. 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.
15. Huang, S. Y.; Zou, X. Q. An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials. J. Comput. Chem. 2006, 27, 1866-1875.
16. Teramoto, R.; Fukunishi, H. Supervised Consensus Scoring for Docking and Virtual Screening. J. Chem. Inf. Model. 2007, 47, 526-534.
17. Vieth, M.; Cummins, D. J. DoMCoSAR: a novel approach for establishing the docking mode that is consistent with the structureactivity relationship. Application to HIV-1 protease inhibitors and VEGF receptor tyrosine kinase inhibitors. J. Med. Chem. 2000, 43, 3020-3032.
18. Huang, S. Y.; Zou, X. Q. An iterative knowledge-based scoring function to predict protein-ligand interactions: U. Validation of the scoring function. J. Comput. Chem. 2006, 27, 1876-1882.
19. Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks. J. Med Chem. 1996, 39, 2887-2893.
20. DockIt http://www.metaphorics.com/products/dockit.html.
21. McMartin, C.; Bohacek, R. S. QXP: Powerful, rapid computer algorithms for structure-based drug design. J. Comput.-Aided Mol. Des. 1997, 11, 333-344.
22. Vieth, M.; Higgs, R. E.; Robertson, D. H.; Shapiro, M.; Gragg, E. A.; et al. Kinomics-structural biology and chemogenomics of kinase inhibitors and targets. Biochim. Biophys. Acta, Proteins Proteomics 2004, 1697, 243-257.
23. ter Haar, E.; Walters, W. P.; Pazhanisamy, S.; Taslimi, P.; Pierce, A. C.; et al. Kinase chemogenomics: Targeting the human kinome for target validation and drug discovery. Mini-Rev. Med. Chem. 2004, 4, 235-253.
24. Team, R. D. C. R Development Core Team R: A language and environment for statistical computing; R Foundation for Statistical Computing: Vienna, Austria, 2008. http://www.r-project.org/. (Accessed March 19, 2008).
25. Wehrens, R.; Mevik, B. H. pis: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR); R package version 1.2-0, 2006. http://www.cran.r-project.org/. (Accessed March 19, 2008).
26. Dayal, B. S.; MacGregor, J. F. Improved PLS Algorithms. J. Chemom. 1997, 11, 73-85.
27. Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed.; Cambridge University Press: Cambridge, 1992; p 1020.
28. Martin, E. J.; Sullivan, D. C. Surrogate AutoShim: Predocking into a Universal Ensemble Kinase Receptor for Three Dimensional Activity Prediction, Very Quickly, without a Crystal Structure. J. Chem. Inf. Model. 2008, 48, 873-881.
29. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-857.
30. Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin, T. E. A geometric approach to macromolecule-ligand interactions. J. Mol. Biol. 1982, 161, 269-288.
31. Breiman, L.; Friedman, J. H.; Olshen, R. A.; Stone, C. J. Classification and Regression Trees; Wadsworth: Monterey, CA, 1984.
32. Therneau, T. M.; Atkinson, B.; Ripley, B. rpart: Recursive Partitioning; R package version 3.1-27, 2005. http://www.cran.r-project.org/ . (Accessed March 19, 2008).
33. 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.
34. Wang, R.; Lu, Y.; Wang, S. Comparative Evaluation of 11 Scoring Functions for Molecular Docking. J. Med. Chem. 2003, 46, 2287-2303.
35. Schneider, G.; Schneider, P.; Renner, S. Scaffold-Hopping: How Far Can You Jump. QSAR Comb. Sci. 2006, 25, 1162-1171.
36. Jain, A. N.; Koile, K.; Chapman, D. Compass: Predicting Biological Activities from Molecular. Performance Comparisons on a Steroid Benchmark. J. Med. Chem. 1994, 37, 2315-2327.
37. Diller, D. J.; Hobbs, D. W. Understanding hERG inhibition with QSAR models based on a one-dimensional molecular representation. J. Comput.-Aided Mol Des. 2007, 21, 379-393.