Abstract
Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structure-based ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method, and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent application of the model to an external test set gives an error of 2.1 kJ/mol, which is remarkably good, considering the simplicity of the model. The structures of the protein-ligand interactions are further analyzed, again demonstrating the large versatility and plasticity of the cytochrome P450 active site.
Footnotes
This work was supported by the Drug Research Academy, Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark (to P.V.); the Carlsberg Foundation, Copenhagen, Denmark (to L.O.); and the Netherlands Organization for Scientific Research [Veni Grant 700.55.401] (to C.O.).
Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.
doi:10.1124/dmd.110.032946.
↵ The online version of this article (available at http://dmd.aspetjournals.org) contains supplemental material.
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ABBREVIATIONS:
- P450
- cytochrome P450
- LIE
- linear interaction energy
- MD
- molecular dynamics
- MOE
- molecular operating environment
- RMS
- root mean square.
- Received February 24, 2010.
- Accepted April 22, 2010.
- Copyright © 2010 by The American Society for Pharmacology and Experimental Therapeutics
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