Abstract
Because of advances in the high-throughput screening technology, identification of a hit that can bind to a target protein has become a relatively easy task; however, in the process of drug discovery, the following hit-to-lead and lead optimization still remain challenging. In a typical hit-to-lead and lead optimization process, the analogues of the most promising hits are synthesized for the development of structure–activity relationship (SAR) analysis, and in turn, in the effort of optimization of lead compounds, such analysis provides guidance for the further synthesis. The synthesis processes are usually long and labor-intensive. In silico searching has becoming an alternative approach to explore SAR especially with millions of compounds ready to be screened and most of them can be easily obtained. Here, we report our discovery of 15 new Dishevelled PDZ domain inhibitors by using such an approach. In our studies, we first developed a pharmacophore model based on NSC668036, an inhibitor previously identified in our laboratory; based on the model, we then screened the ChemDiv database by using an algorithm that combines similarity search and docking procedures; finally, we selected potent inhibitors based on docking analysis and examined them by using NMR spectroscopy. NMR experiments showed that all the 15 compounds we chose bound to the PDZ domain tighter than NSC668036.
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Abbreviations
- Dvl:
-
Dishevelled
- HSQC:
-
Heteronuclear single quantum coherence
- NCI:
-
National Cancer Institute
- NMR:
-
Nuclear magnetic resonance
- PDZ:
-
Post-synaptic density-95/discs large/zonula occludens-1
- SAR:
-
Structure–activity relationship
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Acknowledgments
We thank the Protein Production Facility at St. Jude Children’s Research Hospital and Dr. Ho-Jin Lee, Youming Shao for producing proteins, Dr. Weixing Zhang for his assistance with NMR experiments, Dr. Charles Ross and Scott Malone for their computer support. This work is supported by grants CA21765 and GM061739. We are grateful to the American Heart Association for a Predoctoral Fellowship to J. Shan.
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Shan, J., Zheng, J.J. Optimizing Dvl PDZ domain inhibitor by exploring chemical space. J Comput Aided Mol Des 23, 37–47 (2009). https://doi.org/10.1007/s10822-008-9236-1
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DOI: https://doi.org/10.1007/s10822-008-9236-1