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Comprehensive Structural and Functional Characterization of the Human Kinome by Protein Structure Modeling and Ligand Virtual Screening
| Venue: | J. Chem. Inf. Model 2010 |
| Citations: | 4 - 1 self |
BibTeX
@ARTICLE{Brylinski_comprehensivestructural,
author = {Michal Brylinski and Jeffrey Skolnick},
title = {Comprehensive Structural and Functional Characterization of the Human Kinome by Protein Structure Modeling and Ligand Virtual Screening},
journal = {J. Chem. Inf. Model 2010},
year = {},
pages = {1839}
}
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Abstract
The growing interest in the identification of kinase inhibitors, promising therapeutics in the treatment of many diseases, has created a demand for the structural characterization of the entire human kinome. At the outset of the drug development process, the lead-finding stage, approaches that enrich the screening library with bioactive compounds are needed. Here, protein structure based methods can play an important role, but despite structural genomics efforts, it is unlikely that the three-dimensional structures of the entire kinome will be available soon. Therefore, at the proteome level, structure-based approaches must rely on predicted models, with a key issue being their utility in virtual ligand screening. In this study, we employ the recently developed FINDSITE/Q-Dock ligand homology modeling approach, which is well-suited for proteome-scale applications using predicted structures, to provide extensive structural and functional characterization of the human kinome. Specifically, we construct structure models for the human kinome; these are subsequently subject to virtual screening against a library of more than 2 million compounds. To rank the compounds, we employ a hierarchical approach that combines ligand- and structure-based filters. Modeling accuracy is carefully validated using available experimental data with particularly encouraging results found for the ability to identify, without prior knowledge, specific kinase inhibitors. More generally, the modeling procedure results in a large number of predicted molecular interactions between kinases and small ligands that should be of practical use in the development of novel inhibitors. The data set is freely available to the academic community via a user-friendly Web interface at
Keyphrases
human kinome functional characterization comprehensive structural ligand virtual screening protein structure modeling important role entire kinome key issue three-dimensional structure many disease large number virtual ligand structure-based filter entire human kinome small ligand protein structure academic community structural characterization modeling procedure result specific kinase inhibitor virtual screening novel inhibitor findsite q-dock ligand homology structure model proteome-scale application practical use encouraging result hierarchical approach predicted molecular interaction bioactive compound kinase inhibitor structural genomics effort prior knowledge proteome level drug development process user-friendly web interface structure-based approach predicted structure data set available experimental data lead-finding stage predicted model
