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Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states.

ABSTRACT: Type-II kinase inhibitors represent a class of chemicals that trap their target kinases in an inactive, so-called DFG-out state, occupying a hydrophobic pocket adjacent to the ATP binding site. These compounds are often more specific than those that target active DFG-in kinase conformations. Unfortunately, the discovery of novel type-II scaffolds presents a considerable challenge, partially because the lack of compatible kinase structures makes structure-based methods inapplicable. We present a computational protocol for converting multiple available DFG-in structures of various kinases (approximately 70% of mammalian structural kinome) into accurate and specific models of their type-II bound state. The models, described as deletion-of-loop Asp-Phe-Gly-in (DOLPHIN) kinase models, demonstrate exceptional performance in various inhibitor discovery applications, including compound pose prediction, screening, and in silico activity profiling. Given the abundance of the DFG-in structures, the presented approach opens possibilities for kinome-wide discovery of specific molecules targeting inactive kinase states.

SUBMITTER: Kufareva I

PROVIDER: S-EPMC2669721 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states.

Kufareva Irina I Abagyan Ruben R

Journal of medicinal chemistry 20081201 24

Type-II kinase inhibitors represent a class of chemicals that trap their target kinases in an inactive, so-called DFG-out state, occupying a hydrophobic pocket adjacent to the ATP binding site. These compounds are often more specific than those that target active DFG-in kinase conformations. Unfortunately, the discovery of novel type-II scaffolds presents a considerable challenge, partially because the lack of compatible kinase structures makes structure-based methods inapplicable. We present a ...[more]

PMID: 19053777

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Project description:BackgroundMany kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.ResultsWe have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.ConclusionsWe have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.

Dataset Information

Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states.

Publications

Type-II kinase inhibitor docking, screening, and profiling using modified structures of active kinase states.

Similar Datasets

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