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Multiplex imaging and cellular target identification of kinase inhibitors via an affinity-based proteome profiling approach.

ABSTRACT: MLN8237 is a highly potent and presumably selective inhibitor of Aurora kinase A (AKA) and has shown promising antitumor activities. Like other kinase inhibitors which target the ATP-binding site of kinases, MLN8237 might be expected to have potential cellular off-targets. Herein, we report the first photoaffinity-based, small molecule AKA probe capable of both live-cell imaging of AKA activities and in situ proteome profiling of potential off-targets of MLN8237 (including AKA-associating proteins). By using two mutually compatible, bioorthogonal reactions (copper-catalyzed azide-alkyne cycloaddition chemistry and TCO-tetrazine ligation), we demostrate small molecule-based multiplex bioimaging for simultaneous in situ monitoring of two important cell-cycle regulating kinases (AKA and CDK1). A broad range of proteins, as potential off-targets of MLN8237 and AKA's-interacting partners, is subsequently identified by affinity-based proteome profiling coupled with large-scale LC-MS/MS analysis. From these studies, we discover novel AKA interactions which were further validated by cell-based immunoprecipitation (IP) experiments.

SUBMITTER: Su Y

PROVIDER: S-EPMC4290084 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Multiplex imaging and cellular target identification of kinase inhibitors via an affinity-based proteome profiling approach.

Su Ying Y Pan Sijun S Li Zhengqiu Z Li Lin L Wu Xiaoyuan X Hao Piliang P Sze Siu Kwan SK Yao Shao Q SQ

Scientific reports 20150112

MLN8237 is a highly potent and presumably selective inhibitor of Aurora kinase A (AKA) and has shown promising antitumor activities. Like other kinase inhibitors which target the ATP-binding site of kinases, MLN8237 might be expected to have potential cellular off-targets. Herein, we report the first photoaffinity-based, small molecule AKA probe capable of both live-cell imaging of AKA activities and in situ proteome profiling of potential off-targets of MLN8237 (including AKA-associating protei ...[more]

PMID: 25579846

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Project description:Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.

Dataset Information

Multiplex imaging and cellular target identification of kinase inhibitors via an affinity-based proteome profiling approach.

Publications

Multiplex imaging and cellular target identification of kinase inhibitors via an affinity-based proteome profiling approach.

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