Literature DB >> 26501902

Using Bioluminescent Kinase Profiling Strips to Identify Kinase Inhibitor Selectivity and Promiscuity.

Hicham Zegzouti1, Jacquelyn Hennek2, Said A Goueli3,4.   

Abstract

The advancement of a kinase inhibitor throughout drug discovery and development is predicated upon its selectivity towards the target of interest. Thus, profiling the compound against a broad panel of kinases is important for providing a better understanding of its activity and for obviating any off-target activities that can result in undesirable consequences. To assess the selectivity and potency of an inhibitor against multiple kinases, it is desirable to use a universal assay that can monitor the activity of all classes of kinases regardless of the nature of their substrates. The luminescent ADP-Glo kinase assay is a universal platform that measures kinase activity by quantifying the amount of the common kinase reaction product ADP. Here we present a method using standardized kinase profiling systems for inhibitor profiling studies based on ADP detection by luminescence. The kinase profiling systems are sets of kinases organized by family, presented in multi-tube strips containing eight enzymes, each with corresponding substrate strips, and standardized for optimal kinase activity. We show that using the kinase profiling strips we could quickly and easily generate multiple selectivity profiles using small or large kinase panels, and identify compound promiscuity within the kinome.

Entities:  

Keywords:  ADP detection; Bioluminescence; Kinase assay; Kinase profiling; Selectivity profiles

Mesh:

Substances:

Year:  2016        PMID: 26501902     DOI: 10.1007/978-1-4939-3073-9_5

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  2 in total

1.  A Flexible Workflow for Automated Bioluminescent Kinase Selectivity Profiling.

Authors:  Tracy Worzella; Matt Butzler; Jacquelyn Hennek; Seth Hanson; Laura Simdon; Said Goueli; Cris Cowan; Hicham Zegzouti
Journal:  SLAS Technol       Date:  2016-11-15       Impact factor: 3.047

2.  Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors.

Authors:  Siddharth Vijay; Taranjit S Gujral
Journal:  iScience       Date:  2020-05-01
  2 in total

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