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An optimal set of inhibitors for Reverse Engineering via Kinase Regularization

Scott Rata, Jonathan Scott Gruver, Natalia Trikoz, View ORCID ProfileAlexander Lukyanov, Janelle Vultaggio, Michele Ceribelli, Craig Thomas, View ORCID ProfileTaran Singh Gujral, View ORCID ProfileMarc W. Kirschner, View ORCID ProfileLeonid Peshkin
doi: https://doi.org/10.1101/2020.09.26.312348
Scott Rata
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Jonathan Scott Gruver
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Natalia Trikoz
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Alexander Lukyanov
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Janelle Vultaggio
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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Michele Ceribelli
4Division of Preclinical Innovation, NCATS/NIH, Rockville, MD 20850, USA
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Craig Thomas
4Division of Preclinical Innovation, NCATS/NIH, Rockville, MD 20850, USA
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Taran Singh Gujral
2Division of Human Biology, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA
3Department of Pharmacology, University of Washington, Seattle, WA 98195, USA
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Marc W. Kirschner
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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  • For correspondence: marc@hms.harvard.edu leonid_peshkin@hms.harvard.edu
Leonid Peshkin
1Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
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  • ORCID record for Leonid Peshkin
  • For correspondence: marc@hms.harvard.edu leonid_peshkin@hms.harvard.edu
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Abstract

We present a comprehensive resource of 257 kinase inhibitor profiles against 365 human protein kinases using gold-standard kinase activity assays. We show the utility of this dataset with an improved version of Kinome Regularization (KiR) to deconvolve protein kinases involved in a cellular phenotype. We assayed protein kinase inhibitors against more than 70% of the human protein kinome and chose an optimal subset of 58 inhibitors to assay at ten doses across four orders of magnitude. We demonstrate the effectiveness of KiR to identify key kinases by using a quantitative cell migration assay and updated machine learning methods. This approach can be widely applied to biological problems for which a quantitative phenotype can be measured and which can be perturbed with our set of kinase inhibitors.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 28, 2020.
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An optimal set of inhibitors for Reverse Engineering via Kinase Regularization
Scott Rata, Jonathan Scott Gruver, Natalia Trikoz, Alexander Lukyanov, Janelle Vultaggio, Michele Ceribelli, Craig Thomas, Taran Singh Gujral, Marc W. Kirschner, Leonid Peshkin
bioRxiv 2020.09.26.312348; doi: https://doi.org/10.1101/2020.09.26.312348
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An optimal set of inhibitors for Reverse Engineering via Kinase Regularization
Scott Rata, Jonathan Scott Gruver, Natalia Trikoz, Alexander Lukyanov, Janelle Vultaggio, Michele Ceribelli, Craig Thomas, Taran Singh Gujral, Marc W. Kirschner, Leonid Peshkin
bioRxiv 2020.09.26.312348; doi: https://doi.org/10.1101/2020.09.26.312348

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