RT Journal Article
SR Electronic
T1 An optimal set of inhibitors for Reverse Engineering via Kinase Regularization
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.26.312348
DO 10.1101/2020.09.26.312348
A1 Rata, Scott
A1 Gruver, Jonathan Scott
A1 Trikoz, Natalia
A1 Lukyanov, Alexander
A1 Vultaggio, Janelle
A1 Ceribelli, Michele
A1 Thomas, Craig
A1 Gujral, Taran Singh
A1 Kirschner, Marc W.
A1 Peshkin, Leonid
YR 2020
UL http://biorxiv.org/content/early/2020/09/28/2020.09.26.312348.abstract
AB 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 StatementThe authors have declared no competing interest.