What Makes a Kinase Promiscuous for Inhibitors?. Issue 3 (21st March 2019)

• Record Type: Journal Article
• Authors: Hanson, Sonya M.; Georghiou, George; Thakur, Manish K.; Miller, W. Todd; Rest, Joshua S.; Chodera, John D.; Seeliger, Markus A.
• Is Part Of: Cell chemical biology. Volume 26:Issue 3(2019)
• Page Start: 390
• Page End: 399.e5
• Publication Date: 2019-03-21
• Subjects: drug promiscuity -- kinase inhibition -- DFG -- DDR1 -- ABL -- selectivity -- Markov state model -- Folding@home -- molecular dynamics -- crystallography; Biochemistry -- Periodicals; 572.05
• DOI: 10.1016/j.chembiol.2018.11.005
• Languages: English
• ISSNs: 2451-9456

Abstract

Summary: ATP-competitive kinase inhibitors often bind several kinases due to the high conservation of the ATP binding pocket. Through clustering analysis of a large kinome profiling dataset, we found a cluster of eight promiscuous kinases that on average bind more than five times more kinase inhibitors than the other 398 kinases in the dataset. To understand the structural basis of promiscuous inhibitor binding, we determined the co-crystal structure of the receptor tyrosine kinase DDR1 with the type I inhibitors dasatinib and VX-680. Surprisingly, we find that DDR1 binds these type I inhibitors in an inactive conformation typically reserved for type II inhibitors. Our computational and biochemical studies show that DDR1 is unusually stable in this inactive conformation, giving a mechanistic explanation for inhibitor promiscuity. This phenotypic clustering analysis provides a strategy to obtain functional insights not available by sequence comparison alone. Graphical Abstract: Highlights: Clustering of kinome by inhibition phenotype identifies promiscuous kinases Promiscuous tyrosine kinases are stable in the DFG-Asp-out inactive conformation Residues define promiscuous kinases and stabilize inactive conformation Abstract : Hanson et al. identify a group of kinases that bind a surprising number of inhibitors and reveal the molecular basis for this promiscuity. Functional clustering of proteins identifies groups of co-inhibited kinases. The findings have implications for the development of specific kinase inhibitors and drug resistance.