Leveraging Compound Promiscuity to Identify Targetable Cysteines within the Kinome

Suman Rao, Deepak Gurbani, Guangyan Du, Robert A Everley, Christopher M Browne, Apirat Chaikuad, Tan Li, Martin Schröder, Sudershan Gondi, Scott B Ficarro, Taebo Sim, Nam Doo Kim, Matthew J Berberich, Stefan Knapp, Jarrod A Marto, Kenneth D Westover, Peter K Sorger, Nathanael S Gray

Cell Chemical Biology, 2019
DOI: https://doi.org/10.1016/j.chembiol.2019.02.021

Covalent kinase inhibitors, which typically target cysteine residues, represent an important class of clinically relevant compounds. Approximately 215 kinases are known to have potentially targetable cysteines distributed across 18 spatially distinct locations proximal to the ATP-binding pocket. However, only 40 kinases have been covalently targeted, with certain cysteine sites being the primary focus. To address this disparity, we have developed a strategy that combines the use of a multi-targeted acrylamide-modified inhibitor, SM1-71, with a suite of complementary chemoproteomic and cellular approaches to identify additional targetable cysteines. Using this single multi-targeted compound, we successfully identified 23 kinases that are amenable to covalent inhibition including MKNK2, MAP2K1/2/3/4/6/7, GAK, AAK1, BMP2K, MAP3K7, MAPKAPK5, GSK3A/B, MAPK1/3, SRC, YES1, FGFR1, ZAK (MLTK), MAP3K1, LIMK1, and RSK2. The identification of nine of these kinases previously not targeted by a covalent inhibitor increases the number of targetable kinases and highlights opportunities for covalent kinase inhibitor development.

Modeling Covalent-Modifier Drugs

Ernest Awoonor-Williams, Andrew G. Walsh, Christopher N. Rowley

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

doi: 10.1016/j.bbapap.2017.05.009

In this review, we present a summary of how computer modeling has been used in the development of covalent modifier drugs. Covalent modifier drugs bind by forming a chemical bond with their target. This covalent binding can improve the selectivity of the drug for a target with complementary reactivity and result in increased binding affinities due to the strength of the covalent bond formed. In some cases, this results in irreversible inhibition of the target, but some targeted covalent inhibitor (TCI) drugs bind covalently but reversibly. Computer modeling is widely used in drug discovery, but different computational methods must be used to model covalent modifiers because of the chemical bonds formed. Structural and bioinformatic analysis has identified sites of modification that could yield selectivity for a chosen target. Docking methods, which are used to rank binding poses of large sets of inhibitors, have been augmented to support the formation of protein–ligand bonds and are now capable of predicting the binding pose of covalent modifiers accurately. The pKa’s of amino acids can be calculated in order to assess their reactivity towards electrophiles. QM/MM methods have been used to model the reaction mechanisms of covalent modification. The continued development of these tools will allow computation to aid in the development of new covalent modifier drugs.

Covalent inhibitors that target lysine side chains

Inhibition of Mcl-1 through covalent modification of a noncatalytic lysine side chain

Gizem Akçay, Matthew A Belmonte, Brian Aquila, Claudio Chuaqui, Alexander W Hird, Michelle L Lamb, Philip B Rawlins, Nancy Su, Sharon Tentarelli, Neil P Grimster & Qibin Su

Nature Chemical Biology (2016) doi:10.1038/nchembio.2174