DRUG DISCOVERY GNE-6640
Inhibitors hit deubiquitinase selectively Small molecules block USP7 enzyme to fight cancer In the body, ubiquitinating enzymes regulate proteins—marking them for disposal, for example—by adding ubiquitin peptides to them. And deubiquitinases (DUBs) refine regulation by removing ubiquitins. But DUBs can also promote tumor growth and curb anticancer immune responses. Researchers have been trying to find small molecules that fight cancer by inhibiting DUB activity, but progress has been slow. In two new studies, researchers have passed a milestone in the field by identifying some of the most promising inhibitors yet for USP7 (ubiquitin-specific protease-7), the most common anticancer target among the approximately 100 types of DUBs found in the body. USP7 can promote cancer by reducing levels of the tumor suppressor protein p53 and by impairing the immune system’s ability to detect and eliminate tumors. Inhibiting USP7 can have the opposite effect, killing cancer cells. However, previous inhibitors blocked USP7 essentially by only a single mechanism: bonding covalently or binding noncovalently to the enzyme’s active-site cysteine. Scientists haven’t obtained, or at least haven’t published, small-molecule/ USP7 crystal structures to guide drug discovery. And selective USP7 binding, with its potential for reduced side effects, has been elusive because many DUBs have similar structures and active sites. In the new studies, two research groups
found the most selective USP7 inhibitors yet reported, and enzyme crystal structures they obtained reveal that the compounds bind at novel sites. Till Maurer and Ingrid E. Wertz of Genentech and coworkers used nuclear magnetic resonance spectroscopy-based screening and structure-based design to identify and optimize GNE-6640 and GNE-6776, which inhibit USP7 selectively compared with 36 other DUBs (Nature 2017, DOI: 10.1038/
N
NH2
N N H
OH
GNE-6776
H3C
H N
N
N
NH2
OH
O
apeutics, Andrew P. Turnbull of Cancer Research UK Therapeutic Discovery Laboratories, David Komander of the MRC Laboratory of Molecular Biology, Benedikt M. Kessler of the University of Oxford, Sylvie Urbé and Michael J. Clague of the UniverFT671 sity of Liverpool, and coworkers screened OH a Forma compound library to identify and F N N refine the noncovalent inhibitor FT671 N F O and the covalent inhibitor FT827 (NaF N ture 2017, DOI: 10.1038/nature24451). O N N N The compounds block USP7 but not 38 other DUBs. They target USP7 at or F near its active site but also interact with a uniquely structured pocket, and they inhibit the proliferation of cultured cancer FT827 cells and the growth of tumors in mice. OH The groups used structural biology N N O “to fine-tune inhibition of USP7 in a HN N O H3C N way that exploited differences between S O USP7 and other DUBs, maximizing N O the chances of obtaining high selectivity,” comments Michael Mattern, vice nature24006). The compounds block president of corporate affairs at Progenra, USP7 far from the active-site cysteine the which is developing USP7 inhibitors. enzyme has in common with other DUBs “How these findings translate to clinical and kill tumors in cell culture and in live utility, however, remains to be determice. mined.”—STU BORMAN Stephanos Ioannidis of Forma Ther-
COMPUTATIONAL CHEMISTRY
Machine learning streamlines quantum calculations A machine learning approach could allow computers to determine the electronic structure of molecules without having to use the most resource-intensive equations of density functional theory, new research suggests (Nat. Commun. 2017, DOI: 10.1038/s41467-017-00839-3). Spam filtering, economic forecasting, and other activities, such as predicting material properties, are powered by algorithms that allow computers to learn from and make predictions on the basis of collections of data. In the case of material
properties, computers currently make predictions after being trained with a database of electronic structure information for many types of substances. A team led by Kieron Burke of the University of California, Irvine; Klaus-Robert Müller of Technical University of Berlin; and Mark E. Tuckerman of New York University now proposes training computers to connect molecules’ structure and properties by having the machines learn from maps of molecular electron density determined from molecules’ potential energy.
After training on existing maps, computers could predict a new molecule’s ground-state electron density. The molecule’s ground-state properties can then be extrapolated from its electron density, as theorized by Pierre Hohenberg and Walter Kohn in the 1960s. In the new work, the team was able to use its “machine learning Hohenberg-Kohn” approach to simulate an intramolecular proton transfer in the enol form of malonaldehyde [CH2(CHO)2].—JYLLIAN
KEMSLEY OCTOBER 23, 2017 | CEN.ACS.ORG | C&EN
5
