The Front Cover shows the initial screening strategy for hit identification on WRN helicase. The main challenge was to understand the type of false positives which came out of standard DNA unwinding or ATPase assays. Beyond DNA binders, direct interferences of compounds with WRN protein were the major source of artefacts as illustrated by the extensive characterization of inhibitors (ML216 and NSC617145) wrongly considered as WRN probes. Some learnings on how to minimize and triage out those in vitro artefacts are shared for best hit discovery on WRN helicase. More information can be found in the Research Article by Alisia Heuser, Henrik Möbitz, Jacques Hamon et al.
Imidazoleglycerol-phosphate dehydratase catalyses the sixth step of the histidine-biosynthesis pathway in plants and microorganisms and has been identified as a possible target for the development of novel herbicides. Arabidopsis thaliana IGPD has been cloned and overexpressed in Escherichia coli, purified and subsequently crystallized in the presence of manganese. Under these conditions, the inactive trimeric form of the metal-free enzyme is assembled into a fully active species consisting of a 24-mer exhibiting 432 symmetry. X-ray diffraction data have been collected to 3.0 Å resolution from a single crystal at 293 K. The crystal belongs to space group R3, with approximate unit-cell parameters a = b = 157.9, c = 480.0 Å, α = β = 90, γ = 120° and with either 16 or 24 subunits in the asymmetric unit. A full structure determination is under way in order to provide insights into the mode of subunit assembly and to initiate a programme of rational herbicide design.
Targeted protein degradation (TPD) is a rapidly developing drug discovery technique with unique efficacy and target scope stemming from its degradation-based activity. Molecular glue degraders are a promising arm of TPD, as evidenced by the FDA-approved therapeutics within this class, the increasing number of degraders in clinical development, and their predisposition to drug-likeness. Cereblon (CRBN) glue degraders mediate target degradation by generating a neomorphic interface between CRBN and a protein of interest. While promising, the complicated nature of this CRBN-glue-target ternary complex makes the rational design of molecular glue degraders challenging. For other drug modalities, predictive modeling has been established to leverage existing activity data and generate quantitative structure-activity relationships (QSAR). However, the applicability of QSAR strategies for glues remains under-investigated. Herein, machine learning methodologies were developed to predict glue-mediated recruitment of CRBN to target proteins and achieved promising performance. Generated models leveraged more than a hundred internal screening campaigns across thousands of CRBN glues to predict glue-mediated recruitment of targets to CRBN. Our results show that recruitment activity of CRBN glue degraders can be modeled by machine learning, with 89 % of models producing an area under the receiver operating characteristic curve (ROC AUC) > 0.8 and 70 % of models producing a Matthew's correlation coefficient (MCC) > 0.2 for these primary screening data. Importantly, our findings also indicate that the combination of compound and protein descriptors in the so-called proteochemometric models improves performance, with >80 % of the models exhibiting higher ROC AUC and MCC values than per-target models only based on compound information. Hence, our investigations suggest that proteochemometric modeling is a successful approach for molecular glue degraders. The proposed machine learning strategies can aid compound prioritization based on recruitment efficacy and target selectivity, thus have the potential to facilitate the design and discovery of therapeutic CRBN molecular glues.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
