Evaluating the druggability of TrmD, a potential antibacterial target, through design and microbiological profiling of a series of potent TrmD inhibitors.

The post-transcriptional modifier tRNA-(N1G37) methyltransferase (TrmD) has been proposed to be essential for growth in many Gram-negative and Gram-positive pathogens, however previously reported inhibitors show only weak antibacterial activity. In this work, optimisation of fragment hits resulted in compounds with low nanomolar TrmD inhibition incorporating features designed to enhance bacterial permeability and covering a range of physicochemical space. The resulting lack of significant antibacterial activity suggests that whilst TrmD is highly ligandable, its essentiality and druggability are called into question.

Exploring Ligand Stability in Protein Crystal Structures Using Binding Pose Metadynamics.

Identification of correct protein-ligand binding poses is important in structure-based drug design and crucial for the evaluation of protein-ligand binding affinity. Protein-ligand coordinates are commonly obtained from crystallography experiments that provide a static model of an ensemble of conformations. Binding pose metadynamics (BPMD) is an enhanced sampling method that allows for an efficient assessment of ligand stability in solution. Ligand poses that are unstable under the bias of the metadynamics simulation are expected to be infrequently occupied in the energy landscape, thus making minimal contributions to the binding affinity. Here, the robustness of the method is studied using crystal structures with ligands known to be incorrectly modeled, as well as 63 structurally diverse crystal structures with ligand fit to electron density from the Twilight database. Results show that BPMD can successfully differentiate compounds whose binding pose is not supported by the electron density from those with well-defined electron density.

Optimal water networks in protein cavities with GAsol and 3D-RISM.

Motivation: Water molecules in protein binding sites play essential roles in biological processes. The popular 3D-RISM prediction method can calculate the solvent density distribution within minutes, but is difficult to convert it into explicit water molecules. Results: We present GAsol, a tool that is capable of finding the network of water molecules that best fits a particular 3D-RISM density distribution in a fast and accurate manner and that outperforms other available tools by finding the globally optimal solution thanks to its genetic algorithm. Availability and implementation: https://github.com/accsc/GAsol. BSD 3-clauses license. Contact: alvaro.x.cortes@gsk.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Active learning strategies with COMBINE analysis: new tricks for an old dog.

The COMBINE method was designed to study congeneric series of compounds including structural information of ligand-protein complexes. Although very successful, the method has not received the same level of attention than other alternatives to study Quantitative Structure Active Relationships (QSAR) mainly because lack of ways to measure the uncertainty of the predictions and the need for large datasets. Active learning, a semi-supervised learning approach that makes use of uncertainty to enhance models' performance while reducing the size of the training sets, has been used in this work to address both problems. We propose two estimators of uncertainty: the pool of regressors and the distance to the training set. The performance of the methods has been evaluated by testing the resulting active learning workflows in 3 diverse datasets: HIV-1 protease inhibitors, Taxol-derivatives and BRD4 inhibitors. The proposed strategies were successful in 80% of the cases for the taxol-derivatives and BRD4 inhibitors, while outperformed random selection in the case of the HIV-1 protease inhibitors time-split. Our results suggest that AL-COMBINE might be an effective way of producing consistently superior QSAR models with a limited number of samples.

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen.

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed.

Optimization of Potent Ligands for the E3 Ligase DCAF15 and Evaluation of Their Use in Heterobifunctional Degraders.

Unlocking novel E3 ligases for use in heterobifunctional PROTAC degraders is of high importance to the pharmaceutical industry. Over-reliance on the current suite of ligands used to recruit E3 ligases could limit the potential of their application. To address this, potent ligands for DCAF15 were optimized using cryo-EM supported, structure-based design to improve on micromolar starting points. A potent binder, compound 24, was identified and subsequently conjugated into PROTACs against multiple targets. Following attempts on degrading a number of proteins using DCAF15 recruiting PROTACs, only degradation of BRD4 was observed. Deconvolution of the mechanism of action showed that this degradation was not mediated by DCAF15, thereby highlighting both the challenges faced when trying to expand the toolbox of validated E3 ligase ligands for use in PROTAC degraders and the pitfalls of using BRD4 as a model substrate.

Structure-Guided Design of a Domain-Selective Bromodomain and Extra Terminal N-Terminal Bromodomain Chemical Probe.

Small-molecule-mediated disruption of the protein-protein interactions between acetylated histone tails and the tandem bromodomains of the bromodomain and extra-terminal (BET) family of proteins is an important mechanism of action for the potential modulation of immuno-inflammatory and oncology disease. High-quality chemical probes have proven invaluable in elucidating profound BET bromodomain biology, with seminal publications of both pan- and domain-selective BET family bromodomain inhibitors enabling academic and industrial research. To enrich the toolbox of structurally differentiated N-terminal bromodomain (BD1) BET family chemical probes, this work describes an analysis of the GSK BRD4 bromodomain data set through a lipophilic efficiency lens, which enabled identification of a BD1 domain-biased benzimidazole series. Structure-guided growth targeting a key Asp/His BD1/BD2 switch enabled delivery of GSK023, a high-quality chemical probe with 300-1000-fold BET BD1 domain selectivity and a phenotypic cellular fingerprint consistent with BET bromodomain inhibition.