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The adenosine A3 receptor (A3AR) is a G protein-coupled receptor (GPCR) that exerts immunomodulatory effects in pathophysiological conditions such as inflammation and cancer. Thus far, studies towards the downstream effects of A3AR activation have yielded contradictory results, thereby motivating the need for further investigations. Various chemical and biological tools have been developed for this purpose, ranging from fluorescent ligands to antibodies. Nevertheless, these probes are limited by their reversible mode of binding, relatively large size and often low specificity. Therefore, in this work, we have developed a clickable and covalent affinity-based probe (AfBP) to target the human A3AR. Herein, we show validation of the synthesized AfBP in radioligand displacement, SDS-PAGE and confocal microscopy experiments, as well as utilization of the AfBP for the detection of endogenous A3AR expression in flow cytometry experiments. Ultimately, this AfBP will aid future studies towards the expression and function of the A3AR in pathologies.

The discovery of novel molecules with desirable properties is a classic challenge in medicinal chemistry. With the recent advancements of machine learning, there has been a surge of de novo drug design tools. However, few resources exist that are both user-friendly as well as easily customisable. In this application note, we present the new versatile open-source software package DrugEx for multi-objective reinforcement learning. This package contains the consolidated and redesigned scripts from the prior DrugEx papers including multiple generator architectures and a variety of scoring tools and multi-objective optimisation methods. It has a flexible application programming interface and can readily be used via the command line interface or the graphical user interface GenUI. The DrugEx package is publicly available at https://github.com/CDDLeiden/DrugEx

Protein kinases are a protein family that play an important role in several complex diseases such as cancer, cardiovascular and immunological diseases. Kinases have conserved binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multi-target drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of kinase activity data in the public domain, which can be used in many different ways. Multi-task machine learning models are expected to excel for these kinds of datasets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multi-task modelling of sparse data poses two major challenges: (i) creating a balanced train-test split without data leakage and (ii) handling missing data. In this work, we construct a kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random splits based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multi-task deep learning models, on this very sparse dataset, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set.

Generative deep learning models have emerged as a powerful approach for de novo drug design, as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89 %, with different models having different error type distributions. Post hoc correction of SMILES increases model validity, with the SMILES corrector fixing 35 to 80 % of invalid model outputs. While, corrector models trained with one error per input sequence alter 60 to 90 % of invalid inputs, a higher performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60 to 95 % of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators with regard to novelty and similarity. Additionally, the SMILES corrector can also be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39 % and a novelty of approximately 20 %. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates.

Hypoxia and HIF signaling drive cancer progression and therapy resistance and have been demonstrated in breast cancer. To what extent breast cancer subtypes differ in their response to hypoxia has not been resolved. Here, we show that hypoxia similarly triggers HIF1 stabilization in luminal and basal A triple negative breast cancer cells and we use high throughput targeted RNA sequencing to analyze its effects on gene expression in these subtypes. We focus on regulation of YAP/TAZ/TEAD targets and find overlapping as well as distinct target genes being modulated in luminal and basal A cells under hypoxia. We reveal a HIF1 mediated, basal A specific response to hypoxia by which TAZ, but not YAP, is phosphorylated at Ser89. While total YAP/TAZ localization is not affected by hypoxia, hypoxia drives a shift of [p-TAZ(Ser89)/p-YAP(Ser127)] from the nucleus to the cytoplasm in basal A but not luminal breast cancer cells. Cell fractionation and YAP knock-out experiments confirm cytoplasmic sequestration of TAZ(Ser89) in hypoxic basal A cells. Pharmacological and genetic interference experiments identify c-Src and CDK3 as kinases involved in such phosphorylation of TAZ at Ser89 in hypoxic basal A cells. Hypoxia attenuates growth of basal A cells and the effect of verteporfin, a disruptor of YAP/TAZ-TEAD–mediated transcription, is diminished under those conditions, while expression of a TAZ-S89A mutant does not confer basal A cells with a growth advantage under hypoxic conditions, indicating that other hypoxia regulated pathways suppressing cell growth are dominant.

This manuscript describes the development and architecture of the GenUI software platform for integration of molecular generators. The source code for the components of the platform is available in the following repositories:https://github.com/martin-sicho/genuihttps://github.com/martin-sicho/genui-guihttps://github.com/martin-sicho/genui-docker

In an attempt to exploit the hydrolytic mechanism by which β-lactamase enzymes degrade cephalosporins, we designed and synthesized a series of novel cephalosporin prodrugs aimed at delivering thiol-based inhibitors of metallo-β-lactamases (MBLs) in spatiotemporally controlled fashion. Notably, while enzyme-mediated hydrolysis of the β-lactam ring was found to occur, it was not accompanied by release of the thiol-based inhibitors. Nonetheless, the cephalosporin prodrugs, especially thiomandelic acid conjugate (8), demonstrated potent inhibition of IMP-type MBLs, with IC50 values in the nanomolar range. In addition, conjugate 8 was also found to greatly reduce the MIC of meropenem against an IMP-28 producing clinical isolate of K. pneumoniae. The results of kinetic experiments indicate that these prodrugs inhibit IMP-type MBLs by acting as slowly turned-over substrates. Structure-activity relationship studies revealed that both phenyl and carboxyl moieties of 8 are crucial for its potency. Furthermore, modeling studies indicate that productive interactions of the thiomandelic acid moiety of 8 with residues Trp28 and Lys161 within the IMP active site may contribute to the observed inhibitory potency and selectivity.

To diagnose and treat early-stage (preclinical) Alzheimer’s disease (AD) patients, we need body-fluid-based biomarkers that reflect the processes that occur in this stage, but current knowledge on associated processes is lacking. As human studies on (possible) onset and early-stage AD would be extremely expensive and time-consuming, we investigate the potential value of animal AD models to help to fill this knowledge gap. We provide a comprehensive overview of processes associated with AD pathogenesis and biomarkers, current knowledge on AD-related biomarkers derived from on human and animal brains and body fluids, comparisons of biomarkers obtained in human AD and frequently used animal AD models, and emerging body-fluid-based biomarkers. In human studies, amyloid beta (Aβ), hyperphosphorylated tau (P-tau), total tau (T-tau), neurogranin, SNAP-25, glial fibrillary acidic protein (GFAP), YKL-40, and especially neurofilament light (NfL) are frequently measured. In animal studies, the emphasis has been mostly on Aβ. Although a direct comparison between human (familial and sporadic) AD and (mostly genetic) animal AD models cannot be made, still, in brain, cerebrospinal fluid (CSF), and blood, a majority of similar trends are observed for human AD stage and animal AD model life stage. This indicates the potential value of animal AD models in understanding of the onset and early stage of AD. Moreover, animal studies can be smartly designed to provide mechanistic information on the interrelationships between the different AD processes in a longitudinal fashion and may also include the combinations of different conditions that may reflect comorbidities in human AD, according to the Mastermind Research approach.

International audience; Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.