3D Similarity Research Articles

Discovery of novel 1,4-dicarbonylthiosemicarbazides as DNA gyrase inhibitors for the treatment of MRSA infection

Antibiotic resistance has become a serious threat to public health, thus novel antibiotics are urgently needed to combat drug-resistant bacteria including methicillin-resistant Staphylococcus aureus (MRSA). The 1,4-dicarbonylthiosemicarbazide is an interesting chemotype that could exhibit antibacterial activity. However, the currently available compounds are not as potent as clinical antibiotics. Herein, we adopted the computer-aided drug design strategy, substructure search, to retrieve antibacterial 1,4-dicarbonylthiosemicarbazide derivatives, and identified compound B5 (Specs ID: AG-690/15432331) from the Specs chemical library that exhibited moderate activity (minimum inhibitory concentration (MIC): 6.25 μg/mL) against Staphylococcus aureus ATCC 29213. Based on that compound, we further designed and synthesized 45 derivatives, and evaluated their antibacterial activity. Eight derivatives were more potent than or equivalent to vancomycin (MIC: 1.56 μg/mL). We compared the three most potent ones for their cytotoxicity to HepG2 and HUVEC cells and selected compound 1b as our lead compound for comprehensive biological evaluation. As a result, compound 1b exhibited a bacteriostatic mode, and was active against a panel of Gram-positive bacteria strains, metabolically stable, and effective to protect the mice from MRSA infection. More importantly, we applied 2D similarity calculation and reverse docking to predict potential targets of compound 1b. Through experimental validation and molecular dynamics simulation, we were able to confirm that compound 1b inhibited Staphylococcus aureus DNA gyrase (IC50: 1.81 μM) and DNA supercoiling, potentially by binding to the ATPase domain, where ASP81, GLU58 and GLN91 formed key hydrogen bonds. Taken together, we have discovered a new class of DNA gyrase inhibitors represented by compound 1b for the treatment of MRSA infection, through the design, synthesis, and biological evaluation of novel 1,4-dicarbonylthiosemicarbazides.

Features and strategy influence performance on a chemistry spatial task

Spatial thinking is an important skill for success in the chemistry classroom. Experimental studies on mental rotation, a key skill for spatial thinking, show interesting trends in participant performance on 2D and 3D similarity judgement tasks. In these studies, participants’ use of mental rotation is determined by linear regression and/or correlations of response time versus angular disparity of the task. While judgements with block diagrams are amenable to mental rotation, studies find differences with the more symbolic 2D chemistry diagrams depending in the participants’ expertise: novices tend to use mental rotation, while experts use algorithms or other heuristics. Herein, we study these aspects chemistry, spatial thinking, and mental rotation, adding to prior work by investigating the effect of time limits and how task variables like axis and angular disparity influence task performance. We also incorporated a block design that allowed us to look for practice effects. This study was conducted online with undergraduate students ( N = 162) and designed as a conceptual replication of Stieff et al. (2018) to explore how features of the task (e.g., axis of rotation) influence participants accuracy, response time, and their use of mental rotation, diagrammatic, analytic, or algorithmic strategies. Our results suggest that mental rotation was being used successfully for only z-axis rotations, with other strategies such as symmetry heuristics are likely being used for x- and y-axis rotations. We also found that practice can improve stereochemistry task performance. These findings support chemistry novices’ reliance on mental rotation but also suggest that alternative strategies are used when time is limited.

Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases.

Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors.

The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.

Potential inhibitors of extra-synaptic NMDAR/TRPM4 interaction: Screening, molecular docking, and structure-activity analysis

Over-activation of extra-synaptic NMDARs by excessive glutamate is known to cause excitotoxicity. The molecular mechanism of how this excitotoxicity occurs was revealed recently. This paper presents the results of in silico studies aimed at finding potential small-molecule inhibitors that can block this mechanism, namely the extra-synaptic NMDAR/TRPM4 interaction. We screened for small molecules according to 2D (at least Tanimoto threshold was 90%) and/or 3D similarity, molecular weight, lipophilicity using control compounds (C8 and C19) targeting this interaction. We then pre-filtered these molecules according to their drug-likeness and toxicity profiles. After pre-filtering, we performed a docking study against the extra-synaptic NMDAR/TRPM4 interaction with the remaining 26 compounds. In addition, we determined that selected compounds exhibit low affinity for classical NMDAR ligand binding sites. Ultimately, we identified four novel compounds (C8-12, C8-15, C19-3, C19-4) that could block the extra-synaptic NMDAR/TRPM4 interaction without inhibiting the normal function of synaptic NMDARs.

Efficient virtual high-content screening using a distance-aware transformer model

Molecular similarity search is an often-used method in drug discovery, especially in virtual screening studies. While simple one- or two-dimensional similarity metrics can be applied to search databases containing billions of molecules in a reasonable amount of time, this is not the case for complex three-dimensional methods. In this work, we trained a transformer model to autoencode tokenized SMILES strings using a custom loss function developed to conserve similarities in latent space. This allows the direct sampling of molecules in the generated latent space based on their Euclidian distance. Reducing the similarity between molecules to their Euclidian distance in latent space allows the model to perform independent of the similarity metric it was trained on. While we test the method here using 2D similarity as proof-of-concept study, the algorithm will enable also high-content screening with time-consuming 3D similarity metrics. We show that the presence of a specific loss function for similarity conservation greatly improved the model’s ability to predict highly similar molecules. When applying the model to a database containing 1.5 billion molecules, our model managed to reduce the relevant search space by 5 orders of magnitude. We also show that our model was able to generalize adequately when trained on a relatively small dataset of representative structures. The herein presented method thereby provides new means of substantially reducing the relevant search space in virtual screening approaches, thus highly increasing their throughput. Additionally, the distance awareness of the model causes the efficiency of this method to be independent of the underlying similarity metric.

Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing.

Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC.

Predicting alveolar ventilation heterogeneity in pulmonary fibrosis using a non-uniform polyhedral spring network model.

Pulmonary Fibrosis (PF) is a deadly disease that has limited treatment options and is caused by excessive deposition and cross-linking of collagen leading to stiffening of the lung parenchyma. The link between lung structure and function in PF remains poorly understood, although its spatially heterogeneous nature has important implications for alveolar ventilation. Computational models of lung parenchyma utilize uniform arrays of space-filling shapes to represent individual alveoli, but have inherent anisotropy, whereas actual lung tissue is isotropic on average. We developed a novel Voronoi-based 3D spring network model of the lung parenchyma, the Amorphous Network, that exhibits more 2D and 3D similarity to lung geometry than regular polyhedral networks. In contrast to regular networks that show anisotropic force transmission, the structural randomness in the Amorphous Network dissipates this anisotropy with important implications for mechanotransduction. We then added agents to the network that were allowed to carry out a random walk to mimic the migratory behavior of fibroblasts. To model progressive fibrosis, agents were moved around the network and increased the stiffness of springs along their path. Agents migrated at various path lengths until a certain percentage of the network was stiffened. Alveolar ventilation heterogeneity increased with both percent of the network stiffened, and walk length of the agents, until the percolation threshold was reached. The bulk modulus of the network also increased with both percent of network stiffened and path length. This model thus represents a step forward in the creation of physiologically accurate computational models of lung tissue disease.

Identification of a novel adiponectin receptor and opioid receptor dual acting agonist as a potential treatment for diabetic neuropathy

Diabetic neuropathy (DN) is a long-term complication of diabetes mellitus, affecting different periphery nerve systems including sensory and motor neurons. Hyperglycemia is the major cause of DN with symptoms such as weakness of balance or coordination, insensitivity to sensation, weakness of the muscles as well as numbness and pain in limbs Analgesic drug such as opioids can be effective to relief neuropathy pain but there is no effective treatment. Adiponectin is an anti-diabetic adipokine, which possesses insulin-sensitizing and neuroprotective effects. In this project, we aim to identify an agent which is dual acting to opioid and adiponectin receptors. Within a virtual screening repositioning campaign, a large collection of compounds with different structures comprehensive of adipoRon-like piperidine derivatives was screened by docking. Recently developed opioid receptor benzomorphanic agonists finally emerged as good ligands to adiponectin receptors showing some 2D and 3D structural similarities with AdipoRon. Particularly, we have identified (+)-MML1017, which has high affinity to the same binding domain of AdipoR1 and AdipoR2 as AdipoRon. Our western blot results indicate (+)-MML1017 activates AMPK phosphorylation through both adipoR1 and adipoR2 in neuronal cell line. Moreover, pretreatment of (+)-MML1017 can improve the cell viability with motor neurons under hyperglycermic conditions. The (+)-MML1017 also activates μ-opioid receptor cells in a concentration-dependent manner. Our study identified a novel compound having dual activity on opioid receptors and adiponectin receptors that may have analgesic effects and neuroprotective effects to treat diabetic neuropathy.

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design.

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.

Generating 3D porous structures using machine learning and additive manufacturing

Complex structures, often found in nature, may be difficult to replicate or integrate with human-made designs. Generative machine learning may be a useful tool in extracting and transferring complex structure features. A generative adversarial network (GAN) was trained using x-ray microtomography images of porous and lattice structures. Three types of cellular materials were used. Two-dimensional images were generated by the generative network at two resolutions. A bag of features approach was used to sequence the generated images of porous structures. The combination of 2D GAN method and similarity based stacking resulted in 3D structures. The approach aimed at economising on computational cost whilst ensuring a degree of continuity through the structure. The original and generated open cell porous structure images were binarized and 3D surfaces were created using imaging tools. The surfaces were transformed into solid geometries, using computer aided design tools and exported for 3D printing. The compressive behaviour of the specimens was compared. The method generated qualitatively similar structures of consistent relative densities. However the relative density and compressive response of the generated structures diverged in relation to the reduction in resolution. The method shows promise for biomimicking, or generating hybrid natural-artificial structures, based on training sets.

Study on the Mechanism of Treating Femoral Head Necrosis with Drynariae Rhizoma Based on Network Pharmacology.

Through the network pharmacology thought, the action target of the active ingredients of Drynariae Rhizoma was predicted, and the mapping was combined with the related targets of ONFH, and the key nodes of interaction were identified for enrichment analysis, so as to comprehensively explore the pharmacological mechanism of Drynariae Rhizoma against ONFH. The main active ingredients of Drynariae Rhizoma were screened based on pharmacokinetic characteristics in pharmacokinetic database and analysis platform of TCM system (TCMSP). We used the organic small molecule bioactivity database (PubChem) and Swiss target prediction database to predict related targets based on 2D or 3D structural similarity and then mined the known ONFH therapeutic targets through the Human Mendelian Genetic Database (OMIM) and Pubmed texts. Combined with the predicted targets, String database was imported to construct the OP target interaction network diagram of bone fracture therapy. CytoNCA software was used to topology the key nodes of interaction according to relevant node parameters, and String was imported again to construct the protein interaction network diagram. Finally, biological functions and metabolic pathways of key nodes were analyzed through DAVID database. It was revealed that Drynariae Rhizoma may regulate stem cells, osteoblasts, osteoclasts, and immune cells through multiple pathways, including proliferation, differentiation, immunity, and oxidative stress. Conclusion: Pharmacological studies based on network indicate that Drynariae Rhizoma may participate in the regulation of several major signaling pathways through direct or indirect action targets and affect the proliferation and differentiation of multiple types of cells, thus playing an anti-ONFH role, which provides a scientific basis for explaining the material basis and mechanism of its anti- ONFH.

Inhibition of human androgen receptor by delta 9‐tetrahydro‐cannabinol and cannabidiol related to reproductive dysfunction: A computational study

There have been conflicting reports on the impact of Cannabis sativa impact on reproductive function. Hence this study was aimed to ascertain the impact of tetrahydrocannabinol (THC) and cannabidiol (CBD) binding affinity on human androgen receptor (AR) via computational molecular dynamic simulation. The human AR coordinate in this study is derived from human AR in complex with the ligand metribolone (R18) (PBD ID: 1E3G) template using (MODELER version. 9.15). CBD (PubChem CID: 644019), and THC (PubChem CID: 16078) 2D structures were retrieved from PubChem and docked (Autodock-Vina inbuilt in PyMol into the active site of human AR using the coordinates of the co-crystalized ligand (R18). All atomic representations in this study were created using visual molecular dynamics (VMD) tools. The result revealed that neither CBD nor THC bear significant 2D similarity with R18. Despite the diversity within the chemical space, both CBD and THC poses bond flexibility required to bind avidly to AR with the docking scores comparable to R18. In fully bound state, the three compounds engage the AR pocket hydrophobic residues such as L701, L704, and L707, and aromatic residues such as F764. Polar contacts with T877 observed in R18 bound state is avoided in the THC and CBD bound states. Moreso, the results revealed that CBD has lesser binding energy compared to THC and R18 compound which serves as standard. This study hypothesized that CBD and THC binds complimentarily to the pocket AR, indicating a likely inhibition of reproductive function and prostate cancer progression.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators.

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Three-dimensional similarity law for elastic wave propagation in CFRP with concentrically curved fibers

The wave front of an elastic wave in carbon fiber reinforced plastic (CFRP) with curved fibers is distorted owing to the fiber curvature effect. The similarity law to estimate the two-dimensional (2D) elastic bulk wave behavior in CFRP with concentrically curved fibers was presented in our previous study. In this research, the three-dimensional (3D) propagation of bulk waves in CFRPs with concentrically curved fibers was examined. Using 3D-printed CFRP specimens, the 3D propagation behavior of the quasi-longitudinal wave emitted from the point source was visualized virtually. Considering these results, the similarity law was expanded to the case of the 3D propagation behaviors of the bulk waves. Mathematical discussion based on the governing equations shows that the expansion parameters need to be equal in both the thickness and the radial directions to satisfy the similarity law in the 3D case, in addition to the conditions required to satisfy the 2D similarity law. By conducting the experimental visualization, it was demonstrated that the shape changes of quasi-longitudinal waves follow the similarity law, which was eventually verified for 3D propagation.

ToxProfiler: Toxicity-target profiler based on chemical similarity

Identifying the ability of a chemical to interact with toxicity targets, such as proteins in an adverse outcome pathway, is an essential step in drug discovery and risk assessment. Computational approaches to screen for chemical-toxicity target interaction can serve as a rapid alternative to traditional in vitro/in vivo methods. In this work, we have developed a chemical-similarity based protocol that predicts the potential of a chemical to interact with 64 established toxicity targets. In particular, we created a chemogenomics database from public data sources to identify target representatives, i.e., chemicals that are known to interact with the selected targets. We evaluated the performance of 2D and 3D similarity approaches in correctly ranking known interacting compounds using an external evaluation set from ChEMBL database. We found that the 2D approach outperforms the 3D approach in target prediction. Here, we developed a publically available toxicity profiler website (https://toxpro.bhsai.org/) using 2D similarity-based screening approach that allows user to obtain toxicity target profile for a set of query compounds. We utilized the profiler to screen 649 known acute and highly toxic chemicals with a Globally Harmonized System (GHS) score of less than 2. In this set, acetylcholinesterase was the most frequently occurring target underlying toxicity. The developed toxicity profiler tool provides a rapid means to screen for mechanisms underlying chemical toxicity.

The ubiquinone synthesis pathway is a promising drug target for Chagas disease.

Chagas disease is caused by infection with the protozoan parasite Trypanosoma cruzi (T. cruzi). It was originally a Latin American endemic health problem, but now is expanding worldwide as a result of increasing migration. The currently available drugs for Chagas disease, benznidazole and nifurtimox, provoke severe adverse effects, and thus the development of new drugs is urgently required. Ubiquinone (UQ) is essential for respiratory chain and redox balance in trypanosomatid protozoans, therefore we aimed to provide evidence that inhibitors of the UQ biosynthesis have trypanocidal activities. In this study, inhibitors of the human COQ7, a key enzyme of the UQ synthesis, were tested for their trypanocidal activities because they were expected to cross-react and inhibit trypanosomal COQ7 due to their genetic homology. We show the trypanocidal activity of a newly found human COQ7 inhibitor, an oxazinoquinoline derivative. The structurally similar compounds were selected from the commercially available compounds by 2D and 3D ligand-based similarity searches. Among 38 compounds selected, 12 compounds with the oxazinoquinoline structure inhibited significantly the growth of epimastigotes of T. cruzi. The most effective 3 compounds also showed the significant antitrypanosomal activity against the mammalian stage of T. cruzi at lower concentrations than benznidazole, a commonly used drug today. We found that epimastigotes treated with the inhibitor contained reduced levels of UQ9. Further, the growth of epimastigotes treated with the inhibitors was partially rescued by UQ10 supplementation to the culture medium. These results suggest that the antitrypanosomal mechanism of the oxazinoquinoline derivatives results from inhibition of the trypanosomal UQ synthesis leading to a shortage of the UQ pool. Our data indicate that the UQ synthesis pathway of T. cruzi is a promising drug target for Chagas disease.