Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection.

Complex mixtures containing natural products are still an interesting source of novel drug candidates. High content screening (HCS) is a popular tool to screen for such. In particular, multiplexed HCS assays promise comprehensive bioactivity profiles, but generate also high amounts of data. Yet, only some machine learning (ML) applications for data analysis are available and these usually require a profound knowledge of the underlying cell biology. Unfortunately, there are no applications that simply predict if samples are biologically active or not (any kind of bioactivity). Within this work, we benchmark ML algorithms for binary classification, starting with classical ML models, which are the standard classifiers of the scikit-learn library or ensemble models of these classifiers (a total of 92 models tested). Followed by a partial least square regression (PLSR)-based classification (44 tested models in total) and simple artificial neural networks (ANNs) with dense layers (72 tested models in total). In addition, a novelty detection (ND) was examined, which is supposed to handle unknown patterns. For the final analysis the models, with and without upstream ND, were tested with two independent data sets. In our analysis, a stacking model, an ensamble model of class ML algorithms, performed best to predict new and unknown data. ND improved the predictions of the models and was useful to handle unknown patterns. Importantly, the classifier presented here can be easily rebuilt and be adapted to the data and demands of other groups. The hit detector (ND + stacking model) is universal and suitable for a broader application to support the search for new drug candidates.

E2/E3-independent ubiquitin-like protein conjugation by Urm1 is directly coupled to cysteine persulfidation.

Post-translational modifications by ubiquitin-like proteins (UBLs) are essential for nearly all cellular processes. Ubiquitin-related modifier 1 (Urm1) is a unique UBL, which plays a key role in tRNA anticodon thiolation as a sulfur carrier protein (SCP) and is linked to the noncanonical E1 enzyme Uba4 (ubiquitin-like protein activator 4). While Urm1 has also been observed to conjugate to target proteins like other UBLs, the molecular mechanism of its attachment remains unknown. Here, we reconstitute the covalent attachment of thiocarboxylated Urm1 to various cellular target proteins in vitro, revealing that, unlike other known UBLs, this process is E2/E3-independent and requires oxidative stress. Furthermore, we present the crystal structures of the peroxiredoxin Ahp1 before and after the covalent attachment of Urm1. Surprisingly, we show that urmylation is accompanied by the transfer of sulfur to cysteine residues in the target proteins, also known as cysteine persulfidation. Our results illustrate the role of the Uba4-Urm1 system as a key evolutionary link between prokaryotic SCPs and the UBL modifications observed in modern eukaryotes.

High-Content Screening Identifies Cyclosporin A as a Novel ABCA3-Specific Molecular Corrector.

ABCA3 (ATP-binding cassette subfamily A member 3) is a lipid transporter expressed in alveolar type II cells and localized in the limiting membrane of lamellar bodies. It is crucial for pulmonary surfactant storage and homeostasis. Mutations in the ABCA3 gene are the most common genetic cause of respiratory distress syndrome in mature newborns and of interstitial lung disease in children. Apart from lung transplant, there is no cure available. To address the lack of causal therapeutic options for ABCA3 deficiency, a rapid and reliable approach is needed to investigate variant-specific molecular mechanisms and to identify pharmacologic modulators for monotherapies or combination therapies. To this end, we developed a phenotypic cell-based assay to autonomously identify ABCA3 wild-type-like or mutant-like cells by using machine learning algorithms aimed at identifying morphologic differences in wild-type and mutant cells. The assay was subsequently used to identify new drug candidates for ABCA3-specific molecular correction by using high-content screening of 1,280 Food and Drug Administration-approved small molecules. Cyclosporin A was identified as a potent corrector, specific for some but not all ABCA3 variants. Results were validated by using our previously established functional small-format assays. Hence, cyclosporin A may be selected for orphan drug evaluation in controlled repurposing trials in patients.

Combination therapies induce cancer cell death through the integrated stress response and disturbed pyrimidine metabolism.

By accentuating drug efficacy and impeding resistance mechanisms, combinatorial, multi-agent therapies have emerged as key approaches in the treatment of complex diseases, most notably cancer. Using high-throughput drug screens, we uncovered distinct metabolic vulnerabilities and thereby identified drug combinations synergistically causing a starvation-like lethal catabolic response in tumor cells from different cancer entities. Domperidone, a dopamine receptor antagonist, as well as several tricyclic antidepressants (TCAs), including imipramine, induced cancer cell death in combination with the mitochondrial uncoupler niclosamide ethanolamine (NEN) through activation of the integrated stress response pathway and the catabolic CLEAR network. Using transcriptome and metabolome analyses, we characterized a combinatorial response, mainly driven by the transcription factors CHOP and TFE3, which resulted in cell death through enhanced pyrimidine catabolism as well as reduced pyrimidine synthesis. Remarkably, the drug combinations sensitized human organoid cultures to the standard-of-care chemotherapy paclitaxel. Thus, our combinatorial approach could be clinically implemented into established treatment regimen, which would be further facilitated by the advantages of drug repurposing.

Image-based high-content screening in drug discovery.

While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions.

Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal-ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal-ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally observed temperature-dependent geometric structure changes, by correctly assigning almost all (>95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry.

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

Association between interleukin 1ß and interleukin 10 concentrations: a cross-sectional study in young adolescents in Taiwan.

BACKGROUND: In adults, low circulating interleukin 10 (IL10) has been associated with obesity and type 2 diabetes. However, studies investigating IL10 in overweight and obese children have yielded conflicting results. The aim of this study was to investigate factors associated with serum IL10 concentration in young Chinese adolescents. METHODS: Young adolescents (n=325) ages 13.33±1.10 years were recruited into the cross-sectional study from 2010 to 2011. Parameters of obesity, individual components of MetS, iron status and serum IL10 were evaluated. RESULTS: Compared with their normal weight counterparts, overweight adolescents had lower serum IL10 but higher TNFα, nitric oxide (NO) and IL1ß concentrations (all p<0.05). Obese adolescents had increased IL1ß but decreased hepcidin concentration compared with normal weight (p<0.01 and p<0.05; respectively). A strong inverse relationship (p<0.0001) was found between IL10 and pro-inflammatory cytokines (TNFα and IL1ß). Multivariate linear regression analysis showed serum IL1ß was significantly correlated with IL10 (ß=-0.156, p<0.0001). When overweight and obese adolescents were assessed separately from normal weight, only IL1ß was inversely associated with serum IL10 (ß=-0.231, p=0.0009). The association between IL10 and IL1ß was weaker in adolescents with normal weight (ß=-0.157, p=0.0002), after adjusting for gender, TNFα, IFNγ and NO. CONCLUSIONS: Our study confirmed that low IL10 concentration is associated with overweight and obesity in young adolescents. We also demonstrated for the first time that pro-inflammatory cytokine IL1ß is independently associated with IL10. A decline in IL10 concentration in overweight and obese adolescents may further contribute to the IL1ß-mediated inflammatory environment associated with obesity.

Production of monodisperse epigallocatechin gallate (EGCG) microparticles by spray drying for high antioxidant activity retention.

Epigallocatechin gallate (EGCG) originated from green tea is well-known for its pharmaceutical potential and antiproliferating effect on carcinoma cells. For drug delivery, EGCG in a micro-/nanoparticle form is desirable for their optimized chemopreventive effect. In this study, first time reports that EGCG microparticles produced by low temperature spray drying can maintain high antioxidant activity. A monodisperse droplet generation system was used to realize the production of EGCG microparticles. EGCG microparticles were obtained with narrow size distribution and diameter of 30.24 ± 1.88 µM and 43.39 ± 0.69 µM for pure EGCG and lactose-added EGCG, respectively. The EC50 value (the amount of EGCG necessary to scavenge 50% of free radical in the medium) of spray dried pure EGCG particles obtained from different temperature is in the range of 3.029-3.075 µM compared to untreated EGCG with EC50 value of 3.028 µM. Varying the drying temperatures from 70°C and 130°C showed little detrimental effect on EGCG antioxidant activity. NMR spectrum demonstrated the EGCG did not undergo chemical structural change after spray drying. The major protective mechanism was considered to be: (1) the use of low temperature and (2) the heat loss from water evaporation that kept the particle temperature at low level. With further drier optimization, this monodisperse spray drying technique can be used as an efficient and economic approach to produce EGCG micro-/nanoparticles.

High-throughput, high-accuracy array-based resequencing.

Although genomewide association studies have successfully identified associations of many common single-nucleotide polymorphisms (SNPs) with common diseases, the SNPs implicated so far account for only a small proportion of the genetic variability of tested diseases. It has been suggested that common diseases may often be caused by rare alleles missed by genomewide association studies. To identify these rare alleles we need high-throughput, high-accuracy resequencing technologies. Although array-based genotyping has allowed genomewide association studies of common SNPs in tens of thousands of samples, array-based resequencing has been limited for 2 main reasons: the lack of a fully multiplexed pipeline for high-throughput sample processing, and failure to achieve sufficient performance. We have recently solved both of these problems and created a fully multiplexed high-throughput pipeline that results in high-quality data. The pipeline consists of target amplification from genomic DNA, followed by allele enrichment to generate pools of purified variant (or nonvariant) DNA and ends with interrogation of purified DNA on resequencing arrays. We have used this pipeline to resequence approximately 5 Mb of DNA (on 3 arrays) corresponding to the exons of 1,500 genes in >473 samples; in total >2,350 Mb were sequenced. In the context of this large-scale study we obtained a false positive rate of approximately 1 in 500,000 bp and a false negative rate of approximately 10%.