Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of nonanimal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/.
- MeSH
- Databases, Factual MeSH
- Small Molecule Libraries chemistry pharmacology MeSH
- Skin Tests * MeSH
- Skin drug effects MeSH
- Molecular Structure MeSH
- Mice MeSH
- Organic Chemicals chemistry pharmacology MeSH
- Local Lymph Node Assay MeSH
- Animals MeSH
- Check Tag
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: Quercetin is a natural flavonoid, which widely exists in nature, such as tea, coffee, apples, and onions. Numerous studies have showed that quercetin has multiple biological activities such as anti-oxidation, anti-inflammatory, and anti-aging. Hence, quercetin has a significant therapeutic effect on cancers, obesity, diabetes, and other diseases. In the past decades, a large number of studies have shown that quercetin combined with other agents can significantly improve the overall therapeutic effect, compared to single use. PURPOSE: This work reviews the pharmacological activities of quercetin and its derivatives. In addition, this work also summarizes both in vivo and in vitro experimental evidence for the synergistic effect of quercetin against cancers and metabolic diseases. METHODS: An extensive systematic search for pharmacological activities and synergistic effect of quercetin was performed considering all the relevant literatures published until August 2021 through the databases including NCBI PubMed, Scopus, Web of Science, and Google Scholar. The relevant literatures were extracted from the databases with following keyword combinations: "pharmacological activities" OR "biological activities" OR "synergistic effect" OR "combined" OR "combination" AND "quercetin" as well as free-text words. RESULTS: Quercetin and its derivatives possess multiple pharmacological activities including anti-cancer, anti-oxidant, anti-inflammatory, anti-cardiovascular, anti-aging, and neuroprotective activities. In addition, the synergistic effect of quercetin with small molecule agents against cancers and metabolic diseases has also been confirmed. CONCLUSION: Quercetin cooperates with agents to improve the therapeutic effect by regulating signal molecules and blocking cell cycle. Synergistic therapy can reduce the dose of agents and avoid the possible toxic and side effects in the treatment process. Although quercetin treatment has some potential side effects, it is safe under the expected use conditions. Hence, quercetin has application value and potential strength as a clinical drug. Furthermore, quercetin, as the main effective therapeutic ingredient in traditional Chinese medicine, may effectively treat and prevent coronavirus disease 2019 (COVID-19).
- MeSH
- Antioxidants pharmacology MeSH
- COVID-19 * MeSH
- Humans MeSH
- Quercetin * pharmacology MeSH
- Plant Extracts MeSH
- SARS-CoV-2 MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>-60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.
- Publication type
- Journal Article MeSH
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds", "bad actors", or "nuisance compounds". These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on "privileged scaffolds" to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose-response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de , not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.
- MeSH
- Databases, Pharmaceutical MeSH
- Small Molecule Libraries chemistry MeSH
- Pharmaceutical Preparations chemistry MeSH
- Models, Molecular MeSH
- Proteins chemistry MeSH
- ROC Curve MeSH
- High-Throughput Screening Assays methods MeSH
- Machine Learning * MeSH
- Protein Binding MeSH
- Binding Sites MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de.
BACKGROUND: MicroRNAs are small non-coding one-stranded RNA molecules that play an important role in the post-transcriptional regulation of genes. Bioinformatic predictions indicate that each miRNA can regulate hundreds of target genes. MicroRNA expression can be associated with various cellular processes leading to the metastasis of malignant tumours including non-small cell lung carcinoma. This review summarizes current knowledge on the role of microRNAs in NSCLC metastasis to the brain and lymph nodes. METHODS: A search of the NCBI/PubMed database for publications on expression levels and the mechanisms of microRNA action in NSCLC metastasis. RESULTS AND CONCLUSION: Dysregulation of microRNAs in NSCLC can be associated with brain and lymph node metastasis. There are differences in microRNA expression profiling between NSCLC with and without metastases but it is currently not possible to reliably predict the site of metastasis in NSCLC. Based on data from RNAmicroarrays, bioinformatics analysis is able to predict the target genes of highlighted microRNAs, providing us with complex information about cancer cell features such as enhanced proliferation, migration and invasion. Such microRNAs may then be knocked-down using siRNAs or substituted with miRNA mimics. RNA microarray profiling may thus be a useful tool to select up- or down-regulated microRNAs. A number of authors suggest that microRNAs could serve as biomarkers and therapeutic targets in the treatment of NSCLC metastasis.
- MeSH
- Down-Regulation MeSH
- Humans MeSH
- Lymphatic Metastasis MeSH
- Neoplasm Metastasis MeSH
- MicroRNAs physiology MeSH
- Cell Line, Tumor MeSH
- Bone Neoplasms secondary MeSH
- Brain Neoplasms secondary MeSH
- Lung Neoplasms etiology MeSH
- Carcinoma, Non-Small-Cell Lung etiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
The Protein Data Bank in Europe (PDBe), a founding member of the Worldwide Protein Data Bank (wwPDB), actively participates in the deposition, curation, validation, archiving and dissemination of macromolecular structure data. PDBe supports diverse research communities in their use of macromolecular structures by enriching the PDB data and by providing advanced tools and services for effective data access, visualization and analysis. This paper details the enrichment of data at PDBe, including mapping of RNA structures to Rfam, and identification of molecules that act as cofactors. PDBe has developed an advanced search facility with ∼100 data categories and sequence searches. New features have been included in the LiteMol viewer at PDBe, with updated visualization of carbohydrates and nucleic acids. Small molecules are now mapped more extensively to external databases and their visual representation has been enhanced. These advances help users to more easily find and interpret macromolecular structure data in order to solve scientific problems.
- MeSH
- Databases, Protein * MeSH
- Protein Conformation MeSH
- Cluster Analysis MeSH
- Software * MeSH
- Data Accuracy MeSH
- User-Computer Interface MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
Pre-mRNA splicing represents an important regulatory layer of eukaryotic gene expression. In the simple budding yeast Saccharomyces cerevisiae, about one-third of all mRNA molecules undergo splicing, and splicing efficiency is tightly regulated, for example, during meiotic differentiation. S. cerevisiae features a streamlined, evolutionarily highly conserved splicing machinery and serves as a favourite model for studies of various aspects of splicing. RNA-seq represents a robust, versatile, and affordable technique for transcriptome interrogation, which can also be used to study splicing efficiency. However, convenient bioinformatics tools for the analysis of splicing efficiency from yeast RNA-seq data are lacking. We present a complete workflow for the calculation of genome-wide splicing efficiency in S. cerevisiae using strand-specific RNA-seq data. Our pipeline takes sequencing reads in the FASTQ format and provides splicing efficiency values for the 5' and 3' splice junctions of each intron. The pipeline is based on up-to-date open-source software tools and requires very limited input from the user. We provide all relevant scripts in a ready-to-use form. We demonstrate the functionality of the workflow using RNA-seq datasets from three spliceosome mutants. The workflow should prove useful for studies of yeast splicing mutants or of regulated splicing, for example, under specific growth conditions.
Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.
- MeSH
- Small Molecule Libraries * pharmacology chemistry MeSH
- Humans MeSH
- Ligands MeSH
- Receptors, Glucocorticoid * metabolism chemistry MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) play a crucial role in structure-guided drug discovery and design, and also provide atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. The quality with which small-molecule ligands have been modelled in Protein Data Bank (PDB) entries has been, and continues to be, a matter of concern for many investigators. Correctly interpreting whether electron density found in a binding site is compatible with the soaked or co-crystallized ligand or represents water or buffer molecules is often far from trivial. The Worldwide PDB validation report (VR) provides a mechanism to highlight any major issues concerning the quality of the data and the model at the time of deposition and annotation, so the depositors can fix issues, resulting in improved data quality. The ligand-validation methods used in the generation of the current VRs are described in detail, including an examination of the metrics to assess both geometry and electron-density fit. It is found that the LLDF score currently used to identify ligand electron-density fit outliers can give misleading results and that better ligand-validation metrics are required.
- MeSH
- Databases, Protein * MeSH
- Protein Conformation * MeSH
- Crystallography, X-Ray MeSH
- Humans MeSH
- Ligands MeSH
- Macromolecular Substances chemistry MeSH
- Models, Molecular MeSH
- Molecular Structure MeSH
- Proteins analysis chemistry MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Validation Study MeSH
