Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High-throughput metabolomics predicts drug-target relationships for eukaryotic proteins

ABSTRACT: Chemical probes are important tools for understanding signaling systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high-throughput metabolomics and overexpression to predict drug-target interactions. The metabolome profiles of yeast treated with 1280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug-target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug-target relationships for eukaryotic proteins using high-throughput metabolomics.

SUBMITTER: Dr. Duncan Holbrook-Smith

PROVIDER: S-SCDT-MSB-2021-10767 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

High-throughput metabolomics predicts drug-target relationships for eukaryotic membrane proteins

Project description:Saccharomyces cerevisiae were treated with 1280 drugs from the Prestwick chemical library. Intracellular polar metabolite extracts were collected and measured in negative mode by flow injection analysis. In addition, intracellular metabolite extracts were collected and measured for inducible overexpression mutants in yeast membrane proteins.

2020-11-13 | MSV000086451 | MassIVE

GNPS - High-throughput metabolomics predicts drug-target relationships for eukaryotic membrane proteins

2020-11-13 | MSV000086451 | GNPS

High-throughput affinity denaturation assay for constructing drug target space

Project description:Targeted proteomics data was acquired on a QE-HF mass spectrometer with data-independent acquisition (DIA) model.

2021-04-28 | PXD025669 | JPOST Repository

Domain selection combined with improved cloning strategy for high throughput expression of higher eukaryotic proteins.

Project description:Expression of higher eukaryotic genes as soluble, stable recombinant proteins is still a bottleneck step in biochemical and structural studies of novel proteins today. Correct identification of stable domains/fragments within the open reading frame (ORF), combined with proper cloning strategies, can greatly enhance the success rate when higher eukaryotic proteins are expressed as these domains/fragments. Furthermore, a HTP cloning pipeline incorporated with bioinformatics domain/fragment selection methods will be beneficial to studies of structure and function genomics/proteomics.With bioinformatics tools, we developed a domain/domain boundary prediction (DDBP) method, which was trained by available experimental data. Combined with an improved cloning strategy, DDBP had been applied to 57 proteins from C. elegans. Expression and purification results showed there was a 10-fold increase in terms of obtaining purified proteins. Based on the DDBP method, the improved GATEWAY cloning strategy and a robotic platform, we constructed a high throughput (HTP) cloning pipeline, including PCR primer design, PCR, BP reaction, transformation, plating, colony picking and entry clones extraction, which have been successfully applied to 90 C. elegans genes, 88 Brucella genes, and 188 human genes. More than 97% of the targeted genes were obtained as entry clones. This pipeline has a modular design and can adopt different operations for a variety of cloning/expression strategies.The DDBP method and improved cloning strategy were satisfactory. The cloning pipeline, combined with our recombinant protein HTP expression pipeline and the crystal screening robots, constitutes a complete platform for structure genomics/proteomics. This platform will increase the success rate of purification and crystallization dramatically and promote the further advancement of structure genomics/proteomics.

| S-EPMC1950093 | biostudies-literature

High-Throughput Metabolomics by 1D NMR.

Project description:Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

| S-EPMC6391965 | biostudies-literature

High-throughput affinity responsive denaturation assay for construncting drug target space

Project description:Targeted proteomics data was acquired on a QE-HF mass spectrometer with data-independent acquisition (DIA) model.

2021-03-28 | PXD025037 | JPOST Repository

Predicting network activity from high throughput metabolomics.

Project description:The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

| S-EPMC3701697 | biostudies-literature

Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships.

Project description:The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.

| S-EPMC8533369 | biostudies-literature

A high-throughput drug screen for Entamoeba histolytica identifies a new lead and target.

Project description:Entamoeba histolytica, a protozoan intestinal parasite, is the causative agent of human amebiasis. Amebiasis is the fourth leading cause of death and the third leading cause of morbidity due to protozoan infections worldwide(1), resulting in ~70,000 deaths annually. E. histolytica has been listed by the National Institutes of Health as a category B priority biodefense pathogen in the United States. Treatment relies on metronidazole(2), which has adverse effects(3), and potential resistance of E. histolytica to the drug is an increasing concern(4,5). To facilitate drug screening for this anaerobic protozoan, we developed and validated an automated, high-throughput screen (HTS). This screen identified auranofin, a US Food and Drug Administration (FDA)-approved drug used therapeutically for rheumatoid arthritis, as active against E. histolytica in culture. Auranofin was ten times more potent against E. histolytica than metronidazole. Transcriptional profiling and thioredoxin reductase assays suggested that auranofin targets the E. histolytica thioredoxin reductase, preventing the reduction of thioredoxin and enhancing sensitivity of trophozoites to reactive oxygen-mediated killing. In a mouse model of amebic colitis and a hamster model of amebic liver abscess, oral auranofin markedly decreased the number of parasites, the detrimental host inflammatory response and hepatic damage. This new use of auranofin represents a promising therapy for amebiasis, and the drug has been granted orphan-drug status from the FDA.

| S-EPMC3411919 | biostudies-literature

High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data

Project description: Not available

| S-EPMC10713627 | biostudies-literature