High-throughput affinity responsive denaturation assay for construncting drug target space
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens (human)
SUBMITTER: Mingliang Ye
PROVIDER: PXD025037 | JPOST Repository | Sun Mar 28 00:00:00 GMT 2021
REPOSITORIES: jPOST
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| HF_RCF20200612_heat52_Stau_1.raw | Raw | |||
| HF_RCF20200612_heat52_Stau_2.raw | Raw | |||
| HF_RCF20200612_heat52_Stau_3.raw | Raw | |||
| HF_RCF20200612_heat52_Stau_4.raw | Raw | |||
| HF_RCF20200612_heat52_Stau_5.raw | Raw |
Items per page: 5 1 - 5 of 20 |
Similar Datasets
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
Project description:Identifying the molecular targets for the beneficial or detrimental effects of small-molecule drugs is an important and currently unmet challenge. We have developed a method, drug affinity responsive target stability (DARTS), which takes advantage of a reduction in the protease susceptibility of the target protein upon drug binding. DARTS is universally applicable because it requires no modification of the drug and is independent of the mechanism of drug action. We demonstrate use of DARTS to identify known small-molecule-protein interactions and to reveal the eukaryotic translation initiation machinery as a molecular target for the longevity-enhancing plant natural product resveratrol. We envisage that DARTS will also be useful in global mapping of protein-metabolite interaction networks and in label-free screening of unlimited varieties of compounds for development as molecular imaging agents.
| S-EPMC2789755 | biostudies-literature
Project description:Targeted proteomics data was acquired on a QE-HF mass spectrometer with data-independent acquisition (DIA) model.
2022-01-10 | PXD025039 | JPOST Repository
Project description:Drug affinity responsive target of Benbromarone for the treatment of Staphylococcus aureus infections
2024-05-23 | PXD052501 | iProX
Project description:Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS) screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development. Cryptococcosis is a neglected fungal meningitis that causes approximately half a million deaths annually. The most effective antifungal agent, amphotericin B, was developed in the 1950s, and no effective medicine has been developed for this disease since that time. A key aspect of amphotericin B's effectiveness is thought to be because of its ability to kill the fungus (fungicidal activity), rather than just stop or slow its growth. The present study utilized a recently identified fungicidal agent, bithionol, to identify potential fungicidal drug targets that can be used in developing modern fungicidal agents. A combined protein and genetic analysis approach was used to identify a class of enzymes, dehydrogenases, that the fungus uses to maintain homeostasis with regard to sugar nutrients. Similarities in the drug target site were found that resulted in simultaneous inhibition and killing of the fungus by bithionol. These studies thus identify a common, multitarget site for antifungal development.
| S-EPMC4981720 | biostudies-literature
Project description:Artemetin is a valuable 5-hydroxy-3,6,7,3',4'-pentamethoxyflavone found in many different medicinal plants with a very good oral bioavailability and drug-likeness values endowed with numerous bioactivities such as anti-inflammatory and anti-cancer ones. Here, a multi-disciplinary strategy has been developed and applied for identifying Artemetin target(s) to fully address its mechanism of action, based on drug affinity response target stability and targeted limited proteolysis. Both approaches point to the identification of Filamins A and B as major Artemetin targets in HeLa cell lysates, giving also detailed insights into the ligand/proteins interaction sites. Interestingly, also the 8-prenyl-Artemetin, which is a more permeable semisynthetic analog, directly interacts with Filamin A and B. Both compounds alter filamins conformation in living HeLa cells with an effect on cytoskeleton disassembly and on the disorganization of the F-actin filaments, mediated by the dissociation of filamins. Both the natural compound and its derivative are able to block cell migration, expectantly acting on tumor metastasis’s occurrence and development.
2022-10-14 | PXD034776 | Pride
Project description:MotivationThe identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).ResultsThe results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.Availability and implementationhttps://github.com/hkmztrk/DeepDTA.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6129291 | biostudies-literature
Project description:Various natural agents, including grape seed extract (GSE), have shown considerable chemopreventive and anti-cancer efficacy against different cancers in pre-clinical studies; however, their specific protein targets are largely unknown and thus, their clinical usefulness is marred by limited scientific evidences about their direct cellular targets. Accordingly, herein, employing, for the first time, the recently developed drug affinity responsive target stability (DARTS) technique, we aimed to profile the potential protein targets of GSE in human colorectal cancer (CRC) cells. Unlike other methods, which can cause chemical alteration of the drug components to allow for detection, this approach relies on the fact that a drug bound protein may become less susceptible to proteolysis and hence the enriched proteins can be detected by Mass Spectroscopy methods. Our results, utilizing the DARTS technique followed by examination of the spectral output by LC/MS and the MASCOT data, revealed that GSE targets endoplasmic reticulum (ER) stress response proteins resulting in overall down regulation of proteins involved in translation and that GSE also causes oxidative protein modifications, specifically on methionine amino acids residues on its protein targets. Corroborating these findings, mechanistic studies revealed that GSE indeed caused ER stress and strongly inhibited PI3k-Akt-mTOR pathway for its biological effects in CRC cells. Furthermore, bioenergetics studies indicated that GSE also interferes with glycolysis and mitochondrial metabolism in CRC cells. Together, the present study identifying GSE molecular targets in CRC cells, combined with its efficacy in vast pre-clinical CRC models, further supports its usefulness for CRC prevention and treatment.
| S-EPMC4217504 | biostudies-literature
Project description:The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label information. However, in biomedicine, obtaining labeled training data is an expensive and a laborious process. This paper proposes a semi-supervised generative adversarial networks (GANs)-based method to predict binding affinity. Our method comprises two parts, two GANs for feature extraction and a regression network for prediction. The semi-supervised mechanism allows our model to learn proteins drugs features of both labeled and unlabeled data. We evaluate the performance of our method using multiple public datasets. Experimental results demonstrate that our method achieves competitive performance while utilizing freely available unlabeled data. Our results suggest that utilizing such unlabeled data can considerably help improve performance in various biomedical relation extraction processes, for example, Drug-Target interaction and protein-protein interaction, particularly when only limited labeled data are available in such tasks. To our best knowledge, this is the first semi-supervised GANs-based method to predict binding affinity.
| S-EPMC6962343 | biostudies-literature
Project description:Determining and understanding drug target engagement is critical for drug discovery. This can be challenging within living cells as selective readouts are often unavailable. Here we describe a novel method for measuring target engagement in living cells based on the principle of altered protein thermal stabilization / destabilization in response to ligand binding. This assay (HCIF-CETSA) utilizes high content, high throughput single cell immunofluorescent detection to determine target protein levels following heating of adherent cells in a 96 well plate format. We have used target engagement of Chk1 by potent small molecule inhibitors to validate the assay. Target engagement measured by this method was subsequently compared to target engagement measured by two alternative methods (autophosphorylation and CETSA). The HCIF-CETSA method appeared robust and a good correlation in target engagement measured by this method and CETSA for the selective Chk1 inhibitor V158411 was observed. However, these EC50 values were 23- and 12-fold greater than the autophosphorylation IC50. The described method is therefore a valuable advance in the CETSA method allowing the high throughput determination of target engagement in adherent cells.
| S-EPMC5884524 | biostudies-literature