Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data

ABSTRACT: Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data

PROVIDER: PRJNA950097 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR24010001_1.fastq.gz	Fastqsanger.gz
	SRR24010001_2.fastq.gz	Fastqsanger.gz
	SRR24010002_1.fastq.gz	Fastqsanger.gz
	SRR24010002_2.fastq.gz	Fastqsanger.gz
	SRR24010003_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data

Project description:This study aimed to compare the mechanisms of action (MOA) of similar natural compounds and observed the changes when they are mixed and used.

2023-04-03 | GSE228524 | GEO

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations.

Project description:Natural products have successfully treated several diseases using a multi-component, multi-target mechanism. However, a precise mechanism of action (MOA) has not been identified. Systems pharmacology methods have been used to overcome these challenges. However, there is a limitation as those similar mechanisms of similar components cannot be identified. In this study, comparisons of physicochemical descriptors, molecular docking analysis and RNA-seq analysis were performed to compare the MOA of similar compounds and to confirm the changes observed when similar compounds were mixed and used. Various analyses have confirmed that compounds with similar structures share similar MOA. We propose an advanced method for in silico experiments in herbal medicine research based on the results. Our study has three novel findings. First, an advanced network pharmacology research method was suggested by partially presenting a solution to the difficulty in identifying multi-component mechanisms. Second, a new natural product analysis method was proposed using large-scale molecular docking analysis. Finally, various biological data and analysis methods were used, such as in silico system pharmacology, docking analysis and drug response RNA-seq. The results of this study are meaningful in that they suggest an analysis strategy that can improve existing systems pharmacology research analysis methods by showing that natural product-derived compounds with the same scaffold have the same mechanism.

| S-EPMC10555731 | biostudies-literature

Identification and Validation of Small Molecule Inhibitors Targeting FGFR through Molecular Docking-Based Screening

Project description:Fibroblast growth factors and their receptors (FGFRs) play important roles in multiple cellular processes. FGFR genetic alterations such as mutations, amplifications, and chromosomal translocations are prevalent in various cancer types, contributing to the initiation and progression of tumors by enhancing FGFR signaling. Identifying effective small molecule inhibitors that selectively target FGFR can advance cancers therapy driven by FGFR abnormalities. Here, we conducted molecular docking and dynamics simulations to discover new small compounds targeting FGFRs. By docking with 2.8 million small molecules, we identified three promising FGFR inhibitors ranked in the top average absolute difference in free energy. By evaluating the binding stability of the docking pose of these three compounds, we found that ZINC000101867325 formed the most stable binding interactions with FGFRs. After treatment with ZINC000101867325, colorectal cancer cell lines showed a decrease in FGFR phosphorylation and early apoptosis. In addition, ZINC000101867325 is also predicted to interact with FGFR2 mutations in colorectal cancer (CRC) patients. This study provides the novel FGFR inhibitors, and enriches treatment strategies for cancers.

2024-06-30 | GSE267868 | GEO

Hypoglycemic Efficacy of Docking Selected Natural Compounds against ?-Glucosidase and ?-Amylase.

Project description:The inhibition of ?-glucosidase and ?-amylase is a clinical strategy for the treatment of type II diabetes, and herbal medicines have been reported to credibly alleviate hyperglycemia. Our previous study has reported some constituents from plant or herbal sources targeted to ?-glucosidase and ?-amylase via molecular docking and enzymatic measurement, but the hypoglycemic potencies in cell system and mice have not been validated yet. This study was aimed to elucidate the hypoglycemic efficacy of docking selected compounds in cell assay and oral glucose and starch tolerance tests of mice. All test compounds showed the inhibition of ?-glucosidase activity in Caco-2 cells. The decrease of blood sugar levels of test compounds in 30 min and 60 min of mice after OGTT and OSTT, respectively and the decreased glucose levels of test compounds were significantly varied in acarbose. Taken altogether, in vitro and in vivo experiments suggest that selected natural compounds (curcumin, antroquinonol, HCD, docosanol, tetracosanol, rutin, and actinodaphnine) via molecular docking were confirmed as potential candidates of ?-glucosidase and ?-amylase inhibitors for treating diabetes.

| S-EPMC6225388 | biostudies-other

Profiling of novel p53-inducing compounds

Project description:We describe six novel lead compounds activating the p53 tumor suppressor pathway. Transcriptional profiling indicated that they invoked highly similar responses involving p53 and cell cycle pathways and DNA damage response signaling. Keywords: Stress response Six compounds and one vehicle control, with four biological replicates each, for a total of 28 samples.

2010-10-01 | E-GEOD-12666 | biostudies-arrayexpress

Active natural compounds perturb the melanoma risk-gene network

Project description:Active natural compounds perturb the melanoma risk-gene network

| PRJNA1032634 | ENA

Profiling of novel p53-inducing compounds

2010-10-01 | GSE12666 | GEO

deep sea sulfur structures

Project description:deep sea sulfur structures

| PRJNA691673 | ENA

A scalable organoid model of autosomal dominant polycystic kidney disease for disease mechanism and drug discovery

Project description:Human pluripotent stem cell-derived organoids are models for human development and disease. We report a modified human kidney organoid system that generates thousands of similar kidney organoids, each consisting of 1 to 2 nephron-like structures. Single-cell transcriptomic profiling and immunofluorescence validation highlighted patterned nephron-like structures, utilizing similar pathways, with distinct morphogenesis, to human nephrogenesis. To examine this platform for therapeutic screening, the polycystic kidney disease genes PKD1 and PKD2 were inactivated by gene-editing. PKD1 and PKD2 mutant models exhibited efficient and reproducible cyst formation. Cystic outgrowths could be propagated for months to centimeter-sized cysts. To shed new light on cystogenesis, 247 protein kinase inhibitors (PKI) were screened in a live imaging assay identifying novel compounds blocking cyst formation but not overall organoid growth. Scaling and further development of the organoid platform will enable a broader capability for kidney disease modeling and high-throughput drug screens.

2022-06-23 | GSE164564 | GEO

Active natural compounds perturb the melanoma risk-gene network

Project description:Cutaneous melanoma is an aggressive type of skin cancer with a complex genetic landscape caused by the malignant transformation of melanocytes. The study aimed at providing an in-silico network model based on the systematic profiling of the melanoma-associated genes considering germline mutations, somatic mutations, and genome-wide association studies (GWAS) signals (collectively melanoma risk genes). A protein-protein interaction network (melanoma risk network) was constructed using the melanoma risk genes to describe the functional landscape in which the melanoma genes operate within the cellular milieu. A significant portion of the melanoma risk network showed differential expression when SK-MEL-28 human melanoma cells were exposed to the phytochemicals harmine and berberine chloride reinforcing the hypothesis that network modelling may represent an alternative screening approach to prioritize potentially active compounds.

2023-11-01 | GSE246383 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data