scholarly journals Comprehensive protein-protein interaction analysis (proteome analysis) using "in vitro virus" and its application for drug target exploration

2003 ◽  
Vol 18 (6) ◽  
pp. 519-527
Author(s):  
Hideaki Takashima ◽  
Hiroshi Yanagawa
Keyword(s):  
Protein Interaction ◽  
Drug Target ◽  
Interaction Analysis ◽  
Proteome Analysis ◽  
Protein Protein Interaction

scholarly journals Molecular Mechanism of Gleditsiae Spina for the Treatment of High-grade Serous Ovarian Cancer Based on Network Pharmacology and Experimental Verification

2021 ◽  
Author(s):  
Boran Zhang ◽  
Wenchao Dan ◽  
Xing Chen ◽  
Cunfang Dai ◽  
Guangda Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Protein Interaction ◽  
Interaction Analysis ◽  
Dynamics Simulation ◽  
Network Pharmacology ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Active Ingredients ◽  
Protein Protein Interaction

Abstract Background In this study, we aimed to analyze the pharmacological mechanism of Gleditsiae Spina in the treatment of high-grade serous ovarian cancer (HGSC) based on network pharmacology and in vitro experiments. Methods The main active ingredients of Gleditsiae Spina were identified by high performance liquid chromatography and mass spectrometry, and ADME screening was performed. The component targets of Gleditsiae Spina were screened using the pharmMapper platform, and differentially expressed genes in normal and HGSC tissues were identified through GEO database. Thereafter, Cytoscape 3.7.2 software was used to construct the network of "active ingredient-targets," and the BioGenet database was used for protein-protein interaction analysis. Furthermore, the protein-protein interaction network was established, and the potential protein function module was mined. Biological processes and pathways were analyzed through gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Results The core active ingredients of Gleditsiae Spina for regulating HGSC included luteolin, genistein, D-(+)-tryptophan, ursolic acid, and berberine. The ideal targets were HPSE, PI3KCA, AKT1, and CTNNB1. The prediction results were verified by molecular docking, molecular dynamics simulation, and western blot analysis. Conclusions This study revealed the mechanism of Gleditsiae Spina for the treatment of HGSC based on multi-components, multi-targets, and multi-channels. It also provides a theoretical basis for the prevention of ovarian cancer and its treatment using traditional Chinese medicine in the future.

scholarly journals An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3174 ◽  
Author(s):  
Xin Xue ◽  
Gang Bao ◽  
Hai-Qing Zhang ◽  
Ning-Yi Zhao ◽  
Yuan Sun ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Target ◽  
P53 Protein ◽  
Pharmacophore Model ◽  
Cellular Level ◽  
Complex Structures ◽  
Ligand Complex ◽  
Null Cell ◽  
Protein Protein Interaction ◽  
First Time

: The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of Ki from 102 to 103 nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

Protein–protein interaction analysis using an affinity peptide tag and hydrophilic polystyrene plate

2007 ◽  
Vol 128 (2) ◽  
pp. 354-361 ◽  
Author(s):  
Y KUMADA ◽  
C ZHAO ◽  
R ISHIMURA ◽  
H IMANAKA ◽  
K IMAMURA ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Interaction Analysis ◽  
Protein Protein Interaction ◽  
Polystyrene Plate ◽  
Affinity Peptide ◽  
Peptide Tag

scholarly journals Definition of a minimal munc18c domain that interacts with syntaxin 4

2000 ◽  
Vol 350 (3) ◽  
pp. 741-746 ◽  
Author(s):  
Julian GRUSOVIN ◽  
Violet STOICHEVSKA ◽  
Keith H. GOUGH ◽  
Katrina NUNAN ◽  
Colin W. WARD ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Full Length ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Interaction Studies ◽  
Syntaxin 4 ◽  
Definition Of ◽  
Two Hybrid

munc18c is a critical protein involved in trafficking events associated with syntaxin 4 and which also mediates inhibitory effects on vesicle docking and/or fusion. To investigate the domains of munc18c responsible for its interaction with syntaxin 4, fragments of munc18c were generated and their interaction with syntaxin 4 examined in vivo by the yeast two-hybrid assay. In vitro protein–protein interaction studies were then used to confirm that the interaction between the proteins was direct. Full-length munc18c1–592, munc18c1–139 and munc18c1–225, but not munc18c226–592, munc18c1–100, munc18c43–139 or munc18c66–139, interacted with the cytoplasmic portion of syntaxin 4, Stx42–273, as assessed by yeast two-hybrid assay of growth on nutritionally deficient media and by β-galactosidase reporter induction. The N-terminal predicted helix-a-helix-b-helix-c region of syntaxin 4, Stx429–157, failed to interact with full-length munc18c1–592, indicating that a larger portion of syntaxin 4 is necessary for the interaction. The yeast two-hybrid results were confirmed by protein–protein interaction studies between Stx42–273 and glutathione S-transferase fusion proteins of munc18c. Full-length munc18c1–592, munc18c1–139 and munc18c1–225 interacted with Stx42–273 whereas munc18c1–100 did not, consistent with the yeast two-hybrid data. These data thus identify a region of munc18c between residues 1 and 139 as a minimal domain for its interaction with syntaxin 4.

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