scholarly journals Different phosphorylation and farnesylation patterns tune Rnd3–14-3-3 interaction in distinct mechanisms

2021 ◽  
Author(s):  
Jun Hu ◽  
Xue-Meng Sun ◽  
Jing-Yun Su ◽  
Yu-Fen Zhao ◽  
Yong-Xiang Chen
Keyword(s):  
Molecular Mechanism ◽  
Protein Interaction ◽  
Posttranslational Modifications ◽  
Binding Thermodynamics ◽  
Protein Protein Interaction ◽  
Thermodynamics And Kinetics ◽  
Protein Posttranslational Modifications

Different protein posttranslational modifications (PTMs) patterns affect the binding thermodynamics and kinetics and their molecular mechanism of multivalent protein–protein interaction (PPIs).

Binding thermodynamics and kinetics guided optimization of potent Keap1–Nrf2 peptide inhibitors

RSC Advances ◽  
2015 ◽  
Vol 5 (105) ◽  
pp. 85983-85987 ◽  
Author(s):  
Meng-Chen Lu ◽  
Zhi-Yun Chen ◽  
Ya-Lou Wang ◽  
Yong-Lin Jiang ◽  
Zhen-Wei Yuan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Peptide Inhibitors ◽  
Research Interest ◽  
Novel Agents ◽  
Binding Thermodynamics ◽  
Protein Protein Interaction ◽  
Thermodynamics And Kinetics ◽  
Nrf2 Protein

Activation of Nrf2 by directly inhibiting the Keap1–Nrf2 Protein–Protein Interaction (PPI) has gained research interest with regard to developing novel agents for treating inflammatory related diseases.

scholarly journals My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yadi Zhou ◽  
Junfei Zhao ◽  
Jiansong Fang ◽  
William Martin ◽  
Lang Li ◽  
...  
Keyword(s):  
Posttranslational Modifications ◽  
Genomic Medicine ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Protein Binding Sites ◽  
Functional Sites ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Protein Posttranslational Modifications

AbstractMassive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces (“edgetic”) and 311,022 functional sites (“nodetic”), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org.

scholarly journals Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Si Xu ◽  
Xiaoning Li ◽  
Sha Wu ◽  
Min Yang
Keyword(s):  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Theoretical Basis ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: To provide theoretical basis for the molecular mechanism of the development of diabetic nephropathy and targeted molecular therapy by screening expressed genes based on bioinformatic analysis. Methods: We analyzed diabetic nephropathy microarray datasets derived from GEO database. Perl and R programming packages were used for data processing and analysis and for drawing. STRING online database and Cytoscape software were utilized for protein-protein interaction network analysis and screened for hub genes. Also, WebGestalt was used to analyze the relationship between genes and microRNAs. Nephroseq online tool was used to visualize the correlation between genes and clinical properties.Results: We found 91 differentially expressed genes between diabetic nephropathy tissues and normal control tissues. Protein-protein interaction network analysis screened out 5 key modules and a total of 14 hub genes were identified by integration, also11 microRNAs were associated with hub genes. Especially mir29 could regulate COL6A3 and COL15A1.Conclusions: The internal biological information in diabetic nephropathy can be revealed by integrative bioinformatical analysis, providing theoretical basis for further research on molecular mechanism and potential targets for diagnosis and therapeutics of diabetic nephropathy.

scholarly journals Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT)

2019 ◽  
Vol 20 (9) ◽  
pp. 2058 ◽  
Author(s):  
Laurens Vyncke ◽  
Delphine Masschaele ◽  
Jan Tavernier ◽  
Frank Peelman
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Random Mutagenesis ◽  
Interaction Mechanism ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Multiple Partners ◽  
Interaction Trap ◽  
Mammalian Protein

The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.

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