scholarly journals Arabinogalactan Glycosyltransferases: Enzyme Assay, Protein-Protein Interaction, Subcellular Localization, and Perspectives for Application

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Naomi Geshi
Keyword(s):  
Protein Interactions ◽  
Enzyme Assay ◽  
Molecular Mechanisms ◽  
Gum Arabic ◽  
Arabinogalactan Proteins ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Plant Microbe Interaction ◽  
Cellular Processes ◽  
Survival Pattern

Arabinogalactan proteins (AGPs) are abundant extracellular proteoglycans that are found in most plant species and involved in many cellular processes, such as cell proliferation and survival, pattern formation, and growth, and in plant microbe interaction. AGPs are synthesized by posttranslational O-glycosylation of proteins and attached glycan part often constitutes greater than 90% of the molecule. Subtle altered glycan structures during development have been considered to function as developmental markers on the cell surface, but little is known concerning the molecular mechanisms. My group has been working on glycosylation enzymes (glycosyltransferases) of AGPs to investigate glycan function of the molecule. This review summarizes the recent findings from my group as for AtGalT31A, AtGlcAT14A-C, and AtGalT29A of Arabidopsis thaliana with a specific focus on the (i) biochemical enzyme activities; (ii) subcellular compartments targeted by the glycosyltransferases; and (iii) protein-protein interactions. I also discuss application aspect of glycosyltransferase in improving AGP-based product used in industry, for example, gum arabic.

Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update

2018 ◽  
Vol 25 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Ylenia Cau ◽  
Daniela Valensin ◽  
Mattia Mori ◽  
Sara Draghi ◽  
Maurizio Botta
Keyword(s):  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Physiological Role ◽  
Specific Protein ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Partners ◽  
High Sequence Homology ◽  
Small Molecule Modulators

14-3-3 is a class of proteins able to interact with a multitude of targets by establishing protein-protein interactions (PPIs). They are usually found in all eukaryotes with a conserved secondary structure and high sequence homology among species. 14-3-3 proteins are involved in many physiological and pathological cellular processes either by triggering or interfering with the activity of specific protein partners. In the last years, the scientific community has collected many evidences on the role played by seven human 14-3-3 isoforms in cancer or neurodegenerative diseases. Indeed, these proteins regulate the molecular mechanisms associated to these diseases by interacting with (i) oncogenic and (ii) pro-apoptotic proteins and (iii) with proteins involved in Parkinson and Alzheimer diseases. The discovery of small molecule modulators of 14-3-3 PPIs could facilitate complete understanding of the physiological role of these proteins, and might offer valuable therapeutic approaches for these critical pathological states.

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

scholarly journals Phenotype and target-based chemical biology investigations in cancers

2018 ◽  
Vol 6 (6) ◽  
pp. 1111-1127 ◽  
Author(s):  
Guo-Qiang Chen ◽  
Ying Xu ◽  
Shao-Ming Shen ◽  
Jian Zhang
Keyword(s):  
Protein Interactions ◽  
Cancer Biology ◽  
Chemical Biology ◽  
Molecular Mechanisms ◽  
Target Identification ◽  
Expression System ◽  
Protein Protein Interactions ◽  
Anticancer Compounds ◽  
Protein Protein Interaction ◽  
Mechanisms Of Carcinogenesis

Abstract Chemical biology has been attracting a lot of attention because of the key roles of chemical methods and techniques in helping to decipher and manipulate biological systems. Although chemical biology encompasses a broad field, this review will focus on chemical biology aimed at using exogenous chemical probes to interrogate, modify and manipulate biological processes, at the cellular and organismal levels, in a highly controlled and dynamic manner. In this area, many advances have been achieved for cancer biology and therapeutics, from target identification and validation based on active anticancer compounds (forward approaches) to discoveries of anticancer molecules based on some important targets including protein-protein interaction (reverse approaches). Herein we attempt to summarize some recent progresses mainly from China through applying chemical biology approaches to explore molecular mechanisms of carcinogenesis. Additionally, we also outline several new strategies for chemistry to probe cellular activities such as proximity-dependent labeling methods for identifying protein-protein interactions, genetically encoded sensors, and light activating or repressing gene expression system.

scholarly journals Mimicking Strategy for Protein–Protein Interaction Inhibitor Discovery by Virtual Screening

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4428 ◽  
Author(s):  
Ke-Jia Wu ◽  
Pui-Man Lei ◽  
Hao Liu ◽  
Chun Wu ◽  
Chung-Hang Leung ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Promising Strategy ◽  
Protein Partners ◽  
Inhibitor Discovery ◽  
And Function

As protein–protein interactions (PPIs) are highly involved in most cellular processes, the discovery of PPI inhibitors that mimic the structure of the natural protein partners is a promising strategy toward the discovery of PPI inhibitors. In this review, we discuss recent advances in the application of virtual screening for identifying mimics of protein partners. The classification and function of the mimicking protein partner inhibitor discovery by virtual screening are described. We anticipate that this review would be of interest to medicinal chemists and chemical biologists working in the field of protein–protein interaction inhibitors or probes.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

scholarly journals Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dan Tan ◽  
Qiang Li ◽  
Mei-Jun Zhang ◽  
Chao Liu ◽  
Chengying Ma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Affinity Purification ◽  
Protein Protein Interactions ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Lysine Residues ◽  
Spacer Arm ◽  
Cross Linker ◽  
Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

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