scholarly journals Regulation of all members of the antizyme family by antizyme inhibitor

2004 ◽  
Vol 385 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Ursula MANGOLD ◽  
Ekkehard LEBERER
Keyword(s):  
Protein Interactions ◽  
Cell Transformation ◽  
Enzymic Activity ◽  
Cellular Growth ◽  
Polyamine Biosynthesis ◽  
Protein Protein Interaction ◽  
Antizyme Inhibitor ◽  
Polyamine Transport ◽  
General Inhibitor ◽  
Rate Limiting

ODC (ornithine decarboxylase) is the rate-limiting enzyme in polyamine biosynthesis. Polyamines are essential for cellular growth and differentiation but enhanced ODC activity is associated with cell transformation. Post-translationally, ODC is negatively regulated through members of the antizyme family. Antizymes inhibit ODC activity, promote ODC degradation through the 26 S proteasome and regulate polyamine transport. Besides the ubiquitously expressed antizymes 1 and 2, there is the tissue-specific antizyme 3 and an yet uncharacterized antizyme 4. Antizyme 1 has been shown to be negatively regulated through the AZI (antizyme inhibitor) that binds antizyme 1 with higher affinity compared with ODC. In the present study, we show by yeast two- and three-hybrid protein–protein interaction studies that AZI interacts with all members of the antizyme family and is capable of disrupting the interaction between each antizyme and ODC. In a yeast-based ODC complementation assay, we show that human ODC is able to complement fully the function of the yeast homologue of ODC. Co-expression of antizymes resulted in ODC inhibition and cessation of yeast growth. The antizyme-induced growth inhibition could be reversed by addition of putrescine or by the co-expression of AZI. The protein interactions could be confirmed by immunoprecipitation of the human ODC–antizyme 2–AZI complexes. In summary, we conclude that human AZI is capable of acting as a general inhibitor for all members of the antizyme family and that the previously not yet characterized antizyme 4 is capable of binding ODC and inhibiting its enzymic activity similar to the other members of the antizyme family.

A polyamine pathway-mediated mitogenic mechanism in enterochromaffin-like cells of Mastomys

1998 ◽  
Vol 275 (2) ◽  
pp. G370-G376 ◽  
Author(s):  
M. Kidd ◽  
L. H. Tang ◽  
S. W. Schmid ◽  
K. Miu ◽  
I. M. Modlin
Keyword(s):  
Dna Synthesis ◽  
Proliferative Response ◽  
Transforming Growth Factor ◽  
Cell Transformation ◽  
Polyamine Biosynthesis ◽  
Transforming Growth Factor Α ◽  
Ecl Cells ◽  
Ecl Cell ◽  
Factor Α ◽  
Rate Limiting

We have previously demonstrated that in Mastomys species proliferation of gastric enterochromaffin-like (ECL) cells is predominantly regulated by gastrin and by transforming growth factor-α (TGF-α) in the naive and neoplastic state, respectively. In this study we examined whether these intracellular mitogenic responses are mediated by polyamines and ornithine decarboxylase (ODC), the rate-limiting enzyme for polyamine biosynthesis. An ECL cell preparation of high purity was used to measure the effect of the polyamine derivatives putrescine, spermidine, and spermine on DNA synthesis by bromodeoxyuridine uptake. Both putrescine and spermidine augmented gastrin-stimulated, but not basal, DNA synthesis in naive cells. This proliferative response correlated with an increase in ODC activity that was partially inhibited (20%) by difluoromethylornithine (DFMO), an inhibitor of ODC (IC50, 30 pM). In contrast, all polyamines increased both basal and TGF-α-stimulated DNA synthesis as well as ODC activity in tumor ECL cells. DFMO completely inhibited the proliferative response of TGF-α (IC50, 3 pM). Thus polyamine biosynthesis is involved in proliferation of ECL cells and in particular the mitogenesis of tumor cells, suggesting a role for this pathway in the regulation of ECL cell transformation.

Regulation of cellular polyamine levels and cellular proliferation by antizyme and antizyme inhibitor

2009 ◽  
Vol 46 ◽  
pp. 47-62 ◽  
Author(s):  
Chaim Kahana
Keyword(s):  
Turnover Rate ◽  
Cellular Proliferation ◽  
Cellular Growth ◽  
26S Proteasome ◽  
Cellular Viability ◽  
Cellular Functions ◽  
Polyamine Biosynthesis ◽  
Cellular Processes ◽  
Antizyme Inhibitor ◽  
Key Enzyme

Polyamines are small aliphatic polycations present in all living cells. Polyamines are essential for cellular viability and are involved in regulating fundamental cellular processes, most notably cellular growth and proliferation. Being such central regulators of fundamental cellular functions, the intracellular polyamine concentration is tightly regulated at the levels of synthesis, uptake, excretion and catabolism. ODC (ornithine decarboxylase) is the first key enzyme in the polyamine biosynthesis pathway. ODC is characterized by an extremely rapid intracellular turnover rate, a trait that is central to the regulation of cellular polyamine homoeostasis. The degradation rate of ODC is regulated by its end-products, the polyamines, via a unique autoregulatory circuit. At the centre of this circuit is a small protein called Az (antizyme), whose synthesis is stimulated by polyamines. Az inactivates ODC and targets it to ubiquitin-independent degradation by the 26S proteasome. In addition, Az inhibits uptake of polyamines. Az itself is regulated by another ODC-related protein termed AzI (antizyme inhibitor). AzI is highly homologous with ODC, but it lacks ornithine-decarboxylating activity. Its ability to serve as a regulator is based on its high affinity to Az, which is greater than the affinity Az has to ODC. As a result, it interferes with the binding of Az to ODC, thus rescuing ODC from degradation and permitting uptake of polyamines.

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 Mutations and Protein Interaction Landscape Reveal Key Cellular Events Perturbed in Upper Motor Neurons with HSP and PLS

2021 ◽  
Vol 11 (5) ◽  
pp. 578
Author(s):  
Oge Gozutok ◽  
Benjamin Ryan Helmold ◽  
P. Hande Ozdinler
Keyword(s):  
Motor Neurons ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Cortical Neurons ◽  
Therapeutic Interventions ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Cellular Events ◽  
Interaction Domains ◽  
Upper Motor Neurons

Hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS) are rare motor neuron diseases, which affect mostly the upper motor neurons (UMNs) in patients. The UMNs display early vulnerability and progressive degeneration, while other cortical neurons mostly remain functional. Identification of numerous mutations either directly linked or associated with HSP and PLS begins to reveal the genetic component of UMN diseases. Since each of these mutations are identified on genes that code for a protein, and because cellular functions mostly depend on protein-protein interactions, we hypothesized that the mutations detected in patients and the alterations in protein interaction domains would hold the key to unravel the underlying causes of their vulnerability. In an effort to bring a mechanistic insight, we utilized computational analyses to identify interaction partners of proteins and developed the protein-protein interaction landscape with respect to HSP and PLS. Protein-protein interaction domains, upstream regulators and canonical pathways begin to highlight key cellular events. Here we report that proteins involved in maintaining lipid homeostasis and cytoarchitectural dynamics and their interactions are of great importance for UMN health and stability. Their perturbation may result in neuronal vulnerability, and thus maintaining their balance could offer therapeutic interventions.

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.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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