scholarly journals Nitric Oxide (NO) Scaffolds the Peroxisomal Protein-Protein Interaction Network in Higher Plants

2021 ◽  
Vol 22 (5) ◽  
pp. 2444
Author(s):  
Francisco J. Corpas ◽  
Salvador González-Gordo ◽  
José M. Palma
Keyword(s):  
Nitric Oxide ◽  
Protein Interactions ◽  
Higher Plants ◽  
Interaction Network ◽  
Comprehensive Overview ◽  
Subcellular Compartment ◽  
Peroxisomal Protein ◽  
Protein Protein Interaction ◽  
Adverse Environmental Conditions ◽  
Almost All

The peroxisome is a single-membrane subcellular compartment present in almost all eukaryotic cells from simple protists and fungi to complex organisms such as higher plants and animals. Historically, the name of the peroxisome came from a subcellular structure that contained high levels of hydrogen peroxide (H2O2) and the antioxidant enzyme catalase, which indicated that this organelle had basically an oxidative metabolism. During the last 20 years, it has been shown that plant peroxisomes also contain nitric oxide (NO), a radical molecule than leads to a family of derived molecules designated as reactive nitrogen species (RNS). These reactive species can mediate post-translational modifications (PTMs) of proteins, such as S-nitrosation and tyrosine nitration, thus affecting their function. This review aims to provide a comprehensive overview of how NO could affect peroxisomal metabolism and its internal protein-protein interactions (PPIs). Remarkably, many of the identified NO-target proteins in plant peroxisomes are involved in the metabolism of reactive oxygen species (ROS), either in its generation or its scavenging. Therefore, it is proposed that NO is a molecule with signaling properties with the capacity to modulate the peroxisomal protein-protein network and consequently the peroxisomal functions, especially under adverse environmental conditions.

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 PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Stefan Kalkhof ◽  
Stefan Schildbach ◽  
Conny Blumert ◽  
Friedemann Horn ◽  
Martin von Bergen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Isotope Labeling ◽  
Affinity Purification ◽  
Interaction Network ◽  
Mass Spectrometry Data ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Binding Behavior ◽  
Bona Fide

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

2015 ◽  
Vol 4 (4) ◽  
pp. 35-51 ◽  
Author(s):  
Bandana Barman ◽  
Anirban Mukhopadhyay
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

scholarly journals Comparative proteomics—network analysis of proteins responsible for ursolic acid–induced cytotoxicity in colorectal cancer cells

Tumor Biology ◽  
2017 ◽  
Vol 39 (3) ◽  
pp. 101042831769501 ◽  
Author(s):  
Qiaoyan Cai ◽  
Jing Lin ◽  
Ling Zhang ◽  
Jiumao Lin ◽  
Lili Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Cells ◽  
Ursolic Acid ◽  
Protein Interactions ◽  
Direct Interaction ◽  
Interaction Network ◽  
Two Dimensional Gel Electrophoresis ◽  
Cellular Targets ◽  
Protein Protein Interaction ◽  
Colorectal Cancer Cells

Ursolic acid is a key active compound present in many medicinal herbs that have been widely used in traditional Chinese medicine for the clinical treatment of various cancers. However, the precise mechanisms of its antitumor activity have been poorly understood. To identify the cellular targets of ursolic acid, two-dimensional gel electrophoresis combined with mass spectrometry was performed in this study, which identified 15 proteins with significantly altered levels in protein expression. This demonstrated that ursolic acid–induced cytotoxicity in colorectal cancer cells involves dysregulation in protein folding, signal transduction, cell proliferation, cell cycle, and apoptosis. Corresponding protein regulation was also confirmed by Western blotting. Furthermore, the study of functional association between these 15 proteins revealed that 10 were closely related in a protein–protein interaction network, whereby the proteins either had a direct interaction with each other or were associated via only one intermediary protein. In this instance, the ATP5B/CALR/HSP90B1/HSPB1/HSPD1-signaling network was revealed as the predominant target which was associated with the majority of the observed protein–protein interactions. As a result, the identified targets may be useful in explaining the anticancer mechanisms of ursolic acid and as potential targets for colorectal cancer therapy.

scholarly journals mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

2019 ◽  
Vol 47 (W1) ◽  
pp. W338-W344 ◽  
Author(s):  
Carlos H M Rodrigues ◽  
Yoochan Myung ◽  
Douglas E V Pires ◽  
David B Ascher
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Evolutionary Information ◽  
Biological Processes ◽  
Missense Mutations ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Residue Interaction ◽  
User Friendly ◽  
Network Metrics

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

Prediction of protein–protein interaction network using a multi-objective optimization approach

2016 ◽  
Vol 14 (03) ◽  
pp. 1650008 ◽  
Author(s):  
Archana Chowdhury ◽  
Pratyusha Rakshit ◽  
Amit Konar
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Optimization Approach ◽  
Scoring Functions ◽  
Multi Objective Optimization ◽  
Trial Solution ◽  
Multi Objective ◽  
Random Decision Forest ◽  
Almost All ◽  
Ppi Prediction

Protein–Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

scholarly journals Dynamic rewiring of the human interactome by interferon signalling

2019 ◽  
Author(s):  
Craig H. Kerr ◽  
Michael A. Skinnider ◽  
Angel M. Madero ◽  
Daniel D.T. Andrews ◽  
R. Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Cell Types ◽  
Antiviral Response ◽  
Type I ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Viral Pathogens ◽  
Protein Protein Interaction ◽  
Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

scholarly journals In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Monika Samant ◽  
Nidhi Chadha ◽  
Anjani K. Tiwari ◽  
Yasha Hasija
Keyword(s):  
Plasmodium Falciparum ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Biological Significance ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Pathogen Protein ◽  
Complete Protein

Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.

scholarly journals Protein-protein-interaction Network Organization of the Hypusine Modification System

2012 ◽  
Vol 11 (11) ◽  
pp. 1289-1305 ◽  
Author(s):  
Henning Sievert ◽  
Simone Venz ◽  
Oscar Platas-Barradas ◽  
Vishnu M. Dhople ◽  
Martin Schaletzky ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Affinity Purification ◽  
Interaction Network ◽  
Initiation Factor ◽  
Post Translational Modification ◽  
Comprehensive Overview ◽  
Data Set ◽  
Modification System ◽  
Protein Protein Interaction

Hypusine modification of eukaryotic initiation factor 5A (eIF-5A) represents a unique and highly specific post-translational modification with regulatory functions in cancer, diabetes, and infectious diseases. However, the specific cellular pathways that are influenced by the hypusine modification remain largely unknown. To globally characterize eIF-5A and hypusine-dependent pathways, we used an approach that combines large-scale bioreactor cell culture with tandem affinity purification and mass spectrometry: “bioreactor-TAP-MS/MS.” By applying this approach systematically to all four components of the hypusine modification system (eIF-5A1, eIF-5A2, DHS, and DOHH), we identified 248 interacting proteins as components of the cellular hypusine network, with diverse functions including regulation of translation, mRNA processing, DNA replication, and cell cycle regulation. Network analysis of this data set enabled us to provide a comprehensive overview of the protein-protein interaction landscape of the hypusine modification system. In addition, we validated the interaction of eIF-5A with some of the newly identified associated proteins in more detail. Our analysis has revealed numerous novel interactions, and thus provides a valuable resource for understanding how this crucial homeostatic signaling pathway affects different cellular functions.

scholarly journals Interactome Data and Databases: Different Types of Protein Interaction

2004 ◽  
Vol 5 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Javier De Las Rivas ◽  
Alberto de Luis
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Short Review ◽  
Experimental Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Bioinformatic Tool ◽  
Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

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