scholarly journals Proteinarium: Multi-Sample Protein-Protein Interaction Analysis and Visualization Tool

2019 ◽  
Author(s):  
David Armanious ◽  
Jessica Schuster ◽  
George F. Tollefson ◽  
Anthony Agudelo ◽  
Andrew T. DeWan ◽  
...  
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Visualization Tool ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractBackgroundData analysis has become crucial in the post genomic era where the accumulation of genomic information is mounting exponentially. Analyzing protein-protein interactions in the context of the interactome is a powerful approach to understanding disease phenotypes.ResultsWe describe Proteinarium, a multi-sample protein-protein interaction network analysis and visualization tool. Proteinarium can be used to analyze data for samples with dichotomous phenotypes, multiple samples from a single phenotype or a single sample. Then, by similarity clustering, the network-based relations of samples are identified and clusters of related samples are presented as a dendrogram. Each branch of the dendrogram is built based on network similarities of the samples. The protein-protein interaction networks can be analyzed and visualized on any branch of the dendrogram. Proteinarium’s input can be derived from transcriptome analysis, whole exome sequencing data or any high-throughput screening approach. Its strength lies in use of gene lists for each sample as a distinct input which are further analyzed through protein interaction analyses. Proteinarium output includes the gene lists of visualized networks and PPI interaction files where users can analyze the network(s) on other platforms such as Cytoscape. In addition, since the dendrogram is written in Newick tree format, users can visualize it in other software platforms like Dendroscope, ITOL.ConclusionsProteinarium, through the analysis and visualization of PPI networks, allows researchers to make important observations on high throughput data for a variety of research questions. Proteinarium identifies significant clusters of patients based on their shared network similarity for the disease of interest and the associated genes. Proteinarium is a command-line tool written in Java with no external dependencies and it is freely available at https://github.com/Armanious/Proteinarium.

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 Combining Unique Multiplex Gateway Cloning and Bimolecular Fluorescence Complementation (BiFC) for High-Throughput Screening of Protein–Protein Interactions

2016 ◽  
Vol 21 (10) ◽  
pp. 1100-1111 ◽  
Author(s):  
Adriana Lepur ◽  
Lucija Kovačević ◽  
Robert Belužić ◽  
Oliver Vugrek
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Cellular Localization ◽  
Interaction Analysis ◽  
Bimolecular Fluorescence Complementation ◽  
Galectin 3 ◽  
Fluorescence Complementation ◽  
Bimolecular Fluorescence

Protein interaction networks are the basis for human metabolic and signaling systems. Interaction studies often use bimolecular fluorescence complementation (BiFC) to reveal the formation and cellular localization of protein complexes. However, large-scale studies were either far from native conditions in human cells or limited by laborious restriction/ligation cloning techniques. Here, we describe a new tool for protein interaction screening based on Gateway-compatible BiFC vectors. We made a set of four new vectors that permit fusion of candidate proteins to the N or C fragment of Venus in all fusion positions. We have validated the vectors and confirmed self-association of AHCY, AHCYL1, and galectin-3. In a high-throughput BiFC screen, we identified new AHCY interaction partners: galectin-3 and PUS7L. We also describe additional steps in protein interaction analysis, applied for AHCY–galectin-3 interaction. First, we classified the interaction in intracellular vesicles using CellCognition, machine learning free software. Then we identified the vesicles as endosomal pathway compartments, in line with known galectin-3 trafficking route. This offers a platform to rapidly identify and localize new protein interactions inside living cells, a prerequisite to validate in silico interactome data, and ultimately decode complex protein networks.

scholarly journals Polyplexed Flow Cytometry Protein Interaction Assay: A Novel High-Throughput Screening Paradigm for RGS Protein Inhibitors

2009 ◽  
Vol 14 (6) ◽  
pp. 610-619 ◽  
Author(s):  
David L. Roman ◽  
Shodai Ota ◽  
Richard R. Neubig
Keyword(s):  
Flow Cytometry ◽  
G Protein ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Intracellular Signaling ◽  
High Throughput Screening ◽  
Binding Constants ◽  
Rgs Proteins ◽  
Protein Protein Interaction

Intracellular signaling cascades are a series of regulated protein-protein interactions that may provide a number of targets for potential drug discovery. Here, the authors examine the interaction of regulators of G-protein signaling (RGS) proteins with the G-protein Gαo, using a flow cytometry protein interaction assay (FCPIA). FCPIA accurately measures nanomolar binding constants of this protein-protein interaction and has been used in high-throughput screening. This report focuses on 5 RGS proteins (4, 6, 7, 8, and 16). To increase the content of screens, the authors assessed high-throughput screening of these RGS proteins in multiplex, by establishing binding constants of each RGS with Gαo in isolation, and then in a multiplex format with 5 RGS proteins present. To use this methodology as a higher-content multiplex protein-protein interaction screen, they established Z-factor values for RGS proteins in multiplex of 0.73 to 0.92, indicating this method is suitable for screening using FCPIA. To increase throughput, they also compressed a set of 8000 compounds by combining 4 compounds in a single assay well. Subsequent deconvolution of the compounds mixtures verified the identification of active compounds at specific RGS targets in their mixtures using the polyplexed FCPIA method. ( Journal of Biomolecular Screening 2009: 610-619)

scholarly journals PINOT: An Intuitive Resource for Integrating Protein-Protein Interactions

2019 ◽  
Author(s):  
JE Tomkins ◽  
R Ferrari ◽  
N Vavouraki ◽  
J Hardy ◽  
RC Lovering ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Data Entry ◽  
Interaction Network ◽  
Ease Of Use ◽  
Detection Methods ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Web Resource ◽  
Ppi Networks

AbstractThe past decade has seen the rise of omics data, for the understanding of biological systems in health and disease. This wealth of data includes protein-protein interaction (PPI) derived from both low and high-throughput assays, which is curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from the IMEx consortium associated repositories (members and observers) and from WormBase for constructing, respectively, human and C. elegans PPI networks.Users submit a query containing a list of proteins of interest for which PINOT will mine PPIs. PPI data is downloaded, merged, quality checked, and confidence scored based on the number of distinct methods and publications in which each interaction has been reported. Examples of PINOT applications are provided to highlight the performance, the ease of use and the potential applications of this tool.PINOT is a tool that allows users to survey the literature, extracting PPI data for a list of proteins of interest. The comparison with analogous tools showed that PINOT was able to extract similar numbers of PPIs while incorporating a set of innovative features. PINOT processes both small and large queries, it downloads PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI annotations (i.e. removing the need of manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each of the downloaded interaction data entry and provides results in a table format that can be easily further customised and/or directly uploaded in a network visualization software.

scholarly journals A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Dongjie Wu ◽  
Wang Zhang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Negative Impact ◽  
False Negative ◽  
Interaction Network ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

scholarly journals Development of a Novel High-Throughput Screen and Identification of Small-Molecule Inhibitors of the Gα–RGS17 Protein–Protein Interaction Using AlphaScreen

2011 ◽  
Vol 16 (8) ◽  
pp. 869-877 ◽  
Author(s):  
Duncan I. Mackie ◽  
David L. Roman
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
Protein Interaction ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Screening Method ◽  
Fold Increase ◽  
Rgs Proteins ◽  
High Throughput Screen ◽  
Protein Protein Interaction

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gαo–RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gαo–RGS17 protein–protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 µM in dose–response experiments. Four exhibited IC50 values <6 µM while inhibiting the Gαo–RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.

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.

Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network

2014 ◽  
Vol 934 ◽  
pp. 159-164
Author(s):  
Yun Yuan Dong ◽  
Xian Chun Zhang
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Centrality Measures ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Comparison Results ◽  
A Cell ◽  
High Degree

Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

scholarly journals A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins

2019 ◽  
Author(s):  
Stavros Makrodimitris ◽  
Marcel Reinders ◽  
Roeland van Ham
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Process ◽  
Poor Performance ◽  
Protein Interaction Data ◽  
Physical Interaction ◽  
Cellular Functions ◽  
Data Resource ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractPhysical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extened these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

scholarly journals MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Jannik S Möllmann ◽  
Martin H Schaefer ◽  
Miguel A Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Model Organism ◽  
Web Interface ◽  
Protein Protein Interactions ◽  
Environmental Signals ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
User Friendly

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

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