MIPPIE: the mouse integrated protein–protein interaction reference

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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PINAWeb: A web-based tool for the comparison of Protein-Protein Interaction Networks Aligners

10.1101/2021.04.23.441154 ◽

2021 ◽

Author(s):

A. Alcalá ◽

G. Riera ◽

I. García ◽

R. Alberich ◽

M. Llabrés

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Protein Interactions ◽

Considerable Effort ◽

Web Based ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

User Friendly

AbstractMotivationSeveral protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners.ResultsWe developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered.Availabilityhttps://bioinfo.uib.es/~recerca/PINAWeb

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CellMap visualizes protein-protein interactions and subcellular localization

F1000Research ◽

10.12688/f1000research.12707.2 ◽

2018 ◽

Vol 6 ◽

pp. 1824 ◽

Cited By ~ 2

Author(s):

Christian Dallago ◽

Tatyana Goldberg ◽

Miguel Angel Andrade-Navarro ◽

Gregorio Alanis-Lobato ◽

Burkhard Rost

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Cellular Component ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

A Cell ◽

Areas Of Interest

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.

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CellMap visualizes protein-protein interactions and subcellular localization

F1000Research ◽

10.12688/f1000research.12707.1 ◽

2017 ◽

Vol 6 ◽

pp. 1824 ◽

Cited By ~ 2

Author(s):

Christian Dallago ◽

Tatyana Goldberg ◽

Miguel Angel Andrade-Navarro ◽

Gregorio Alanis-Lobato ◽

Burkhard Rost

Keyword(s):

Subcellular Localization ◽

Protein Interaction ◽

Protein Interactions ◽

Cellular Component ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

A Cell ◽

Areas Of Interest

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

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.3.17 ◽

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.

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

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

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.

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Furan warheads for covalent trapping of weak protein-protein interactions: cross-linking of thymosin β4 to actin

Chemical Communications ◽

10.1039/d1cc01731d ◽

2021 ◽

Author(s):

Laia Miret Casals ◽

Willem Vannecke ◽

Kurt Hoogewijs ◽

Gianluca Arauz ◽

Marina Gay ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

3D Structure ◽

Cross Linking ◽

Thymosin Β4 ◽

Protein Protein Interactions ◽

Site Specific ◽

Protein Protein Interaction ◽

Covalent Trapping

We describe furan as a triggerable ‘warhead’ for site-specific cross-linking using the actin and thymosin β4 (Tβ4)-complex as model of a weak and dynamic protein-protein interaction with known 3D structure...

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

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2015100103 ◽

2015 ◽

Vol 4 (4) ◽

pp. 35-51 ◽

Cited By ~ 1

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.

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Molecular Biology of Protein-Protein Interactions for Computer Scientists

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch001 ◽

2009 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Christian Schönbach

Keyword(s):

Molecular Biology ◽

Protein Interactions ◽

Wnt Signaling Pathway ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Detection Technology ◽

Challenges And Opportunities ◽

Computer Scientists ◽

Assay Methods

Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate new interactions and disease pathways. The success of computational analyses considerably depends on PPI data understanding. Exploration of the data and verification of their quality requires basic knowledge of the molecular biology of PPIs and familiarity with the assay methods used to detect PPIs. Both topics are reviewed in this chapter. After introducing various types of PPIs the principles of selected PPI assays are explained and their limitations discussed. Case studies of the Wnt signaling pathway and splice regulation demonstrate some of the challenges and opportunities that arise from assaying and analyzing PPIs. The chapter is concluded with an extrapolation to human systems biology that offers a glimpse into the future of PPI networks.

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HMNPPID—human malignant neoplasm protein–protein interaction database

Human Genomics ◽

10.1186/s40246-019-0223-5 ◽

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.

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

Nucleic Acids Research ◽

10.1093/nar/gkz383 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W338-W344 ◽

Cited By ~ 51

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/.

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