RNP-MaP: In-cell analysis of protein interaction networks defines functional hubs in RNA

ABSTRACTRNAs interact with networks of proteins to form complexes (RNPs) that govern many biological processes, but these networks are currently impossible to examine in a comprehensive way. We developed a live-cell chemical probing strategy for mapping protein interaction networks in any RNA with single-nucleotide resolution. This RNP-MaP strategy (RNP network analysis by mutational profiling) simultaneously detects binding by and cooperative interactions involving multiple proteins with single RNA molecules. RNP-MaP revealed that two structurally related, but sequence-divergent noncoding RNAs, RNase P and RMRP, share nearly identical RNP networks and, further, that protein interaction network hubs identify function-critical sites in these RNAs. RNP-MaP identified numerous protein interaction networks within the XIST long noncoding RNA that are conserved between mouse and human RNAs and distinguished communities of proteins that network together on XIST. RNP-MaP data show that the Xist E region is densely networked by protein interactions and that PTBP1, MATR3, and TIA1 proteins each interface with the XIST E region via two distinct interaction modes; and we find that the XIST E region is sufficient to mediate RNA foci formation in cells. RNP-MaP will enable discovery and mechanistic analysis of protein interaction networks across any RNA in cells.

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Evolution of Protein-protein Interaction Networks in Duplication-Divergence Model

Communications in Physics ◽

10.15625/0868-3166/22/1/629 ◽

2012 ◽

Vol 22 (1) ◽

pp. 7-14

Author(s):

Bui Phuong Thuy ◽

Trinh Xuan Hoang

Keyword(s):

Experimental Data ◽

Protein Interaction ◽

Protein Interactions ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Protein Interactions ◽

Scale Free ◽

Protein Protein Interaction ◽

Living Organisms

Protein interacts with one another resulting in complex functions in living organisms. Like many other real-world networks, the networks of protein-protein interactions possess a certain degree of ordering, such as the scale-free property. The latter means that the probability $P$ to find a protein that interacts with $k$ other proteins follows a power law, $P(k) \sim k^{-\gamma}$. Protein interaction networks (PINs) have been studied by using a stochastic model, the duplication-divergence model, which is based on mechanisms of gene duplication and divergence during evolution. In this work, we show that this model can be used to fit experimental data on the PIN of yeast Saccharomyces cerevisae at two different time instances simultaneously. Our study shows that the evolution of PIN given by model is consistent with growing experimental data over time, and that the scale-free property of protein interaction network is robust against random deletion of interactions.

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ProtFinder: finding subcellular locations of proteins using protein interaction networks

10.1101/2022.01.11.475836 ◽

2022 ◽

Author(s):

Aayush Grover ◽

Laurent Gatto

Keyword(s):

Protein Interaction ◽

Intelligent System ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Subcellular Localization ◽

Human Protein Atlas ◽

Protein Subcellular Localization Prediction ◽

Localization Prediction ◽

Roc Score

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.

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XlinkCyNET: a Cytoscape application for visualization of protein interaction networks based on cross-linking mass-spectrometry identifications

10.1101/2020.12.20.423654 ◽

2020 ◽

Author(s):

Diogo Borges Lima ◽

Ying Zhu ◽

Fan Liu

Keyword(s):

Mass Spectrometry ◽

Protein Interaction ◽

Large Scale ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Cross Linking ◽

Protein Interaction Data ◽

Interaction Data ◽

Link Type

ABSTRACTSoftware tools that allow visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a large selection of plugins for interpretation of protein interaction data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for such interaction data, but there are currently no Cytoscape tools to analyze XL-MS results. In light of the suitability of Cytoscape platform but also to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plugin for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers rapid and easy visualization of intra and intermolecular cross-links and the locations of protein domains in a rectangular bar style, allowing subdomain-level interrogation of the interaction network. XlinkCyNET is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/xlinkcynet and at https://www.theliulab.com/software/xlinkcynet.

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Discovering Network Motifs in Protein Interaction Networks

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch008 ◽

2009 ◽

pp. 117-143 ◽

Cited By ~ 1

Author(s):

Raymond Wan ◽

Hiroshi Mamitsuka

Keyword(s):

Protein Interaction ◽

Graph Algorithms ◽

Protein Interaction Network ◽

Undirected Graph ◽

Interaction Network ◽

Building Blocks ◽

Software Tools ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Network Motifs

This chapter examines some of the available techniques for analyzing a protein interaction network (PIN) when depicted as an undirected graph. Within this graph, algorithms have been developed which identify “notable” smaller building blocks called network motifs. The authors examine these algorithms by dividing them into two broad categories based on two de?nitions of “notable”: (a) statistically-based methods and (b) frequency-based methods. They describe how these two classes of algorithms differ not only in terms of ef?ciency, but also in terms of the type of results that they report. Some publicly-available programs are demonstrated as part of their comparison. While most of the techniques are generic and were originally proposed for other types of networks, the focus of this chapter is on the application of these methods and software tools to PINs.

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Discovering Interaction Motifs from Protein Interaction Networks

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch007 ◽

2009 ◽

pp. 99-116 ◽

Cited By ~ 1

Author(s):

Hugo Willy

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Patterns ◽

Protein Protein Interaction ◽

Family Based ◽

High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

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SHAPE reveals transcript-wide interactions, complex structural domains, and protein interactions across the Xist lncRNA in living cells

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1600008113 ◽

2016 ◽

Vol 113 (37) ◽

pp. 10322-10327 ◽

Cited By ~ 123

Author(s):

Matthew J. Smola ◽

Thomas W. Christy ◽

Kaoru Inoue ◽

Cindo O. Nicholson ◽

Matthew Friedersdorf ◽

...

Keyword(s):

Conformational Changes ◽

Protein Interactions ◽

Rna Structure ◽

Noncoding Rna ◽

Ex Vivo ◽

Living Cells ◽

Mammalian Development ◽

Cellular Environment ◽

Nucleotide Resolution ◽

In Cells

The 18-kb Xist long noncoding RNA (lncRNA) is essential for X-chromosome inactivation during female eutherian mammalian development. Global structural architecture, cell-induced conformational changes, and protein–RNA interactions within Xist are poorly understood. We used selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) to examine these features of Xist at single-nucleotide resolution both in living cells and ex vivo. The Xist RNA forms complex well-defined secondary structure domains and the cellular environment strongly modulates the RNA structure, via motifs spanning one-half of all Xist nucleotides. The Xist RNA structure modulates protein interactions in cells via multiple mechanisms. For example, repeat-containing elements adopt accessible and dynamic structures that function as landing pads for protein cofactors. Structured RNA motifs create interaction domains for specific proteins and also sequester other motifs, such that only a subset of potential binding sites forms stable interactions. This work creates a broad quantitative framework for understanding structure–function interrelationships for Xist and other lncRNAs in cells.

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Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments

Journal of The Royal Society Interface ◽

10.1098/rsif.2015.0573 ◽

2015 ◽

Vol 12 (110) ◽

pp. 20150573 ◽

Cited By ~ 1

Author(s):

A. Annibale ◽

A. C. C. Coolen ◽

N. Planell-Morell

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Yeast Two Hybrid ◽

Binary Matrix ◽

Interaction Detection ◽

Experimental Protocols ◽

True Protein ◽

Two Hybrid

Protein interaction networks (PINs) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy and yeast two-hybrid. We start from the observation that the adjacency matrix of a PIN, i.e. the binary matrix which defines, for every pair of proteins in the network, whether or not there is a link, has a special form, that we call separable. This induces precise relationships between the moments of the degree distribution (i.e. the average number of links that a protein in the network has, its variance, etc.) and the number of short loops (i.e. triangles, squares, etc.) along the links of the network. These relationships provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.

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Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

BioMed Research International ◽

10.1155/2016/4658506 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Sandip Chakraborty ◽

David Alvarez-Ponce

Keyword(s):

Positive Selection ◽

Protein Interaction ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

Interspecific Comparisons ◽

High Degree

Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

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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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Computational identification of signaling pathways in protein interaction networks

F1000Research ◽

10.12688/f1000research.7591.1 ◽

2015 ◽

Vol 4 ◽

pp. 1522

Author(s):

Angela U. Makolo ◽

Temitayo A. Olagunju

Keyword(s):

Signaling Pathways ◽

High Throughput ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Protein Interaction

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.

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