scholarly journals Hacking the Cell: Network Intrusion and Exploitation by Adenovirus E1A

mBio ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Cason R. King ◽  
Ali Zhang ◽  
Tanner M. Tessier ◽  
Steven F. Gameiro ◽  
Joe S. Mymryk
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Basis ◽  
Human Adenovirus ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Viral Control ◽  
Adenovirus E1a ◽  
Network Intrusion

ABSTRACTAs obligate intracellular parasites, viruses are dependent on their infected hosts for survival. Consequently, viruses are under enormous selective pressure to utilize available cellular components and processes to their own advantage. As most, if not all, cellular activities are regulated at some level via protein interactions, host protein interaction networks are particularly vulnerable to viral exploitation. Indeed, viral proteins frequently target highly connected “hub” proteins to “hack” the cellular network, defining the molecular basis for viral control over the host. This widespread and successful strategy of network intrusion and exploitation has evolved convergently among numerous genetically distinct viruses as a result of the endless evolutionary arms race between pathogens and hosts. Here we examine the means by which a particularly well-connected viral hub protein, human adenovirus E1A, compromises and exploits the vulnerabilities of eukaryotic protein interaction networks. Importantly, these interactions identify critical regulatory hubs in the human proteome and help define the molecular basis of their function.

New horizons for antiviral drug discovery from virus–host protein interaction networks

2012 ◽  
Vol 2 (5) ◽  
pp. 606-613 ◽  
Author(s):  
Benoît de Chassey ◽  
Laurène Meyniel-Schicklin ◽  
Anne Aublin-Gex ◽  
Patrice André ◽  
Vincent Lotteau
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Antiviral Drug ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
New Horizons

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
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.

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

2015 ◽  
Vol 12 (110) ◽  
pp. 20150573 ◽  
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.

scholarly journals Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0236304
Author(s):  
Mercè Llabrés ◽  
Gabriel Valiente
Keyword(s):  
Linear Programming ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Host Interactions ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Consensus Alignment ◽  
Protein Protein Interaction Networks

Motivation Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. Results We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

scholarly journals Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Mercè Llabrés ◽  
Gabriel Riera ◽  
Francesc Rosselló ◽  
Gabriel Valiente
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Protein Interaction ◽  
Software Tools ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Protein ◽  
Protein Protein Interaction ◽  
Programming Software ◽  
Protein Protein Interaction Networks

Abstract Background The alignment of protein-protein interaction networks was recently formulated as an integer quadratic programming problem, along with a linearization that can be solved by integer linear programming software tools. However, the resulting integer linear program has a huge number of variables and constraints, rendering it of no practical use. Results We present a compact integer linear programming reformulation of the protein-protein interaction network alignment problem, which can be solved using state-of-the-art mathematical modeling and integer linear programming software tools, along with empirical results showing that small biological networks, such as virus-host protein-protein interaction networks, can be aligned in a reasonable amount of time on a personal computer and the resulting alignments are structurally coherent and biologically meaningful. Conclusions The implementation of the integer linear programming reformulation using current mathematical modeling and integer linear programming software tools provided biologically meaningful alignments of virus-host protein-protein interaction networks.

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

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

scholarly journals Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

2017 ◽  
Vol 4 (1) ◽  
pp. 100056
Author(s):  
Gregorio Alanis-Lobato ◽  
Spyros Petrakis
Keyword(s):  
Computational Biology ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Current Status ◽  
Protein Interaction Data ◽  
Cellular Functions ◽  
Human Disorders

Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current status and limitations of protein interaction data and computational resources. This editorial outlines the meeting's background and programme, putting special emphasis on the extended abstracts of contributed talks collected in the present issue of Genomics and Computational Biology.

scholarly journals Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

2021 ◽  
Author(s):  
Mark A. Zaydman ◽  
Alexander Little ◽  
Fidel Haro ◽  
Valeryia Aksianiuk ◽  
William J. Buchser ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Protein Interaction Networks ◽  
Gene Set Enrichment Analysis ◽  
Interaction Networks ◽  
Spectral Correlation ◽  
Hierarchical Networks ◽  
Statistical Pattern ◽  
Cellular Phenotypes

AbstractCellular phenotypes emerge from a hierarchy of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be extracted from the statistical pattern of proteome variation as measured across thousands of bacteria and that these hierarchies reflect the emergence of complex bacterial phenotypes. We describe the mathematics underlying our statistical approach and validate our results through gene-set enrichment analysis and comparison to existing experimentally-derived hierarchical databases. We demonstrate the biological utility of our unbiased hierarchical models by creating a model of motility in Pseudomonas aeruginosa and using it to discover a previously unappreciated genetic effector of twitch-based motility. Overall, our approach, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), predicts hierarchies of protein interaction networks describing emergent biological function using only the statistical pattern of bacterial proteome variation.

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