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

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.

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 PPI layouts: BioJS components for the display of Protein-Protein Interactions

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 50 ◽  
Author(s):  
Gustavo A. Salazar ◽  
Ayton Meintjes ◽  
Nicola Mulder
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Third Party ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Protein Protein Interaction ◽  
Link Type ◽  
Protein Protein Interaction Networks

Summary: We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. Availability: http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753

scholarly journals AligNet: Alignment of Protein-Protein Interaction Networks

2019 ◽  
Author(s):  
R. Alberich ◽  
A. Alcalá ◽  
M. Llabrés ◽  
F. Rosselló ◽  
G. Valiente
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Information ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

AbstractOne of the most difficult problems difficult problem in systems biology is to discover protein-protein interactions as well as their associated functions. The analysis and alignment of protein-protein interaction networks (PPIN), which are the standard model to describe protein-protein interactions, has become a key ingredient to obtain functional orthologs as well as evolutionary conserved pathways and protein complexes. Several methods have been proposed to solve the PPIN alignment problem, aimed to match conserved subnetworks or functionally related proteins. However, the right balance between considering network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm which, unfortunately, remains unsolved. Therefore, in this work, we propose AligNet, a new method and software tool for the pairwise global alignment of PPIN that produces biologically meaningful alignments and more efficient computations than state-of-the-art methods and tools, by achieving a good balance between structural matching and protein function conservation as well as reasonable running times.

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.

DISCOVERING PROTEIN–PROTEIN INTERACTIONS

2004 ◽  
Vol 01 (04) ◽  
pp. 711-741 ◽  
Author(s):  
SEE-KIONG NG ◽  
SOON-HENG TAN
Keyword(s):  
Protein Interactions ◽  
Experimental Methods ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Comprehensive Description ◽  
New Genes ◽  
Genomics And Proteomics ◽  
Living Organisms

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein–protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein–protein interactions, covering both the conventional experimental methods and new computational approaches.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

2014 ◽  
Vol 4 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Cellular Systems ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Prediction and Analysis of Protein-Protein Interactions

2009 ◽  
pp. 29-44 ◽  
Author(s):  
Tatsuya Akutsu ◽  
Morihiro Hayashida
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Interaction Data ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This chapter overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, and linear programmingbased method. This chapter also reviews a simple evolutionary model of protein domains, which yields a scalefree distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

Biotechnology ◽  
2019 ◽  
pp. 406-427
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Interaction Model ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions ◽  
Protein Sequence Data

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

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