Building and analyzing protein interactome networks by cross-species comparisons

BACKGROUND: A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions f...

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Autores principales: Wiles, Amy M, Doderer, Mark, Ruan, Jianhua, Gu, Ting-Ting, Ravi, Dashnamoorthy, Blackman, Barron, Bishop, Alexander JR
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2859380/
https://www.ncbi.nlm.nih.gov/pubmed/20353594
http://dx.doi.org/10.1186/1752-0509-4-36
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author Wiles, Amy M
Doderer, Mark
Ruan, Jianhua
Gu, Ting-Ting
Ravi, Dashnamoorthy
Blackman, Barron
Bishop, Alexander JR
author_facet Wiles, Amy M
Doderer, Mark
Ruan, Jianhua
Gu, Ting-Ting
Ravi, Dashnamoorthy
Blackman, Barron
Bishop, Alexander JR
author_sort Wiles, Amy M
collection PubMed
description BACKGROUND: A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species. RESULTS: The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced. CONCLUSIONS: Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.
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spelling pubmed-28593802010-04-27 Building and analyzing protein interactome networks by cross-species comparisons Wiles, Amy M Doderer, Mark Ruan, Jianhua Gu, Ting-Ting Ravi, Dashnamoorthy Blackman, Barron Bishop, Alexander JR BMC Syst Biol Research article BACKGROUND: A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species. RESULTS: The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced. CONCLUSIONS: Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data. BioMed Central 2010-03-30 /pmc/articles/PMC2859380/ /pubmed/20353594 http://dx.doi.org/10.1186/1752-0509-4-36 Text en Copyright ©2010 Wiles et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research article
Wiles, Amy M
Doderer, Mark
Ruan, Jianhua
Gu, Ting-Ting
Ravi, Dashnamoorthy
Blackman, Barron
Bishop, Alexander JR
Building and analyzing protein interactome networks by cross-species comparisons
title Building and analyzing protein interactome networks by cross-species comparisons
title_full Building and analyzing protein interactome networks by cross-species comparisons
title_fullStr Building and analyzing protein interactome networks by cross-species comparisons
title_full_unstemmed Building and analyzing protein interactome networks by cross-species comparisons
title_short Building and analyzing protein interactome networks by cross-species comparisons
title_sort building and analyzing protein interactome networks by cross-species comparisons
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2859380/
https://www.ncbi.nlm.nih.gov/pubmed/20353594
http://dx.doi.org/10.1186/1752-0509-4-36
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