Simulated evolution of protein-protein interaction networks with realistic topology.

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a...

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Autores principales:	G Jack Peterson, Steve Pressé, Kristin S Peterson, Ken A Dill
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9a92a006a67d4041b32984a651dc0582
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spelling	oai:doaj.org-article:9a92a006a67d4041b32984a651dc05822021-11-18T07:14:00ZSimulated evolution of protein-protein interaction networks with realistic topology.1932-620310.1371/journal.pone.0039052https://doaj.org/article/9a92a006a67d4041b32984a651dc05822012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22768057/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.G Jack PetersonSteve PresséKristin S PetersonKen A DillPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e39052 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q G Jack Peterson Steve Pressé Kristin S Peterson Ken A Dill Simulated evolution of protein-protein interaction networks with realistic topology.
description	We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.
format	article
author	G Jack Peterson Steve Pressé Kristin S Peterson Ken A Dill
author_facet	G Jack Peterson Steve Pressé Kristin S Peterson Ken A Dill
author_sort	G Jack Peterson
title	Simulated evolution of protein-protein interaction networks with realistic topology.
title_short	Simulated evolution of protein-protein interaction networks with realistic topology.
title_full	Simulated evolution of protein-protein interaction networks with realistic topology.
title_fullStr	Simulated evolution of protein-protein interaction networks with realistic topology.
title_full_unstemmed	Simulated evolution of protein-protein interaction networks with realistic topology.
title_sort	simulated evolution of protein-protein interaction networks with realistic topology.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/9a92a006a67d4041b32984a651dc0582
work_keys_str_mv	AT gjackpeterson simulatedevolutionofproteinproteininteractionnetworkswithrealistictopology AT stevepresse simulatedevolutionofproteinproteininteractionnetworkswithrealistictopology AT kristinspeterson simulatedevolutionofproteinproteininteractionnetworkswithrealistictopology AT kenadill simulatedevolutionofproteinproteininteractionnetworkswithrealistictopology
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Simulated evolution of protein-protein interaction networks with realistic topology.

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