Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the e...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Apichat Suratanee, Martin H Schaefer, Matthew J Betts, Zita Soons, Heiko Mannsperger, Nathalie Harder, Marcus Oswald, Markus Gipp, Ellen Ramminger, Guillermo Marcus, Reinhard Männer, Karl Rohr, Erich Wanker, Robert B Russell, Miguel A Andrade-Navarro, Roland Eils, Rainer König
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
Materias:
Acceso en línea:https://doaj.org/article/e01205940d804bc0bcc38759f7abd7ed
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:e01205940d804bc0bcc38759f7abd7ed
record_format dspace
spelling oai:doaj.org-article:e01205940d804bc0bcc38759f7abd7ed2021-11-25T05:40:43ZCharacterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.1553-734X1553-735810.1371/journal.pcbi.1003814https://doaj.org/article/e01205940d804bc0bcc38759f7abd7ed2014-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1003814https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.Apichat SurataneeMartin H SchaeferMatthew J BettsZita SoonsHeiko MannspergerNathalie HarderMarcus OswaldMarkus GippEllen RammingerGuillermo MarcusReinhard MännerKarl RohrErich WankerRobert B RussellRobert B RussellMiguel A Andrade-NavarroRoland EilsRainer KönigPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 9, p e1003814 (2014)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Apichat Suratanee
Martin H Schaefer
Matthew J Betts
Zita Soons
Heiko Mannsperger
Nathalie Harder
Marcus Oswald
Markus Gipp
Ellen Ramminger
Guillermo Marcus
Reinhard Männer
Karl Rohr
Erich Wanker
Robert B Russell
Robert B Russell
Miguel A Andrade-Navarro
Roland Eils
Rainer König
Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
description Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.
format article
author Apichat Suratanee
Martin H Schaefer
Matthew J Betts
Zita Soons
Heiko Mannsperger
Nathalie Harder
Marcus Oswald
Markus Gipp
Ellen Ramminger
Guillermo Marcus
Reinhard Männer
Karl Rohr
Erich Wanker
Robert B Russell
Robert B Russell
Miguel A Andrade-Navarro
Roland Eils
Rainer König
author_facet Apichat Suratanee
Martin H Schaefer
Matthew J Betts
Zita Soons
Heiko Mannsperger
Nathalie Harder
Marcus Oswald
Markus Gipp
Ellen Ramminger
Guillermo Marcus
Reinhard Männer
Karl Rohr
Erich Wanker
Robert B Russell
Robert B Russell
Miguel A Andrade-Navarro
Roland Eils
Rainer König
author_sort Apichat Suratanee
title Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
title_short Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
title_full Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
title_fullStr Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
title_full_unstemmed Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.
title_sort characterizing protein interactions employing a genome-wide sirna cellular phenotyping screen.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/e01205940d804bc0bcc38759f7abd7ed
work_keys_str_mv AT apichatsuratanee characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT martinhschaefer characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT matthewjbetts characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT zitasoons characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT heikomannsperger characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT nathalieharder characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT marcusoswald characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT markusgipp characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT ellenramminger characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT guillermomarcus characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT reinhardmanner characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT karlrohr characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT erichwanker characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT robertbrussell characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT robertbrussell characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT miguelaandradenavarro characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT rolandeils characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
AT rainerkonig characterizingproteininteractionsemployingagenomewidesirnacellularphenotypingscreen
_version_ 1718414549215870976