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Predicting the Fission Yeast Protein Interaction Network

A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic prot...

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Autores principales: Pancaldi, Vera, Saraç, Ömer S., Rallis, Charalampos, McLean, Janel R., Převorovský, Martin, Gould, Kathleen, Beyer, Andreas, Bähler, Jürg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337474/
https://www.ncbi.nlm.nih.gov/pubmed/22540037
http://dx.doi.org/10.1534/g3.111.001560
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author Pancaldi, Vera
Saraç, Ömer S.
Rallis, Charalampos
McLean, Janel R.
Převorovský, Martin
Gould, Kathleen
Beyer, Andreas
Bähler, Jürg
author_facet Pancaldi, Vera
Saraç, Ömer S.
Rallis, Charalampos
McLean, Janel R.
Převorovský, Martin
Gould, Kathleen
Beyer, Andreas
Bähler, Jürg
author_sort Pancaldi, Vera
collection PubMed
description A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).
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spelling pubmed-33374742012-04-26 Predicting the Fission Yeast Protein Interaction Network Pancaldi, Vera Saraç, Ömer S. Rallis, Charalampos McLean, Janel R. Převorovský, Martin Gould, Kathleen Beyer, Andreas Bähler, Jürg G3 (Bethesda) Investigations A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt). Genetics Society of America 2012-04-01 /pmc/articles/PMC3337474/ /pubmed/22540037 http://dx.doi.org/10.1534/g3.111.001560 Text en Copyright © 2012 Pancaldi et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Investigations
Pancaldi, Vera
Saraç, Ömer S.
Rallis, Charalampos
McLean, Janel R.
Převorovský, Martin
Gould, Kathleen
Beyer, Andreas
Bähler, Jürg
Predicting the Fission Yeast Protein Interaction Network
title Predicting the Fission Yeast Protein Interaction Network
title_full Predicting the Fission Yeast Protein Interaction Network
title_fullStr Predicting the Fission Yeast Protein Interaction Network
title_full_unstemmed Predicting the Fission Yeast Protein Interaction Network
title_short Predicting the Fission Yeast Protein Interaction Network
title_sort predicting the fission yeast protein interaction network
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337474/
https://www.ncbi.nlm.nih.gov/pubmed/22540037
http://dx.doi.org/10.1534/g3.111.001560
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