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Predicting the fission yeast protein interaction network.

Pancaldi, Vera and Saraç, Omer S and Rallis, Charalampos and McLean, Janel R and Převorovský, Martin and Gould, Kathleen and Beyer, Andreas and Bähler, Jürg (2012) 'Predicting the fission yeast protein interaction network.' G3: Genes, Genomes, Genetics, 2 (4). pp. 453-467. ISSN 2160-1836

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Abstract

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).

Item Type: Article
Uncontrolled Keywords: Cbf11; TOR; Mak1/2/3; support vector machine; random forest
Divisions: Faculty of Science and Health
Faculty of Science and Health > Life Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 18 Jun 2021 14:28
Last Modified: 18 Aug 2022 10:32
URI: http://repository.essex.ac.uk/id/eprint/26722

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