Predicting the fission yeast protein interaction network

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Vera Pancaldi - (Author)
  • Ömer S. Saraç - , Dresden University of Technology (Author)
  • Charalampos Rallis - (Author)
  • Janel R. McLean - (Author)
  • Martin Převorovský - (Author)
  • Kathleen Gould - (Author)
  • Andreas Beyer - , Cellular networks and systems biology (Author)
  • Jürg Bähler - (Author)

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 subnetworks 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).

Details

Original languageEnglish
Pages (from-to)453-467
Number of pages15
JournalG3: Genes, genomes, genetics
Volume2
Issue number4
Publication statusPublished - Apr 2012
Peer-reviewedYes

Keywords

Sustainable Development Goals

Keywords

  • Cbf11, Mak1/2/3 support vector machine random forest, TOR