Predicting the Fission Yeast Protein Interaction Network

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).

Oxidative stress response pathways: Fission yeast as archetype

“…Despite the wealth of existing knowledge, some aspects of the fission yeast response to HP stress require further investigation. For instance, existing in silico predictions on Csx1 targets (Pancaldi & Bähler, 2011) and predicted interactions between the Mak1-3 proteins and a number of TFs (Pancaldi et al, 2012) need to be validated. To refine the current view of transcriptional regulation in oxidative stress, the binding specificities of the less studied transcriptional regulators that are highly induced upon HP exposure (Chen et al, 2003(Chen et al, , 2008 need to be defined as well.…”

“…Both domains bind diverse cofactors to sense different stimuli, with the PAS domain being also involved in protein-protein interactions (Erbel et al, 2003). Interestingly, 18 TFs have been identified as potential interaction partners of the Mak1-3 proteins, with Mak2 alone being predicted to physically interact with six TFs (Atf31, Hsr1, Rsv1, Res1, Moc3 and Prr1) (Pancaldi et al, 2012).…”

Mga2 Transcription Factor Regulates an Oxygen-responsive Lipid Homeostasis Pathway in Fission Yeast

“…Although cbf11 is not itself an Mga2 target gene (supplemental Table S4), deletion of cbf11 results in a low oxygen growth defect in our screen (supplemental Tables S1 and S2), and Cbf11 was found to bind Mga2 in an affinity capture screen for the fission yeast protein interactome network (57). The Cbf11 human homologs RBPJ and RBPJL share ϳ19% protein sequence identity with Cbf11 (clustered in the functional domains), raising the possibility that these proteins perform similar functions.…”

Antifungal compound honokiol triggers oxidative stress responsive signalling pathway and modulates central carbon metabolism

“…The enriched gene ontology category was selected based on biological process and molecular function using list frequency >5% and p -value <0.01 as the selection criterion. The protein–protein interaction network was constructed by the online tool Pint (Pombe Interactome) based on the support vector machine and random forest algorithm (Pancaldi et al 2012). …”