Identifying functional modules in the physical interactome of Saccharomyces cerevisiae.

TitleIdentifying functional modules in the physical interactome of Saccharomyces cerevisiae.
Publication TypeJournal Article
Year of Publication2007
AuthorsPu, S., Vlasblom J., Emili A., Greenblatt J., and Wodak S. J.
Date Published2007 Mar
KeywordsAlgorithms, Computer Simulation, Models, Biological, Multiprotein Complexes, Protein Interaction Mapping, Reproducibility of Results, ROC Curve, Saccharomyces cerevisiae Proteins

Reliable information on the physical and functional interactions between the gene products is an important prerequisite for deriving meaningful system-level descriptions of cellular processes. The available information about protein interactions in Saccharomyces cerevisiae has been vastly increased recently by two comprehensive tandem affinity purification/mass spectrometry (TAP/MS) studies. However, using somewhat different approaches, these studies produced diverging descriptions of the yeast interactome, clearly illustrating the fact that converting the purification data into accurate sets of protein-protein interactions and complexes remains a major challenge. Here, we review the major analytical steps involved in this process, with special focus on the task of deriving complexes from the network of binary interactions. Applying the Markov Cluster procedure to an alternative yeast interaction network, recently derived by combining the data from the two latest TAP/MS studies, we produce a new description of yeast protein complexes. Several objective criteria suggest that this new description is more accurate and meaningful than those previously published. The same criteria are also used to gauge the influence that different methods for deriving binary interactions and complexes may have on the results. Lastly, it is shown that employing identical procedures to process the latest purification datasets significantly improves the convergence between the resulting interactome descriptions.

Alternate JournalProteomics
PubMed ID17370254