Analysis of Protein Interactions and Complexes - Wodak Lab

Here the compass complex is shown in red, and proteins that are predicted to interact with members of this complex are shown in blue
Complexes predicted using the MCL graph clustering algorithm on the consolidated interaction network of Collins et al. Edges denote components shared by two predicted complexes, and nodes represent the complexes themselves - with size proportional to the number of proteins in that complex. The small piegraphs overlaid on each node display how the components of each complex distribute amongst "gold standard" MIPS complexes. The red colour is reserved for unassigned components - those proteins which are not found in any of the MIPS complexes.

Proteins tend to associate with one another, often forming large edifices that act as complex molecular machines. Although this has long been realized, the prevalence of such interactions and complexes in living cells only became apparent in recent years due to technological advances enabling large scale studies of protein-protein interactions and complexes in organisms such as yeast, bacteria worm, fly and more recently in humans. In parallel, methods have been developed for inferring physical and functional interactions from information on genome and protein sequences, as well as structural information stored in the Protein Data Bank. These various efforts have produced data typically describing thousands of interactions that can be grouped into hundreds of protein complexes. Processing and analyzing these data and extracting biologically meaningful information from them is an exciting but challenging undertaking. The data tend to be noisy and their coverage is still short of comprehensive, as many biologically important interactions (for instance, transient interactions and those involving membrane proteins) are not readily detected by current methods.

Our laboratory has been involved in analyzing genome-wide protein-protein interaction data for several years. Together with colleagues and former students in Brussels (J. vanHelden, N. Simonis, D. Gonze) we investigated the transcriptional regulation of protein complexes in the yeast S. cerevisiae. We used computational approaches to identify regulatory motifs in proteins belonging to the same complex and analyzed the overlap between genes coding for components of protein complexes and groups of genes known to be co-regulated. In addition we investigated the extent to which components of protein complexes are coherently expressed as judged by the mRNA expression profiles.

More recently we collaborated with the teams of Jack Greenblatt and Andrew Emili at the University of Toronto, analyzing one of the latest comprehensive dataset on protein-protein interactions in yeast that they produced. Our main role was in generating meaningful protein complexes from the binary interaction data, and in developing the necessary software and methodology for analyzing and validating these complexes. In another study we investigated the influence that the computational methodology can have on the final description of protein complexes from high throughput Tap-tag/MS data. Currently we are examining ways of improving the ranking of interactions derived from the TAP-tag/MS data so that fewer true interactions are discarded, while maintaining a low false positive error rate. This is achieved by using various machine learning techniques to incorporate additional biological evidence into the calculation of the confidence score that is associated with each interaction in the dataset.

As part of this work we are developing software for visualizing the analyzing protein interaction networks and complexes. Our software tools are developed as plug-ins to the Cytoscape platform. See for example GenePro, which allow flexible display and analysis of genome scale protein-protein interactions networks and complexes. Much of the supplementary material of the Nature paper of Krogan et al. was presented using a web-based version of GenePro, which allowed reader to examine the network and complexes in an interactive fashion. This was the first time that such interactive software was used in this context.

For further details see:

  • 18799807
    Wodak SJ, Pu S, Vlasblom J, Séraphin B.
    Challenges and rewards of interaction proteomics.
    Mol Cell Proteomics. 2008 Sep 17
  • 16973176
    Simonis N, Gonze D, Orsi C, van Helden J, Wodak SJ.
    Modularity of the transcriptional response of protein complexes in yeast.
    J Mol Biol. Oct 20;363(2):589-610
  • 15128447
    Simonis N. van Helden J. Cohen G. and Wodak SJ.,
    Transcriptional regulation of protein complexes in Yeast.
    Genome Biology :5(5) :R33
  • 16554755
    Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, Li J, Pu S, Datta N, Tikuisis AP, Punna T, Peregrin-Alvarez JM, Shales M, Zhang X, Davey M, Robinson MD, Paccanaro A, Bray JE, Sheung A, Beattie B, Richards DP, Canadien V, Lalev A, Mena F, Wong P, Starostine A, Canete MM, Vlasblom J, Wu S, Orsi C, Collins SR, Chandran S, Haw R, Rilstone JJ, Gandi K, Thompson NJ, Musso G, St Onge P, Ghanny S, Lam MH, Butland G, Altaf-Ul AM, Kanaya S, Shilatifard A, O'Shea E, Weissman JS, Ingles CJ, Hughes TR, Parkinson J, Gerstein M, Wodak SJ, Emili A, Greenblatt JF.
    Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.
    Nature. Mar 30;440(7084):637-43. Epub Mar 22.
  • 17370254
    Pu, S., Vlasblom, J., Emili, A., Greenblatt, J. & Wodak, S.J.
    Identifying functional modules in the physical interactome of Saccharomyces cerevisiae.
    Proteomics 7, 944-60.
  • 16921162
    Vlasblom J, Wu S, Pu S, Superina M, Liu G, Orsi C, Wodak SJ.
    GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.
    Bioinformatics. Sep 1;22(17):2178-9
Wodak Lab:
Analysis of Protein Interactions and Complexes