Resource: STRING v8.1

After months of hard work from the entire STRING team – thanks everyone -  I am pleased to be able to say that STRING v8.1 has now been put into production. Here is a screen shot of the start page:

This is a minor release of STRING, which means that the imported databases of microarray expression data, protein interactions, genetic interactions, and pathways as well as text-mining evidence have all been updated. We have also fixed a bug that affected the minority of bacteria that have multiple chromosomes.

Another notable feature of STRING v8.1 is the new interactive network viewer that is implemented in Adobe Flash:

For further details please see the post on the official STRING/STITCH blog.

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Analysis: Four complementary yeast interactomes

The latest issue of Science features a paper by Yu et al. in which they report the results of a comprehensive yeast two-hybrid (Y2H) screen for interactions between budding yeast proteins. Just a few months earlier, Science published a paper by Tarassov et al. that describes a similar screen performed using a novel protein fragment complementation assay (PCA). Peer Bork and I wrote a Perspectives piece on these two papers, showing that the different assays for detecting protein interactions are complementary in the sense that they capture interactions for different subsets of the proteome. For example, PCA detects many interactions for membrane proteins whereas Y2H detects many interactions for nuclear proteins.

As part of writing the Perspectives piece, I performed numerous analyses that were not included in the final publication, because they were either too technical for a broad audience, not interesting enough to spend valuable space on, or would involve additional figures. Thankfully, my blog imposes no limitations on the number of words or figures (nor is it required that the content is interesting, although that is desirable).

The comparison included, in addition to the two interactomes introduced above, a third interactome that consists of all the high-confidence interactions identified by Gavin et al. and Krogan et al. using the tandem affinity purification (TAP) method. Also included in the comparison (but not in the Perspectives piece) was the literature-curated (LC) set of interactions published by Reguly et al. in 2006.

The Venn diagram below shows the overlap of the four interactomes in terms of proteins, that is a protein is considered to belong to an interactome if the method in question suggested at least one interaction partner:

The numbers outside the ellipses specify the total number of proteins for which a given method identified interactions. Notably, the PCA, Y2H, and TAP interactomes cover only approximately one sixth, one third, and half of the yeast proteome, respectively, despite all three assays having been tested on all yeast ORFs. This suggests that only a fraction of proteins can be targeted with a given assay.

A second way to compare the four interactomes is to count their overlaps in terms of pairs of interacting proteins. To provide additional detail, I distinguished between interactions that are not found in a given interactome because one or both proteins are not covered by the interactome in question (dashed lines in the diagrams), and interactions that were not found despite both proteins being covered (full lines in the diagrams). The Venn diagrams below show all twelve pairwise comparisions of the four interactomes:

As expected, the largest overlap is observed when comparing the two largest interactomes (LC and TAP), whereas the smallest overlap is observed when comparing the smallest interactomes (PCA and Y2H). Even if taking into account the differences in terms of protein coverage, however, the the overlaps between the interactomes leave a lot to be desired.

There are several reasons for the poor overlap at the level of pairwise interactions. One is that false positive interactions are unlikely to be reproducible by a different assay. A second is that the assays measure fundamentally different types of interactions: PCA and Y2H measure direct binary interactions between proteins, whereas TAP measures co-complex interactions, that is whether two proteins are part of the same complex or not. This is illustrated in the figure below, which shows the binary and co-complex networks for three different scenarios:

The two types of assays have different strengths and weaknesses. Binary interaction assays can in principle distinguish between the two first complexes, which only differ in that the subunits B and C are in direct contact in first complex but not in the second. However, binary assays are not able to distinguish between the second and the third scenario, that is whether A, B, and C form a single complex (ABC) or two complexes (AB and AC). Conversely, data from co-complex assays are able to answer the latter question but are unable to distinguish between the two first scenarios. The different assays thus complement each other, not only because they are able to interrogate different subsets of the proteome, but also because they provide us with complementary information about the composition and topology of protein complexes.

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Commentary: On large protein complexes and the essentiality of hubs

In 2001, Jeong and coworkers published a paper in Nature in which they showed that the central proteins in interaction networks, that is the proteins with the highest connectivity, are enriched for essential proteins. This publication has been highly influential as evidenced by the numerous subsequent publications on the importance of “hub” proteins. Several hypothesis have been published that try to explain why hubs are essential, for example that certain protein interactions are essential and that a protein with many interactions is thus more likely to be involved in at least one essential interaction (He and Zhang, 2006).

Yesterday, Zotenko and coworkers published a paper in PLoS Computational Biology in which they take a closer look at the cause of this phenomenon:

Why Do Hubs in the Yeast Protein Interaction Network Tend To Be Essential: Reexamining the Connection between the Network Topology and Essentiality.

The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein-protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model.

What Zotenko et al. show is, in other words, that essential hubs tend to be highly connected with each other and hence form large “Essential Complex Biological Modules”. Table 7 in their paper lists the Gene Ontology terms associated with these modules; among the recurring themes are “rRNA metabolic process”, “mRNA metabolic process”, “RNA splicing”, “ribosome biogenesis and assembly”, and “proteolysis”. These Gene Ontology terms obviously correspond to well known protein complexes, namely the RNA polymerases, the spliceosome, the ribosome, and the proteoasome. The analysis of Zotenko et al. thus suggests that the much debated correlation between centrality and essentiality is simply a consequence of the fact that many of the large protein complexes in a eukaryotic cell are essential, which is hardly surprising considering that they have been conserved through more than two billion years of evolution (Brocks et al., 1999).

Edit: For more views on the results of Zotenko et al. see the discussion on FriendFeed.

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