Abstract
The yeast protein-protein interaction network has been shown to have distinct topological features such as a scale free degree distribution and a high level of clustering. Here we analyze an additional feature which is called Neighbor Overlap. This feature reflects the number of shared neighbors between a pair of proteins. We show that Neighbor Overlap is enriched in the yeast protein-protein interaction network compared with control networks carefully designed to match the characteristics of the yeast network in terms of degree distribution and clustering coefficient. Our analysis also reveals that pairs of proteins with high Neighbor Overlap have higher sequence similarity, more similar GO annotations and stronger genetic interactions than pairs with low ones. Finally, we demonstrate that pairs of proteins with redundant functions tend to have high Neighbor Overlap. We suggest that a combination of three mechanisms is the basis for this feature: The abundance of protein complexes, selection for backup of function, and the need to allow functional variation.
Highlights
The yeast Saccharomyces cerevisiae protein interaction network is probably the most studied protein interaction network both experimentally and computationally
We show that high Neighbor Overlap (NO) is associated with functional similarity and is enriched in pairs of proteins that participate in genetic interactions and that supply backup to each other
Definitions of Neighbor Overlap NO is a measure of how many common neighbors a pair of proteins has in the protein interaction network
Summary
The yeast Saccharomyces cerevisiae protein interaction network is probably the most studied protein interaction network both experimentally and computationally. In addition the network was shown to have large clustering coefficients (CC), [2,3] meaning that neighbors of nodes in the network tend to interact amongst themselves (a property sometimes referred to as locality or modularity). We explore a measure called Neighbor Overlap (NO) which reflects the number of common neighbors a protein pair has in the protein interaction network, normalized in various ways. Ravasz et al utilized this measure to study the hierarchical organization of modularity in metabolic networks [4]. A related measure that calculates an edge clustering coefficient between directly connected nodes was used [5,6] to detect communities in complex networks, including the C. elegans metabolic network
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