Abstract
Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks.
Highlights
Recent high-throughput experimental methods have generated proteome-scale protein-protein physical interaction maps for many organisms
Any property can be used to annotate proteins in schemas, and different types of interactions may be specified, we focus on direct physical protein-protein interactions with proteins described via Pfam sequence motifs [14] and a set of GO molecular function terms [15]; such schemas with multiple instances in an interactome are likely to correspond to shared mechanisms that underlie a range of biological activities
We propose the language of network schemas for describing recurring patterns of specific types of proteins and their interactions
Summary
Recent high-throughput experimental methods have generated proteome-scale protein-protein physical interaction maps for many organisms (review, [1]). We aim to explicitly incorporate known attributes of individual proteins into the analysis of biological networks We conceptualize this with network schemas, which are a general means for representing organizational patterns within interactomes where groups of proteins are described by arbitrary known characteristics along with the desired network topology of interactions among them (Figure 1A). Because we expect the largest number of schemas with multiple instances to be associated with small topologies, we begin to address these questions by considering four basic network topologies (Figure 1C) varying from two interacting proteins (pair schemas) to higher-order schemas containing up to three interactions (triplet, triangle, and Y-star schemas); we choose these particular linear, cyclical, and branched topologies because they are the simplest patterns in physical interactomes that may intuitively be associated with signaling pathways, complexes, and switch-like patterns, respectively
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