Local Topological Signatures for Network-Based Prediction of Biological Function

In biology, similarity in structure or sequence between molecules is often used as evidence of functional similarity. In protein interaction networks, structural similarity of nodes (i.e., proteins) is often captured by comparing node signatures (vectors of topological properties of neighborhoods surrounding the nodes). In this paper, we ask how well such topological signatures predict protein function, using protein interaction networks of the organism Saccharomyces cerevisiae. To this end, we compare two node signatures from the literature – the graphlet degree vector and a signature based on the graph spectrum – and our own simple node signature based on basic topological properties. We find the connection between topology and protein function to be weak but statistically significant. Surprisingly, our node signature, despite its simplicity, performs on par with the other more sophisticated node signatures. In fact, we show that just two metrics, the link count and transitivity, are enough to classify protein function at a level on par with the other signatures suggesting that detailed topological characteristics are unlikely to aid in protein function prediction based on protein interaction networks.