Essential Protein Discovery Based on Network Motif and Gene Ontology

Essential proteins are indispensable to support cellular life and constitute a minimal set required for a living cell. Fast progress in high-throughput technologies and large amount of data enable to discover essential proteins in system level by analyzing protein-protein interaction networks. A number of centrality algorithms are suggested to detect essential proteins, but they focus only on network structures. In this paper, we develop a new centrality algorithm, named MCGO which uses network motifs for centrality measure in the graph pruned by EDGEGO. EDGEGO algorithm utilizes Gene Ontology(GO) to trim a number of uninformative edges from the network. We compare the performance of our algorithm with DC (degree centrality) and SoECC (sum of edge clustering coefficient) against various evaluation measures. Experimental results applied to an yeast protein-protein interaction network downloaded from DIP database show that MCGO performs significantly better than DC and SoECC. We also show that DC and SoECC improve greatly when EDGEGO is applied to them.