Identifying protein complexes based on density and modularity in protein-protein interaction network

Background: Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. Results: To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f(rho), as f(rho)= (density)(rho)*(modularity)(1-rho), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S. cerevisiae show that compared with the results of fitness equal to density (rho = 1) or equal to modularity (rho = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<rho<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S. cerevisiae and E. coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity. Conclusions: By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.