Identifying of protein complexes and functional modules in E.coli PPI networks

Abstract Background: Escherichia coli has been at the center of microbial research for decades, making it a standard microorganism for studying molecular mechanism. Molecular complexes, operons and functional modules are important molecular functional domains of Escherichia coli. Most previous studies focused on the detection of E. coli protein complexes based on the experimental methods. While the research of prediction of protein complexes in E. coli based on large-scale proteomic data, especially the functional modules of E. coli are relatively few. Identifying protein complexes and functional modules of E. coli is crucial to reveal principles of cellular organizations, processes and functions. Results: In this study, the protein complexes and functional modules of two high-quality binary interaction datasets of E. coli are predicted by an efficient edge clustering algorithm (ELPA) for complex biological network, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, the experimental results show that most topological modules predicted in the two datasets match very well with the real protein complexes, cellular processes and biological functions. By analyzing the corresponding complexes and functional modules shows that all predicted protein complexes are fully covered by one or more functional modules. Furthermore, we compared the results of ELPA with a famous node clustering algorithm (MCL) on the same PPI network of E. coli , and found that ELPA outperforms MCL in terms of matching with gold standard complexes. Conclusions: As a consequence, we surmise that topological modules of PPI network detected by ELPA fits well with real protein complexes and functional units. In most predicted topological modules, the protein complexes and corresponding functional modules are highly overlapping. ELPA is an effective tool to predict protein complexes and functional modules in PPI networks of E. coli.