Identifying functions of protein complexes based on topology similarity with random forest.

Elucidating the functions of protein complexes is critical for understanding disease mechanisms, diagnosis and therapy. In this study, based on the concept that protein complexes with similar topology may have similar functions, we firstly model protein complexes as weighted graphs with nodes representing the proteins and edges indicating interaction between proteins. Secondly, we use topology features derived from the graphs to characterize protein complexes based on the graph theory. Finally, we construct a predictor by using random forest and topology features to identify the functions of protein complexes. Effectiveness of the current method is evaluated by identifying the functions of mammalian protein complexes. And then the predictor is also utilized to identify the functions of protein complexes retrieved from human protein-protein interaction networks. We identify some protein complexes with significant roles in the occurrence of tumors, vesicles and retinoblastoma. It is anticipated that the current research has an important impact on pathogenesis and the pharmaceutical industry. The source code of Matlab and the dataset are freely available on request from the authors.