Algorithms For Detecting Protein Complexes In Protein Interaction Networks Based On Gene Ontology

Identification of modular structures in protein-protein interaction (PPI) networks is the first step in understanding the organization and dynamics of cell functions, so how to detect protein complexes in PPI networks becomes a very important and challenging task now. Many methods have been used to identify protein complexes from PPI networks. However, most of them detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent biological function of them. Inspired by this insight, in this paper we present a method CoAchGO to identify protein complexes in yeast PPI networks.CoAchGO consists of two main stages:Firstly, it identifies protein complex cores as the "heart" of protein complexes. Not only can our method consider the topological properties of the PPI network, but it effectively uses the protein’s functional properties to solve the problem that too much false negative results exist in the PPI data. Secondly, it reuses the protein’s functional properties based on Gene Ontology to involve attachments into these protein complex cores to form biologically meaningful protein complexes.We experimentally evaluate the performance of our method CoAchGO on two different yeast PPI networks. Using gold standard sets of protein complexes, Gene Ontology, and localization annotations, we show our method gains a significant improvement over previous algorithms in terms of precision, recall, and biological significance of the predicted complexes and thus provides more insights for future biological study.