A multiobjective approach for identifying protein complexes and studying their association in multiple disorders

Background: Detecting protein complexes within protein-protein interaction (PPI) networks is a major step toward the analysis of biological processes and pathways. Identification and characterization of protein complexes in PPI network is an ongoing challenge. Several high-throughput experimental techniques provide substantial number of PPIs which are widely utilized for compiling the PPI network of a species. Results: Here we focus on detecting human protein complexes by developing a multiobjective framework. For this large human PPI network is partitioned into modules which serves as protein complex. For building the objective functions we have utilized topological properties of PPI network and biological properties based on Gene Ontology semantic similarity. The proposed method is compared with that of some state-of-the-art algorithms in the context of different performance metrics. For the purpose of biological validation of our predicted complexes we have also employed a Gene Ontology and pathway based analysis here. Additionally, we have performed an analysis to associate resulting protein complexes with 22 key disease classes. Two bipartite networks are created to clearly visualize the association of identified protein complexes with the disorder classes. Conclusions: Here, we present the task of identifying protein complexes as a multiobjective optimization problem. Identified protein complexes are found to be associated with several disorders classes like 'Cancer','Endocrine' and 'Multiple'. This analysis uncovers some new relationships between disorders and predicted complexes that may take a potential role in the prediction of multi target drugs.