Abstract
BackgroundDetecting 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.ResultsHere 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.ConclusionsHere, 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-015-0056-2) contains supplementary material, which is available to authorized users.
Highlights
Detecting protein complexes within protein–protein interaction (PPI) networks is a major step toward the analysis of biological processes and pathways
Here we illustrate the performance of our proposed technique and compare this with three well known algorithms Molecular Complex Detection (MCODE) [5], clusterONE [6] and Affinity propagation [7]
This study introduces a multiobjective approach for detection of protein complexes in human PPI network
Summary
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. Recent advancement in biotechnology produces lots of information about protein–protein interactions. Those information act as a potential source to construct the protein–protein interaction network (PPIN) for a single species. Protein complexes are generally described as molecular aggregation of a set of proteins connected by multiple protein–protein interactions. Protein complexes play different functions in the cell. It can serve as cellular machines, rigid structures, and posttranslational modification systems.
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