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From the July/August 2014 Issue

From Function to Interaction: A New Paradigm for Accurately Predicting Protein Complexes Based on Protein-to-Protein Interaction Networks

By Bin Xu and Jihong Guan

Featured article thumbnail imageIdentification of protein complexes is critical to understand complex formation and protein functions. Recent advances in high-throughput experiments have provided large data sets of protein-protein interactions (PPIs). Many approaches, based on the assumption that complexes are dense subgraphs of PPI networks (PINs in short), have been proposed to predict complexes using graph clustering methods. In this paper, we introduce a novel from-function-to-interaction paradigm for protein complex detection. As proteins perform biological functions by forming complexes, we first cluster proteins using biology process (BP) annotations from gene ontology (GO). Then, we map the resulting protein clusters onto a PPI network (PIN in short), extract connected subgraphs consisting of clustered proteins from the PPI network and expand each connected subgraph with protein nodes that have rich links to the proteins in the subgraph. Such expanded subgraphs are taken as predicted complexes. We apply the proposed method (called CPredictor) to two PPI data sets of S. cerevisiae for predicting protein complexes. Experimental results show that CPredictor outperforms the existing methods. The outstanding precision of CPredictor proves that the from-function-to-interaction paradigm provides a new and effective way to computational detection of protein complexes.

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  • TCBB celebrates its 10th Anniversary. Editor-in-Chief Ying Xu says, "The emergence and maturation of increasingly more and powerful molecular measurement technologies such as next generation sequencing and chromosome conformation capture allow scientists to tackle biological problems at the depth and breadth that we have never seen before. At the same time the enormity and complexity of the data generated using these technologies raised tremendous challenges to computational scientists to develop more effective techniques to store, transmit, organize, process, analyze and mine the data, and to construct models to assist interpreting the data. Since its creation ten years ago, TCBB has been playing a major role in bridging the world of computing and the world of biology. I want to congratulate what the journal has done in providing biologists with the most powerful computational tools to help address their data and modeling needs. I fully expect that TCBB will continue to play increasingly significant roles in attracting more computational scientists to address the ever increasing needs for new and more powerful computational techniques and to introduce to new comers the important and challenging computational biology problems in a timely fashion."

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TCBB is a joint publication of the IEEE Computer Society, Association for Computing Machinery, IEEE Computational Intelligence Society, and the IEEE Engineering in Medicine and Biology Society.

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) is a bimonthly journal that publishes archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology. 
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