Suk Hoon Jung (email@example.com)
Woo-Hyuk Jang (firstname.lastname@example.org)
Hee-Yyun Hur (email@example.com)
Bora Hyun (firstname.lastname@example.org)
Dong-Soo Han (email@example.com)
School of Engineering, Information and Communications University, 119, Munjiro, Yuseong-gu,Daejeon, 305-714, Korea
The increasing amount of available Protein-Protein Interaction (PPI) data enables scalable methods for the protein complex prediction. A protein complex is a group of two or more proteins formed by interactions that are stable over time, and it generally corresponds to a dense sub-graph in PPI Network (PPIN). However, dense sub-graphs correspond not only to stable protein complexes but also to sets of proteins including dynamic interactions. As a result, conventional simple PPIN based graph-theoretic clustering methods have high false positive rates in protein complex prediction. In this paper, we propose an approach to predict protein complexes based on the integration of PPI data and mutually exclusive interaction information drawn from structural interface data of protein domains. The extraction of Simultaneous Protein Interaction Cluster (SPIC) is the essence of our approach, which excludes interaction conflicts in network clusters by achieving mutually exclusion among interactions. The concept of SPIC was applied to conventional graph-theoretic clustering algorithms, MCODE and LCMA, to evaluate the density of clusters for protein complex prediction. The comparison with original graph-theoretic clustering algorithms verified the effectiveness of our approach; SPIC based methods refined false positives of original methods to be true positive complexes, without any loss of true positive predictions yielded by original methods.
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