Protein complex prediction based on simultaneous protein interaction network

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Motivation: The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. Results: In this article, a method of re. ning PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network ( SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions.
Publisher
OXFORD UNIV PRESS
Issue Date
2010-02
Language
English
Article Type
Article
Keywords

YEAST; MAP; RECOGNITION; ALGORITHM; BINDING; MODULES

Citation

BIOINFORMATICS, v.26, no.3, pp.385 - 391

ISSN
1367-4803
DOI
10.1093/bioinformatics/btp668
URI
http://hdl.handle.net/10203/174460
Appears in Collection
CS-Journal Papers(저널논문)
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