Abstract
BackgroundProteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.ResultsIn this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.ConclusionsExtensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.
Highlights
Proteins dynamically interact with each other to perform their biological functions
As physical interactions determined by popular high-throughput technologies, e.g. yeast two-hybrid (Y2H) and Tandem Affinity Purification with mass spectrometry (TAP-MS) lack of temporal information, majority of existing complex detection methods treat the protein-protein interaction (PPI) network as a static network that can not be used to detect temporal protein complexes
To address the above two issues, in this paper we propose a novel technique to detect temporal protein complexes from the dynamic PPI networks
Summary
Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Computational detection of complexes has attracted tremendous attentions during the past decade [6,8,9,10,11,12,13,14] According to their life time, PPIs could be classified into stable or transient PPIs [15,16]. As physical interactions determined by popular high-throughput technologies, e.g. yeast two-hybrid (Y2H) and Tandem Affinity Purification with mass spectrometry (TAP-MS) lack of temporal information, majority of existing complex detection methods treat the PPI network as a static network that can not be used to detect temporal protein complexes. It is desirable to design novel computational methods that can take the inherent dynamic characteristics of PPI networks into consideration to better detect temporal protein complexes
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