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. 2016 Jul 25;17 Suppl 7(Suppl 7):229.
doi: 10.1186/s12859-016-1101-y.

A method for predicting protein complex in dynamic PPI networks

Yijia Zhang  1 , Hongfei Lin  2 , Zhihao Yang  2 , Jian Wang  2 , Yiwei Liu  2 , Shengtian Sang  2
Affiliations

A method for predicting protein complex in dynamic PPI networks

Yijia Zhang et al. BMC Bioinformatics. .

Abstract

Background: Accurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.

Results: The gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.

Conclusions: We construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.

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Figures

Fig. 1
Fig. 1
An illustration example of dynamic PPI networks construction. a construction of static PPI networks based on high-throughput PPI data. b calculation of dynamic information based on gene expression data. ATP, AP and PCC denote active time points, active probability and Pearson correlation coefficient, respectively. c construction of dynamic PPI networks
Fig. 2
Fig. 2
RNA polymerase I complex predicted by our method on STRING dataset
Fig. 3
Fig. 3
Examples of protein complexes predicted by our method
See this image and copyright information in PMC

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