A method for predicting protein complex in dynamic PPI networks

BMC Bioinformatics

Research

Shengtian Sang¹  Hongfei Lin¹  Yiwei Liu¹  Zhihao Yang¹  Jian Wang¹  Yijia Zhang¹ 

[1] College of Computer Science and Technology, Dalian University of Technology Dalian, Liaoning, China;

关键词: Positive Predictive Value;    Predict Protein Complex;    Protein Complex Prediction;    Active Time Point;    Integrate Gene Expression Data;   

DOI  :  10.1186/s12859-016-1101-y

来源: Springer

【 摘 要 】

BackgroundAccurate 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.ResultsThe 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.ConclusionsWe 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.

【 授权许可】

CC BY   
© The Author(s). 2016