A method for predicting protein complex in dynamic PPI networks.

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

doi:10.1186/s12859-016-1101-y

Hongfei Lin, Yijia Zhang + Show 4 more

Open Access

https://doi.org/10.1186/s12859-016-1101-y

Copy DOI

Journal: BMC bioinformatics	Publication Date: Jul 1, 2016
Citations: 56	License type: cc-by

Affiliation: Dalian University of Technology

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1101-y) contains supplementary material, which is available to authorized users.

Highlights

Accurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function
Our method is compared with current state-of-the-art protein complex prediction methods
This implement of our algorithm and the experimental datasets are avialable in the Additional files 1, 2 and 3

Summary

Introduction

Accurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. The protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. Prediction of protein complexes from protein-protein interaction (PPI) networks has become a key problem for revealing cellular function and organization of biological systems in post-genomic era. Protein complexes are of great importance for understanding the principles of cellular organization and function [1,2,3]. Great efforts have been made to detect protein complexes in these PPI networks through the computational methods [6,7,8,9,10,11,12,13]. Liu et al [9] present a method called CMC (Clusteringbased on Maximal Cliques) which identifies protein

Methods

Results

Conclusion