Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks.

Mian Wang,Hai Xiang Lin,Cheng Zhong,Jinxiong Zhang

doi:10.1155/2019/3726721

Abstract

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

Highlights

The majority of proteins interact with each other to perform a specific biological process [1]
By adopting a dynamic model-based method to filter the noisy data from gene expression profiles, Xiao et al [21] proposed a k-sigma method to determine whether a protein at a time point is active and constructed a noise-filtered active protein interaction network (NF-APIN) to detect protein complexes
Let a graph G=(V, E) represent a static protein-protein interaction network (SPIN), where V is a set of nodes and N=|V|, E is a set of edges, and e(i, j) denotes the edge between nodes i and j, where i, j=1, 2, . . ., N

Summary

Introduction

The majority of proteins interact with each other to perform a specific biological process [1]. According to the periodicity of gene expression, De Lichtenberg et al [24] constructed dynamic PPI networks over the yeast mitotic cell cycle by determining active time points of each protein. Instead of using a global threshold, Wang et al [23] presented a three-sigma method, which uses the sum of the gene expression mean and three standard deviations as a threshold, to determine active time points of each protein, and constructed dynamic protein interaction networks (DPIN) and identified complexes from DPIN. By adopting a dynamic model-based method to filter the noisy data from gene expression profiles, Xiao et al [21] proposed a k-sigma method to determine whether a protein at a time point is active and constructed a noise-filtered active protein interaction network (NF-APIN) to detect protein complexes. We evaluated our constructed TI-PINs and other dynamic PPI networks and compared our proposed identification method with four other competing methods

Methods

Experiments and Results

Conclusions