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

• Published in: BioMed research international Vol. 2019; no. 2019; pp. 1 - 17
• Main Authors: Wang, Mian, Lin, Hai Xiang, Zhong, Cheng, Zhang, Jinxiong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Acids; Algorithms; Bioinformatics; Biological activity; Biological computing; Cell cycle; Cluster Analysis; Clustering; Computational Biology - methods; Computer applications; Gene expression; Gene Expression - physiology; Genes; Homogeneity; Identification methods; Metabolism; Methods; Networks; Ontology; Protein interaction; Protein Interaction Maps - physiology; Protein-protein interactions; Proteins; Proteins - metabolism; Semantics; Yeast; Yeasts
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2019/3726721

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.