Detecting Protein Complexes from Signed Protein-Protein Interaction Networks

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2015-11, Vol.12 (6), p.1333-1344
• Main Authors: Ou-Yang, Le, Dai, Dao-Qing, Zhang, Xiao-Fei
• Format: Article
• Language: English
• Subjects: Animals; Bioinformatics; Biology; complex-complex interaction; Computation; Computational biology; Computer Simulation; Detection algorithms; Factorization; Gene Expression Profiling - methods; Humans; Mathematical models; Models, Biological; Multiprotein Complexes - metabolism; Networks; Organizations; Pattern Recognition, Automated - methods; protein complex; Protein Interaction Mapping - methods; Protein-protein interaction; Proteins; Proteome - metabolism; Signal Transduction - physiology; signed graph regularization; signed network
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2401014

Identification of protein complexes is fundamental for understanding the cellular functional organization. With the accumulation of physical protein-protein interaction (PPI) data, computational detection of protein complexes from available PPI networks has drawn a lot of attentions. While most of the existing protein complex detection algorithms focus on analyzing the physical protein-protein interaction network, none of them take into account the "signs" (i.e., activation-inhibition relationships) of physical interactions. As the "signs" of interactions reflect the way proteins communicate, considering the "signs" of interactions can not only increase the accuracy of protein complex identification, but also deepen our understanding of the mechanisms of cell functions. In this study, we proposed a novel Signed Graph regularized Nonnegative Matrix Factorization (SGNMF) model to identify protein complexes from signed PPI networks. In our experiments, we compared the results collected by our model on signed PPI networks with those predicted by the state-of-the-art complex detection techniques on the original unsigned PPI networks. We observed that considering the "signs" of interactions significantly benefits the detection of protein complexes. Furthermore, based on the predicted complexes, we predicted a set of signed complex-complex interactions for each dataset, which provides a novel insight of the higher level organization of the cell. All the experimental results and codes can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail. sysu.edu.cn/dai/others/SGNMF.zip.