Proteome-wide prediction of signal flow direction in protein interaction networks based on interacting domains.
Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.
Liu
Wei
W
Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Institutes of Biomedical Sciences, Fudan University, 100850 Beijing, China.
Li
Dong
D
Wang
Jian
J
Xie
Hongwei
H
Zhu
Yunping
Y
He
Fuchu
F
D000818:Animals; D006801:Humans; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D021381:Protein Transport; D020543:Proteome / Q000737:chemistry* / Q000378:metabolism*; D015203:Reproducibility of Results; D015398:Signal Transduction; D013045:Species Specificity; D013347:Subcellular Fractions / Q000378:metabolism
D020543:Proteome