Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7447
Title: Full characterization of localization diversity in the human protein interactome
Authors: Cheng, Lixin 
Fan, Kaili 
Huang, Yan 
Wang, Dong 
Prof. LEUNG Kwong Sak 
Issue Date: 2017
Source: Journal of Proteome Research, 2017, vol.16 (8), pp. 3019–3029.
Journal: J. Proteome Res 
Abstract: Spatial–temporal regulation among proteins forms dynamic networks in cells. Coexistence in common cell compartments can improve biological reliability of the protein–protein interactions. However, this is usually overlooked by most proteomic studies and leads to unrealistic discoveries. In this paper, we systematically characterize the interaction localization diversity in the human protein interactome using the localization coefficient, a novel metric proposed for assessing how diversely the interactions localize among cell compartments. Our analysis reveals the following: (1) the subcellular networks of the nucleus, cytosol, and mitochondrion are dense but the interactions tend to localize in specific cell compartments, whereas the subnetworks of the secretory-pathway, membrane, and extracellular region are sparse but the interactions are diversely localized; (2) the housekeeping proteins tend to appear in multiple compartments, while the tissue-specific proteins present a relatively flat profile of localization breadth; (3) the autophagy proteins tend to diversely localize in multiple compartments, especially those with high connectivity, compared with the apoptosis proteins; (4) the proteins targeted by small-molecule drugs show no preference for compartments, whereas the proteins directed by antibody-based drugs tend to belong to transmembrane regions with a strong diversity. In summary, our analysis provides a comprehensive view of the subcellular localization for interacting proteins, demonstrates that localization diversity is an important feature of protein interactions, and shows its ability to highlight meaningful biological functions.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7447
ISSN: 1535-3893
1535-3907
DOI: 10.1021/acs.jproteome.7b00306
Appears in Collections:Applied Data Science - Publication

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