Dataset Information

Toward an understanding of the protein interaction network of the human liver.

ABSTRACT: Proteome-scale protein interaction maps are available for many organisms, ranging from bacteria, yeast, worms and flies to humans. These maps provide substantial new insights into systems biology, disease research and drug discovery. However, only a small fraction of the total number of human protein-protein interactions has been identified. In this study, we map the interactions of an unbiased selection of 5026 human liver expression proteins by yeast two-hybrid technology and establish a human liver protein interaction network (HLPN) composed of 3484 interactions among 2582 proteins. The data set has a validation rate of over 72% as determined by three independent biochemical or cellular assays. The network includes metabolic enzymes and liver-specific, liver-phenotype and liver-disease proteins that are individually critical for the maintenance of liver functions. The liver enriched proteins had significantly different topological properties and increased our understanding of the functional relationships among proteins in a liver-specific manner. Our data represent the first comprehensive description of a HLPN, which could be a valuable tool for understanding the functioning of the protein interaction network of the human liver.

SUBMITTER: Wang J

PROVIDER: S-EPMC3261708 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Toward an understanding of the protein interaction network of the human liver.

Wang Jian J Huo Keke K Ma Lixin L Tang Liujun L Li Dong D Huang Xiaobi X Yuan Yanzhi Y Li Chunhua C Wang Wei W Guan Wei W Chen Hui H Jin Chaozhi C Wei Juncheng J Zhang Wanqiao W Yang Yongsheng Y Liu Qiongming Q Zhou Ying Y Zhang Cuili C Wu Zhihao Z Xu Wangxiang W Zhang Ying Y Liu Tao T Yu Donghui D Zhang Yaping Y Chen Liang L Zhu Dewu D Zhong Xing X Kang Lixin L Gan Xiang X Yu Xiaolan X Ma Qi Q Yan Jing J Zhou Li L Liu Zhongyang Z Zhu Yunping Y Zhou Tao T He Fuchu F Yang Xiaoming X

Molecular systems biology 20111011

Proteome-scale protein interaction maps are available for many organisms, ranging from bacteria, yeast, worms and flies to humans. These maps provide substantial new insights into systems biology, disease research and drug discovery. However, only a small fraction of the total number of human protein-protein interactions has been identified. In this study, we map the interactions of an unbiased selection of 5026 human liver expression proteins by yeast two-hybrid technology and establish a human ...[more]

PMID: 21988832

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Project description:Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Dataset Information

Toward an understanding of the protein interaction network of the human liver.

Publications

Toward an understanding of the protein interaction network of the human liver.

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