Targeting the human cancer pathway protein interaction network by structural genomics.

@article{citeulike:2822871, abstract = {Structural genomics provides an important approach for characterizing and understanding systems biology. As a step towards better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well-known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. While some 45\% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Blast E-val < 10-6), only \~{} 20\% of residues in these proteins are structurally covered using high-accuracy homology modeling criteria (i.e. Blast E\_val < 10-6 and at least 80\% sequence identity) or by actual experimental structures. The HCPIN website (http://nmr.cabm.rutgers.edu/hcpin) provides a comprehensive description of this biomedical important multi-pathway network, together with experimental and homology models of HCPIN proteins useful for cancer biology research. In order to complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium (NESG) (www.nesg.org) is targeting > 1,000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long-range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes, in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.}, address = {Center for advanced biotechnology and medicine, Rutgers University, Piscataway, NJ 08854.}, author = {Huang, Yuanpeng Janet J. and Hang, Dehua and Lu, Long Jason J. and Tong, Liang and Gerstein, Mark B B. and Montelione, Gaetano T T. }, citeulike-article-id = {2822871}, doi = {http://dx.doi.org/10.1074/mcp.M700550-MCP200}, issn = {1535-9484}, journal = {Molecular \& cellular proteomics : MCP}, keywords = {databases, networks, pathways}, month = {May}, posted-at = {2008-06-10 16:45:25}, priority = {2}, title = {Targeting the human cancer pathway protein interaction network by structural genomics.}, url = {http://dx.doi.org/10.1074/mcp.M700550-MCP200}, year = {2008} }

TY - JOUR ID - citeulike:2822871 L3 - citeulike-article-id:2822871 TI - Targeting the human cancer pathway protein interaction network by structural genomics. JF - Molecular & cellular proteomics : MCP AD - Center for advanced biotechnology and medicine, Rutgers University, Piscataway, NJ 08854. SN - 1535-9484 N2 - Structural genomics provides an important approach for characterizing and understanding systems biology. As a step towards better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well-known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. While some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Blast E-val < 10-6), only ~ 20% of residues in these proteins are structurally covered using high-accuracy homology modeling criteria (i.e. Blast E_val < 10-6 and at least 80% sequence identity) or by actual experimental structures. The HCPIN website (http://nmr.cabm.rutgers.edu/hcpin) provides a comprehensive description of this biomedical important multi-pathway network, together with experimental and homology models of HCPIN proteins useful for cancer biology research. In order to complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium (NESG) (www.nesg.org) is targeting > 1,000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long-range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes, in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects. KW - databases KW - networks KW - pathways AU - Huang, Yuanpeng Janet AU - Hang, Dehua AU - Lu, Long Jason AU - Tong, Liang AU - Gerstein, Mark B AU - Montelione, Gaetano T PY - 2008/05/18/ UR - http://dx.doi.org/10.1074/mcp.M700550-MCP200 ER -