%0 Journal Article %1 Huang2008Targeting %A Huang, Yuanpeng J. %A Hang, Dehua %A Lu, Long J. %A Tong, Liang %A Gerstein, Mark B. %A Montelione, Gaetano T. %C Center for advanced biotechnology and medicine, Rutgers University, Piscataway, NJ 08854. %D 2008 %I American Society for Biochemistry and Molecular Biology %J Molecular & Cellular Proteomics %K cancer networks protein-protein-interactions structural-genomics %N 10 %P 2048--2060 %R 10.1074/mcp.m700550-mcp200 %T Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics %U http://dx.doi.org/10.1074/mcp.m700550-mcp200 %V 7 %X Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward 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. Although some 45\% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10−6), only ∼20\% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10−6 and at least 80\% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 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.

@article{Huang2008Targeting, abstract = {Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward 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. Although some 45\% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool ({BLAST}) E-value <10−6), only ∼20\% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. {BLAST} E-value <10−6 and at least 80\% sequence identity) or by actual experimental structures. The {HCPIN} Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of {HCPIN} proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 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.}, added-at = {2018-12-02T16:09:07.000+0100}, address = {Center for advanced biotechnology and medicine, Rutgers University, Piscataway, NJ 08854.}, author = {Huang, Yuanpeng J. and Hang, Dehua and Lu, Long J. and Tong, Liang and Gerstein, Mark B. and Montelione, Gaetano T.}, biburl = {https://www.bibsonomy.org/bibtex/2a4ec049455298253043baefba2053824/karthikraman}, citeulike-article-id = {2822871}, citeulike-linkout-0 = {http://dx.doi.org/10.1074/mcp.m700550-mcp200}, citeulike-linkout-1 = {http://www.mcponline.org/content/7/10/2048.abstract}, citeulike-linkout-2 = {http://www.mcponline.org/content/7/10/2048.full.pdf}, citeulike-linkout-3 = {http://www.mcponline.org/cgi/content/abstract/7/10/2048}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/18487680}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=18487680}, day = 01, doi = {10.1074/mcp.m700550-mcp200}, interhash = {b73845302545f504d87edccd8d556ac5}, intrahash = {a4ec049455298253043baefba2053824}, issn = {1535-9484}, journal = {Molecular \& Cellular Proteomics}, keywords = {cancer networks protein-protein-interactions structural-genomics}, month = oct, number = 10, pages = {2048--2060}, pmid = {18487680}, posted-at = {2010-07-12 14:11:59}, priority = {2}, publisher = {American Society for Biochemistry and Molecular Biology}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics}, url = {http://dx.doi.org/10.1074/mcp.m700550-mcp200}, volume = 7, year = 2008 }