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Large-scale de novo prediction of physical protein-protein association

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons50573

Stelzl,  U.
Molecular Interaction Networks (Ulrich Stelzl), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Elefsinioti, A., Sarac, O. S., Hegele, A., Plake, C., Hubner, N. C., Poser, I., et al. (2011). Large-scale de novo prediction of physical protein-protein association. Mol Cell Proteomics, 10(11), M111 010629. Retrieved from http://www.mcponline.org/content/10/11/M111.010629.full.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-785E-F
Zusammenfassung
Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.