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Dual Coordination of Post Translational Modifications in Human Protein Networks

MPG-Autoren
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Woodsmith,  J.
Molecular Interaction Networks (Ulrich Stelzl), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Kamburov,  A.
Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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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

Woodsmith, J., Kamburov, A., & Stelzl, U. (2013). Dual Coordination of Post Translational Modifications in Human Protein Networks. PLoS Computational Biology, 9(3), e1002933-e1002933. doi:10.1371/journal.pcbi.1002933.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0018-F3D3-6
Zusammenfassung
Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling.