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Structural Propensities of Human Ubiquitination Sites: Accessibility, Centrality and Local Conformation

Overview of attention for article published in PLOS ONE, December 2013
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Title
Structural Propensities of Human Ubiquitination Sites: Accessibility, Centrality and Local Conformation
Published in
PLOS ONE, December 2013
DOI 10.1371/journal.pone.0083167
Pubmed ID
Authors

Yuan Zhou, Sixue Liu, Jiangning Song, Ziding Zhang

Abstract

The existence and function of most proteins in the human proteome are regulated by the ubiquitination process. To date, tens of thousands human ubiquitination sites have been identified from high-throughput proteomic studies. However, the mechanism of ubiquitination site selection remains elusive because of the complicated sequence pattern flanking the ubiquitination sites. In this study, we perform a systematic analysis of 1,330 ubiquitination sites in 505 protein structures and quantify the significantly high accessibility and unexpectedly high centrality of human ubiquitination sites. Further analysis suggests that the higher centrality of ubiquitination sites is associated with the multi-functionality of ubiquitination sites, among which protein-protein interaction sites are common targets of ubiquitination. Moreover, we demonstrate that ubiquitination sites are flanked by residues with non-random local conformation. Finally, we provide quantitative and unambiguous evidence that most of the structural propensities contain specific information about ubiquitination site selection that is not represented by the sequence pattern. Therefore, the hypothesis about the structural level of the ubiquitination site selection mechanism has been substantially approved.

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Geographical breakdown

Country Count As %
Hungary 1 6%
Germany 1 6%
Unknown 15 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Researcher 3 18%
Student > Postgraduate 2 12%
Student > Master 1 6%
Other 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 47%
Biochemistry, Genetics and Molecular Biology 4 24%
Computer Science 1 6%
Unknown 4 24%