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Local Network Patterns in Protein-Protein Interfaces

Overview of attention for article published in PLOS ONE, March 2013
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Title
Local Network Patterns in Protein-Protein Interfaces
Published in
PLOS ONE, March 2013
DOI 10.1371/journal.pone.0057031
Pubmed ID
Authors

Qiang Luo, Rebecca Hamer, Gesine Reinert, Charlotte M. Deane

Abstract

Protein-protein interfaces hold the key to understanding protein-protein interactions. In this paper we investigated local interaction network patterns beyond pair-wise contact sites by considering interfaces as contact networks among residues. A contact site was defined as any residue on the surface of one protein which was in contact with a residue on the surface of another protein. We labeled the sub-graphs of these contact networks by their amino acid types. The observed distributions of these labeled sub-graphs were compared with the corresponding background distributions and the results suggested that there were preferred chemical patterns of closely packed residues at the interface. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which were involved in binding to residues which were close on the interacting partner. Interaction interfaces were far from random and contain information beyond pairs and triangles. To illustrate the possible application of the local network patterns observed, we introduced a signature method, called iScore, based on these local patterns to assess interface predictions. On our data sets iScore achieved 83.6% specificity with 82% sensitivity.

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Geographical breakdown

Country Count As %
United Kingdom 2 6%
Japan 1 3%
Portugal 1 3%
Unknown 32 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 36%
Researcher 9 25%
Student > Master 4 11%
Student > Doctoral Student 3 8%
Other 2 6%
Other 4 11%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 33%
Biochemistry, Genetics and Molecular Biology 6 17%
Computer Science 4 11%
Chemistry 3 8%
Physics and Astronomy 2 6%
Other 6 17%
Unknown 3 8%