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Coherent Conformational Degrees of Freedom as a Structural Basis for Allosteric Communication

Overview of attention for article published in PLoS Computational Biology, December 2011
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Title
Coherent Conformational Degrees of Freedom as a Structural Basis for Allosteric Communication
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002301
Pubmed ID
Authors

Simon Mitternacht, Igor N. Berezovsky

Abstract

Conformational changes in allosteric regulation can to a large extent be described as motion along one or a few coherent degrees of freedom. The states involved are inherent to the protein, in the sense that they are visited by the protein also in the absence of effector ligands. Previously, we developed the measure binding leverage to find sites where ligand binding can shift the conformational equilibrium of a protein. Binding leverage is calculated for a set of motion vectors representing independent conformational degrees of freedom. In this paper, to analyze allosteric communication between binding sites, we introduce the concept of leverage coupling, based on the assumption that only pairs of sites that couple to the same conformational degrees of freedom can be allosterically connected. We demonstrate how leverage coupling can be used to analyze allosteric communication in a range of enzymes (regulated by both ligand binding and post-translational modifications) and huge molecular machines such as chaperones. Leverage coupling can be calculated for any protein structure to analyze both biological and latent catalytic and regulatory sites.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Norway 2 3%
United Kingdom 2 3%
Chile 1 1%
Italy 1 1%
Argentina 1 1%
Canada 1 1%
Unknown 60 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 33%
Researcher 23 32%
Professor 5 7%
Student > Master 4 6%
Student > Bachelor 4 6%
Other 10 14%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 40%
Biochemistry, Genetics and Molecular Biology 17 24%
Chemistry 10 14%
Physics and Astronomy 5 7%
Computer Science 3 4%
Other 2 3%
Unknown 6 8%