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Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003767
Pubmed ID
Authors

Shuo Gu, Daniel-Adriano Silva, Luming Meng, Alexander Yue, Xuhui Huang

Abstract

Protein-ligand recognition plays key roles in many biological processes. One of the most fascinating questions about protein-ligand recognition is to understand its underlying mechanism, which often results from a combination of induced fit and conformational selection. In this study, we have developed a three-pronged approach of Markov State Models, Molecular Dynamics simulations, and flux analysis to determine the contribution of each model. Using this approach, we have quantified the recognition mechanism of the choline binding protein (ChoX) to be ∼90% conformational selection dominant under experimental conditions. This is achieved by recovering all the necessary parameters for the flux analysis in combination with available experimental data. Our results also suggest that ChoX has several metastable conformational states, of which an apo-closed state is dominant, consistent with previous experimental findings. Our methodology holds great potential to be widely applied to understand recognition mechanisms underlining many fundamental biological processes.

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Geographical breakdown

Country Count As %
Germany 3 4%
Russia 1 1%
United Kingdom 1 1%
Unknown 65 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 39%
Researcher 17 24%
Student > Bachelor 6 9%
Student > Master 4 6%
Student > Postgraduate 2 3%
Other 4 6%
Unknown 10 14%
Readers by discipline Count As %
Chemistry 19 27%
Biochemistry, Genetics and Molecular Biology 12 17%
Physics and Astronomy 10 14%
Agricultural and Biological Sciences 9 13%
Computer Science 3 4%
Other 7 10%
Unknown 10 14%