%0 Journal Article %1 Gu2014MSMBindingMechanismsCholine %A Gu, Shuo %A Silva, Daniel-Adriano %A Meng, Luming %A Yue, Alexander %A Huang, Xuhui %D 2014 %I Public Library of Science %J PLOS Computational Biology %K MSM conformational-selection induced-fit ligand-binding ligand-binding-mechanism molecular-dynamics %N 8 %P 1-11 %R 10.1371/journal.pcbi.1003767 %T Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis %U http://dx.doi.org/10.1371%2Fjournal.pcbi.1003767 %V 10 %X Author Summary Molecular recognition plays important roles in numerous biological processes including gene regulation, cell signaling and enzymatic activity. It has been suggested that molecular recognition employs a variety of mechanisms, ranging from induced fit to conformational selection. In many realistic systems, conformational selection and induced fit are not mutually exclusive. An analytical flux analysis has been developed to determine the contribution of each model, but it is extremely challenging to obtain the necessary kinetic parameters for this flux analysis through experimental techniques. In this work, we have developed an approach integrating Markov State Models, molecular dynamics simulations, and flux analysis to tackle this problem. Using this approach, we have quantified the recognition mechanism of the choline binding protein to be ∼90% conformational selection dominant in the experimental conditions. Our methodology provides a way to quantify the molecular recognition mechanisms that are extremely difficult to be directly accessed by experiments. This opens up numerous possibilities for in silico design to fine tune the recognition event either to increase the degree of conformational selection or induced fit, so that new properties could be created to accommodate the needs of protein engineering, drug development and beyond.

@article{Gu2014MSMBindingMechanismsCholine, abstract = {Author Summary Molecular recognition plays important roles in numerous biological processes including gene regulation, cell signaling and enzymatic activity. It has been suggested that molecular recognition employs a variety of mechanisms, ranging from induced fit to conformational selection. In many realistic systems, conformational selection and induced fit are not mutually exclusive. An analytical flux analysis has been developed to determine the contribution of each model, but it is extremely challenging to obtain the necessary kinetic parameters for this flux analysis through experimental techniques. In this work, we have developed an approach integrating Markov State Models, molecular dynamics simulations, and flux analysis to tackle this problem. Using this approach, we have quantified the recognition mechanism of the choline binding protein to be ∼90% conformational selection dominant in the experimental conditions. Our methodology provides a way to quantify the molecular recognition mechanisms that are extremely difficult to be directly accessed by experiments. This opens up numerous possibilities for in silico design to fine tune the recognition event either to increase the degree of conformational selection or induced fit, so that new properties could be created to accommodate the needs of protein engineering, drug development and beyond.}, added-at = {2017-02-27T23:10:29.000+0100}, author = {Gu, Shuo and Silva, Daniel-Adriano and Meng, Luming and Yue, Alexander and Huang, Xuhui}, biburl = {https://www.bibsonomy.org/bibtex/2f252ca456aa48495f7a29a80856dba46/salotz}, description = {Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis}, doi = {10.1371/journal.pcbi.1003767}, interhash = {5b69a544d1a3a2f954fc10e3dbb33bd9}, intrahash = {f252ca456aa48495f7a29a80856dba46}, journal = {PLOS Computational Biology}, keywords = {MSM conformational-selection induced-fit ligand-binding ligand-binding-mechanism molecular-dynamics}, month = {08}, number = 8, pages = {1-11}, publisher = {Public Library of Science}, timestamp = {2017-02-27T23:10:29.000+0100}, title = {Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1003767}, volume = 10, year = 2014 }