Building a macro-mixing dual-basin Gō model using the Multistate Bennett Acceptance Ratio
Building a macro-mixing dual-basin Gō model using the Multistate Bennett Acceptance Ratio
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使用多状态 Bennett 接受率构建宏观混合双流域 Gō 模型
DOI：
10.2142/biophysico.16.0_310
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发表时间：
2019
影响因子：
1.5
通讯作者：
Y. Sugita
中科院分区：
文献类型：
--
作者：
Ai Shinobu;C. Kobayashi;Y. Matsunaga;Y. Sugita
双盆G-ō模型是一个基于结构的粗粒度模型，用于模拟蛋白质的两个已知结构之间的构象转换。虽然混合参数的确定通常不是一目了然的，但要产生使用两个单盆地势混合的双盆地势，需要两个参数。在这里，我们开发了一种有效的方案来确定混合参数使用多态Bennett接受率(MBAR)方法后，用一组参数进行了简短的模拟。在该方案中，MBAR允许我们预测各种未模拟条件下的可观量，这对于在下一轮迭代模拟中改进混合参数是有用的。在该格式中，获得收敛的混合参数所需的迭代次数显著减少。我们将该方案应用于谷氨酰胺结合蛋白和核糖结合蛋白两个蛋白质，以显示其在参数确定中的有效性。在获得收敛参数后，两种蛋白质都表现出频繁的开放和闭合状态之间的构象转换，为研究蛋白质的结构-动力学-功能关系提供了理论基础。
The dual-basin Gō-model is a structural-based coarsegrained model for simulating a conformational transition between two known structures of a protein. Two parameters are required to produce a dual-basin potential mixed using two single-basin potentials, although the determination of mixing parameters is usually not straightforward. Here, we have developed an efficient scheme to determine the mixing parameters using the Multistate Bennett Acceptance Ratio (MBAR) method after short simulations with a set of parameters. In the scheme, MBAR allows us to predict observables at various unsimulated conditions, which are useful to improve the mixing parameters in the next round of iterative simulations. The number of iterations that are necessary for obtaining the converged mixing parameters are significantly reduced in the scheme. We applied the scheme to two proteins, the glutamine binding protein and the ribose binding protein, for showing the effectiveness in the parameter determination. After obtaining the converged parameters, both proteins show frequent conformational transitions between open and closed states, providing the theoretical basis to investigate structure-dynamics-function relationships of the proteins.
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影响因子：
--
作者：
J. Ponder;D. Case
通讯作者：
J. Ponder;D. Case
影响因子：
5.6
作者：
L. Wong;Scot A Lieser;O. Miyashita;Meghan T. Miller;K. Taskén;J. Onuchic;J. Adams;Virgil L. Woods;P. Jennings
通讯作者：
L. Wong;Scot A Lieser;O. Miyashita;Meghan T. Miller;K. Taskén;J. Onuchic;J. Adams;Virgil L. Woods;P. Jennings
影响因子：
5.6
作者：
Whitford, Paul C.;Miyashita, Osamu;Onuchic, Jose N.
通讯作者：
Onuchic, Jose N.
影响因子：
5.6
作者：
Ravindranathan, KP;Gallicchio, E;Levy, RM
通讯作者：
Levy, RM
DOI：
10.1073/pnas.100547897
发表时间：
2000-05-23
影响因子：
11.1
作者：
Clementi, C;Jennings, PA;Onuchic, JN
通讯作者：
Onuchic, JN