Monte Carlo simulation of mechanical unfolding of proteins based on a simple two-state model.
Monte Carlo simulation of mechanical unfolding of proteins based on a simple two-state model.
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DOI：
10.1016/j.ijbiomac.2009.12.001
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发表时间：
2010-03-01
影响因子：
8.2
通讯作者：
Yang G
中科院分区：
文献类型：
--
作者：
King WT;Su M;Yang G
单分子方法正在成为研究生物大分子的常规生物物理技术。在蛋白质的机械展开中，使用外部施加力来诱导单个蛋白质分子的展开。这样的实验揭示了新的信息，这些信息显着增强了我们对几种类型蛋白质功能和折叠机制的理解。为了从机械展开数据中获取有关蛋白质分子的展开动力学和自由能景观的信息，经常使用基于简单的两态动力学模型的蒙特卡洛模拟。在本文中，我们提供了执行此类模拟过程并讨论所涉及的近似值和假设的程序的详细说明。我们表明，蛋白质机械展开的力与延伸曲线的外观受到各种实验参数的影响，例如蛋白质聚合物的长度和悬臂的力常数。我们还分析了与不同的数据汇集方法相关的误差，并对模拟结果拟合实验数据的良好程度提出了定量度量。这些发现将有助于使用机械展开方法对各种蛋白质进行单分子研究的实验设计，伪影识别和数据分析。
Single molecule methods are becoming routine biophysical techniques for studying biological macromolecules. In mechanical unfolding of proteins, an externally applied force is used to induce the unfolding of individual protein molecules. Such experiments have revealed novel information that has significantly enhanced our understanding of the function and folding mechanisms of several types of proteins. To obtain information on the unfolding kinetics and the free energy landscape of the protein molecule from mechanical unfolding data, a Monte Carlo simulation based on a simple two-state kinetic model is often used. In this paper, we provide a detailed description of the procedure to perform such simulations and discuss the approximations and assumptions involved. We show that the appearance of the force versus extension curves from mechanical unfolding of proteins is affected by a variety of experimental parameters, such as the length of the protein polymer and the force constant of the cantilever. We also analyze the errors associated with different methods of data pooling and present a quantitative measure of how well the simulation results fit experimental data. These findings will be helpful in experimental design, artifact identification, and data analysis for single molecule studies of various proteins using the mechanical unfolding method.
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影响因子：
56.9
作者：
Rief, M;Oesterhelt, F;Gaub, HE
通讯作者：
Gaub, HE
影响因子：
8.6
作者：
Rief, M;Fernandez, JM;Gaub, HE
通讯作者：
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影响因子：
3.4
作者：
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DOI：
10.1073/pnas.96.7.3694
发表时间：
1999-03-30
影响因子：
11.1
作者：
Carrion-Vazquez, M;Oberhauser, AF;Fernandez, JM
通讯作者：
Fernandez, JM
DOI：
10.1073/pnas.192351899
发表时间：
2002-09-17
影响因子：
11.1
作者：
Best, RB;Fowler, SB;Clarke, J
通讯作者：
Clarke, J