Estimation of protein folding free energy barriers from calorimetric data by multi-model Bayesian analysis.
Estimation of protein folding free energy barriers from calorimetric data by multi-model Bayesian analysis.
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通过多模型贝叶斯分析从量热数据估计蛋白质折叠自由能垒。
DOI：
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
J. M. Sanchez
中科院分区：
文献类型：
--
作者：
Athi N. Naganathan;R. Perez;V. Muñoz;J. M. Sanchez
折叠自由能势垒可以小到足以在势垒顶部产生大量物种，这一认识已经在几种通过平衡实验估计折叠势垒的方法中萌芽。其中一些方法基于将差示扫描量热法 (DSC) 测量的实验热分析图拟合到折叠自由能表面 (FES) 的一维表示。不同的物理模型被用来表示 FES：（1）朗道四次多项式作为总焓的函数，充当序参数； (2) 类伊辛统计力学模型生成的所有构象的自由能在结构有序参数（即天然残基或天然接触的数量）上的投影； (3) 平均场模型，将构象熵和稳定能定义为连续局部有序参数的函数。出现的基本问题是我们如何从 DSC 实验分析中获得稳健的、独立于模型的热力学折叠势垒估计。在这里，我们通过比较各种 FES 模型在解释具有边缘折叠屏障的蛋白质的热谱图方面的性能来解决这个问题。我们选择了小的 α-螺旋蛋白 PDD，它在微秒内折叠-展开，跨越了先前估计为约 1 RT 的自由能垒。 PDD 热分析图与各种模型和假设的拟合产生了 FES，其具有始终较小的自由能势垒，将折叠和展开的整体分开。然而，拟合的质量以及估计的障碍各不相同。应用贝叶斯概率分析，我们使用严格的统计标准对拟合性能进行排名，从而对折叠屏障及其精度进行全局估计，对于 PDD 来说，其精度为 1.3 ± 0.4 kJ mol(-1)。这一结果证实了 PDD 折叠超过了与下坡折叠方式一致的较小障碍。我们通过分析另外两个蛋白质系统进一步验证了多模型贝叶斯方法：gpW，一种具有 α + β 拓扑结构的中型单结构域，也在微秒内折叠，之前被归类为下坡折叠；以及 α-血影蛋白 SH3，一种大小相似但具有 β 桶折叠、慢折叠动力学和类二态热力学的结构域。从一般角度来看，这里开发的贝叶斯分析产生了一种统计上稳健的、几乎独立于模型的方法，用于根据 DSC 热分析图估计蛋白质折叠的热力学自由能障碍。我们的方法似乎足够准确，可以一致地检测势垒高度的微小差异，从而开辟了通过实验表征下坡区域内蛋白质单点突变引起的热力学折叠势垒变化的可能性。
The realization that folding free energy barriers can be small enough to result in significant population of the species at the barrier top has sprouted in several methods to estimate folding barriers from equilibrium experiments. Some of these approaches are based on fitting the experimental thermogram measured by differential scanning calorimetry (DSC) to a one-dimensional representation of the folding free-energy surface (FES). Different physical models have been used to represent the FES: (1) a Landau quartic polynomial as a function of the total enthalpy, which acts as an order parameter; (2) the projection onto a structural order parameter (i.e. number of native residues or native contacts) of the free energy of all the conformations generated by Ising-like statistical mechanical models; and (3) mean-field models that define conformational entropy and stabilization energy as functions of a continuous local order parameter. The fundamental question that emerges is how can we obtain robust, model-independent estimates of the thermodynamic folding barrier from the analysis of DSC experiments. Here we address this issue by comparing the performance of various FES models in interpreting the thermogram of a protein with a marginal folding barrier. We chose the small α-helical protein PDD, which folds-unfolds in microseconds crossing a free energy barrier previously estimated as ~1 RT. The fits of the PDD thermogram to the various models and assumptions produce FES with a consistently small free energy barrier separating the folded and unfolded ensembles. However, the fits vary in quality as well as in the estimated barrier. Applying Bayesian probabilistic analysis we rank the fit performance using a statistically rigorous criterion that leads to a global estimate of the folding barrier and its precision, which for PDD is 1.3 ± 0.4 kJ mol(-1). This result confirms that PDD folds over a minor barrier consistent with the downhill folding regime. We have further validated the multi-model Bayesian approach through the analysis of two additional protein systems: gpW, a midsize single-domain with α + β topology that also folds in microseconds and has been previously catalogued as a downhill folder, and α-spectrin SH3, a domain of similar size but with a β-barrel fold, slow-folding kinetics and two-state-like thermodynamics. From a general viewpoint, the Bayesian analysis developed here results in a statistically robust, virtually model-independent, method to estimate the thermodynamic free-energy barriers to protein folding from DSC thermograms. Our method appears to be sufficiently accurate to consistently detect small differences in the barrier height, and thus opens up the possibility of characterizing experimentally the changes in thermodynamic folding barriers induced by single-point mutations on proteins within the downhill regime.
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影响因子：
5.6
作者：
Ferguson, N;Day, R;Fersht, AR
通讯作者：
Fersht, AR
DOI：
10.1073/pnas.96.22.12512
发表时间：
1999-10-26
影响因子：
11.1
作者：
Shea, JE;Onuchic, JN;Brooks, CL
通讯作者：
Brooks, CL
DOI：
10.1006/jmbi.1999.3189
发表时间：
1999
期刊：
Journal of molecular biology.
影响因子：
--
作者：
Spector,S;Raleigh,DP
通讯作者：
Raleigh,DP
影响因子：
3.8
作者：
Privalov, Peter L.;Dragan, Anatoly I.
通讯作者：
Dragan, Anatoly I.