Mills Eric A, Plotkin Steven S
Department of Physics & Astronomy, University of British Columbia , Vancouver, British Columbia V6T1Z4, Canada.
J Phys Chem B. 2015 Nov 5;119(44):14130-44. doi: 10.1021/acs.jpcb.5b09219. Epub 2015 Oct 26.
We have found significant entropy-enthalpy compensation for the transfer of a diverse set of two-state folding proteins from water into water containing a diverse set of cosolutes, including osmolytes, denaturants, and crowders. In extracting thermodynamic parameters from experimental data, we show the potential importance of accounting for the cosolute concentration-dependence of the heat capacity change upon unfolding, as well as the potential importance of the temperature-dependence of the heat capacity change upon unfolding. We introduce a new Monte Carlo method to estimate the experimental uncertainty in the thermodynamic data and use this to show by bootstrapping methods that entropy-enthalpy compensation is statistically significant, in spite of large, correlated scatter in the data. We show that plotting the data at the transition midpoint provides the most accurate experimental values by avoiding extrapolation errors due to uncertainty in the heat capacity, and that this representation exhibits the strongest evidence of compensation. Entropy-enthalpy compensation is still significant at lab temperature however. We also find that compensation is still significant when considering variations due to heat capacity models, as well as typical measurement discrepancies lab-to-lab when such data is available. Extracting transfer entropy and enthalpy along with their uncertainties can provide a valuable consistency check between experimental data and simulation models, which may involve tests of simulated unfolded ensembles and/or models of the transfer free energy; we include specific applications to cold shock protein and protein L.
我们发现，多种两态折叠蛋白从水转移到含有多种共溶质（包括渗透剂、变性剂和拥挤剂）的水中时，存在显著的熵-焓补偿。在从实验数据中提取热力学参数时，我们表明了考虑共溶质浓度对展开时热容变化的依赖性的潜在重要性，以及展开时热容变化对温度依赖性的潜在重要性。我们引入了一种新的蒙特卡罗方法来估计热力学数据中的实验不确定性，并通过自举法表明，尽管数据中存在大量相关散射，但熵-焓补偿在统计上是显著的。我们表明，在转变中点绘制数据可避免因热容不确定性导致的外推误差，从而提供最准确的实验值，并且这种表示形式显示出最强的补偿证据。然而，在实验室温度下，熵-焓补偿仍然显著。我们还发现，在考虑热容模型引起的变化以及不同实验室之间典型测量差异（如果有此类数据）时，补偿仍然显著。提取转移熵和焓及其不确定性可以在实验数据和模拟模型之间提供有价值的一致性检查，这可能涉及对模拟未折叠系综的测试和/或转移自由能模型；我们包括对冷休克蛋白和蛋白L的具体应用。
