论文标题
剖析确定蛋白质稳定性的线性外推法的统计特性
Dissecting the statistical properties of the Linear Extrapolation Method of determining protein stability
论文作者
论文摘要
当通过变性诱导的展开蛋白质稳定性测量时，通常使用线性外推法来分析数据。该方法基于这样的观察，即与展开相关的Gibbs自由能的变化通常被认为是变性浓度的线性函数，$ d $。在没有变性剂的情况下，展开的自由能变化是$Δ_RG_0$，是通过从这种线性关系中推断出来的。数据分析通常是通过非线性最小二乘回归进行的，以获得参数和置信区间的估计。我们已经比较了用于计算参数置信区间的不同方法，并发现基于线性理论的简单方法比更高级的方法具有好的（即使不是更好）的结果。我们还比较了线性外推法的三种不同参数化，并表明其中一种形式为$Δ_RG（d）=δ_rg_0-md $，因为$Δ_RG_0$的值和$ m $ $ - value的值在非线性最低squares分析中相关。参数相关在某些情况下可能会在置信区间和区域的估计中引起问题，并且应在可能的情况下避免。两个替代参数化，$Δ_RG（d）= -m（d-d_ {50}）$和$δ_rg（d）=δ_rg_0（1-d/d/d/d_ {50}）$，其中$ d_ {50} $是过渡区域的中点显示出参数之间的相关性要小得多。
When protein stability is measured by denaturant induced unfolding the linear extrapolation method is usually used to analyse the data. This method is based on the observation that the change in Gibbs free energy associated with unfolding, $Δ_rG$, is often found to be a linear function of the denaturant concentration, $D$. The free energy change of unfolding in the absence of denaturant, $Δ_rG_0$, is estimated by extrapolation from this linear relationship. Data analysis is generally done by nonlinear least-squares regression to obtain estimates of the parameters as well as confidence intervals. We have compared different methods for calculating confidence intervals of the parameters and found that a simple method based on linear theory gives as good, if not better, results than more advanced methods. We have also compared three different parameterizations of the linear extrapolation method and show that one of the forms, $Δ_rG(D) = Δ_rG_0 - mD$, is problematic since the value of $Δ_rG_0$ and that of the $m$-value are correlated in the nonlinear least-squares analysis. Parameter correlation can in some cases cause problems in the estimation of confidence-intervals and -regions and should be avoided when possible. Two alternative parameterizations, $Δ_rG(D) = -m(D-D_{50})$ and $Δ_rG(D) = Δ_rG_0(1-D/D_{50})$, where $D_{50}$ is the midpoint of the transition region show much less correlation between parameters.
