Department of Chemistry, Columbia University, New York, New York.
Laboratoire des Biomolécules, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS, Paris, France.
Biophys J. 2018 Dec 18;115(12):2301-2309. doi: 10.1016/j.bpj.2018.10.030. Epub 2018 Nov 6.
Spin relaxation in solution-state NMR spectroscopy is a powerful approach to explore the conformational dynamics of biological macromolecules. Probability distribution functions for overall or internal correlation times have been used previously to model spectral density functions central to spin-relaxation theory. Applications to biological macromolecules rely on transverse relaxation rate constants, and when studying nanosecond timescale motions, sampling at ultralow frequencies is often necessary. Consequently, appropriate distribution functions necessitate spectral density functions that are accurate and convergent as frequencies approach zero. In this work, the inverse Gaussian probability distribution function is derived from general properties of spectral density functions at low and high frequencies for macromolecules in solution, using the principle of maximal entropy. This normalized distribution function is first used to calculate the correlation function, followed by the spectral density function. The resulting model-free spectral density functions are finite at a frequency of zero and can be used to describe distributions of either overall or internal correlation times using the model-free ansatz. To validate the approach, N spin-relaxation data for the bZip transcription factor domain of the Saccharomyces cerevisiae protein GCN4, in the absence of cognate DNA, were analyzed using the inverse Gaussian probability distribution for intramolecular correlation times. The results extend previous models for the conformational dynamics of the intrinsically disordered, DNA-binding region of the bZip transcription factor domain.
在溶液核磁共振波谱学中，自旋弛豫是一种探索生物大分子构象动力学的强大方法。整体或内部相关时间的概率分布函数以前曾被用于模拟对自旋弛豫理论至关重要的谱密度函数。将其应用于生物大分子依赖于横向弛豫率常数，而在研究纳秒时间尺度的运动时，通常需要在超低频率下进行采样。因此，适当的分布函数需要谱密度函数在接近零时准确且收敛。在这项工作中，使用最大熵原理，从溶液中大分子在低频和高频时的谱密度函数的一般性质中推导出了逆高斯概率分布函数。该归一化分布函数首先用于计算相关函数，然后计算谱密度函数。由此得到的无模型谱密度函数在频率为零时是有限的，可以使用无模型假设来描述整体或内部相关时间的分布。为了验证该方法，使用无规卷曲转录因子结构域的 Saccharomyces cerevisiae 蛋白 GCN4 的 N 自旋弛豫数据，在没有同源 DNA 的情况下，使用分子内相关时间的逆高斯概率分布进行了分析。结果扩展了无规卷曲转录因子结构域 DNA 结合区的构象动力学的先前模型。
