Meirovitch Eva, Shapiro Yury E, Polimeno Antonino, Freed Jack H
Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.
J Phys Chem A. 2006 Jul 13;110(27):8366-96. doi: 10.1021/jp056975t.
(15)N-(1)H spin relaxation is a powerful method for deriving information on protein dynamics. The traditional method of data analysis is model-free (MF), where the global and local N-H motions are independent and the local geometry is simplified. The common MF analysis consists of fitting single-field data. The results are typically field-dependent, and multifield data cannot be fit with standard fitting schemes. Cases where known functional dynamics has not been detected by MF were identified by us and others. Recently we applied to spin relaxation in proteins the slowly relaxing local structure (SRLS) approach, which accounts rigorously for mode mixing and general features of local geometry. SRLS was shown to yield MF in appropriate asymptotic limits. We found that the experimental spectral density corresponds quite well to the SRLS spectral density. The MF formulas are often used outside of their validity ranges, allowing small data sets to be force-fitted with good statistics but inaccurate best-fit parameters. This paper focuses on the mechanism of force-fitting and its implications. It is shown that MF analysis force-fits the experimental data because mode mixing, the rhombic symmetry of the local ordering and general features of local geometry are not accounted for. Combined multifield multitemperature data analyzed with the MF approach may lead to the detection of incorrect phenomena, and conformational entropy derived from MF order parameters may be highly inaccurate. On the other hand, fitting to more appropriate models can yield consistent physically insightful information. This requires that the complexity of the theoretical spectral densities matches the integrity of the experimental data. As shown herein, the SRLS spectral densities comply with this requirement.
（15）N - （1）H自旋弛豫是获取蛋白质动力学信息的有力方法。传统的数据分析方法是无模型（MF）方法，其中全局和局部N - H运动是独立的，并且局部几何结构被简化。常见的MF分析包括拟合单场数据。结果通常依赖于场，并且多场数据不能用标准拟合方案进行拟合。我们和其他人发现了一些通过MF未检测到已知功能动力学的情况。最近，我们将缓慢弛豫局部结构（SRLS）方法应用于蛋白质的自旋弛豫，该方法严格考虑了模式混合和局部几何结构的一般特征。SRLS在适当的渐近极限下被证明能产生MF。我们发现实验光谱密度与SRLS光谱密度相当吻合。MF公式经常在其有效范围之外使用，这使得小数据集能够被强制拟合，且具有良好的统计量，但最佳拟合参数不准确。本文重点关注强制拟合的机制及其影响。结果表明，MF分析对实验数据进行强制拟合是因为未考虑模式混合、局部有序的菱形对称性和局部几何结构的一般特征。用MF方法分析的组合多场多温度数据可能会导致检测到错误的现象，并且从MF序参量导出的构象熵可能非常不准确。另一方面，拟合更合适的模型可以产生一致的、具有物理洞察力的信息。这要求理论光谱密度的复杂性与实验数据的完整性相匹配。如本文所示，SRLS光谱密度符合这一要求。
