Chylla R A, Volkman B F, Markley J L
National Magnetic Resonance Facility at Madison, Department of Biochemistry, University of Wisconsin-Madison 53706, USA.
J Biomol NMR. 1998 Aug;12(2):277-97. doi: 10.1023/a:1008254432254.
A maximum likelihood (ML)-based approach has been established for the direct extraction of NMR parameters (e.g., frequency, amplitude, phase, and decay rate) simultaneously from all dimensions of a D-dimensional NMR spectrum. The approach, referred to here as HTFD-ML (hybrid time frequency domain maximum likelihood), constructs a time-domain model composed of a sum of exponentially-decaying sinusoidal signals. The apodized Fourier transform of this time-domain signal is a model spectrum that represents the 'best-fit' to the equivalent frequency-domain data spectrum. The desired amplitude and frequency parameters can be extracted directly from the signal model constructed by the HTFD-ML algorithm. The HTFD-ML approach presented here, as embodied in the software package CHIFIT, is designed to meet the challenges posed by model fitting of D-dimensional NMR data sets, where each consists of many data points (10(8) is not uncommon) encoding information about numerous signals (up to 10(5) for a protein of moderate size) that exhibit spectral overlap. The suitability of the approach is demonstrated by its application to the concerted analysis of a series of ten 2D 1H-15N HSQC experiments measuring 15N T1 relaxation. In addition to demonstrating the practicality of performing maximum likelihood analysis on large, multidimensional NMR spectra, the results demonstrate that this parametric model-fitting approach provides more accurate amplitude and frequency estimates than those obtained from conventional peak-based analysis of the FT spectrum. The improved performance of the model fitting approach derives from its ability to take into account the simultaneous contributions of all signals in a crowded spectral region (deconvolution) as well as to incorporate prior knowledge in constructing models to fit the data.
已经建立了一种基于最大似然（ML）的方法，用于从D维核磁共振（NMR）谱的所有维度中同时直接提取NMR参数（例如频率、幅度、相位和衰减率）。这里称为HTFD-ML（混合时间频率域最大似然）的方法构建了一个由指数衰减正弦信号之和组成的时域模型。该时域信号的变迹傅里叶变换是一个模型谱，它表示对等效频域数据谱的“最佳拟合”。所需的幅度和频率参数可以直接从由HTFD-ML算法构建的信号模型中提取。这里介绍的HTFD-ML方法，体现在软件包CHIFIT中，旨在应对D维NMR数据集的模型拟合所带来的挑战，其中每个数据集由许多数据点（10^8并不罕见）组成，这些数据点编码了关于众多信号（对于中等大小的蛋白质，多达10^5个）的信息，这些信号表现出谱重叠。该方法的适用性通过将其应用于一系列十个二维^1H-^15N HSQC实验的协同分析来证明，这些实验测量^15N T1弛豫。除了证明对大型多维NMR谱进行最大似然分析的实用性之外，结果还表明，这种参数模型拟合方法比从FT谱的传统基于峰的分析中获得的方法提供了更准确的幅度和频率估计。模型拟合方法性能的提高源于其能够考虑拥挤谱区域中所有信号的同时贡献（去卷积），以及在构建拟合数据的模型时纳入先验知识。
