NMR relaxation dispersion experiments play a central role in exploring molecular motion over an important range of timescales, and are an example of a broader class of multidimensional NMR experiments that probe important biomolecules. However, resolving the spectral features of these experiments using the Fourier transform requires sampling the full Nyquist grid of data, making these experiments very costly in time. Practitioners often reduce the experiment time by omitting 1D experiments in the indirectly observed dimensions, and reconstructing the spectra using one of a variety of post-processing algorithms. In prior work, we described a fast, Fourier-based reconstruction method using iterated maps according to the Difference Map algorithm of Veit Elser (DiffMap). Here we describe coDiffMap, a new reconstruction method that is based on DiffMap, but which exploits the strong correlations between 2D data slices in a pseudo-3D experiment. We apply coDiffMap to reconstruct dispersion curves from an [Formula: see text] relaxation dispersion experiment, and demonstrate that the method provides fast reconstructions and accurate relaxation curves down to very low numbers of sparsely-sampled data points.

中文翻译：

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使用迭代图加速 2D NMR 弛豫色散实验。


NMR 弛豫色散实验在探索重要时间尺度范围内的分子运动方面发挥着核心作用，并且是探测重要生物分子的更广泛的多维 NMR 实验的一个例子。然而，使用傅里叶变换解析这些实验的光谱特征需要对完整的奈奎斯特网格数据进行采样，这使得这些实验的时间成本非常高。从业者通常通过省略间接观察维度中的一维实验并使用多种后处理算法之一重建光谱来减少实验时间。在之前的工作中，我们根据 Veit Elser 的差分图算法 (DiffMap) 描述了一种使用迭代图的快速、基于傅立叶的重建方法。在这里，我们描述了 coDiffMap，一种基于 DiffMap 的新重建方法，但它利用了伪 3D 实验中 2D 数据切片之间的强相关性。我们应用 coDiffMap 从[公式：参见文本]松弛色散实验重建色散曲线，并证明该方法可以提供快速重建和精确的松弛曲线，直至非常少量的稀疏采样数据点。