Department of Chemical and Process Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand.
Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK.
J Biomol NMR. 2019 Apr;73(3-4):93-104. doi: 10.1007/s10858-018-00224-2. Epub 2019 May 4.
We present a model-based method for estimation of relaxation parameters from time-domain NMR data specifically suitable for processing data in popular 2D phase-sensitive experiments. Our model is formulated in terms of commutative bicomplex algebra, which allows us to use the complete information available in an NMR signal acquired with principles of quadrature detection without disregarding any of its dimensions. Compared to the traditional intensity-analysis method, our model-based approach offers an important advantage for the analysis of overlapping peaks and is robust over a wide range of signal-to-noise ratios. We assess its performance with simulated experiments and then apply it for determination of [Formula: see text], [Formula: see text], and [Formula: see text] relaxation rates in datasets of a protein with more than 100 cross peaks.
我们提出了一种基于模型的方法，用于从时域 NMR 数据估计弛豫参数，特别适合处理流行的 2D 相敏实验中的数据。我们的模型是用可交换双复数代数来表述的，这使我们能够利用在具有正交检测原理采集的 NMR 信号中可用的完整信息，而不会忽略其任何维度。与传统的强度分析方法相比，我们的基于模型的方法在分析重叠峰方面具有重要优势，并且在广泛的信噪比范围内具有稳健性。我们使用模拟实验评估其性能，然后将其应用于具有 100 多个交叉峰的蛋白质数据集来确定 [Formula: see text]、[Formula: see text] 和 [Formula: see text] 弛豫率。
