Center for Biomolecular Magnetic Resonance, Institute of Biophysical Chemistry, Frankfurt am Main, Germany.
J Biomol NMR. 2013 Jun;56(2):113-23. doi: 10.1007/s10858-013-9727-9. Epub 2013 Apr 13.
Peak overlap is one of the major factors complicating the analysis of biomolecular NMR spectra. We present a general method for predicting the extent of peak overlap in multidimensional NMR spectra and its validation using both, experimental data sets and Monte Carlo simulation. The method is based on knowledge of the magnetization transfer pathways of the NMR experiments and chemical shift statistics from the Biological Magnetic Resonance Data Bank. Assuming a normal distribution with characteristic mean value and standard deviation for the chemical shift of each observable atom, an analytic expression was derived for the expected overlap probability of the cross peaks. The analytical approach was verified to agree with the average peak overlap in a large number of individual peak lists simulated using the same chemical shift statistics. The method was applied to eight proteins, including an intrinsically disordered one, for which the prediction results could be compared with the actual overlap based on the experimentally measured chemical shifts. The extent of overlap predicted using only statistical chemical shift information was in good agreement with the overlap that was observed when the measured shifts were used in the virtual spectrum, except for the intrinsically disordered protein. Since the spectral complexity of a protein NMR spectrum is a crucial factor for protein structure determination, analytical overlap prediction can be used to identify potentially difficult proteins before conducting NMR experiments. Overlap predictions can be tailored to particular classes of proteins by preparing statistics from corresponding protein databases. The method is also suitable for optimizing recording parameters and labeling schemes for NMR experiments and improving the reliability of automated spectra analysis and protein structure determination.
峰重叠是使生物分子 NMR 谱分析复杂化的主要因素之一。我们提出了一种通用的方法来预测多维 NMR 谱中峰重叠的程度，并使用实验数据集和蒙特卡罗模拟对其进行验证。该方法基于 NMR 实验的磁化转移途径和生物磁共振数据银行中的化学位移统计知识。假设每个可观测原子的化学位移具有特征平均值和标准偏差的正态分布，推导出了交叉峰的预期重叠概率的解析表达式。验证了该解析方法与使用相同化学位移统计数据模拟的大量单个峰列表中的平均峰重叠一致。该方法应用于包括一个无序蛋白在内的 8 个蛋白质，可将预测结果与基于实验测量化学位移的实际重叠进行比较。仅使用统计化学位移信息预测的重叠程度与在虚拟光谱中使用实测位移时观察到的重叠程度非常吻合，除了无序蛋白之外。由于蛋白质 NMR 谱的谱复杂性是蛋白质结构确定的关键因素，因此在进行 NMR 实验之前，可以使用分析重叠预测来识别潜在困难的蛋白质。通过从相应的蛋白质数据库中准备统计数据，可以针对特定类别的蛋白质定制重叠预测。该方法还适用于优化 NMR 实验的记录参数和标记方案，以及提高自动光谱分析和蛋白质结构确定的可靠性。
