Facilitated assignment of large protein NMR signals with covariance sequential spectra using spectral derivatives.
Facilitated assignment of large protein NMR signals with covariance sequential spectra using spectral derivatives.
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使用光谱导数促进大蛋白质 NMR 信号与协方差序列光谱的分配。
DOI：
10.1021/ja5058407
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发表时间：
2014-09-24
影响因子：
15
通讯作者：
Frueh, Dominique P.
中科院分区：
文献类型：
--
作者：
Harden, Bradley J.;Nichols, Scott R.;Frueh, Dominique P.
较大蛋白质的核磁共振（NMR）研究受到NMR共振分配困难的阻碍。通常需要人为干预来识别3D光谱中的NMR信号，并且后续程序取决于这种所谓的峰拾取的准确性。我们提出了一种方法，通过协方差NMR构建的相关图，提供连续的连接性，绕过了初步的峰值拾取的需要。我们介绍了两种新的技术，以尽量减少错误的相关性和合并的信息，从所有原始的3D光谱。首先，我们在执行协方差之前进行频谱导数，以强调重合的峰值最大值。其次，我们乘上不同的3D光谱计算的协方差图，以破坏错误的序列相关性。该地图易于使用，可以很容易地从传统的三重共振实验产生。该方法的优点被证明对37 kDa的非核糖体肽合成酶域的光谱重叠。
Nuclear magnetic resonance (NMR) studies of larger proteins are hampered by difficulties in assigning NMR resonances. Human intervention is typically required to identify NMR signals in 3D spectra, and subsequent procedures depend on the accuracy of this so-called peak picking. We present a method that provides sequential connectivities through correlation maps constructed with covariance NMR, bypassing the need for preliminary peak picking. We introduce two novel techniques to minimize false correlations and merge the information from all original 3D spectra. First, we take spectral derivatives prior to performing covariance to emphasize coincident peak maxima. Second, we multiply covariance maps calculated with different 3D spectra to destroy erroneous sequential correlations. The maps are easy to use and can readily be generated from conventional triple-resonance experiments. Advantages of the method are demonstrated on a 37 kDa nonribosomal peptide synthetase domain subject to spectral overlap.
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