Backbone resonance assignment and order tensor estimation using residual dipolar couplings.
Backbone resonance assignment and order tensor estimation using residual dipolar couplings.
复制标题
使用残余偶极耦合进行主干共振分配和阶张量估计。
DOI：
10.1007/s10858-011-9521-5
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发表时间：
2011
影响因子：
2.7
通讯作者：
Valafar,Homayoun
中科院分区：
文献类型：
--
作者：
Shealy,Paul;Liu,Yizhou;Simin,Mikhail;Valafar,Homayoun
对具有已知 X 射线结构的蛋白质进行 NMR 研究引起了许多研究的兴趣。通过核磁共振 (NMR) 进行这些研究需要昂贵的共振分配步骤。普遍的分配策略不利用现有的结构信息并且需要统一的同位素标记。在这里，我们提出了一种快速且经济高效的方法，将 NMR 数据分配给现有结构（X 射线结构或计算建模结构）。所提出的方法，即 RDC 的穷举排列分配 (EPAR)，利用可通过 NMR 光谱轻松获得的未分配残余偶极耦合 (RDC) 数据。该算法仅使用来自多种比对介质的主链 N-H RDC 以及 RDC 的氨基酸类型。它的灵感来自于 Zweckstetter 之前的工作，并提供了一些扩展。我们展示了来自 8 种不同结构（包括两种同二聚体）的 13 个合成和实验数据集的结果。仅使用两种对准介质，EPAR 即可实现大于 80% 的平均分配精度。三种媒体的平均准确率高于 94%。该算法还输出分配精度的预测，其与真实精度的相关性为 0.77。该预测分数可用于建立分配准确性所需的置信度。
An NMR investigation of proteins with known X-ray structures is of interest in a number of endeavors. Performing these studies through nuclear magnetic resonance (NMR) requires the costly step of resonance assignment. The prevalent assignment strategy does not make use of existing structural information and requires uniform isotope labeling. Here we present a rapid and cost-effective method of assigning NMR data to an existing structure—either an X-ray or computationally modeled structure. The presented method, Exhaustively Permuted Assignment of RDCs (EPAR), utilizes unassigned residual dipolar coupling (RDC) data that can easily be obtained by NMR spectroscopy. The algorithm uses only the backbone N–H RDCs from multiple alignment media along with the amino acid type of the RDCs. It is inspired by previous work from Zweckstetter and provides several extensions. We present results on 13 synthetic and experimental datasets from 8 different structures, including two homodimers. Using just two alignment media, EPAR achieves an average assignment accuracy greater than 80%. With three media, the average accuracy is higher than 94%. The algorithm also outputs a prediction of the assignment accuracy, which has a correlation of 0.77 to the true accuracy. This prediction score can be used to establish the needed confidence in assignment accuracy.