Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, United States.
J Am Chem Soc. 2012 Nov 14;134(45):18619-30. doi: 10.1021/ja307445y. Epub 2012 Sep 21.
In structural studies of large proteins by NMR, global fold determination plays an increasingly important role in providing a first look at a target's topology and reducing assignment ambiguity in NOESY spectra of fully protonated samples. In this work, we demonstrate the use of ultrasparse sampling, a new data processing algorithm, and a 4-D time-shared NOESY experiment (1) to collect all NOEs in (2)H/(13)C/(15)N-labeled protein samples with selectively protonated amide and ILV methyl groups at high resolution in only four days, and (2) to calculate global folds from this data using fully automated resonance assignment. The new algorithm, SCRUB, incorporates the CLEAN method for iterative artifact removal but applies an additional level of iteration, permitting real signals to be distinguished from noise and allowing nearly all artifacts generated by real signals to be eliminated. In simulations with 1.2% of the data required by Nyquist sampling, SCRUB achieves a dynamic range over 10000:1 (250× better artifact suppression than CLEAN) and completely quantitative reproduction of signal intensities, volumes, and line shapes. Applied to 4-D time-shared NOESY data, SCRUB processing dramatically reduces aliasing noise from strong diagonal signals, enabling the identification of weak NOE crosspeaks with intensities 100× less than those of diagonal signals. Nearly all of the expected peaks for interproton distances under 5 Å were observed. The practical benefit of this method is demonstrated with structure calculations for 23 kDa and 29 kDa test proteins using the automated assignment protocol of CYANA, in which unassigned 4-D time-shared NOESY peak lists produce accurate and well-converged global fold ensembles, whereas 3-D peak lists either fail to converge or produce significantly less accurate folds. The approach presented here succeeds with an order of magnitude less sampling than required by alternative methods for processing sparse 4-D data.
在通过 NMR 对大型蛋白质进行结构研究中，全局折叠测定在提供目标拓扑结构的初步观察以及减少完全质子化样品的 NOESY 谱中的分配歧义方面发挥着越来越重要的作用。在这项工作中，我们展示了使用超稀疏采样、一种新的数据处理算法和 4-D 分时 NOESY 实验（1），仅用四天时间就在（2）H/（13）C/（15）N 标记的蛋白质样品中选择性质子化酰胺和 ILV 甲基基团的情况下以高分辨率收集所有 NOE，并（2）使用全自动共振分配从该数据计算全局折叠。新算法 SCRUB 结合了用于迭代去除伪影的 CLEAN 方法，但应用了额外的迭代级别，允许区分真实信号和噪声，并允许从真实信号生成的几乎所有伪影都被消除。在需要 Nyquist 采样的 1.2%数据的模拟中，SCRUB 实现了超过 10000:1 的动态范围（比 CLEAN 好 250 倍的伪影抑制），并且完全定量地再现了信号强度、体积和线形状。将 SCRUB 处理应用于 4-D 分时 NOESY 数据，可以大大减少来自强对角信号的混叠噪声，从而能够识别强度比对角信号低 100 倍的弱 NOE 交叉峰。观察到了所有预期的小于 5 Å 的质子间距离的峰。该方法的实际好处通过使用 CYANA 的自动分配协议对 23 kDa 和 29 kDa 测试蛋白质进行结构计算来证明，其中未分配的 4-D 分时 NOESY 峰列表产生准确且收敛良好的全局折叠总体，而 3-D 峰列表要么无法收敛，要么产生明显不准确的折叠。与处理稀疏 4-D 数据的替代方法相比，该方法的采样量减少了一个数量级。
