Ontario Cancer Institute and The Campbell Family Cancer Research Institute, Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON M5G 1L7, Canada.
J Biomol NMR. 2011 Jan;49(1):27-38. doi: 10.1007/s10858-010-9458-0. Epub 2010 Dec 14.
The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution - especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.
利用核磁共振(NMR)光谱法测定的蛋白质结构的质量取决于实验得出的共振分配、距离和角度约束的数量和质量。有两个关键的蛋白质 NMR 数据特征对中小规模蛋白质的常规和自动化结构测定构成了挑战;(1)谱分辨率 - 特别是拥挤的核 Overhauser 效应光谱(NOESY)谱,以及(2)依赖于大多数常见分配方案中连续弱标量耦合网络。为了促进 NMR 结构测定,我们开发了一种半自动策略,该策略利用非均匀采样(NUS)和多维分解(MDD)来优化选定的高分辨率多维 NMR 实验的数据收集和处理,将其与 ABACUS 协议结合用于顺序和侧链共振分配,并简化此过程,分别在 CYANA 和 CNS 中执行结构和精炼计算。开发了两个图形用户界面(GUI),以方便有效地分析和编译数据,并指导自动化结构测定。该综合方法已在大小为 5.5 至 16.5 kDa 的 30 多个高质量蛋白质结构上实施和完善。