Grishaev Alexander, Llinás Miguel
National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA.
Methods Enzymol. 2005;394:261-95. doi: 10.1016/S0076-6879(05)94010-X.
In this chapter we review automated methods of protein NMR data analysis and expand on the assignment-independent CLOUDS approach. As presented, given a set of reliable NOEs it is feasible to derive a spatial H-atom distribution that provides a low-resolution image of the protein structure. In order to generate such a list of unambiguous NOEs, a probabilistic assessment of the NOE identities (in terms of frequency-labeled H-atom sources) was developed on the basis of Bayesian inference. The methodology, encompassing programs SPI and BACUS, provides a list of "clean" NOEs that does not hinge on prior knowledge of sequence-specific resonance assignments or a preliminary structural model. As such, the combined SPI/BACUS approach, intrinsically adaptable to include 13C- and/or 15N-edited experiments, affords a useful tool for the analysis of NMR data irrespective of whether the adopted structure calculation protocol is assignment-dependent.
在本章中,我们回顾了蛋白质核磁共振(NMR)数据分析的自动化方法,并详细阐述了与归属无关的CLOUDS方法。如前所述,给定一组可靠的核Overhauser效应(NOE),推导提供蛋白质结构低分辨率图像的空间氢原子分布是可行的。为了生成这样一份明确的NOE列表,基于贝叶斯推理对NOE归属(根据频率标记的氢原子来源)进行了概率评估。该方法包括SPI和BACUS程序,提供了一份“纯净”的NOE列表,该列表不依赖于序列特异性共振归属的先验知识或初步的结构模型。因此,结合的SPI/BACUS方法本质上适用于包括碳-13(13C)和/或氮-15(15N)编辑实验,无论所采用的结构计算方案是否依赖于归属,它都为NMR数据分析提供了一个有用的工具。