Insights into Protein Dynamics from <sup>15</sup>N-<sup>1</sup>H HSQC

crossref(2021)

引用 0|浏览0
暂无评分
摘要
Abstract. Protein dynamic information is customarily extracted from 15N NMR spin-relaxation experiments. These experiments can only be applied to (small) proteins that can be dissolved to high concentrations. However, most proteins of interest to the biochemical and biomedical community are large and relatively insoluble. These proteins often have functional conformational changes, and it is particularly regretful that these processes cannot be supplemented by dynamical information from NMR. We ask here whether (some) dynamic information can be obtained from the 1H line widths in 15N-1H HSQC spectra. Such spectra are widely available, also for larger proteins. We developed computer programs to predict amide proton line widths from (crystal) structures. We aim to answer the following basic questions: is the 1H linewidth of a HSQC cross peak smaller than average because its 1H nucleus has few dipolar neighbors, or because the resonance is motionally narrowed? Is a broad line broad because of conformational exchange, or because the 1H nucleus resides in a dense proton environment? We calibrate our programs by comparing computational and experimental results for GB1 (58 residues). We deduce that GB1 has low average 1HN order parameters (0.8), in broad agreement with what was found by others from 15N relaxation experiments (Idiyatullin et al., 2003). We apply the program to the BPTI crystal structure and compare the results with a 15N-1H HSQC spectrum of BPTI (56 residues) and identify a cluster of conformationally broadened 1HN resonances that belong to an area, for which millisecond dynamics has been previously reported from 15N relaxation data (Szyperski et al., J. Biomol. NMR 3, 151-164, 1993). We feel that our computational approach is useful to glean insights into the dynamical properties of larger biomolecules for which high-quality 15N relaxation data cannot be recorded.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要