Tycko Robert
Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA.
J Magn Reson. 2015 Apr;253:166-72. doi: 10.1016/j.jmr.2015.02.006.
Determination of accurate resonance assignments from multidimensional chemical shift correlation spectra is one of the major problems in biomolecular solid state NMR, particularly for relative large proteins with less-than-ideal NMR linewidths. This article investigates the difficulty of resonance assignment, using a computational Monte Carlo/simulated annealing (MCSA) algorithm to search for assignments from artificial three-dimensional spectra that are constructed from the reported isotropic (15)N and (13)C chemical shifts of two proteins whose structures have been determined by solution NMR methods. The results demonstrate how assignment simulations can provide new insights into factors that affect the assignment process, which can then help guide the design of experimental strategies. Specifically, simulations are performed for the catalytic domain of SrtC (147 residues, primarily β-sheet secondary structure) and the N-terminal domain of MLKL (166 residues, primarily α-helical secondary structure). Assuming unambiguous residue-type assignments and four ideal three-dimensional data sets (NCACX, NCOCX, CONCA, and CANCA), uncertainties in chemical shifts must be less than 0.4 ppm for assignments for SrtC to be unique, and less than 0.2 ppm for MLKL. Eliminating CANCA data has no significant effect, but additionally eliminating CONCA data leads to more stringent requirements for chemical shift precision. Introducing moderate ambiguities in residue-type assignments does not have a significant effect.
从多维化学位移相关谱中确定准确的共振归属是生物分子固态核磁共振中的主要问题之一,特别是对于具有不太理想的核磁共振线宽的相对较大的蛋白质。本文研究了共振归属的困难,使用计算蒙特卡罗/模拟退火(MCSA)算法从人工三维谱中搜索归属,这些人工三维谱由两种蛋白质的报道的各向同性(15)N和(13)C化学位移构建而成,这两种蛋白质的结构已通过溶液核磁共振方法确定。结果表明归属模拟如何能够为影响归属过程的因素提供新的见解,进而有助于指导实验策略的设计。具体而言,对SrtC的催化结构域(147个残基,主要为β-折叠二级结构)和MLKL的N端结构域(166个残基,主要为α-螺旋二级结构)进行了模拟。假设明确的残基类型归属和四个理想的三维数据集(NCACX、NCOCX、CONCA和CANCA),对于SrtC的归属要唯一,化学位移的不确定性必须小于0.4 ppm,对于MLKL则必须小于0.2 ppm。去除CANCA数据没有显著影响,但额外去除CONCA数据会导致对化学位移精度的要求更严格。在残基类型归属中引入适度的模糊性没有显著影响。