Chao Fa-An, Byrd R Andrew
Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA.
J Magn Reson. 2017 Apr;277:8-14. doi: 10.1016/j.jmr.2017.01.022. Epub 2017 Feb 4.
The Carr-Purcell-Meiboom-Gill (CPMG) experiment is one of the most classical and well-known relaxation dispersion experiments in NMR spectroscopy, and it has been successfully applied to characterize biologically relevant conformational dynamics in many cases. Although the data analysis of the CPMG experiment for the 2-site exchange model can be facilitated by analytical solutions, the data analysis in a more complex exchange model generally requires computationally-intensive numerical analysis. Recently, a powerful computational strategy, geometric approximation, has been proposed to provide approximate numerical solutions for the adiabatic relaxation dispersion experiments where analytical solutions are neither available nor feasible. Here, we demonstrate the general potential of geometric approximation by providing a data analysis solution of the CPMG experiment for both the traditional 2-site model and a linear 3-site exchange model. The approximate numerical solution deviates less than 0.5% from the numerical solution on average, and the new approach is computationally 60,000-fold more efficient than the numerical approach. Moreover, we find that accurate dynamic parameters can be determined in most cases, and, for a range of experimental conditions, the relaxation can be assumed to follow mono-exponential decay. The method is general and applicable to any CPMG RD experiment (e.g. N, C', C, H, etc.) The approach forms a foundation of building solution surfaces to analyze the CPMG experiment for different models of 3-site exchange. Thus, the geometric approximation is a general strategy to analyze relaxation dispersion data in any system (biological or chemical) if the appropriate library can be built in a physically meaningful domain.
卡尔-珀塞尔-迈博姆-吉尔(CPMG)实验是核磁共振光谱学中最经典且著名的弛豫色散实验之一,在许多情况下已成功应用于表征与生物相关的构象动力学。尽管对于双位点交换模型的CPMG实验,其数据分析可通过解析解来简化,但在更复杂的交换模型中,数据分析通常需要计算量很大的数值分析。最近,一种强大的计算策略——几何近似法被提出来,用于为绝热弛豫色散实验提供近似数值解,而这类实验既没有解析解也无法获得可行的解析解。在此,我们通过给出传统双位点模型和线性三位点交换模型的CPMG实验的数据分析解决方案,展示了几何近似法的普遍潜力。该近似数值解与数值解的平均偏差小于0.5%,并且新方法在计算效率上比数值方法高60000倍。此外,我们发现大多数情况下可以确定准确的动力学参数,并且在一系列实验条件下,可以假定弛豫遵循单指数衰减。该方法具有通用性,适用于任何CPMG弛豫色散实验(例如针对氮、碳-13、碳、氢等的实验)。这种方法构成了构建求解曲面以分析不同三位点交换模型的CPMG实验的基础。因此,如果能在物理意义明确的域中构建合适的库,几何近似法就是分析任何系统(生物或化学)中弛豫色散数据的通用策略。