Dörfler Thilo, Eilert Tobias, Röcker Carlheinz, Nagy Julia, Michaelis Jens
Institute of Biophysics, Ulm University.
Institute of Biophysics, Ulm University;
J Vis Exp. 2017 Feb 9(120):54782. doi: 10.3791/54782.
Single-molecule Förster Resonance Energy Transfer (smFRET) can be used to obtain structural information on biomolecular complexes in real-time. Thereby, multiple smFRET measurements are used to localize an unknown dye position inside a protein complex by means of trilateration. In order to obtain quantitative information, the Nano-Positioning System (NPS) uses probabilistic data analysis to combine structural information from X-ray crystallography with single-molecule fluorescence data to calculate not only the most probable position but the complete three-dimensional probability distribution, termed posterior, which indicates the experimental uncertainty. The concept was generalized for the analysis of smFRET networks containing numerous dye molecules. The latest version of NPS, Fast-NPS, features a new algorithm using Bayesian parameter estimation based on Markov Chain Monte Carlo sampling and parallel tempering that allows for the analysis of large smFRET networks in a comparably short time. Moreover, Fast-NPS allows the calculation of the posterior by choosing one of five different models for each dye, that account for the different spatial and orientational behavior exhibited by the dye molecules due to their local environment. Here we present a detailed protocol for obtaining smFRET data and applying the Fast-NPS. We provide detailed instructions for the acquisition of the three input parameters of Fast-NPS: the smFRET values, as well as the quantum yield and anisotropy of the dye molecules. Recently, the NPS has been used to elucidate the architecture of an archaeal open promotor complex. This data is used to demonstrate the influence of the five different dye models on the posterior distribution.
单分子荧光共振能量转移(smFRET)可用于实时获取生物分子复合物的结构信息。因此,通过三边测量法,利用多次smFRET测量来确定蛋白质复合物内部未知染料的位置。为了获得定量信息,纳米定位系统(NPS)使用概率数据分析,将X射线晶体学的结构信息与单分子荧光数据相结合,不仅计算最可能的位置,还计算完整的三维概率分布,即后验概率,它表示实验的不确定性。该概念已推广到包含众多染料分子的smFRET网络分析中。NPS的最新版本Fast-NPS具有一种新算法,该算法基于马尔可夫链蒙特卡罗采样和并行回火进行贝叶斯参数估计,能够在相对较短的时间内分析大型smFRET网络。此外,Fast-NPS通过为每个染料选择五种不同模型之一来计算后验概率,这些模型考虑了染料分子因其局部环境而表现出的不同空间和取向行为。在此,我们展示了获取smFRET数据并应用Fast-NPS的详细方案。我们提供了获取Fast-NPS三个输入参数的详细说明:smFRET值以及染料分子的量子产率和各向异性。最近,NPS已被用于阐明古菌开放启动子复合物的结构。该数据用于展示五种不同染料模型对后验分布的影响。