DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data.
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data.
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DOI：
10.5194/mr-1-209-2020
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Stoll S
中科院分区：
文献类型：
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作者：
Fábregas Ibáñez L;Jeschke G;Stoll S
偶极EPR光谱（DEER和其他技术）通过测量纳米尺度上未成对电子之间的距离分布来实现大分子和生物系统的结构表征。由于逆问题的不适定性质，从测量信号推断这些分布是具有挑战性的。现有的分析工具分散在几个具有专门图形用户界面的应用程序中。这使得比较、再现性和方法开发变得困难。为了弥补这种情况，我们提出了DeerLab，一个开源的软件包，用于分析偶极EPR数据，是模块化的，并实现了广泛的方法。我们表明，DeerLab可以基于可分离的非线性最小二乘法执行一步分析，将偶极多通路模型拟合到多脉冲DEER数据，使用非参数分布进行全局分析，并使用自举方法完全量化分析中的不确定性。
Dipolar EPR spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source software package for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, run global analysis with non-parametric distributions, and use a bootstrapping approach to fully quantify the uncertainty in the analysis.