BASDet: Bayesian Approach(es) for Structure Determination from Single Molecule X-ray Diffraction Images

X-ray free electron lasers (XFEL) are expected to enable molecular structure determination in single molecule diffraction experiments. In this paper, we describe an implementation of two orthogonal Bayesian approaches, previously introduced in Walczak and Grubmüller (2014), capable of extracting structure information from sparse and noisy diffraction images obtained in these experiments. In the ‘Orientational Bayes’ approach, a ‘seed’ model is used to determine for every recorded diffraction image the underlying molecular orientation. The molecular transform of the irradiated molecule is obtained by aligning and averaging those images in three-dimensional reciprocal space. By contrast, in the ‘Structural Bayes’ approach, a real space structure model is optimized to fit best to an entire set of diffraction images. This approach is used in a Monte Carlo structure refinement procedure.

Both presented approaches were implemented in C; previous tests (Walczak and Grubmüller, 2014) suggest that the algorithms are robust against low signal to noise ratios and can deliver high resolution structural information.

X射线自由电子激光器（X-ray free electron lasers，XFEL）有望支持单分子衍射实验中的分子结构解析。本文介绍了两种正交贝叶斯方法的实现方案，该类方法最早由Walczak与Grubmüller于2014年提出，可从此类实验获取的稀疏且含噪的衍射图像中提取结构信息。在“取向贝叶斯方法”中，会通过种子模型为每张已记录的衍射图像确定其对应的分子本征取向；受辐照分子的衍射变换可通过将这些图像在三维倒易空间中对齐并取平均得到。与之相对，“结构贝叶斯方法”会对实空间结构模型进行优化，使其与整套衍射图像实现最优拟合，该方法被应用于蒙特卡洛结构精修流程中。上述两种方法均以C语言实现；此前的测试（Walczak与Grubmüller，2014）表明，这些算法对低信噪比具备良好鲁棒性，且可获取高分辨率的结构信息。