Jiao Zhichao, Geng Zhi, Ding Wei
Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
IUCrJ. 2024 Sep 1;11(Pt 5):891-900. doi: 10.1107/S2052252524007851.
Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction pattern and reconstruct the 3D diffraction intensity. Most of these algorithms rely on the premise that all diffraction patterns originate from identical protein molecules. However, in actual experiments, diffraction patterns from multiple different molecules may be collected simultaneously. Here, we propose a predicted model-aided one-step classification-multireconstruction algorithm that can handle mixed diffraction patterns from various molecules. The algorithm uses predicted structures of different protein molecules as templates to classify diffraction patterns based on correlation coefficients and determines orientations using a correlation maximization method. Tests on simulated data demonstrated high accuracy and efficiency in classification and reconstruction.
超快、高强度的X射线自由电子激光能够对单个蛋白质分子进行衍射成像。人们已经开发出各种算法来确定每个单粒子衍射图案的方向并重建三维衍射强度。这些算法大多基于所有衍射图案都源自相同蛋白质分子这一前提。然而，在实际实验中，可能会同时收集到来自多个不同分子的衍射图案。在此，我们提出一种预测模型辅助的一步分类-多重重建算法，该算法能够处理来自各种分子的混合衍射图案。该算法使用不同蛋白质分子的预测结构作为模板，基于相关系数对衍射图案进行分类，并使用相关最大化方法确定方向。对模拟数据的测试表明，该算法在分类和重建方面具有很高的准确性和效率。
