A predicted model-aided one-step classification–multireconstruction algorithm for X-rayfree-electron laser single-particle imaging

Abstract

A predicted model-aided one-step classification–multireconstruction algorithm for X-ray free-electron laser single-particle imaging is proposed. The algorithm is capable of processing mixed diffraction patterns from multiple molecules, classifying diffraction patterns by different molecules, determining their orientations and reconstructing multiple 3D diffraction intensities, in one step.

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.