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Significance Light-driven rhodopsin proteins pump ions across cell membranes. They have applications in optogenetics and can potentially be used to develop solar energy–harvesting devices. A detailed understanding of rhodopsin dynamics and functions may therefore assist research in medicine, health, and clean energy. This time-resolved crystallography study carried out with X-ray free-electron lasers reveals detailed dynamics of chloride ion–pumping rhodopsin (ClR) within 100 ps of light activation. It shows the dissociation of Cl⁻from the Schiff base binding site upon light-triggered retinal isomerization. This Cl⁻dissociation is followed by diffusion toward the intracellular direction. The results hint at a common ion-pumping mechanism across rhodopsin families. 

Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.