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The three-dimensional structure of a protein plays a key role in determining its function. Methods like AlphaFold have revolutionized protein structure prediction based only on the amino-acid sequence. However, proteins often appear in multiple different conformations, and it is highly relevant to resolve the full conformational distribution. Single-particle cryo-electron microscopy (cryo EM) is a powerful tool for capturing a large number of images of a given protein, frequently in different conformations (referred to as particles). The images are, however, very noisy projections of the protein, and traditional methods for cryo EM reconstruction are limited to recovering a single, or a few, conformations. In this paper, we introduce cryoSPHERE, a deep learning method that takes as input a nominal protein structure, e.g. from AlphaFold, learns how to divide it into segments, and how to move these as approximately rigid bodies to fit the different conformations present in the cryo EM dataset. This formulation is shown to provide enough constraints to recover meaningful reconstructions of single protein structures. This is illustrated in three examples where we show consistent improvements over the current state-of-the-art for heterogeneous reconstruction.

X-ray Free Electron Lasers (XFELs) can probe chemical and biological reactions as they unfold with unprecedented spatial and temporal resolution. A principal challenge in this pursuit is the delivery of samples to the X-ray interaction point in a way that produces data of the highest possible quality and efficiency. This is hampered by constraints posed by the light source and operation within a beamline environment. For liquid samples, the solution typically involves a high-speed liquid jet, capable of keeping up with the rate of X-ray pulses. However, conventional jets are not ideal because of radiation-induced explosions of the jet, as well as their cylindrical geometry combined with the X-ray pointing instability of many beamlines causes the interaction volume to differ for every pulse. This complicates data analysis and contributes to measurement errors. An alternative geometry is a liquid sheet jet which, with its constant thickness over large areas, eliminates the X-ray pointing related problems. Since liquid sheets can be made very thin, the radiation-induced explosion is reduced, boosting their stability. They are especially attractive for experiments which benefit from small interaction volumes such as fluctuation X-ray scattering and several types of spectroscopy. Although they have seen increasing use for soft X-ray applications in recent years, there has not yet been wide-scale adoption at XFELs. Here, we demonstrate liquid sheet jet sample injection at the European XFEL. We evaluate several aspects of its performance relative to a conventional liquid jet including thickness profile, stability, and radiation-induced explosion dynamics at high repetition rates. The sheet jet exhibits superior performance across these critical experimental parameters. Its minute thickness also suggests ultrafast single-particle solution scattering is a possibility.