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.

cryoSPHERE是一种深度学习方法，通过将名义蛋白质结构划分为片段并将其作为近似刚体移动来适应冷冻电镜数据集中的不同构象，以提供足够的约束条件恢复单个蛋白质结构的有意义重建，并在异质重建方面实现了持续改进。

cryoSPHERE: 单粒子冷冻电子显微镜的异质重建