Structural dynamics of macromolecules is critical to their structural-function relationship. Cryogenic electron microscopy (CryoEM) provides snapshots of vitrified protein at different compositional and conformational states, and the structural heterogeneity of proteins can be characterized through computational analysis of the images. For protein systems with multiple degrees of freedom, it is still challenging to disentangle and interpret the different modes of dynamics. Here, by implementing Point Transformer, a self-attention network designed for point cloud analysis, we are able to improve the performance of heterogeneity analysis on CryoEM data, and characterize the dynamics of highly complex protein systems in a more human-interpretable way.

翻译：大分子的结构动力学对其结构-功能关系至关重要。冷冻电子显微镜（CryoEM）提供了蛋白质在不同组成和构象状态下的玻璃化快照，通过图像的计算分析可以表征蛋白质的结构异质性。对于具有多个自由度的蛋白质系统，解析和解释不同的动力学模式仍然具有挑战性。本文通过实现Point Transformer（一种专为点云分析设计的自注意力网络），提升了冷冻电镜数据异质性分析的性能，并以更易于人类理解的方式表征了高度复杂蛋白质系统的动力学。