Cryo-electron microscopy (cryo-EM) is an experimental technique for protein structure determination that images an ensemble of macromolecules in near-physiological contexts. While recent advances enable the reconstruction of dynamic conformations of a single biomolecular complex, current methods do not adequately model samples with mixed conformational and compositional heterogeneity. In particular, datasets containing mixtures of multiple proteins require the joint inference of structure, pose, compositional class, and conformational states for 3D reconstruction. Here, we present Hydra, an approach that models both conformational and compositional heterogeneity fully ab initio by parameterizing structures as arising from one of K neural fields. We employ a new likelihood-based loss function and demonstrate the effectiveness of our approach on synthetic datasets composed of mixtures of proteins with large degrees of conformational variability. We additionally demonstrate Hydra on an experimental dataset of a cellular lysate containing a mixture of different protein complexes. Hydra expands the expressivity of heterogeneous reconstruction methods and thus broadens the scope of cryo-EM to increasingly complex samples.

翻译：冷冻电子显微镜（cryo-EM）是一种通过近生理环境下对生物大分子集合成像以确定蛋白质结构的实验技术。尽管近期进展已能实现单个生物分子复合物动态构象的重构，现有方法仍无法充分建模同时存在构象异质性与组分异质性的样本。特别地，包含多种蛋白质混合物的数据集需要进行结构、空间姿态、组分类别及构象状态的联合推断以完成三维重构。本文提出Hydra方法，通过将结构参数化为K个神经场之一的生成结果，实现了完全从数据出发的构象与组分异质性建模。我们采用新型基于似然的损失函数，并在由高构象可变性蛋白质混合物构成的合成数据集上验证了方法的有效性。此外，我们在包含不同蛋白质复合物混合物的细胞裂解液实验数据集上展示了Hydra的性能。Hydra拓展了异构重构方法的表达能力，从而将冷冻电镜的适用范围扩展至日益复杂的样本体系。