Structural flexibility and/or dynamic interactions with other molecules is a critical aspect of protein function. Cryogenic electron microscopy (cryo-EM) provides direct visualization of individual macromolecules sampling different conformational and compositional states. While numerous methods are available for computational classification of discrete states, characterization of continuous conformational changes or large numbers of discrete state without human supervision remains challenging. Here we present e2gmm, a machine learning algorithm to determine a conformational landscape for proteins or complexes using a three-dimensional Gaussian mixture model mapped onto two-dimensional particle images in known orientations. Using a deep neural network architecture, e2gmm can automatically resolve the structural heterogeneity within the protein complex and map particles onto a small latent space describing conformational and compositional changes. This system presents a more intuitive and flexible representation than other manifold methods currently in use. We demonstrate this method on both simulated data and three biological systems to explore compositional and conformational changes at a range of scales. The software is distributed as part of EMAN2.

结构灵活性和/或与其他分子的动态相互作用是蛋白质功能的一个关键方面。低温电子显微镜 (cryo-EM) 提供了对采样不同构象和组成状态的单个大分子的直接可视化。虽然有许多方法可用于离散状态的计算分类，但在没有人工监督的情况下对连续构象变化或大量离散状态进行表征仍然具有挑战性。在这里，我们提出了 e2gmm，这是一种机器学习算法，使用映射到已知方向的二维粒子图像上的三维高斯混合模型来确定蛋白质或复合物的构象景观。使用深度神经网络架构，e2gmm 可以自动解决蛋白质复合物中的结构异质性，并将粒子映射到描述构象和组成变化的小潜在空间。与当前使用的其他多种方法相比，该系统提供了更直观和灵活的表示。我们在模拟数据和三个生物系统上演示了这种方法，以探索一系列尺度的成分和构象变化。该软件作为 EMAN2 的一部分分发。