美国贝勒医学院Steven J. Ludtke团队开发出基于深度学习的混合维度高斯混合模型，可用于表征cryo-EM的变异性。相关论文于2021年7月29日在线发表于国际学术期刊《自然—方法学》。

研究人员提出了e2gmm，这是一种机器学习算法，使用三维高斯混合模型映射到已知方向的二维粒子图像来确定蛋白质或复合体的构象景观。使用深度神经网络架构，e2gmm可以自动解析蛋白质复合物内部的结构异质性，并将颗粒映射到描述构象和成分变化的小的潜在空间。与目前使用的其他流形方法相比，这个系统提出了一个更直观和灵活的表述。研究人员在模拟数据和三个生物系统上展示了这种方法，可用于探索一系列尺度上的组成和构象变化。该软件作为EMAN2的一部分发布。

据介绍，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。cryo-EM可以直接观察单个大分子的不同构象和组成状态。虽然有许多方法可用于离散状态的计算分类，但连续的构象变化或大量的离散状态的表征在没有人类监督的情况下仍然具有挑战性。

附：英文原文

Title: Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM

Author: Chen, Muyuan, Ludtke, Steven J.

Issue&Volume: 2021-07-29

Abstract: 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. e2gmm uses a deep neural network with a Gaussian representation to resolve the compositional and conformational variability within biomolecules using cryo-EM data.

DOI: 10.1038/s41592-021-01220-5

Source: https://www.nature.com/articles/s41592-021-01220-5