德国马克斯普朗克研究所Stefan Raunser团队近期取得重要工作进展。他们研究开发了TomoTwin工具，该工具可以通过结构数据挖掘在低温电子断层图中实现大分子的广义三维（3D）定位。相关研究成果2023年5月15日在线发表于《自然—方法学》杂志上。

据介绍，低温电子断层扫描技术能够极其详细地显示细胞环境，然而，仍然需要分析这些密集体积中包含的全部信息的工具。通过亚断层图平均对大分子进行详细分析需要首先将颗粒定位在断层图体积内，这项任务因为低信噪比和细胞空间拥挤在内的几个因素而变得复杂。用于该任务的可用方法要么容易出错，要么需要对训练数据进行手动注释。

为了帮助完成这一关键的粒子信息拾取步骤，研究人员提出了TomoTwin：一个基于深度度量学习的低温电子断层图开源通用拾取模型。通过将断层图像嵌入信息丰富的高维空间，根据大分子的三维结构将其分离，TomoTwin允许用户在断层图像中从头识别蛋白质，而无需手动创建训练数据或重新训练网络来定位新的蛋白质。

附：英文原文

Title: TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining

Author: Rice, Gavin, Wagner, Thorsten, Stabrin, Markus, Sitsel, Oleg, Prumbaum, Daniel, Raunser, Stefan

Issue&Volume: 2023-05-15

Abstract: Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.

DOI: 10.1038/s41592-023-01878-z

Source: https://www.nature.com/articles/s41592-023-01878-z