美国杜克大学Alberto Bartesaghi研究小组开发出了MiLoPYP学习框架，可用于自我监督的分子模式挖掘与原位颗粒定位。该项研究成果发表在2024年9月9日出版的《自然—方法学》上。

据悉，低温电子断层扫描允许在纳米范围分辨率，对三维细胞图谱进行常规可视化。当与单粒子断层扫描相结合时，可以在其天然环境中获得频繁发生的大分子的近原子分辨率结构。与冷冻电子断层扫描或单颗粒断层扫描相关的两大主要挑战，是蛋白质的自动识别和定位。

这些任务受到细胞内分子拥挤、冷冻电子断层扫描图像特有的成像畸变，以及断层扫描数据集庞大规模的阻碍。目前的方法准确性低，需要大量和耗时的人工标记，或者仅限于检测特定类型的蛋白质。

该研究组提出了MiLoPYP，这是一个两步数据集特定的基于对比学习的框架，可以实现快速的分子模式挖掘，然后是准确的蛋白质定位。MiLoPYP能够有效地检测和定位广泛的靶标，包括球状和管状复合物以及大的膜蛋白，将有助于简化和扩大高分辨率工作流程的适用性，用于原位结构测定。

附：英文原文

Title: MiLoPYP: self-supervised molecular pattern mining and particle localization in situ

Author: Huang, Qinwen, Zhou, Ye, Bartesaghi, Alberto

Issue&Volume: 2024-09-09

Abstract: Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination.

DOI: 10.1038/s41592-024-02403-6

Source: https://www.nature.com/articles/s41592-024-02403-6