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IsoNet2 以亚分子分辨率确定细胞结构,没有平均值

Yun-Tao Liu^1,2, Hongcheng Fan^1,2, Jonathan Jih^1,2

¹Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.

bioRxiv : the preprint server for biology

|December 19, 2025

概括

IsoNet2 是一种用于冷电子断层扫描的新型深度学习工具,可以在20 Å分辨率下重建3D分子结构. 这种方法可以在没有平均值的情况下对细胞组件进行详细的分析.

科学领域:

结构生物学是结构生物学.
生物物理学的生物物理.
计算生物学是一种计算生物学.

背景情况:

低温电子断层扫描 (cryo-ET) 对于可视化细胞结构至关重要.
从冷ET数据中重建高分辨率的3D密度仍然具有挑战性,因为噪音和信息缺失.
现有的方法往往需要平均或广泛的手工处理.

研究的目的:

从冷ET数据开发一个端到端的深度学习方法,用于直接,高质量的3D密度重建.
提高分辨率,并使细胞环境的原子层次解释成为可能.
为研究人员提供一个用户友好的工具.

主要方法:

推出了IsoNet2,一个自我监督的深度学习网络.
统一的网络同时执行无声化,对比转移功能的校正和缺失的恢复.
一个图形用户界面 (GUI) 允许对数据集进行特定的微调.

主要成果:

在没有平均值的情况下实现了~20 Å分辨率的重建.
在HIV囊蛋白中成功解决了域组织.
在单个核糖体和线粒体呼吸复合体的in situ架构中确定tRNA占用.

结论:

IsoNet2可以直接从冷ET数据中实现高质量的3D密度重建.
该方法促进了对复杂的细胞环境的原子层次解释.
IsoNet2为结构和细胞生物学研究提供了一种强大且易于使用的工具.

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