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使用SIREn对冷EM体积组合的自动化无模型分析

Laurel F Kinman¹, Maria V Carreira¹, Barrett M Powell¹

¹Department of Biology, Massachusetts Institute of Technology, Cambridge, MA.

bioRxiv : the preprint server for biology

|October 17, 2024

概括

新的机器学习工具有助于分析冷电子显微镜 (cryo-EM) 数据,以识别蛋白质结构状态及其数量. 这有助于我们更好地理解蛋白质的动态和功能.

科学领域:

结构生物学结构生物学
生物物理学的生物物理.
计算生物学计算生物学

背景情况:

低温电子显微镜 (cryo-EM) 可以生成生物分子的3D密度图.
机器学习的进步使大型冷电磁数据集能够被处理成众多的3D地图.
从这些地图中识别不同的蛋白质构造状态及其种群是具有挑战性的.

研究的目的:

开发用于分析冷EM体积集的新型计算工具.
允许在冷EM数据中自动和公正地识别结构异质性.
为了方便构建蛋白质的定量构造景观.

主要方法:

在冷电磁体体积集团内对voxels的统计共占用分析可以推断出可变区域.
3D卷积神经网络的开发和应用,用于自动化二进制化值预测.
将新工具与现有的冷电磁数据处理管道集成.

主要成果:

开发的方法成功地在模拟和真实冷EM数据集中确定了已知的结构异质性.
该方法允许从体积集合中直接推断可变区域.
三维卷积神经网络提供了公正和自动的二进制化值预测.
整合改进了颗粒固,并使定量构造构成的格局景观成为可能.

结论:

提出的计算方法增强了冷EM数据的分析,通过使蛋白质结构状态的稳健识别.
这些工具解决了从冷EM数据集中量化结构异质性和占用率的关键挑战.
这些发现为深入了解蛋白质动态及其功能影响铺平了道路.

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Cryo-electron Microscopy

Cryo-electron Microscopy

Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...