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使用3D变压器和隐藏的马尔科夫模型为冷EM密度图的De Novo原子蛋白质结构建模

Nabin Giri^1,2, Jianlin Cheng^1,2

¹Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA.

bioRxiv : the preprint server for biology

|January 23, 2024

概括

Cryo2Struct是一种新的自动化方法,用于从冷电子显微镜 (cryo-EM) 密度图中构建蛋白质原子结构. 它准确地模拟复杂的结构 de novo,优于现有的方法改善结构生物学见解.

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:

从3D冷电子显微镜 (cryo-EM) 密度图中精确的3D原子结构确定对于理解蛋白质复杂的功能至关重要.
从冷EM数据对蛋白质结构的新建建模,特别是对于缺乏模板的复合体,仍然是一个重要的计算挑战.

研究的目的:

推出Cryo2Struct,这是一个全自动化的初始方法,用于新的冷EM结构建模.
为了应对从冷电磁密度图构建精确的3D原子结构的挑战,而不依赖同类模板.

主要方法:

Cryo2Struct使用3D变压器在冷EM密度图中识别原子和氨基酸类型.
一个新的隐藏马尔科夫模型 (HMM) 用于连接预测的原子并构建蛋白质骨干结构.

主要成果:

与Phenix在标准数据集 (128张地图) 上相比,Cryo2Struct在构建准确和完整的蛋白质结构模型方面表现出卓越的表现.
该方法在更大,更近期的数据集 (500张地图) 上显示出强大的准确性,分辨率和蛋白质大小各不相同.
性能在不同的冷EM图像分辨率和蛋白质结构大小中保持一致.

结论:

Cryo2Struct为初始冷EM结构建模提供了一个强大的,自动化的解决方案.
该方法显著推进了从冷EM数据中重新确定蛋白质复杂结构的方法.
Cryo2Struct是结构生物学研究的一个有价值的工具,能够更准确,更有效地阐明结构.

关键词:

隐藏的马尔科夫模型原子蛋白结构建模原子蛋白结构建模低温电磁波冷却器 (Cryo-EM) 是一个非常好的方法.深度学习是一种深度学习.变压器的变压器是一个变压器.

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