De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model.
De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model.
复制标题
使用 3D Transformer 和隐马尔可夫模型对冷冻电镜密度图进行从头原子蛋白质结构建模。
DOI：
10.1101/2024.01.02.573943
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Cheng,Jianlin
中科院分区：
文献类型：
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作者：
Giri,Nabin;Cheng,Jianlin
从三维冷冻电子显微镜(Cryo-EM)密度图精确构建三维(3D)原子结构是基于Cryo-EM确定蛋白质复合体结构的关键步骤。尽管3D低温EM密度图的分辨率有所提高，但对于没有准确的同源或预测结构可用作模板的蛋白质复合体，将密度图从头转换为3D原子结构仍然是一个重大挑战。在这里，我们介绍了一种全自动从头计算低温EM结构建模方法Cryo2Struct，该方法首先利用3D转换器识别低温EM密度图中的原子和氨基酸类型，然后使用一种新的隐马尔可夫模型(HMM)连接预测的原子来构建蛋白质的主干结构。在128张不同分辨率(2.1-5.6°A)和不同残基数量(730-8,416)的低温EM密度图的标准测试数据集上进行测试，Cryo2Struct在多个评估指标方面比广泛使用的从头计算方法Phenix建立了更准确和更完整的蛋白质结构模型。此外，在一个由500张最近发布的不同分辨率(1.9-4.0°A)和不同残基数量(234-8828)的密度图组成的新测试数据集上，它比在标准数据集上建立了更准确的模型。对于密度图的分辨率和蛋白质结构大小的变化，它的性能是相当稳健的。
Accurately building three-dimensional (3D) atomic structures from 3D cryo-electron microscopy (cryo-EM) density maps is a crucial step in the cryo-EM-based determination of the structures of protein complexes. Despite improvements in the resolution of 3D cryo-EM density maps, the de novo conversion of density maps into 3D atomic structures for protein complexes that do not have accurate homologous or predicted structures to be used as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated ab initio cryo-EM structure modeling method that utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps first, and then employs a novel Hidden Markov Model (HMM) to connect predicted atoms to build backbone structures of proteins. Tested on a standard test dataset of 128 cryo-EM density maps with varying resolutions (2.1 – 5.6 °A) and different numbers of residues (730 – 8,416), Cryo2Struct built substantially more accurate and complete protein structural models than the widely used ab initio method - Phenix in terms of multiple evaluation metrics. Moreover, on a new test dataset of 500 recently released density maps with varying resolutions (1.9 – 4.0 °A) and different numbers of residues (234 – 8,828), it built more accurate models than on the standard dataset. And its performance is rather robust against the change of the resolution of density maps and the size of protein structures.