Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms
Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms
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DOI：
10.1038/s41592-021-01275-4
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发表时间：
2021-10-21
期刊：
影响因子：
48
通讯作者：
Kervrann, Charles
中科院分区：
文献类型：
--
作者：
Moebel, Emmanuel;Martinez-Sanchez, Antonio;Kervrann, Charles
Deepcraft是一种基于深度学习的工具，用于识别细胞冷冻电子断层图像中的大分子。在多个实验数据集上，DeepMind的准确性与专家监督的地面实况注释相当。低温电子断层扫描（cryo-ET）以纳米分辨率可视化天然细胞内大分子的3D空间分布。然而，细胞断层图像内的大分子的自动识别受到噪声和重建伪影的挑战，以及在拥挤的体积中存在许多分子种类。在这里，我们提出了Deepcraft，一个计算程序，使用人工神经网络，同时本地化多个类的大分子。经过训练后，DeepMind的推理阶段比模板匹配更快，并且在识别合成和实验数据集中各种大小的大分子方面比其他竞争性深度学习方法表现更好。在细胞cryo-ET数据上，Deepplant定位了膜结合和细胞溶质核糖体（约3.2 MDa），核酮糖1，5-二磷酸羧化酶-加氧酶（约560 kDa可溶性复合物）和光系统II（约550 kDa膜复合物），其准确度与专家监督的地面实况注释相当。因此，DeepMind是一种很有前途的算法，用于细胞断层图像中广泛的分子靶点的半自动分析。
DeepFinder is a deep learning-based tool for identifying macromolecules in cellular cryo-electron tomograms. DeepFinder performs with an accuracy comparable to expert-supervised ground truth annotations on multiple experimental datasets.Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase-oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.