Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions
Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions
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DOI：
10.1016/j.sbi.2023.102536
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发表时间：
2023-02-09
影响因子：
6.8
通讯作者：
Cheng, Jianlin
中科院分区：
文献类型：
--
作者：
Giri, Nabin;Roy, Raj S.;Cheng, Jianlin
低温电子显微镜（cryo-EM）是近年来发现的一种确定蛋白质结构的关键技术，特别是大的蛋白质复合物和组装体。冷冻电镜数据分析的一个关键挑战是从冷冻电镜密度图自动重建准确的蛋白质结构。在这篇综述中，我们简要概述了从cryo-EM密度图构建蛋白质结构的各种深度学习方法，分析了它们的影响，并讨论了为训练深度学习模型准备高质量数据集的挑战。展望未来，需要开发更先进的深度学习模型，将cryo-EM数据与其他补充数据源（如蛋白质序列和AlphaFold-predicted结构）有效整合，以进一步推动该领域的发展。
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically recon-struct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.