论文标题
从冷冻EM密度图中重建蛋白质结构的深度学习：最新进展和未来方向
Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions
论文作者
论文摘要
冷冻电子显微镜（Cryo-EM）已成为确定蛋白质结构的关键技术，尤其是近年来大型蛋白质复合物和组件。 Cryo-EM数据分析中的一个关键挑战是从冷冻EM密度图自动重建准确的蛋白质结构。在这篇综述中，我们简要概述了从冷冻EM密度图建立蛋白质结构的各种深度学习方法，分析其影响，并讨论准备高质量数据集以培训深度学习模型的挑战。展望未来，需要开发更先进的深度学习模型，以有效地将冷冻EM数据与其他互补数据（例如蛋白质序列和Alphafold预测的结构）相结合，以进一步推进该领域。
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 reconstruct 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.
