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从冷冻电镜图谱中快速且自动化地构建蛋白质-DNA/RNA 大分子复合物模型。

Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps.

机构信息

Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA.

Department of Computer Science, Duke University, Durham, NC 27708, USA.

出版信息

Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbac632.

Abstract

Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.

摘要

低温电子显微镜(cryo-EM)可以在三维库仑势图中重建蛋白质-DNA/RNA 复合物等大分子结构。这些大分子复合物的结构信息为理解分子机制提供了基础,包括许多人类疾病。然而,大型大分子复合物的模型构建通常具有难度和耗时的特点。我们最近开发了基于人工智能的 DeepTracer-2.0 流水线,可以从单个低温电子显微镜图中构建氨基酸和核酸骨架,甚至根据侧链密度预测最佳拟合残基。在对蛋白质-DNA/RNA 复合物的独立实验图谱进行基准测试时,该实验显示出了改进的准确性和效率,并展示了从低温电子显微镜图谱进行大分子建模的广阔前景。我们的方法和流水线可以使从事分子生物医学和药物发现的全球研究人员受益,并极大地提高低温电子显微镜模型构建的通量。该流水线已整合到网页门户 https://deeptracer.uw.edu/ 中。

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