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A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion
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在中等分辨率的冷电子密度图中识别氨基酸侧链.

Dibyendu Mondal1, Vipul Kumar1, Tadej Satler1,2

  • 1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.

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PubMed
概括
此摘要是机器生成的。

EMSequenceFinder在冷电子显微镜图中准确地将氨基酸序列分配给蛋白质骨干片段. 这种方法改善了蛋白质结构建模,特别是在较低分辨率下.

关键词:
冷电子显微镜的使用方法整合性结构建模 整合性结构建模蛋白质结构建模模型序列线程线程是什么

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科学领域:

  • 结构生物学 结构生物学
  • 生物物理学的生物物理.
  • 计算生物学 计算生物学

背景情况:

  • 准确的原子模型构建成冷电子显微镜 (cryo-EM) 地图具有挑战性,特别是在分辨率低于3 Å.
  • 在冷EM地图中确定蛋白质骨干片段的氨基酸序列是模型构建的关键步骤.

研究的目的:

  • 开发和验证一种计算方法,EMSequenceFinder,用于在冷EM地图中将氨基酸序列分配给蛋白质骨干片段.
  • 在冷电磁结构研究中提高原子模型构建的准确性和效率.

主要方法:

  • EMSequenceFinder使用贝叶斯分数函数来根据地图密度合适度,分辨率和次要结构倾向来对氨基酸残留类型进行排名.
  • 一个卷积神经网络在电子显微镜数据库 (EMDB) 中从冷电磁图和原子模型中训练了数百万个残留密度.
  • 该方法在已知的蛋白质片段的广泛数据集上进行了基准测试,并应用于SARS-CoV-2的冷EM图.

主要成果:

  • EMSequenceFinder正确地确定了氨基酸序列作为77.8%测试的α-螺旋和β-链片段的顶级预测.
  • 在4至6 Å分辨率的冷电磁图上,EMSequenceFinder获得了63.5%的准确性,超过了三种最先进的方法.
  • 通过将SARS-CoV-2 NSP2序列连接到一系列分辨率的多个冷EM地图中,证明了成功的应用.

结论:

  • EMSequenceFinder提供了一个强大的,准确的方法,用于在冷-EM密度图中进行序列分配,这有助于构建原子模型.
  • 该工具通过将冷EM数据与其他生物信息相结合,增强了整合性结构建模.
  • 作为一个开源工具,EMSequenceFinder可在集成建模平台 (IMP) 中使用.