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相关概念视频

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The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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相关实验视频

Updated: Jan 21, 2026

Preparation of High-Temperature Sample Grids for Cryo-EM
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精细调整的AlphaFold与有限的冷电磁观测.

Junwen Liao1, Dihan Zheng2,3, Hui Zhang1

  • 1Qiuzhen College, Tsinghua University, Beijing, China.

Communications chemistry
|January 19, 2026
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概括

CoCoFold通过将粒子图像集成到AlphaFold以进行直接的原子模型预测来增强冷电子显微镜 (cryo-EM). 这种方法提高了结构准确性,特别是在冷电磁单粒子分析 (SPA) 中数据有限或视图缺失的情况下.

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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:

  • 低温电子显微镜单颗粒分析 (cryo-EM SPA) 对于确定宏分子结构至关重要.
  • 目前的冷-EM SPA 方法由于粒子数量不足和视图缺失而面临局限性,阻碍了准确的结构确定.

研究的目的:

  • 引入CoCoFold,这是一个新的框架,旨在克服冷-EM SPA中的数据限制.
  • 使用与AlphaFold集成的原始冷电磁粒子图像直接指导原子模型预测.

主要方法:

  • CoCoFold通过整合原始冷电磁粒子图像来微调AlphaFold,以直接指导原子模型.
  • 一个记忆效率高的调整策略在AlphaFold的结构模块中采用了融合的注意力机制.
  • 一个可差异化的网络使预测结构与实验冷电磁数据的端到端精细化成为可能.

主要成果:

  • 在具有有限粒子数量和缺失视图的基准实验中,CoCoFold表现出卓越的性能.
  • 该框架在各种评估指标中始终优于最先进的方法.
  • CoCoFold有效地通过利用实验性冷电磁观测来改进原子模型.

结论:

  • CoCoFold显著提高了冷-EM SPA 中原子模型预测的准确性,特别是在数据稀缺的条件下.
  • 将原始冷电磁数据直接集成到预测框架中,为结构生物学提供了一个强大的方法.