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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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在CAPRI第47-55轮中与罗塞塔和深度学习方法对接.

Ameya Harmalkar1, Lee-Shin Chu1, Samuel W Canner2

  • 1Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, USA.

Proteins
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概括
此摘要是机器生成的。

最近的相互作用预测临界评估 (CAPRI) 轮显示,蛋白质-蛋白质相互作用预测的准确性略有提高,特别是对于灵活的复合体. 将罗塞塔对接与像AlphaFold2这样的深度学习方法相结合提供了改进,但复杂的组件和抗体-抗原接口仍然存在挑战.

关键词:
形状的变化 形状的变化深度学习是一种深度学习.蛋白质对接的对接方式结构预测 结构预测

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物学是结构生物学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 相互作用预测的批判性评估 (CAPRI) 挑战社区评估蛋白质-蛋白质相互作用预测方法.
  • 之前的CAPRI轮已经推动了对接和结构预测算法的进步.

研究的目的:

  • 在最近的CAPRI轮 (47-55) 中评估Rosetta对接和深度学习方法的性能.
  • 识别和解决预测蛋白质-蛋白质相互作用的关键挑战,包括构造变化和复杂组合.

主要方法:

  • 将罗塞塔对接工具 (RosettaDock,ReplicaDock,SymDock) 与深度学习预测器 (AlphaFold2,IgFold,AlphaRED) 集成在一起.
  • 开发了针对形状变化的增强采样方法,以及大型异质多元器件的折叠和停靠方法.
  • 基于Rosetta的SymDock 2.0的应用用于对称复合体和抗体-抗原相互作用的分析.

主要成果:

  • 在AlphaFold2后,对更简单的目标进行预测准确度的轻微改进,但对灵活复杂的目标的性能仍然有限.
  • 通过使用增强的RosettaDock.成功预测结合诱导的细菌蛋白 (T194) 的构造变化.
  • 使用折叠和码头策略和对称复合体 (T230) 使用SymDock 2.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.
  • 尽管使用深度学习工具,但在模拟抗体-抗原接口,特别是CDR H3循环方面仍然存在挑战.

结论:

  • 结合对接和深度学习方法有希望,但需要进一步精细化复杂的蛋白质相互作用.
  • 解决构造灵活性和大型多重体结构对于推进蛋白质-蛋白质相互作用预测至关重要.
  • 未来的努力应集中在针对抗体-抗原相互作用的专门策略上,包括增强的采样和CDR特定建模.