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相关概念视频

Protein Complex Assembly02:41

Protein Complex Assembly

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Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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EGGNet是一个可泛化的几何深度学习框架,用于蛋白质复合体的姿势评分.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物物理学的生物物理.
  • 机器学习是机器学习.

背景情况:

  • 预测分子-蛋白相互作用对于药物开发至关重要.
  • 现有的方法经常单独对待蛋白质与蛋白质相互作用 (PPI) 和药物向相互作用 (DTI).
  • 这种隔离限制了对非共价相互作用的计算模型的概括性.

研究的目的:

  • 开发一种统一的计算框架,用于预测蛋白质和多种分子 (小分子,,蛋白质) 之间的相互作用.
  • 提高预测模型在不同类型的蛋白质相互作用中的概括能力.
  • 通过增强的计算预测加速药物发现和蛋白质工程.

主要方法:

  • 开发了Graphs神经网络的等价图 (EGGNet),一个几何深度学习 (GDL) 框架.
  • 在原子分辨率上使用图形的图形 (GoG) 表示图形.
  • 采用多分辨率等价图形神经网络,结合了原子和残留水平的生物物理相互作用.

主要成果:

  • EGGNet在蛋白质-小分子结合亲和力预测方面表现出竞争力 (80.2%在CASF-2016上排名前1的成功率).
  • 在合成蛋白界面预测任务 (88.4% AUC) 中获得了高精度.
  • 成功处理小分子,合成和与目标蛋白相互作用的天然蛋白质的预测.

结论:

  • EGGNet为各种蛋白质相互作用预测任务提供了一个通用的GDL框架.
  • 该模型能够整合多种分子类型和生物物理相互作用的能力提高了预测能力.
  • 这种方法具有显著的潜力,可以加速基于结构的药物开发和蛋白质工程.