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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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使用机器学习预测复杂吸附物的结合基因,并使用以物理为灵感的图形表示.

Wenbin Xu1,2, Karsten Reuter2, Mie Andersen3,4

  • 1Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany.

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概括

机器学习模型预测了催化过程中的吸附剂结合和度. 这种数据效率高的方法使用图核和高斯过程,在过渡金属和合金上表现出强的性能,即使对于新元素也是如此.

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科学领域:

  • 催化剂是一种催化剂.
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 异质催化中的计算选对于发现新材料和新反应至关重要.
  • 第一原则计算在计算上昂贵,限制了对复杂材料和反应机制的探索.
  • 机器学习为预测催化性质提供了一个更有效的替代方案.

研究的目的:

  • 开发一个数据效率高的机器学习模型,用于预测吸附剂结合动机和吸附度.
  • 将模型应用于过渡金属及其合金,包括复杂吸附剂.
  • 为了使材料空间在催化研究中的有效探索.

主要方法:

  • 使用定制的Wasserstein Weisfeiler-Lehman图形内核进行特征提取.
  • 采用高斯过程回归用于预测建模.
  • 纳入了积极学习的整体不确定性估计方法.

主要成果:

  • 该模型准确地预测了元素过渡金属的结合动机和吸附度.
  • 在过渡金属合金上表现出良好的预测性能,超越了训练集.
  • 展示了用最小的新数据预测域外过渡金属属性的能力.

结论:

  • 开发的数据效率高的机器学习模型有效地预测了异质催化中的吸附剂结合和度.
  • 该方法适用于合金等复杂材料,并可扩展到新元素.
  • 该模型显示了将其整合到积极学习策略中的承诺,以加速催化剂的发现.