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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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,...
4.0K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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星球:一个多目标图形神经网络模型,用于蛋白质 - 配体结合的亲和力预测.

Xiangying Zhang1, Haotian Gao1, Haojie Wang1

  • 1Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.

Journal of chemical information and modeling
|June 15, 2023
PubMed
概括

我们开发了PLANET,这是一个图形神经网络,用于预测蛋白质-连接体结合亲和力. 这种高效的模型在虚拟查和药物设计任务中表现出强的表现.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 在生物信息学中的机器学习.

背景情况:

  • 预测蛋白质 - 配体结合亲和力对于药物设计至关重要.
  • 现有的深度学习模型通常需要3D复杂的结构,并且仅专注于亲和力预测.

研究的目的:

  • 开发一个高效的图形神经网络模型,PLANET,用于预测蛋白质-连接体结合亲和力.
  • 通过结合多目标学习和多样化的培训数据来改进现有方法.

主要方法:

  • 开发了PLANET,一个图形神经网络模型.
  • 输入包括3D绑定口袋图和2D连接体结构.
  • 使用多目标方法进行训练,具有绑定亲和力,联系地图和距离矩阵任务.
  • 集成的PDBbind数据和非绑定诱用于培训.

主要成果:

  • 在CASF-2016基准中,PLANET的得分能力与顶级深度学习模型相美.
  • 与其他模型相比,在DUD-E的虚拟选中表现出卓越的性能.
  • 显示了与LIT-PCBA上的Glide相似的准确性,计算时间大大缩短.

结论:

  • PLANET提供了准确性和效率的平衡,用于绑定亲和力预测.
  • 该模型显示了作为药物发现中大规模虚拟查的有价值工具的潜力.
  • 它的多目标培训和输入灵活性有助于其强大的表现.