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相关概念视频

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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Protein Networks

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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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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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生物活性 深度学习用于复杂的无结构化合物-蛋白质相互作用预测.

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  • 1Department of Chemistry, New York University, New York, New York 10003, United States.

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|September 16, 2025
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概括
此摘要是机器生成的。

我们介绍了CPI2M,一个大型生物活性数据集,以及GGAP-CPI,一个深度学习模型. GGAP-CPI有效地预测化合物-蛋白相互作用 (CPI) 和处理活动悬崖 (ACs),优于药物选中的现有方法.

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科学领域:

  • 计算化学和化学信息学
  • 生物信息学和计算生物学
  • 药物发现和开发 药物发现和开发

背景情况:

  • 准确的蛋白质 - 配体结合亲和力预测对于虚拟药物查至关重要.
  • 传统的方法依赖于有限的蛋白质-连接体晶体结构.
  • 无结构化合物-蛋白相互作用 (CPI) 方法提供使用生物活性数据的替代方案,但面临数据异质性和活性悬崖 (AC) 的挑战.

研究的目的:

  • 解决无结构CPI预测的局限性,特别是数据异质性和ACs.
  • 引入一个具有AC注释的大规模基准数据集 (CPI2M).
  • 开发和验证一种新的深度学习模型 (GGAP-CPI) 以进行可靠的CPI预测.

主要方法:

  • 创建CPI2M,一个数据集,包含四种活动类型 (Ki,Kd,EC50,IC50) 的约200万个生物活性点和AC注释.
  • 开发GGAP-CPI,这是一个无结构的深度学习模型,利用集成的生物活性学习和高级蛋白质表示.
  • 对GGAP-CPI进行综合评估,与4种预测场景和7种基准数据集中的19种基线方法对比.

主要成果:

  • GGAP-CPI显著超过了12个特定目标和7个一般CPI基线.
  • 该模型在一般CPI预测,罕见蛋白质预测,转移学习和虚拟查方面表现出卓越的表现.
  • GGAP-CPI提供稳定的生物活性预测,测量预测不确定性,并识别绑定口袋残留物和相互作用.

结论:

  • GGAP-CPI代表了无结构CPI预测的重大进步,有效地解决了数据异质性和活动悬崖.
  • 该模型能够预测生物活性,量化不确定性和丰富相互作用数据,这突显了其在药物发现中的实际实用性.
  • CPI2M数据集和GGAP-CPI模型为推进计算药物查和生物活性评估提供了宝贵的资源.