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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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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.
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Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
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基于拓增强图形神经网络的药物目标亲和力预测.

Hengliang Guo1,2, Congxiang Zhang2, Jiandong Shang1,2

  • 1National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China.

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概括

这项研究引入了一个新的图形神经网络 (GNN),它使用蛋白质口袋数据进行更准确的药物向亲和力 (DTA) 预测,提高药物发现效率.

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 图形神经网络 (GNN) 通过分析分子结构,在药物向亲和力 (DTA) 预测方面表现出色.
  • 当前的GNN模型往往忽略了关键的蛋白质腔信息,限制了预测准确度.
  • 药物开发需要有效和准确的方法来预测药物和蛋白质标之间的相互作用.

研究的目的:

  • 开发一种新的拓增强的GNN,用于改进DTA预测.
  • 将蛋白质口袋数据集成到GNN中,以增强特征表示.
  • 优化GNN培训和消息传递,以提高性能.

主要方法:

  • 设计了一个全新的拓增强图形神经网络 (GNN) 架构.
  • 蛋白质口袋结构数据被纳入了GNN模型.
  • 训练和消息传递策略被优化为特征学习.

主要成果:

  • 拟议的GNN模型在DTA预测中表现出卓越的性能.
  • 整合蛋白质口袋数据显著提高了预测准确度.
  • 该模型有效地捕获了复杂的药物向相互作用.

结论:

  • 拓增强的GNN为DTA预测提供了一个强大的方法.
  • 纳入蛋白质腔信息对于准确的药物向相互作用建模至关重要.
  • 这种方法可以通过提高预测效率来加速药物发现.