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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-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...
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相关实验视频

Updated: Sep 12, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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整合基于物理的模拟与数据驱动的深度学习是开发针对主要蛋白酶的抑制剂的强有力的战略.

Yanqing Yang1, Yangwei Jiang1, Dong Zhang1

  • 1Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Journal of chemical information and modeling
|August 6, 2025
PubMed
概括

一个新的计算管道Deep-CovBoost结合了人工智能和模拟来优化冠状病毒蛋白酶抑制剂. 这种方法通过识别增强病毒结合亲和力的有力化合物来加速药物发现.

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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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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
  • 医学中的人工智能

背景情况:

  • 新冠病毒主要蛋白酶是抗病毒药物开发的关键目标.
  • 现有的抑制剂往往不能充分利用全结合位点.

研究的目的:

  • 开发和验证一个计算管道 (Deep-CovBoost) 用于基于结构优化冠状病毒主要蛋白酶抑制剂.
  • 通过准未充分利用的子口袋来识别具有增强结合亲和力的新型抑制剂.

主要方法:

  • 深度学习与自由能量扰动 (FEP) 模拟的整合.
  • 基于结构的药物设计和抑制剂类似物的虚拟选.
  • 分子动力学模拟用于严格验证结合亲和力.

主要成果:

  • 深度CovBoost成功生成并优先考虑了新型抑制剂类似物.
  • 优化的化合物,包括I3C-1,I3C-2和I3C-35,表现出增强的结合亲和力.
  • 化合物有效地激活了主要蛋白酶的S4和S5子囊.

结论:

  • 人工智能和基于物理的模拟的结合加快了抗病毒优化.
  • 深度CovBoost是一种有前途的方法,用于设计针对冠状病毒目标的强效抑制剂.
  • 针对未充分利用的子口袋可以显著提高抑制剂的疗效.