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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
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
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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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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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...
12.9K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

47
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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相关实验视频

Updated: Jul 9, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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一个多模式的深度学习框架,用于预测PPI-调节器交互.

Heqi Sun1, Jianmin Wang2, Hongyan Wu3

  • 1State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Journal of chemical information and modeling
|December 1, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度学习框架,MultiPPIMI,只使用序列数据准确地预测蛋白质-蛋白质相互作用 (PPI) 调节器. 这种方法有助于通过克服现有的计算方法的局限性来发现疾病的新药标.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 蛋白与蛋白相互作用 (PPI) 对生物功能和疾病发展至关重要.
  • 目前用于识别PPI调节器的计算方法通常需要目标结构或已知的调节器,限制它们用于新目标的使用.
  • 需要多功能计算工具来预测PPI调制器适用于新目标.

研究的目的:

  • 开发一种新的基于序列的深度学习框架,MultiPPIMI,用于预测蛋白质标和调节器之间的相互作用.
  • 解决现有方法的局限性,用于识别新型PPI目标的调节器.
  • 提供一种计算工具,可以协助选针对PPI的潜在治疗剂.

主要方法:

  • 开发了基于序列的深度学习框架MultiPPIMI.
  • 集成的PPI目标和调制器的多式联运表示.
  • 采用双线性注意网络来捕捉分子间相互作用.
  • 在冷启动和随机分割场景中使用AUROC指标在精选的基准数据集上评估性能.

主要成果:

  • 在三个冷启动场景中,MultiPPIMI实现了0.837的平均AUROC,证明了对新目标的有效性.
  • 在随机分割场景中获得了0.994的AUROC,表明了高精度.
  • 一个案例研究证明了MultiPPIMI在选抑制剂中对Keap1/Nrf2 PPI相互作用的有用性,有助于分子对接模拟.

结论:

  • MultiPPIMI提供了一个有前途的基于序列的深度学习方法,用于预测PPI调节器.
  • 该框架有效地处理新的PPI目标,而不需要结构信息或参考调制器.
  • 多PPIMI可以作为一种有价值的工具,加速治疗PPI的调节器的发现和选.