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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...
12.9K
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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.0K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
13.0K
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...
4.2K
Protein Networks02:26

Protein Networks

4.0K
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
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
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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相关实验视频

Updated: Jul 16, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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ML-PLIC:一个用于表征蛋白质-连接体相互作用和开发基于机器学习的评分函数的网络平台.

Xujun Zhang1, Chao Shen1,2, Tianyue Wang1

  • 1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Briefings in bioinformatics
|September 22, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了ML-PLIC,这是一个用于表征蛋白质-连接体相互作用 (PLI) 和通过虚拟查生成基于机器学习的评分函数 (MLSFs) 的网络平台,用于通过虚拟查发现药物.

关键词:
机器学习是机器学习.评分功能是一个得分函数.虚拟选 虚拟选 虚拟选网络平台 网络平台 网络平台

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科学领域:

  • 计算化学和化学信息学
  • 药物的发现和开发.
  • 在生物信息学中的机器学习.

背景情况:

  • 蛋白-连接体相互作用 (PLI) 对于基于结构的药物设计至关重要.
  • 基于机器学习的评分函数 (MLSFs) 提供了一种有前途的方法来分析PLI.
  • 现有的方法需要强大的平台来实现自动化的PLI表征和MLSF生成.

研究的目的:

  • 推出ML-PLIC,这是一个用于自动化蛋白质 - 连接体相互作用 (PLI) 表征的新型网络平台.
  • 为虚拟选 (VS) 提供基于机器学习的评分函数 (MLSFs) 的生成.
  • 通过识别潜在的蛋白质结合剂,促进基于结构的药物设计.

主要方法:

  • ML-PLIC集成了五个模块:对接,描述器,建模,选和管道.
  • 该平台自动生成PLI的物理和生物化学表示.
  • 对于后续的VS,MLSF使用这些描述符进行训练.
  • 在基准数据集和涉及氨酸/氨酸蛋白激酶WEE1.1的案例研究上进行了验证.

主要成果:

  • 与传统的对接工具相比,ML-PLIC生成的MLSF显示出更高的准确性.
  • 该平台实现了与基于深度学习的评分函数相比的竞争性表现.
  • 一个成功的案例研究强调了ML-PLIC在为WEE1激酶开发MLSF中的实用性.

结论:

  • ML-PLIC提供了一个强大的,集成的平台,用于PLI表征和MLSF生成.
  • 该平台增强了基于结构的虚拟选管道的设计.
  • ML-PLIC是免费可用的,促进药物发现和设计的进步.