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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
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...
12.6K
Protein Organization01:24

Protein Organization

6.0K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.0K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.7K
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:
12.7K

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相关实验视频

Updated: May 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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基于结构预测蛋白质-蛋白质结合亲和力的PCANN计划:与其他神经网络预测器的比较

Olga O Lebedenko1, Mikhail S Polovinkin1, Anastasiia A Kazovskaia1,2

  • 1Laboratory of Biomolecular NMR, St. Petersburg State University, St. Petersburg, Russia.

Proteins
|March 21, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了PCANN,这是一个新的AI工具,用于使用神经网络预测蛋白质与蛋白质的结合亲和力. PCANN的性能优于现有的方法,为了解蛋白质相互作用提供了更准确的方法.

关键词:
语言模型ESM-2语言模型在Kd的预测程序.对Kd预测器进行比较.深度学习是一种深度学习.图表注意力网络 图表注意力网络蛋白质结合数据库 蛋白质结合数据库蛋白质蛋白质结合 蛋白质结合

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 人工智能在药物发现中的作用

背景情况:

  • 准确预测蛋白质-蛋白质结合亲和力对于理解生物过程和开发治疗方法至关重要.
  • 现有的计算预测器由于数据稀缺和准确性问题而面临限制.

研究的目的:

  • 介绍PCANN,一种基于蛋白质-蛋白质复合体亲和力的基于结构的新型预测器.
  • 根据现有的最先进的方法评估PCANN的表现.

主要方法:

  • 利用ESM-2语言模型编码蛋白质结合接口信息.
  • 使用图表注意力网络 (GAT) 进行亲和力预测.
  • 在两个新的文献提取数据集上接受PCANN的培训和测试.

主要成果:

  • 与公开可用的预测器BindPPI相比,PCANN表现优越.
  • 获得了1.3kcal/mol的平均绝对误差 (MAE),表现优于BindPPI的1.4kcal/mol.

结论:

  • PCANN代表了蛋白质复合体基于结构的亲和力预测的重大进步.
  • 通过人工智能杆的文献搜索和人类策划来解决数据限制,可以进一步改进基于深度学习的预测器.