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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

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
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
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.9K
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...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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相关实验视频

Updated: Jul 12, 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

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使用转移学习的蛋白质-蛋白质相互作用和位点预测.

Tuoyu Liu1, Han Gao2, Xiaopu Ren1

  • 1Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

Briefings in bioinformatics
|October 23, 2023
PubMed
概括

这项研究引入了MP-BERT,一种用于识别蛋白质与蛋白质相互作用 (PPI) 和相互作用部位的新模型. 该模型显示了优异的性能和跨生物体的概括性,有助于蛋白质研究.

关键词:
贝尔特 (BERT) 公司在 PPI 站点上.蛋白质蛋白质相互作用转移学习转移学习变压器变压器变压器变压器

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A Protocol for Computer-Based Protein Structure and Function Prediction
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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.
  • 在蛋白质组学中的机器学习.

背景情况:

  • 先进的语言模型越来越多地用于生物序列分析.
  • 蛋白与蛋白相互作用 (PPI) 对细胞功能至关重要.
  • 识别PPI及其相互作用部位对于理解生物过程至关重要.

研究的目的:

  • 开发和评估一种新的深度学习模型,用于预测蛋白质-蛋白质相互作用 (PPI) 和相互作用地点.
  • 评估模型的性能和对不同生物体的概括能力.
  • 为了证明转移学习对蛋白质对任务的有效性.

主要方法:

  • 从变压器 (BERT) 模型中训练一个双向编码器表示,命名为MindSpore ProteinBERT (MP-BERT),使用蛋白质对.
  • 微调MP-BERT用于PPI预测 (MPB-PPI) 和相互作用地点预测 (MPB-PPISP).
  • 在不同的基准数据集和多个生物体上评估模型性能.

主要成果:

  • 微调的MPB-PPI模型在PPI预测中超过了最先进的方法.
  • 一个合并生物模型实现了92.65%的准确性,显示出高度的概括性.
  • MPB-PPISP模型在预测交互地点倾向方面表现出能力.

结论:

  • MP-BERT框架有效地预测了PPI及其交互地点.
  • 转移学习显著提高了蛋白质对任务的性能.
  • 开发的模型为推进蛋白相互作用研究提供了强大的工具.