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相关概念视频

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
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
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

6.8K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
6.8K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.5K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.5K
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

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相关实验视频

Updated: Jun 29, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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一个层次图神经网络框架用于预测蛋白质-蛋白质相互作用调节器与功能组信息和超图结构.

Zitong Zhang, Lingling Zhao, Junjie Wang

    IEEE journal of biomedical and health informatics
    |April 2, 2024
    PubMed
    概括

    预测蛋白质与蛋白质相互作用的小分子调节器 (PPIM) 是一个挑战. 一个新的层次图形神经网络 (HiGPPIM) 集成了原子和功能组特征,在PPIM识别和强度预测方面取得了最先进的结果.

    科学领域:

    • 计算化学是一种计算化学.
    • 药物发现 药物发现
    • 机器学习 机器学习

    背景情况:

    • 预测蛋白质与蛋白质相互作用的小分子调节器 (PPIM) 是至关重要的,但具有挑战性.
    • 当前的机器学习模型需要广泛的手动功能工程.
    • 深度学习,特别是图形神经网络,显示出希望,但往往忽视了分子层次和领域知识.

    研究的目的:

    • 开发一种新的层次图神经网络框架 (HiGPPIM),用于改进PPIM预测.
    • 以化学知识为指导,整合原子层面和功能组层面的分子特征.
    • 加强分子表示学习,用于PPIM识别和强度预测.

    主要方法:

    • 基于化学原理构建了原子级和功能组级图形.
    • 使用图表注意力网络,从这些图表中学习表征.
    • 利用超图注意力网络汇总和转换两级图信息.

    主要成果:

    • 在PPIM识别和强度预测任务中,HiGPPIM实现了最先进的性能.
    • 在八个蛋白质与蛋白质相互作用 (PPI) 家族中进行了评估,证明了强大的预测能力.
    • 证实了将功能组信息用于指导PPIM预测的有效性.

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    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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    结论:

    • 拟议的HiGPPIM框架有效地利用层次分子结构来进行PPIM预测.
    • 整合功能组信息显著提高了PPIM识别和强度预测的准确性.
    • HiGPPIM提供了一种强大的,以知识为导向的深度学习方法,以加速药物发现工作.