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相关概念视频

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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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,...
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Protein Families02:47

Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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...
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Protein Organization

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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....
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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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MKFGO:将多源知识融合与预训练的语言模型集成为高精度的蛋白质功能预测.

Yi-Heng Zhu1, Shuxin Zhu1, Xuan Yu2

  • 1College of Artificial Intelligence, Nanjing Agricultural University, 666 Binjiang Avenue, Jiangbei New District, Nanjing, Jiangsu Province, 211800, China.

Briefings in bioinformatics
|August 15, 2025
PubMed
概括

一种新的深度学习方法,即用于基因本体学预测的多源知识融合 (MKFGO),可以准确地识别蛋白质功能. MKFGO集成了多个数据源,以超越基因本体学属性预测中的现有方法.

关键词:
这是LSTM-注意力网络.深度学习是一种深度学习.多种来源的知识融合融合.预训练的语言模型蛋白质的功能 蛋白质的功能

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相关实验视频

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 准确的蛋白质功能识别对于理解生物机制和推动药物发现至关重要.
  • 测定蛋白质功能的实验方法往往是费力和耗时的.
  • 需要计算方法来加速蛋白质功能预测.

研究的目的:

  • 开发一种新的深度学习方法,用于准确的基因本体学 (GO) 属性预测.
  • 整合多个来源的生物数据,以增强蛋白质功能推断.
  • 改进现有的最先进的蛋白质功能预测方法.

主要方法:

  • 为基因本体学预测 (MKFGO) 提出的多源知识融合,是一种复合深度学习模型.
  • 整合了五个互补的管道,利用多个来源的生物数据.
  • 开发了两个核心深度学习组件:手工制作的基于特征表示的GO预测 (HFRGO) 和基于蛋白质大语言模型 (PLM) 的GO预测 (PLMGO).
  • 采用LSTM注意网络与三重损失和PLM用于特征提取和知识融合.

主要成果:

  • 与12种最先进的方法相比,MKFGO在1522种非冗余蛋白质上表现出更高的性能.
  • HFRGO和PLMGO的组合显著促进了MKFGO的准确性.
  • 从蛋白质-蛋白质相互作用,GO术语概率和基因序列的补充见解进一步增强了预测.

结论:

  • 使用深度学习,MKFGO提供了一种强大而准确的方法来预测蛋白质功能.
  • 该方法的优势在于其多源数据集成和决策层面的知识融合.
  • 开发的模型和源代码是公开可用的,用于研究.