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相关概念视频

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 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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Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
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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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相关实验视频

Updated: Jun 8, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
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Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

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基于多任务协作训练的蛋白质多标签亚细胞定位和功能预测的深度学习模型.

Peihao Bai1, Guanghui Li1, Jiawei Luo2

  • 1School of Information and Software Engineering, East China Jiaotong University, No. 808 Shuanggang East Road, Nanchang 330013, China.

Briefings in bioinformatics
|November 3, 2024
PubMed
概括
此摘要是机器生成的。

DeepMTC是一种新的深度学习模型,可以准确地预测蛋白质细胞下定位和功能,而不需要依赖基因本体学数据库. 这一进步有助于了解疾病机制,并为新发现的蛋白质发现药物.

关键词:
图形变压器 图形变压器多任务协作培训多任务协作培训预先训练的语言模型语言模型.蛋白质功能的预测和预测.亚细胞局部化的局部化

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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.
  • 在蛋白质科学中的深度学习应用.

背景情况:

  • 蛋白质功能性研究对于理解病变发生,药物开发和目标发现至关重要.
  • 目前用于细胞下定位的计算模型存在局限性,包括对基因本体学 (GO) 数据库的依赖以及忽视GO-蛋白定位关系.

研究的目的:

  • 开发一个先进的计算模型,DeepMTC,用于准确的蛋白质细胞下定位和功能预测.
  • 通过整合蛋白质功能和局部化而克服现有模型的局限性,而不依赖已知的GO注释.
  • 为了能够预测新发现的缺乏先前功能数据的蛋白质.

主要方法:

  • 开发了DeepMTC,这是一个端到端的深度学习多任务协作培训模型.
  • 利用预训练的语言模型提取蛋白质的3D结构和序列特征.
  • 采用图形变压器模块来编码蛋白质序列特征并解决远程依赖.
  • 集成了一个功能性交叉注意力机制,以结合学习的功能特征以进行亚细胞局部化.

主要成果:

  • 与最先进的模型相比,DeepMTC在蛋白质功能预测和亚细胞局部化方面都表现出优异的性能.
  • 该模型成功地预测了蛋白质的亚细胞定位和功能,而没有先前的GO注释.
  • 可解释性实验证实了DeepMTC识别关键蛋白质残留物和功能域的能力.

结论:

  • DeepMTC为蛋白质功能研究提供了一种强大而通用的工具,特别是用于新型蛋白质.
  • 该模型能够整合功能和本地化预测的能力增强了生物洞察力.
  • 开发的方法通过减少对广泛的注释数据库的依赖来推进计算生物学.