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相关概念视频

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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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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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Tagging and Fusion Proteins01:24

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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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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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相关实验视频

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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

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标注蛋白质通过融合多种生物模式来起作用.

Wenjian Ma1, Xiangpeng Bi1, Huasen Jiang1

  • 1College of Computer Science and Technology, Ocean University of China, Qingdao, China.

Communications biology
|December 28, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了MIF2GO,这是一种新的深度学习方法,可以有效地融合多种生物数据类型,以改善蛋白质功能注释. 这种方法提高了对疾病的理解,并有助于发现新的治疗点.

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相关实验视频

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科学领域:

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

背景情况:

  • 蛋白质功能注释对于了解疾病机制和确定治疗点至关重要.
  • 目前的深度学习方法难以整合多样化的生物数据,导致蛋白质表征不足,功能预测不准确.
  • 现有模型中的稀疏标签表示阻碍了对最佳解决方案的趋同.

研究的目的:

  • 为精确的蛋白质功能注释开发一个强大的框架,用于融合异质的生物模式.
  • 解决现有的深度学习方法在多式联运数据集成和稀疏标签表示方面的局限性.
  • 提高蛋白质表示的质量,以获得更好的生物洞察力.

主要方法:

  • 拟议的MIF2GO (多模式信息融合以推断基因本体学术语),一个连续的,多步骤的方法来融合多达六种生物模式.
  • 实现了一种新的深度学习架构,旨在有效整合各种生物数据类型.
  • 在不同物种的七个基准数据集上验证了该方法.

主要成果:

  • 在蛋白质功能注释方面,MIF2GO显著超过了最先进的方法.
  • 该方法在各种物种中表现出高度的稳定性和通用性.
  • 通过多式生物数据的有效融合,实现了强大的蛋白质表示.

结论:

  • MIF2GO提供了一个可扩展和强大的框架,用于整合用于蛋白质功能注释的多模式生物数据.
  • 这些发现有助于精准医学的进步和发现新的治疗策略.
  • 提供了宝贵的生物学见解,了解不同生物模式和蛋白质功能之间的关联.