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相关概念视频

Protein Families02:47

Protein Families

15.3K
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...
15.3K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
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...
10.8K
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
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
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.
18.8K
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
Conservation of Protein Domains02:26

Conservation of Protein Domains

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相关实验视频

Updated: Jun 21, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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ProFun-SOM:基于多个序列对齐重建的特定本体学的蛋白质功能预测.

JiangYi Shao, JunJie Chen, Bin Liu

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    |July 9, 2024
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    概括
    此摘要是机器生成的。

    ProFun-SOM使用增强的多重序列对齐 (MSA) 和深度学习准确地注释基因本体. 这种方法克服了蛋白质功能预测的挑战,提高了进化和病毒突变研究的准确性.

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    相关实验视频

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    An Integrated Approach for Microprotein Identification and Sequence Analysis
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    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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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:

    • 蛋白质功能预测对于理解进化和病毒突变至关重要.
    • 基因本体学 (GO) 为蛋白质功能注释提供了一个标准化的框架,创造了一个层次化的多标签分类问题.
    • 由于标签依赖性和GO注释中的数据稀疏性,现有的方法面临性能瓶,特别是混合本体学问题.

    研究的目的:

    • 提出ProFun-SOM,一个创新的多标签分类器,以准确地注释基因本体学.
    • 为了克服由标签依赖和数据稀疏引起的蛋白质功能预测中的性能瓶.
    • 为了利用多重序列对齐 (MSAs) 进行增强的GO注释.

    主要方法:

    • ProFun-SOM使用通过重建过程增强的多重序列对齐 (MSA).
    • 将增强的MSA集成到深度学习架构中,用于预测.
    • 预测跨细胞组件,分子功能,生物过程和混合本体学的注释.

    主要成果:

    • 与最先进的方法相比,ProFun-SOM在CAFA3,SwissProt和NetGO2数据集上表现出更高的性能.
    • 使用MSA有效地解决了标签依赖性和数据稀疏性问题.
    • 该方法减轻了GO注释中的混合本体学问题.

    结论:

    • ProFun-SOM为准确的蛋白质功能预测提供了一个有效的解决方案.
    • 该方法成功地克服了对GO术语的层次多标签分类中的关键挑战.
    • 这项工作为推进进化生物学和病毒学研究提供了宝贵的工具.