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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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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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

Updated: Jan 11, 2026

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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阶段网络:基于蛋白质语言模型的阶段分离蛋白的识别计算框架.

Xuxin He1, Jiahui Guan2, Peilin Xie1

  • 1Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172, Shenzhen, China.

International journal of biological macromolecules
|November 15, 2025
PubMed
概括
此摘要是机器生成的。

PhaseNet准确地识别和分类分相蛋白,这对于理解细胞过程和疾病至关重要. 这种计算工具增强了参与液态液态相分离 (LLPS) 的蛋白质的发现.

关键词:
深度学习是一种深度学习.组合学习学习 组合学习阶段分离蛋白质是分离阶段的蛋白质.蛋白质语言模型的模型

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相关实验视频

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科学领域:

  • 生物化学和分子生物学
  • 计算生物学 计算生物学
  • 基因组学和蛋白质组学

背景情况:

  • 液-液相分离 (LLPS) 驱动生物分子凝聚物的形成,这对于细胞功能至关重要.
  • 对LLPS的失调与各种人类疾病有关,这突显了对精确蛋白质识别的需要.
  • 鉴定相分离蛋白质对于理解凝结机制和疾病病原性至关重要.

研究的目的:

  • 开发PhaseNet,一种双重任务的计算框架,用于区分分相分离蛋白 (PSP) 和非PSP.
  • 将PSP分为自组装 (PS-Self) 和依赖合作伙伴 (PS-Part) 的类别.
  • 为系统发现和注释PSP提供一个强大的和可解释的工具.

主要方法:

  • PhaseNet集成了来自蛋白质语言模型 (ESM),序列编码 (ZSCALE,BLOSUM) 和具有多头注意力的CNN-BiGRU的功能.
  • 不同质的特征通过以注意为导向的策略被融合在一起,并通过HSIC规范化进行优化,以加强歧视.
  • 一个次要的任务是使用基于Lasso的特征选择ESM嵌入式和五个分类器 (随机森林,额外树木,GBDT,XGBoost,MLP) 的堆叠组合.

主要成果:

  • 与现有预测器相比,PhaseNet在PSP的一般识别和细粒度分类方面表现出卓越的表现.
  • 该框架有效区分LLPS蛋白与非LLPS蛋白.
  • 阶段网络准确地将LLPS蛋白分为PS-Self和PS-Part类别.

结论:

  • 阶段网络在相分离蛋白的计算预测方面取得了重大进展.
  • 模块化和可解释的设计有助于系统地发现和注释LLPS所涉及的蛋白质.
  • 该工具有助于阐明生物分子凝聚物的分子机制及其在健康和疾病中的作用.