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相关概念视频

Proteomics01:33

Proteomics

7.2K
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...
7.2K
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

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BMI-genome interactions regulate global gene expression with emphasis in brain and gut.

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相关实验视频

Updated: Jun 8, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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对于复杂的生物系统而言,人工智能赋能扰动蛋白质组学.

Liujia Qian1, Rui Sun1, Ruedi Aebersold2

  • 1School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.

Cell genomics
|November 2, 2024
PubMed
概括
此摘要是机器生成的。

扰动蛋白质组学通过测量扰动后的蛋白质变化,提供了一种全面的方法来理解生物系统. 这种方法与计算建模相结合,增强了系统生物学应用的预测能力.

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP
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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP

Published on: June 15, 2018

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相关实验视频

Last Updated: Jun 8, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP
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Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 系统生物学 系统生物学
  • 计算生物学 计算生物学

背景情况:

  • 有限的蛋白质水平扰动数据阻碍了系统生物学的采用.
  • 扰动蛋白质组学通过整合各种测量来解决这个差距.

研究的目的:

  • 介绍扰动蛋白质组学的逻辑,基本性和实用性.
  • 提出一个通用的扰动,测量,建模到预测 (PMMP) 管道.
  • 突出扰动蛋白质组学在促进生物学理解和预测建模方面的作用.

主要方法:

  • 通过各种因素 (生物,化学,物理) 扰乱生物系统.
  • 测量蛋白质变化 (表达,周转,PTM,相互作用,定位) 和表型数据.
  • 应用计算模型 (机器学习,深度学习) 进行响应预测和功能识别.

主要成果:

  • 开发一个通用的PMMP管道.
  • 从大规模扰动蛋白质原子数据构建基础模型的潜力.
  • 区分人工和自然扰乱系统的建模.

结论:

  • 扰乱蛋白质组学对于推进系统生物学至关重要.
  • 集成的数据和建模改善了对生物反应和功能的预测.
  • 这种方法有助于治疗选择,化合物设计和实验优化.