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相关概念视频

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,...
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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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Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
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Protein Complex Assembly02:41

Protein Complex Assembly

10.6K
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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相关实验视频

Updated: Jun 11, 2025

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

Published on: March 3, 2015

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蜘蛛:构建细胞类型特定的蛋白质-蛋白质相互作用网络.

Yael Kupershmidt1, Simon Kasif2, Roded Sharan1

  • 1Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.

Bioinformatics advances
|September 30, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了SPIDER,这是一个监督模型,可以比以前的方法更准确地预测细胞类型特定的蛋白质-蛋白质相互作用 (PPI). 该工具增强了对复杂细胞信号的理解,并有助于识别与疾病相关的基因.

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相关实验视频

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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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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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科学领域:

  • 计算生物学是一种计算生物学.
  • 系统生物学 系统生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白与蛋白相互作用 (PPI) 对细胞功能至关重要,但依赖于环境,在细胞类型,时间和空间上有所不同.
  • 现有的PPI检测试验在非内源条件下提供静态网络,无法捕捉动态细胞复杂性.
  • 对于计算方法来预测特定于细胞类型的PPI网络,有极大的需求.

研究的目的:

  • 开发一种新的计算方法,用于预测细胞类型特定的蛋白质-蛋白质相互作用 (PPI) 网络.
  • 通过结合实验数据来提高PPI网络预测的准确性和生物相关性.
  • 使用预测PPI网络,促进组织特异性疾病基因的识别.

主要方法:

  • 介绍了SPIDER (监督蛋白相互作用DEtectoR),一个基于注意力的图形模型.
  • 利用实验测量细胞类型特定网络来指导模型的监督训练.
  • 评估了模型在人类和小鼠实验数据上的性能.

主要成果:

  • 在预测特定于细胞类型的PPI网络方面,SPIDER显著优于现有的无监督方法.
  • 该模型展示了强大的概括能力,预测了缺乏先前实验PPI数据的组织网络.
  • 预测网络促进了特定组织疾病基因的识别.

结论:

  • 蜘蛛提供了一个强大的,数据驱动的方法来建模动态的,特定于细胞类型的蛋白质-蛋白质相互作用.
  • 该方法促进了对细胞复杂性的理解,并为疾病基因发现提供了宝贵的工具.
  • 开发的代码和数据是公开可用的,用于进一步的研究和应用.