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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
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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相关实验视频

Updated: Jun 12, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

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通过深度学习揭示了人类病原体中的蛋白质相互作用.

Ian R Humphreys1,2, Jing Zhang3,4,5, Minkyung Baek6

  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.

Nature microbiology
|September 18, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一个深度学习模型来预测细菌蛋白相互作用及其结构. 这种工具确定了数千种新的蛋白质复合体,有助于开发治疗传染病的方法.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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相关实验视频

Last Updated: Jun 12, 2025

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物学是结构生物学.
  • 传染病研究传染病研究.

背景情况:

  • 了解细菌蛋白与蛋白相互作用 (PPI) 对于破译致病机制和开发新疗法至关重要.
  • 目前用于识别和表征PPI的方法可能耗时且范围有限.

研究的目的:

  • 开发一个快速的,基于深度学习的计算管道,用于蛋白质组范围的识别和细菌PPI的结构性表征.
  • 为了利用残留-残留共演和蛋白质结构预测,提高PPI预测的准确性.

主要方法:

  • 开发RoseTTAFold2-Lite,这是一个集同进化数据和结构预测的深度学习模型.
  • 在19种人类细菌病原体中对7800万对蛋白质进行系统选.
  • 选择预测PPI的实验验证.

主要成果:

  • 确定了1,923个涉及必需基因的可靠预测蛋白质复合体.
  • 发现了与毒性因子相关的256个预测复合体.
  • 实验验证了12个测试的PPI预测中的6个,显示出高准确度.

结论:

  • RoseTTAFold2-Lite管道为大规模的细菌PPI发现提供了一种强大的方法.
  • 已识别的PPI为细菌病原体的基本细胞过程和毒性机制提供了洞察力.
  • 这些发现可以指导针对性抗微生物疗法的开发.