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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,...
3.9K
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...
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相关实验视频

Updated: Jun 23, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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通过面具语言建模对配相互作用的蛋白序列.

Umberto Lupo1,2, Damiano Sgarbossa1,2, Anne-Florence Bitbol1,2

  • 1Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland.

Proceedings of the National Academy of Sciences of the United States of America
|June 24, 2024
PubMed
概括
此摘要是机器生成的。

我们使用基于对齐的语言模型 (DiffPALM) 开发了差异配对,以预测相互作用的蛋白质序列. DiffPALM利用蛋白质语言模型,优于现有的方法,改善了蛋白质复杂结构预测.

关键词:
共同进化的共同进化机器学习是机器学习.蛋白质复杂结构结构 蛋白质复杂结构蛋白质语言模型的模型蛋白质蛋白质相互作用

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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相关实验视频

Last Updated: Jun 23, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算生物学是一种计算生物学.
  • 蛋白质的生物信息学
  • 结构生物学中的机器学习

背景情况:

  • 从氨基酸序列预测蛋白质-蛋白质相互作用对于理解生物功能至关重要.
  • 现有的方法经常与浅的多重序列对齐 (MSAs) 斗争.
  • 相互作用蛋白序列的准确配对对于预测蛋白质复杂结构至关重要.

研究的目的:

  • 开发一种使用蛋白质语言模型预测相互作用蛋白质序列的新方法.
  • 以一种可差分的方式制定蛋白质配对问题.
  • 为了提高蛋白质复杂结构预测的准确性.

主要方法:

  • 开发了使用基于对齐的语言模型 (DiffPALM) 的可差配对.
  • 利用MSA转换器,一种在MSA上训练的蛋白质语言模型,来预测交互伙伴.
  • 利用了MSA变压器在链间相互作用中推断同进化信号的能力.
  • 以可分化的方式制定了来自两个蛋白质家族的对应物对的配对.

主要成果:

  • 在具有浅层MSA的具有挑战性的基准指标上,DiffPALM的表现优于现有的基于共同进化的配对方法.
  • DiffPALM超越了使用单个序列训练蛋白质语言模型的方法.
  • 使用DiffPALM配对序列显著提高了使用AlphaFold-Multimer的真核蛋白质复合体的结构预测.
  • 与基于orthology的配对相比,获得了竞争性表现.

结论:

  • DiffPALM提供了一种强大的新方法来预测相互作用蛋白序列,特别是在有限的MSA数据上.
  • 该方法证明了基于MSA的语言模型在捕捉链际共进化的实用性.
  • DiffPALM生成的序列对是基于深度学习的蛋白质结构预测工具的宝贵输入.