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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
The Proteasome02:18

The Proteasome

Eukaryotic cells can degrade proteins through several pathways. One of the most important amongst these is the ubiquitin-proteasome pathway. It helps the cell eliminate the misfolded, damaged, or unwarranted cytoplasmic proteins in a highly specific manner.
In this pathway, the target proteins are first tagged with small proteins called ubiquitin. A series of enzymes carry out the ubiquitination of the target proteins - E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme), and E3...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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 polypeptide...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
The Proteasome01:13

The Proteasome

Eukaryotic cells can degrade proteins through several pathways. One of the most important among these is the ubiquitin-proteasome pathway. It helps the cell eliminate the misfolded, damaged, or unwarranted cytoplasmic proteins in a highly specific manner.
In this pathway, the target proteins are first tagged with small proteins called ubiquitin. This involves participation of a series of enzymes including— E1 (ubiquitin-activating enzyme), E2 (ubiquitin-conjugating enzyme), and E3 (ubiquitin...

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相关实验视频

Updated: Jun 19, 2026

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

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蛋白MAE:用于蛋白质表面自我监督学习的蒙面自编码器.

Mingzhi Yuan1,2, Ao Shen1,2, Kexue Fu1,2

  • 1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.

Bioinformatics (Oxford, England)
|November 29, 2023
PubMed
概括
此摘要是机器生成的。

蛋白MAE是一个自我监督的框架,通过利用未标记的数据来克服标签稀缺性来增强蛋白质表面表示. 这种方法可以提高各种任务的性能,并大大降低计算成本.

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科学领域:

  • 计算生物学 计算生物学
  • 结构生物信息学 结构生物信息学
  • 机器学习 机器学习

背景情况:

  • 蛋白质功能是由表面特性决定的,对于蛋白质设计和相互作用预测等任务至关重要.
  • 目前用于蛋白质表面分析的深度学习方法受到有限的实验数据 (标签稀缺) 的阻碍.
  • 自主监督学习 (SSL) 在克服其他领域的数据限制方面表现有前途.

研究的目的:

  • 介绍ProteinMAE,一种用于蛋白质表面表示的新型自我监督框架.
  • 解决基于学习的蛋白质表面分析中标签稀缺的挑战.
  • 开发一种计算效率高的方法,用于预训练蛋白质表面模型.

主要方法:

  • 为蛋白质表面表示开发了一个高效的网络架构.
  • 利用大量未标记的蛋白质数据进行自我监督预训 (ProteinMAE).
  • 在下游任务上微调预训练模型:结合位点识别,蛋白质口袋分类和蛋白质-蛋白质相互作用预测.

主要成果:

  • 在所有评估的下游任务中,ProteinMAE显著提高了性能.
  • 与最先进的方法相比,该方法取得了具有竞争力的结果.
  • 蛋白MAE网络展示了实质性的计算优势,需要以前方法内存成本的不到10%.

结论:

  • 蛋白MAE通过自我监督学习有效地减轻了蛋白质表面表示的标签稀缺性.
  • 该框架为分析蛋白质表面提供了一种强大且计算效率高的替代方案.
  • 这种方法具有很大的潜力,可以在结构生物学和药物发现中推进各种应用.