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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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...
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Protein Networks02:26

Protein Networks

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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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Proteins: From Genes to Degradation02:11

Proteins: From Genes to Degradation

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Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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相关实验视频

Updated: Sep 13, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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PLMFit:比较转移学习与蛋白质语言模型用于蛋白质工程.

Thomas Bikias1,2, Evangelos Stamkopoulos1,2, Sai T Reddy1,2

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

Briefings in bioinformatics
|July 30, 2025
PubMed
概括
此摘要是机器生成的。

蛋白质语言模型 (PLM) 与转移学习 (TL) 结合,提供了强大的蛋白质工程工具. 我们的研究对不同的TL方法进行了基准测试,发现微调 (FT) 对于有限的数据和概括需求是最好的.

关键词:
基准测试 (benchmarking) 是一种比较的方法.参数高效的微调.蛋白质工程工程 蛋白质工程蛋白质健身 蛋白质健身是一种健身.蛋白质语言模型的模型转移学习转移学习

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相关实验视频

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科学领域:

  • 计算生物学是一种计算生物学.
  • 蛋白质工程是一种蛋白质工程.
  • 在生物信息学中的机器学习.

背景情况:

  • 蛋白质语言模型 (PLM) 对蛋白质工程非常有价值.
  • 转移学习 (TL) 通过特征提取或微调 (FT) 来提高PLM性能.
  • 缺乏比较分析阻碍了PLM的最佳TL策略选择.

研究的目的:

  • 对应用到最先进的蛋白质语言模型 (PLM) 的转移学习 (TL) 方法进行基准测试.
  • 在蛋白质工程任务中确定知识转移的最佳策略.
  • 为特定蛋白质工程应用确定最合适的TL方法.

主要方法:

  • 结合了三个最先进的PLM (ESM2,ProGen2,ProteinBert) 和三个TL方法 (特征提取,低级适应,瓶适应器).
  • 进行了>3150个in silico实验,不同的PLM大小,层次,TL超参数和培训程序.
  • 在五个不同的蛋白质工程数据集中评估了性能.

主要成果:

  • 使用TL的部分PLM不会显著影响业绩.
  • 特性提取 (FE) 和微调 (FT) 之间的选择取决于数据量和多样性.
  • 用有限的数据进行概括时,FT最有效.

结论:

  • PLMFit为PLM中的TL提供了一个全面的基准.
  • 该研究为选择蛋白质工程的最佳FE或FT策略提供了指导.
  • PLMFit是一个开源资源,旨在促进PLM在科学界的应用.