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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.1K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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相关实验视频

Updated: Sep 11, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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EZpred:改进基于深度学习的酶功能预测,使用未标记的序列同类物.

Chengxin Zhang1,2, Quancheng Liu2, Lydia Freddolino1,3

  • 1CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

bioRxiv : the preprint server for biology
|August 12, 2025
PubMed
概括
此摘要是机器生成的。

EZpred是一种新的深度学习模型,使用未标记的序列同类来改善蛋白质功能预测. 这种方法提高了酶委员会数量预测的准确性,优于现有的方法.

关键词:
生物科学 生物科学生物物理和计算生物学深度学习 (Deep Learning) 是一种深度学习.酶委员会 (EC) 编号 编号 编号蛋白质功能预测的预测序列对应物 序列对应物

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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相关实验视频

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 深度学习模型擅长使用序列同类特征进行蛋白质结构预测.
  • 然而,序列同类特征在蛋白质功能预测中未得到充分利用.
  • 现有的方法很难利用未标记的序列同类来进行函数预测.

研究的目的:

  • 开发第一个深度学习模型EZpred,用于使用未标记的序列同类物进行蛋白质功能预测.
  • 为了提高酶委员会 (EC) 数字预测的准确性.

主要方法:

  • EZpred使用MMseqs2.2识别了序列同类.
  • 序列特征通过ESMC蛋白语言模型提取.
  • 一个深度学习模型使用这些特性预测EC数字.

主要成果:

  • 与没有使用序列同类模型的模型相比,EZpred获得了4%的F1得分.
  • EZpred的性能至少比最先进的EC数字预测模型高出10%.
  • 该研究表明,序列同类对酶功能预测准确性的显著影响.

结论:

  • 未标记的序列同类对基于深度学习的蛋白质功能预测有价值.
  • EZpred在预测酶委员会数字方面取得了重大进展.
  • 这些发现突出了改善蛋白质功能预测模型的新方向.