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相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.5K
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...
6.5K
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.7K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.7K
Peptide Bonds02:43

Peptide Bonds

74.6K
A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
74.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K

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相关实验视频

Updated: Jul 12, 2025

Bacterial Peptide Display for the Selection of Novel Biotinylating Enzymes
10:43

Bacterial Peptide Display for the Selection of Novel Biotinylating Enzymes

Published on: October 3, 2019

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TPpred-LE:基于标签嵌入的治疗功能预测.

Hongwu Lv1, Ke Yan1, Bin Liu2,3

  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.

BMC biology
|October 31, 2023
PubMed
概括

这项研究介绍了TPpred-LE,这是一种通过利用功能相关性来预测治疗性功能的新方法. 它还包含多标签分类器再培训 (MCRT),以有效地识别有限数据的新功能.

关键词:
多个标签分类的分类.多标签分类器重新训练关系信息 关系信息治疗性质预测的预测

更多相关视频

Peptide-based Identification of Functional Motifs and their Binding Partners
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Peptide-based Identification of Functional Motifs and their Binding Partners

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A Tripeptide-Stabilized Nanoemulsion of Oleic Acid
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A Tripeptide-Stabilized Nanoemulsion of Oleic Acid

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相关实验视频

Last Updated: Jul 12, 2025

Bacterial Peptide Display for the Selection of Novel Biotinylating Enzymes
10:43

Bacterial Peptide Display for the Selection of Novel Biotinylating Enzymes

Published on: October 3, 2019

5.8K
Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

12.5K
A Tripeptide-Stabilized Nanoemulsion of Oleic Acid
10:42

A Tripeptide-Stabilized Nanoemulsion of Oleic Acid

Published on: February 27, 2019

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科学领域:

  • 生物化学和生物信息学
  • 药物发现和开发 药物发现和开发

背景情况:

  • 治疗性在生理学,治疗和生物制药方面至关重要.
  • 预测函数的现有计算方法缺乏明确利用功能间关系.
  • 对于先进的计算方法的需求是由新的功能和有限的标记数据的发现所驱动的.

研究的目的:

  • 提出一种新的计算方法,用于预测治疗性的多重功能.
  • 为了明确提取函数相关性信息,并利用函数特定的分类器.
  • 开发一种策略,以有限的标记数据检测新的治疗性功能.

主要方法:

  • 开发了一种基于变压器框架的新方法TPpred-LE.
  • 使用标签嵌入方法来提取函数相关信息.
  • 功能特定的分类器和多标签分类器再培训 (MCRT) 被纳入.

主要成果:

  • 与现有的最先进的方法相比,TPpred-LE表现出优越的性能.
  • 集成的MCRT与TPpred-LE证明了强大的预测功能与有限的标记数据.
  • 这项研究强调了功能间关系在治疗性功能预测中的重要性.

结论:

  • TPpred-LE是一个有效的功能特定分类器,用于准确的治疗功能预测.
  • 利用函数之间的关系信息对于提高预测准确性至关重要.
  • MCRT提供了一种简单而有效的方法,用于用有限的标记数据进行函数检测.