Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Threonine Phosphorylation Is a Bioreversible Surrogate of Proline Hydroxylation in a Collagen Triple Helix.

Journal of the American Chemical Society·2026
Same author

Reductive Methylation: An Alternative to Lysine → Arginine Mutagenesis.

Journal of peptide science : an official publication of the European Peptide Society·2026
Same author

A peptide catalyst can replace an essential enzyme in a eukaryotic cell.

bioRxiv : the preprint server for biology·2026
Same author

Nuclear Localization Signals Enable the Cellular Delivery of an Anti-CRISPR Protein to Control Genome Editing.

bioRxiv : the preprint server for biology·2025
Same author

Functional Group Compatibility of the Oxidation-Resistant Benzoxaborolone Pharmacophore.

The Journal of organic chemistry·2025
Same author

Anfinsen Redux: Ribonuclease Folding in the Single-Molecule Regime.

Journal of the American Chemical Society·2025

相关实验视频

Updated: Jun 10, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K

Sitetack:一个深度学习模型,通过使用已知的PTM来改进PTM预测.

Clair S Gutierrez1,2, Alia A Kassim1, Benjamin D Gutierrez3

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.

Bioinformatics (Oxford, England)
|October 10, 2024
PubMed
概括

将已知的翻译后修改 (PTM) 站点纳入深度学习模型可以显著提高PTM预测的准确性. 这种方法提高了其他PTM的可预测性,突出了它们在蛋白质调节中的关键作用.

更多相关视频

Utilizing a Comprehensive Immunoprecipitation Enrichment System to Identify an Endogenous Post-translational Modification Profile for Target Proteins
08:12

Utilizing a Comprehensive Immunoprecipitation Enrichment System to Identify an Endogenous Post-translational Modification Profile for Target Proteins

Published on: January 8, 2018

11.2K
A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.1K

相关实验视频

Last Updated: Jun 10, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K
Utilizing a Comprehensive Immunoprecipitation Enrichment System to Identify an Endogenous Post-translational Modification Profile for Target Proteins
08:12

Utilizing a Comprehensive Immunoprecipitation Enrichment System to Identify an Endogenous Post-translational Modification Profile for Target Proteins

Published on: January 8, 2018

11.2K
A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

9.1K

科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 翻译后修改 (PTM) 对蛋白质组多样性至关重要,并具有治疗意义.
  • 深度学习模型越来越多地用于预测PTM网站.
  • 目前的预测模型面临由于数据集限制和分析方法的限制.

研究的目的:

  • 评估已知的PTM站点对基于序列的深度学习算法的PTM预测准确性的影响.
  • 调查一个PTM的位置是否可以提高其他PTM的预测.

主要方法:

  • 使用卷积神经网络开发了基于序列的深度学习模型.
  • 将已知的PTM位置编码为序列内的不同氨基酸.
  • 使用词嵌入用于序列表示.
  • 与已知PTM站点标记和不标记的模型性能进行比较.

主要成果:

  • 模型的性能与没有标记PTM数据的现有方法相当.
  • 标记已知的PTM地点导致对现有模型的显著改进.
  • 了解PTM位置提高了其他PTM的可预测性.

结论:

  • 包括已知的PTM位置在内,大大提高了用于PTM预测的深度学习模型的性能.
  • 在随后发生的PTM中,PTM起着至关重要的作用.
  • 这种方法预计将提高各种蛋白质学机器学习算法的性能.