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

Affinity and Avidity01:41

Affinity and Avidity

35.9K
Overview
35.9K
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

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

Interpretable agentic AI system with localized reasoning for radiology.

NPJ digital medicine·2026
Same author

Global Research Trends of Diabetes Mellitus and Metabolic Reprogramming: A Bibliometric and Visualization Analysis.

Journal of visualized experiments : JoVE·2026
Same author

Retained Gastric Substance Requiring Endoscopic Removal in a Patient with Prolonged Toxicity from Acute Metaldehyde Poisoning.

The Journal of emergency medicine·2026
Same author

Data-driven deformation correction in X-ray spectro-tomography with implicit neural networks.

Patterns (New York, N.Y.)·2026
Same author

Correlating Descriptors of Chiral Au Nanoparticles with Their Capability toward Electrochemical Sensing of Enantiomers.

ACS applied materials & interfaces·2026
Same author

Role of the transcription factor Wor2 in biofilm formation of <i>Candidozyma auris</i>.

mSphere·2026

相关实验视频

Updated: Jun 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K

MVSF-AB:通过多视图序列特征学习来准确预测抗体-抗原结合亲和力.

Minghui Li1, Yao Shi1, Shengqing Hu2

  • 1School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430000, China.

Bioinformatics (Oxford, England)
|October 4, 2024
PubMed
概括

准确预测抗体-抗原结合亲和力至关重要. 一种新的多视图序列特征 (MVSF-AB) 学习方法使用序列数据来改善预测,优于治疗抗体开发的现有方法.

更多相关视频

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

12.3K
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.4K

相关实验视频

Last Updated: Jun 11, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K
Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
08:51

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

12.3K
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.4K

科学领域:

  • 生物化学 生物化学
  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学

背景情况:

  • 准确预测抗体-抗原结合亲和力对于治疗性抗体开发,抗体工程和疫苗设计至关重要.
  • 传统的基于结构的机器学习方法受到大多数抗体和抗原的高成本和结构数据的不可用性所限制.
  • 现有的基于序列的方法难以预测抗体-抗原亲和力,原因是数据不平衡和模型设计不特定于抗体-抗原相互作用.

研究的目的:

  • 开发一种基于序列的准确预测方法,用于抗体-抗原结合亲和力.
  • 为了解决处理抗体-抗原相互作用和不平衡数据集的现有方法的局限性.
  • 提出一种新的多视图序列特征 (MVSF-AB) 学习方法.

主要方法:

  • 开发了MVSF-AB,这是一种多视图学习方法,集成了序列数据中的语义和残留特征.
  • 专门为抗体-抗原相互作用设计模型框架,以捕获关键特征.
  • 利用序列信息来预测结合亲和力,而不依赖结构数据.

主要成果:

  • MVSF-AB有效地融合了多视图序列特征,用于增强抗体-抗原亲和力预测.
  • 与预测天然抗体-抗原亲和力的现有方法相比,提出的方法显示出更高的性能.
  • 即使在处理抗体突变时,MVSF-AB也保持了预测准确度.

结论:

  • MVSF-AB提供了一个强大而准确的基于序列的解决方案,用于预测抗体-抗原结合亲和力.
  • 该方法对加速治疗性抗体的发现和工程具有重大意义.
  • 该方法克服了结构数据依赖的局限性,并改进了现有的基于序列的预测模型.