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

B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

1.2K
The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
When naive B cells encounter a specific antigen that can bind to the B cell receptor (BCR) on their surface, they undergo sensitization to respond to the antigen's presence. Sensitization begins with...
1.2K

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

Machine learning framework to extract physicochemical features of B-cell epitopes recognized by a cross-reactive antibody.

NPJ systems biology and applications·2025
Same author

Integrating machine learning to advance epitope mapping.

Frontiers in immunology·2024
Same author

A genetic screen to uncover mechanisms underlying lipid transfer protein function at membrane contact sites.

Life science alliance·2024
Same author

A conserved epitope in VAR2CSA is targeted by a cross-reactive antibody originating from <i>Plasmodium vivax</i> Duffy binding protein.

Frontiers in cellular and infection microbiology·2023
Same author

Pediatric Malaria with Respiratory Distress: Prognostic Significance of Point-of-Care Lactate.

Microorganisms·2023
Same author

MOF-derived Co/Cu-embedded N-doped carbon for trifunctional ORR/OER/HER catalysis in alkaline media.

Dalton transactions (Cambridge, England : 2003)·2021

相关实验视频

Updated: May 12, 2025

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

9.3K

一个机器学习框架来识别B细胞表位的复杂的物理化学特征.

Simranjit Grewal1, Uwa Iyamu1, Daniel Vinals1

  • 1University of Alberta.

Research square
|May 5, 2025
PubMed
概括

通过分析抗体结合,一种机器学习方法通过分析疟疾寄生虫关键蛋白质VAR2CSA的保存表位. 这种方法有助于开发针对胎盘疟疾的广泛保护性疫苗.

科学领域:

  • 寄生虫学的寄生虫学
  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学

背景情况:

  • 怀孕期间的*Plasmodium falciparum*感染涉及病毒性因子VAR2CSA,介导受感染的红细胞粘附于胎盘.
  • 开发针对胎盘疟疾的疫苗需要针对VAR2CSA的抗体,但其高多态性对抗菌株超越免疫构成挑战.
  • 一个单克隆抗体 (3D10),对*P. vivax*达菲结合蛋白 (DBPII) 产生,意外地与各种VAR2CSA等位基因发生交叉反应.

研究的目的:

  • 通过交叉反应抗体3D10.10识别VAR2CSA上的保存表位.
  • 开发和应用一种机器学习框架,用于分析抗体-表皮质相互作用和预测保存区域.
  • 探索多活性抗体在理解和向多态寄生虫抗原方面的潜力.

主要方法:

  • 使用决策树和430个特征的机器学习框架被开发出来,用于分析3D10抗体对VAR2CSA等位基因,DBPII和PvEBP2.2的反应性.
  • 用配列来评估抗体结合,然后对特征模式的分析和突变的设计来测试序列动机.
  • 识别的特征被映射到预测的3D蛋白质结构上,并使用对重组抗原的反应性进行验证.

主要成果:

  • 机器学习框架成功识别了与3D10与VAR2CSA结合相关的特征.

更多相关视频

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.8K
Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

17.9K

相关实验视频

Last Updated: May 12, 2025

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

9.3K
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

14.8K
Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells
13:58

Peptide:MHC Tetramer-based Enrichment of Epitope-specific T cells

Published on: October 22, 2012

17.9K
  • 在VAR2CSA上预测了线性和构造性表位,构造性表位是这种方法的一个新发现.
  • 该研究表明,可以挖掘基阵列数据,以检测多活性抗体识别的表位体的物理化学性质.
  • 结论:

    • 机器学习框架可以有效地挖掘基阵列数据,以识别像VAR2CSA这样的多态抗原上的保守线性和构造性表位.
    • 这种方法有助于了解交叉反应性抗体的结合,并可以指导设计更有效的针对胎盘疟疾的疫苗.
    • 这些发现突显了计算方法在表位发现中的实用性,用于开发针对复杂病原体的疫苗.