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

14.5K
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...
14.5K

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

Leveraging an Explainable Machine Learning Model for Early Identification of Acute Kidney Injury: A Retrospective Study.

The journal of applied laboratory medicine·2026
Same author

Proteomic signatures as biomarkers of atherosclerosis burden.

Cardiovascular research·2026
Same author

From mechanism to substratome: unraveling mysteries of γ-secretase.

The Journal of biological chemistry·2026
Same author

Clinical Validation and Comparative Study Between the KDIGO 2012 AKI Criteria and the AACC Guidance Document 2020.

Indian journal of clinical biochemistry : IJCB·2026
Same author

Urine Proteins Stratify Patient Symptom Severity Phenotypes in Urologic Chronic Pelvic Pain Syndrome: A Multidisciplinary Approach to the Study of Chronic Pelvic Pain Research Network Study.

Urology·2026
Same author

Integrated transcriptomic and proteomic analyses identify novel biomarkers of bladder outlet obstruction.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: May 5, 2026

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

15.6K

BLMPred:使用预训练的蛋白质语言模型和机器学习预测线性B细胞表位.

Barnali Das1, Dmitrij Frishman1

  • 1Department of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.

Computational and structural biotechnology journal
|January 15, 2026
PubMed
概括
此摘要是机器生成的。

BLMPred使用蛋白质语言模型准确地识别了线性B细胞表位. 这种计算工具有助于开发诊断和疫苗,通过从主要蛋白质序列预测抗体表位.

关键词:
B细胞的表观特征嵌入器 嵌入器 嵌入器机器学习是机器学习.每个蛋白质的嵌入物.蛋白质语言模型的模型

更多相关视频

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

10.0K
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

2.5K

相关实验视频

Last Updated: May 5, 2026

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

15.6K
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

10.0K
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

2.5K

科学领域:

  • 免疫学 免疫学 免疫学
  • 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:

  • B细胞表位是免疫反应的关键,对诊断,疫苗和免疫治疗至关重要.
  • 实验性表位标识是昂贵和缓慢的,需要高效的计算方法.

研究的目的:

  • 介绍BLMPred,这是一种用于预测线性B细胞表位的新型计算工具.
  • 为了利用蛋白质语言模型嵌入,在没有3D结构的情况下进行表位预测.

主要方法:

  • BLMPred采用基于序列的方法,使用预训练的蛋白质语言模型嵌入.
  • 它作为二进制分类器来预测序列中的表位素存在.
  • 该方法从主要的氨基酸序列中获得本地和全球蛋白质结构特征.

主要成果:

  • 与独立数据集上的现有工具相比,BLMPred表现出优越的性能.
  • 该工具仅基于序列信息准确预测线性B细胞表位.
  • 预测不需要依赖3D蛋白质结构.

结论:

  • BLMPred提供了一种快速,准确和易于使用的B细胞表位预测方法.
  • 该工具可以加速疫苗设计,抗体开发和诊断应用.
  • 在https://github.com/bdbarnalidas/BLMPred.git免费提供,BLMPred支持进一步的研究和开发.