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

Antibody Structure01:10

Antibody Structure

65.2K
Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
65.2K
Antibody Structure and Classes01:25

Antibody Structure and Classes

8.2K
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
8.2K
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

14.4K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
14.4K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

481
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
481

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

Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.

Journal of computer-aided molecular design·2026
Same author

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026
Same author

MuloAD: A Multiomics Integration Model Utilizing Graph Convolutional Networks for Alzheimer's Disease Diagnosis and Biomarker Identification.

The European journal of neuroscience·2026
Same author

DeepMoDRP: A Multi-Omics-Based Deep Learning Framework for Drug Response Prediction in Brain Cancer.

Molecular informatics·2026
Same author

DeepMCL-DTI: predicting drug-target interactions using multi-channel deep learning with attention mechanism.

Molecular diversity·2025
Same author

MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions.

Interdisciplinary sciences, computational life sciences·2025

相关实验视频

Updated: Jan 12, 2026

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

12.9K

AbEgDiffuser:抗体序列结构编码设计与等价图神经网络和扩散模型.

Yibo Zhu1, Xiumin Shi1, Jingjuan Zhang1

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.

Journal of chemical theory and computation
|October 30, 2025
PubMed
概括

AbEgDiffuser是一个深度生成框架,为药物发现设计了抗体序列和结构. 这种人工智能模型产生具有高结合亲和力的精确抗体,优于传统方法.

更多相关视频

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K
Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray
09:05

Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray

Published on: January 6, 2016

20.6K

相关实验视频

Last Updated: Jan 12, 2026

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

12.9K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K
Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray
09:05

Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray

Published on: January 6, 2016

20.6K

科学领域:

  • 生物技术是生物技术.
  • 免疫学 免疫学 免疫学
  • 人工智能在药物发现中的作用

背景情况:

  • 抗体是具有高特异性的重要免疫蛋白.
  • 传统的抗体工程是低效和耗时的.
  • 深度学习为抗体设计和药物发现提供了一种创新的方法.

研究的目的:

  • 引入AbEgDiffuser,一个用于抗体序列和结构代码设计的深度生成框架.
  • 为了使对特定向抗原有条件的抗体设计.
  • 提高治疗应用抗体开发的效率和准确性.

主要方法:

  • 扩散模型与等价图神经网络的集成.
  • 使用ESM-2蛋白语言模型将进化约束纳入.
  • 逐渐腐蚀和重建抗体序列,Cα原子坐标和残留方向.

主要成果:

  • AbEgDiffuser成功地产生了具有精确序列和结构的抗体.
  • 设计的抗体对目标抗原具有很高的结合亲和力.
  • 该框架的性能优于现有的新型抗体设计和优化方法.

结论:

  • AbEgDiffuser为抗体设计提供了一个强大的AI驱动的解决方案.
  • 该模型通过提高抗体工程效率来增强新疗法的开发.
  • 这项工作推进了用于药物发现的计算抗体设计领域.