Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
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

Related Experiment Video

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: Antibody Sequence-Structure Codesign with Equivariant Graph Neural Networks and Diffusion Models.

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
Summary

AbEgDiffuser, a deep generative framework, codesigns antibody sequences and structures for drug discovery. This AI model generates accurate antibodies with high binding affinity, outperforming traditional methods.

More Related Videos

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

Related Experiment Videos

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

Area of Science:

  • Biotechnology
  • Immunology
  • Artificial Intelligence in Drug Discovery

Background:

  • Antibodies are vital immune proteins with high specificity.
  • Conventional antibody engineering is inefficient and time-consuming.
  • Deep learning presents an innovative approach for antibody design and drug discovery.

Purpose of the Study:

  • To introduce AbEgDiffuser, a deep generative framework for antibody sequence and structure codesign.
  • To enable antibody design conditioned on specific target antigens.
  • To improve the efficiency and accuracy of antibody development for therapeutic applications.

Main Methods:

  • Integration of diffusion models with equivariant graph neural networks.
  • Incorporation of evolutionary constraints using the ESM-2 protein language model.
  • Progressive corruption and reconstruction of antibody sequences, Cα atom coordinates, and residue orientations.

Main Results:

  • AbEgDiffuser successfully generates antibodies with accurate sequences and structures.
  • The designed antibodies exhibit high binding affinity to target antigens.
  • The framework outperforms existing de novo antibody design and optimization methods.

Conclusions:

  • AbEgDiffuser offers a powerful AI-driven solution for antibody design.
  • The model enhances the development of novel therapeutics by improving antibody engineering efficiency.
  • This work advances the field of computational antibody design for drug discovery.