Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Antibody Structure

Antibody Structure

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...

Antibody Structure and Classes

Antibody Structure and Classes

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.

You might also read

Related Articles

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

Sort by

Same author

Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.

Proteins·2026

Same author

Overcoming structural complexity in Galectin-3BP through an integrative computational antibody design workflow.

Computers in biology and medicine·2026

Same author

Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design.

Journal of chemical information and modeling·2026

Same author

Lithium Chloride rescues Monensin-potentiated Wnt signaling inhibition in inflammatory bone loss in rats.

Brazilian dental journal·2025

Same author

In vitro morphological profiling of T cells predicts clinical response to natalizumab therapy in patients with multiple sclerosis.

Nature communications·2025

Same author

Molecular and Immunological Properties of a Chimeric Glycosyl Hydrolase 18 Based on Immunoinformatics Approaches: A Design of a New Anti-<i>Leishmania</i> Vaccine.

ACS pharmacology & translational science·2025

Search research articles

Related Experiment Video

Updated: May 13, 2025

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

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

Published on: March 15, 2019

AbSet: A Standardized Data Set of Antibody Structures for Machine Learning Applications.

Diego S Almeida^1,2, Matheus V Almeida¹, Jean V Sampaio^1,2

¹Laboratory of Structural and Functional Biology Applied to Biopharmaceuticals, Fundação Oswaldo Cruz, Fiocruz Ceará, Eusébio 61773-270, Brazil.

Journal of Chemical Information and Modeling

|May 11, 2025

Summary

A new dataset, AbSet, offers over 800,000 antibody structures and molecular descriptors to improve machine learning models for therapeutic antibody development. This resource addresses limitations in existing structural data for antibody-antigen complexes.

More Related Videos

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Related Experiment Videos

Last Updated: May 13, 2025

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

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

Published on: March 15, 2019

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Area of Science:

Biochemistry
Structural Biology
Computational Biology

Background:

Machine learning (ML) is crucial for therapeutic antibody development, relying on sequence and structural data.
Existing structural datasets are limited, particularly for antibody-antigen complexes, and often lack standardization and essential molecular descriptors.
This data scarcity hinders the development of robust ML models for antibody design and engineering.

Purpose of the Study:

To introduce AbSet, a comprehensive and curated dataset of antibody structures and molecular descriptors.
To address the limitations of existing structural data for machine learning applications in therapeutic antibody development.
To provide a valuable resource for researchers aiming to improve antibody design and predict antibody-antigen interactions.

Main Methods:

Systematic retrieval of antibody structures from the Protein Data Bank (PDB).
Application of rigorous standardization protocols to ensure data consistency.
Expansion of the dataset through large-scale protein-protein docking to generate in silico antibody-antigen complexes.
Quality classification of generated models based on structural similarity to experimental data.

Main Results:

Creation of AbSet, a dataset containing over 800,000 antibody structures and molecular descriptors.
Inclusion of both experimentally determined and computationally generated antibody-antigen complexes.
Development of a quality assessment system for structural models, enabling the creation of decoy sets and high-confidence data.
Public availability of the AbSet dataset and associated scripts.

Conclusions:

AbSet provides a standardized, high-quality, and expanded resource for machine learning in therapeutic antibody development.
The dataset facilitates the creation of improved ML models by offering diverse structural data and molecular descriptors.
AbSet supports the advancement of antibody engineering and the prediction of antibody-antigen interactions.