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

Antibody Structure

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

Analysis of monoclonal antibodies against the malaria invasion complex protein RIPR reveals the structural basis for synergistic antibody protection.

Immunity·2026

Same author

ANARCII enables alignment-free antigen receptor numbering using a generalised language model.

Communications biology·2026

Same author

Machine learning in AIRR diagnostics: Advances and applications.

Immunoinformatics (Amsterdam, Netherlands)·2026

Same author

Engineering of acidic pH-responsive anti-CD3 binding antibodies.

mAbs·2026

Same author

iNOS modulates inflammatory responses in an NO-independent manner through direct interaction with IRG1 in mitochondria.

Nature metabolism·2026

Same author

Rational discovery of therapeutic PAK1 allosteric activators.

Cell·2026

Same journal

Discovery and optimization of a pH-responsive ultra-long-acting VHH-based growth hormone mimetic.

mAbs·2026

Same journal

Efficient inference of non-polyreactive antibody variants dependent on local fine-tuning.

mAbs·2026

Same journal

Impact of ASO conjugation and receptor binding affinity on intracellular transport of mono- and bispecific TfR- and CD98-Brainshuttle<sup>TM</sup> variants.

mAbs·2026

Same journal

Development of single-chain C1q affinity chromatography-mass spectrometry for the glycoform-resolved characterization of low-affinity immunoglobulin G interactions.

mAbs·2026

Same journal

Biparatopic targeting of IL-23 enables dual-epitope engagement and enhanced neutralization potency.

mAbs·2026

Same journal

Comparison of knockdown approaches for the generation of stable cell populations expressing afucosylated antibodies.

mAbs·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 18, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation.

Jinwoo Leem¹, James Dunbar^1,2, Guy Georges²

¹a Department of Statistics , University of Oxford , Oxford , UK.

|July 9, 2016

Summary

This summary is machine-generated.

ABodyBuilder is a new computational tool for modeling antibody structures, including nanobodies. It provides model accuracy estimates and flags potential development issues, aiding therapeutic antibody design.

Keywords:

Antibody modeling model quality assessment modeling nanobody protein structure prediction

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

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

Related Experiment Videos

Last Updated: Mar 18, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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

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

Area of Science:

Structural biology
Computational chemistry
Immunology

Background:

Therapeutic antibody design relies heavily on computational modeling.
Existing antibody modeling pipelines lack support for nanobodies and accuracy estimations.

Purpose of the Study:

To introduce ABodyBuilder, an automated pipeline for antibody modeling.
To address limitations of current methods by modeling nanobodies, estimating accuracy, and identifying development liabilities.

Main Methods:

ABodyBuilder employs a four-step algorithm: template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction.
The pipeline annotates model confidence and flags structural motifs associated with experimental development issues.

Main Results:

ABodyBuilder generates models comparable in quality to existing methods, with high accuracy for canonical CDR loops.
The tool successfully models nanobodies and produces models rapidly (approximately 30 seconds per model).

Conclusions:

ABodyBuilder enhances therapeutic antibody design by providing confidence estimates and identifying potential liabilities.
Its ability to model nanobodies and rapid generation capabilities make it a valuable tool for researchers.