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

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

Engineering strategies and binding mechanisms of therapeutic anti-PD-1 antibodies approved by regulatory agencies globally.

Frontiers in immunology·2026

Same author

PPIscreenML is a method for structure-based screening of protein-protein interactions using AlphaFold.

eLife·2026

Same author

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports.

Nature communications·2026

Same author

Defining the safe operational window for holmium laser lithotripsy in impacted ureteral stones: an analysis of power, operator duty cycle, and irrigation flow.

Scientific reports·2026

Same author

Affinity-maturation engineering via phage display to optimize anti-idiotype antibodies for neutralizing antibody assays.

Bioanalysis·2026

Same author

Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization.

mAbs·2026

Same journal

Engineered HSP90-MP65 Bivalent Fusion Antigen: A Novel Vaccine Candidate Against Invasive Candidiasis.

Proteins·2026

Same journal

Physics-Based Energy Functions for Computational Protein Design.

Proteins·2026

Same journal

Impact of Stabilizing Osmolytes on the Conformational Dynamics of Human and Rat Islet Amyloid Polypeptides.

Proteins·2026

Same journal

Stabilization of Bone Morphogenetic Protein-2 at Physiological pH: Contrasting Roles of CHAPS and Arginine in Aggregation Inhibition.

Proteins·2026

Same journal

Structural Insights Into the Function of Leishmania major Adenylosuccinate Lyase.

Proteins·2026

Same journal

Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.

Proteins·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Published on: June 16, 2023

Antibody modeling assessment.

Juan C Almagro¹, Mary Pat Beavers, Francisco Hernandez-Guzman

¹Centocor R&D, Inc, Radnor, Pennsylvania 19087, USA. jcalmagro@hotmail.com

|September 22, 2011

Summary

This summary is machine-generated.

This study benchmarks antibody variable region (Fv) modeling methods. Four techniques showed good agreement with crystal structures, with root mean square deviation values around 1.2 Å.

More Related Videos

Antigenic Liposomes for Generation of Disease-specific Antibodies

Antigenic Liposomes for Generation of Disease-specific Antibodies

Published on: October 25, 2018

Related Experiment Videos

Last Updated: May 29, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Published on: June 16, 2023

Antigenic Liposomes for Generation of Disease-specific Antibodies

Antigenic Liposomes for Generation of Disease-specific Antibodies

Published on: October 25, 2018

Area of Science:

Structural biology
Computational biology
Immunoinformatics

Background:

Accurate three-dimensional (3D) structure modeling of antibody variable regions (Fv) is crucial for antibody engineering and drug design.
Assessing the performance of different Fv modeling methodologies is essential for advancing the field.

Purpose of the Study:

To evaluate and compare the accuracy of four different Fv structure prediction methodologies.
To benchmark these methods against high-resolution X-ray crystal structures of antibodies.

Main Methods:

A blinded study was conducted using nine high-resolution X-ray Fab crystal structures as a benchmark.
Four Fv modeling methodologies were tested: two homology modeling strategies (CCG and Accelrys) and two automated servers (PIGS and Rosetta Antibody Modeling).
Models generated by each method were compared to the experimental crystal structures using root mean square deviation (rmsd).

Main Results:

Good agreement was observed between the predicted Fv models and the experimental X-ray structures across most methods.
The average backbone rmsd was approximately 1.2 Å, with framework and hypervariable loops (L1-L3, H1-H2) showing rmsd values close to 1.0 Å.
Prediction of the H3 loop, a highly variable region, resulted in higher rmsd values around 3.0 Å.

Conclusions:

The evaluated Fv modeling methodologies demonstrate considerable accuracy in predicting antibody structures.
The study highlights the strengths and weaknesses of each method, providing insights for future antibody modeling efforts.
This benchmarking initiative serves as a model for scientific collaboration in antibody modeling assessment.