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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.

Antibody Actions

Antibody Actions

Antibodies, or immunoglobulins, are critical players in the immune system's arsenal against invading pathogens. Produced by B cells and plasma cells, their primary role is to detect and bind to specific antigens, molecules found on the surface of pathogens like bacteria or viruses. Beyond antigen recognition, antibodies perform several vital functions that contribute to immune defense.
Neutralization
Antibodies can bind to pathogens, preventing them from infecting host cells. This process...

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Related Experiment Video

Updated: Mar 23, 2026

Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques

Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques

Published on: July 5, 2018

Evaluating deep learning based structure prediction methods on antibody-antigen complexes.

Samuel Fromm¹, Marko Ludaic¹, Arne Elofsson¹

¹Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Solna 171 21, Sweden.

Bioinformatics (Oxford, England)

|March 21, 2026

Summary

This summary is machine-generated.

AlphaFold3 demonstrates superior performance in predicting protein complex structures, especially for antibody-antigen interactions. While increased sampling aids prediction, accurately identifying the best model remains a challenge for all methods.

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Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

AlphaFold2 advanced protein complex structure prediction but struggled with interactions lacking coevolutionary signals, such as host-pathogen and antibody-antigen complexes.
Strategies to improve prediction accuracy include massive sampling and architectural changes, like AlphaFold3's pairformer, which enhances structural reasoning without relying on coevolution.

Purpose of the Study:

To benchmark protein structure prediction methods on novel antibody-antigen complexes.
To evaluate the impact of sampling and model quality estimation on prediction accuracy.
To compare the performance of AlphaFold3 against other state-of-the-art methods.

Main Methods:

Benchmarking structure prediction tools on a dataset of unseen antibody-antigen complexes.
Analyzing the effect of increased sampling on the generation of accurate protein models.
Assessing the reliability of internal quality estimates in identifying the best predicted structures.
Comparing AlphaFold3 with AlphaFold2, Boltz-1, and Chai-1.

Main Results:

Increased sampling improves protein model generation in a log-linear fashion.
AlphaFold's internal quality estimates often fail to identify the best predicted models, impacting performance.
AlphaFold3 significantly outperforms AlphaFold2, Boltz-1, and Chai-1.
AlphaFold3's performance decreases for complexes dissimilar to the training set, suggesting an ability to recognize remote structural similarities.

Conclusions:

AlphaFold3 represents a significant advancement in protein complex structure prediction, particularly for challenging interactions.
Accurate identification of the best predicted model remains a critical challenge across all evaluated methods.
The ability of AlphaFold3 to generalize to complexes with low structural similarity to training data warrants further investigation.