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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...

Immunoprecipitation

Immunoprecipitation

Immunoprecipitation, or IP, is a widely used technique that employs protein-antibody interactions to isolate proteins or protein complexes in their native state for studying protein-protein interactions, quaternary structures, or supramolecular complexes. Various modifications of the technique, including chromatin IP, cross-linking IP, and fluorescence IP, are commonly used.
Chromatin Immunoprecipitation
Chromatin immunoprecipitation, also known as ChIP, is used to study protein-DNA or...

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: Jun 27, 2026

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Published on: April 17, 2017

AI-augmented physics-based docking for antibody-antigen complex prediction.

Francis Gaudreault¹, Traian Sulea^1,2, Christopher R Corbeil^1,3

¹Human Health Therapeutics Research Centre, National Research Council Canada, Montreal, Quebec H4P 2R2, Canada.

Bioinformatics (Oxford, England)

|March 26, 2025

Summary

This summary is machine-generated.

AI-augmented docking improves antibody-antigen structure prediction, especially for high-quality models. Ensemble quality and model prioritization are key for successful antibody design and epitope mapping.

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Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Published on: February 23, 2024

Area of Science:

Structural biology
Computational chemistry
Immunoinformatics

Background:

Predicting antibody-antigen complex structures is crucial for therapeutic antibody design but remains challenging.
Artificial intelligence (AI) has advanced antibody and antigen structure prediction.
High-quality models are essential for efficient antibody design.

Purpose of the Study:

To evaluate an AI-augmented physics-based docking pipeline for antibody-antigen structure prediction.
To compare AI-augmented docking performance against AlphaFold2 and Boltz-1.
To define criteria for epitope mapping and antibody engineering applications.

Main Methods:

Utilized AI-guided antibody modeling to generate diverse complementarity-determining region (CDR) ensembles.
Integrated ensembles into an AlphaFold2-rescored docking pipeline (AI-augmented physics-based docking).
Compared docking performance with AlphaFold2 and Boltz-1 for epitope mapping and engineering.

Main Results:

Ensemble quality and model prioritization are critical for successful docking.
AI-augmented docking outperforms AlphaFold2, particularly for high-quality model requirements.
Performance improvements over Boltz-1 were less pronounced; however, AlphaFold3 showed superior results.
Physics-based docking success is dependent on CDR-H3 loop length, defining its applicability range.

Conclusions:

AI-augmented physics-based docking offers advantages for antibody-antigen structure prediction, especially when high-quality models are needed.
The method's performance is influenced by ensemble characteristics and CDR-H3 loop length.
This approach provides a competitive option within a defined applicability range compared to newer AI tools.