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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...

Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

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Related Experiment Video

Updated: Aug 22, 2025

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Published on: January 14, 2020

Hallucinating structure-conditioned antibody libraries for target-specific binders.

Sai Pooja Mahajan¹, Jeffrey A Ruffolo², Rahel Frick¹

¹Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States.

Frontiers in Immunology

|November 7, 2022

Summary

This summary is machine-generated.

FvHallucinator uses deep learning to design antibody sequences, especially CDR loops, for improved therapeutic potential. This computational approach generates targeted libraries for faster, cost-effective antibody affinity maturation.

Keywords:

affinity maturation antibody libraries antibody therapeutics artificial intelligence (AI)deep learning

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Area of Science:

Biotechnology and Bioinformatics
Protein Engineering
Immunology

Background:

Antibodies are crucial therapeutics for cancer, infections, and inflammation.
Current experimental antibody affinity maturation is costly and inefficient.
Deep learning offers potential for advanced protein design.

Purpose of the Study:

To develop a specialized deep learning model for antibody sequence design.
To generate targeted CDR libraries that maintain binding conformation.
To create a pipeline for efficient antibody affinity maturation.

Main Methods:

Developed FvHallucinator, a deep learning model for antibody sequence design.
Leveraged structure prediction models and hallucination frameworks.
Applied a screening pipeline to generate antigen-specific CDR libraries.

Main Results:

FvHallucinator generates natural-like CDR sequences and diverse interfacial amino acid substitutions.
Designs were validated on a benchmark set of 60 antibodies.
In silico designs for Trastuzumab-HER2 showed potential for improved binding affinity.

Conclusions:

FvHallucinator enables cost-effective generation of diverse, targeted antibody libraries.
The pipeline facilitates antibody affinity maturation for therapeutic development.
This deep learning approach accelerates the design of novel antibody therapeutics.