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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...

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Related Experiment Video

Updated: Jul 12, 2025

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

IgLM: Infilling language modeling for antibody sequence design.

Richard W Shuai¹, Jeffrey A Ruffolo², Jeffrey J Gray³

¹Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.

|November 1, 2023

Summary

This summary is machine-generated.

This study introduces the Immunoglobulin Language Model (IgLM), a new AI tool for designing synthetic antibody libraries. IgLM improves antibody developability by using bidirectional context for sequence generation.

Keywords:

antibodies deep learning language modeling

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Last Updated: Jul 12, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

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Published on: March 15, 2019

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Published on: July 8, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Biotechnology
Artificial Intelligence
Immunology

Background:

Therapeutic antibody discovery requires large sequence libraries but faces challenges with antibody developability, including low solubility, aggregation, and immunogenicity.
Generative language models offer a powerful approach for creating diverse and realistic protein sequences on demand.

Purpose of the Study:

To introduce the Immunoglobulin Language Model (IgLM), a deep generative language model for designing synthetic antibody libraries.
To leverage bidirectional context and a text-infilling approach for antibody sequence generation and redesign.

Main Methods:

Trained the IgLM on 558 million antibody heavy- and light-chain variable sequences, conditioning on chain type and species.
Employed a text-infilling formulation for sequence generation, enabling bidirectional context utilization.
Evaluated the model's ability to generate full-length antibody sequences and infilled complementarity-determining region (CDR) loop libraries.

Main Results:

IgLM successfully generated full-length antibody sequences across various species.
The infilling formulation enabled the generation of CDR loop libraries with enhanced in silico developability profiles.
Demonstrated improved sequence generation capabilities compared to unidirectional context methods.

Conclusions:

The Immunoglobulin Language Model (IgLM) represents a significant advancement in AI-driven antibody design.
IgLM's bidirectional context and infilling approach enhance the generation of synthetic antibody libraries with improved developability.
This technology has the potential to accelerate the discovery and optimization of therapeutic antibodies.