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 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 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 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...

Immunological Memory

Immunological Memory

Immunological memory, a pivotal pillar of the adaptive immune system, is responsible for the body's ability to remember and respond more swiftly and effectively to previously encountered pathogens. This remarkable feature is what makes vaccines so effective in preventing diseases.
What is Immunological Memory?
Immunological memory is an integral function of the immune system that allows it to recognize and react more rapidly and effectively to pathogens previously encountered. This feature...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

Language Development

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Evaluating Expert Specialization in Mixture-of-Experts Antibody Language Models.

bioRxiv : the preprint server for biology·2026

Same author

A Single-Cell Atlas of Transcriptional and Immunoglobulin Repertoire Evolution in Early B Cell Development.

bioRxiv : the preprint server for biology·2025

Same author

GhostFold: Accurate protein structure prediction using structure-constrained synthetic coevolutionary signals.

bioRxiv : the preprint server for biology·2025

Same author

Data-optimal scaling of paired antibody language models.

bioRxiv : the preprint server for biology·2025

Same author

Longitudinal analysis of drift in the circulating human antibody repertoire over four years.

bioRxiv : the preprint server for biology·2025

Same author

A curriculum learning approach to training antibody language models.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A curriculum learning approach to training antibody language models.

Sarah M Burbach¹, Bryan Briney^1,2,3,4,5

¹Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States of America.

Plos Computational Biology

|September 11, 2025

Summary

We introduce curriculum learning for antibody language models (AbLMs) to effectively train on mixed unpaired and paired sequence data. This method, CurrAb, outperforms existing models in key antibody prediction tasks.

More Related Videos

Antigenic Liposomes for Generation of Disease-specific Antibodies

Antigenic Liposomes for Generation of Disease-specific Antibodies

Published on: October 25, 2018

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

Related Experiment Videos

Last Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Antigenic Liposomes for Generation of Disease-specific Antibodies

Antigenic Liposomes for Generation of Disease-specific Antibodies

Published on: October 25, 2018

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

Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Antibody language models (AbLMs) are increasingly pre-trained using both unpaired and paired antibody sequences.
A key challenge is optimizing training strategies for mixed datasets due to size imbalances.

Purpose of the Study:

To systematically evaluate data processing and training strategies for AbLMs trained on mixed sequence data.
To introduce and optimize a curriculum learning approach for AbLM pre-training.

Main Methods:

Developed a curriculum learning method for AbLMs, gradually transitioning from unpaired to paired sequences.
Compared curriculum learning with constant mix and fine-tuning data sampling strategies.
Evaluated model performance on downstream residue prediction and classification tasks.

Main Results:

Curriculum learning and constant mix approaches outperformed fine-tuning in large-scale models.
These methods mitigate catastrophic forgetting and slow overfitting.
A 650M-parameter curriculum model, CurrAb, achieved superior performance.

Conclusions:

Curriculum learning is an effective strategy for pre-training AbLMs on imbalanced mixed datasets.
CurrAb demonstrates state-of-the-art performance in antibody sequence analysis tasks.
Optimized training strategies are crucial for leveraging large-scale antibody sequence data.