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Affinity and Avidity

Affinity and Avidity

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...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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.

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Deep geometric framework to predict antibody-antigen binding affinity.

Nuwan Bandara¹, Dasun Premathilaka², Sachini Chandanayake²

¹Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka; School of Computing and Information Systems, Singapore Management University, Singapore.

Journal of Structural Biology

|October 25, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for predicting antibody-antigen binding affinity, improving accuracy by incorporating structural and evolutionary data. The new method enhances prediction accuracy for antibody efficacy in drug development.

Keywords:

Antibody Antigen Binding affinity Deep geometric framework Proteins

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Area of Science:

Computational Biology
Immunology
Drug Discovery

Background:

Antibody efficacy in drug development relies on binding affinity to target antigens.
Traditional binding affinity quantification is computationally complex.
Current deep learning methods often overlook protein evolutionary details and antigen variants.

Purpose of the Study:

To develop a more accurate and generalized deep learning model for antibody-antigen binding affinity prediction.
To address limitations of existing methods regarding structural data quality and evolutionary information.
To create comprehensive datasets for advancing data-driven approaches in this field.

Main Methods:

Curated the largest generalized datasets for antibody-antigen binding affinity prediction (>100K sequence pairs, 8K structure pairs).
Proposed a novel deep geometric neural network combining structure-based and sequence-based models with cross-attention mechanisms.
Utilized multi-scale hierarchical attention blocks to model antibody-antigen interactions.

Main Results:

Achieved a 10% improvement in mean absolute error compared to state-of-the-art models.
Demonstrated a strong correlation (>0.87) between predicted and target binding affinity values.
The proposed framework effectively integrates structural and evolutionary information.

Conclusions:

The novel deep geometric neural network offers a significant advancement in antibody-antigen binding affinity prediction.
The developed datasets and framework facilitate further research in antibody design and drug development.
Public release of datasets and code supports the scientific community.