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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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Updated: Sep 17, 2025

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

AntiBMPNN: Structure-Guided Graph Neural Networks for Precision Antibody Engineering.

Ze-Yu Sun^1,2, Jiayi Yuan², Divya Jaiswal²

¹College of Pharmacology Sciences, Zhejiang University of Technology, Hangzhou, 310014, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|June 27, 2025

Summary

This summary is machine-generated.

A new deep-learning framework, AntiBMPNN, significantly improves antibody sequence design accuracy and binding affinity compared to existing methods. This advancement holds great potential for developing more effective therapeutic antibodies.

Keywords:

deep learning message passing neuron networks (MPNNs)precision antibody engineering sequence designs

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Last Updated: Sep 17, 2025

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

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Published on: March 24, 2017

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Published on: January 17, 2015

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Traditional antibody sequence design methods are inefficient.
Antibodies are critical for various medical applications.

Purpose of the Study:

To introduce AntiBMPNN, a novel deep-learning framework for highly accurate antibody sequence design.
To evaluate AntiBMPNN's performance against existing state-of-the-art methods.

Main Methods:

Utilized an antibody-specific 3D dataset and a fine-tuned message-passing neural network (MPNN).
Incorporated a frequency-based scoring function and AlphaFold 3 for sequence prioritization.
Employed experimental validation for single-point antibody design and binding affinity assessments.

Main Results:

AntiBMPNN achieved over 80% sequence recovery and a perplexity of 1.5, outperforming ProteinMPNN.
Demonstrated a 75% success rate in single-point antibody design.
Designed sequences with significantly enhanced binding affinities, notably for CDR1, CDR2, and CDR3 regions of specific nanobodies, outperforming multiple benchmarks.

Conclusions:

AntiBMPNN represents a significant advancement in antibody sequence design, offering superior accuracy and binding capabilities.
The framework's ability to design high-affinity antibodies, validated experimentally, underscores its potential to revolutionize therapeutic antibody development.