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Updated: Jul 11, 2025

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

DG-Affinity: predicting antigen-antibody affinity with language models from sequences.

Ye Yuan¹, Qushuo Chen², Jun Mao²

¹Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China. yuanye_auto@sjtu.edu.cn.

BMC Bioinformatics

|November 14, 2023

Summary

This summary is machine-generated.

A new method, DG-Affinity, accurately predicts antigen-antibody affinity using only sequences and deep learning. This advances antibody design for therapeutic development.

Keywords:

Affinity Antibody–antigen interaction Deep learning Sequence embedding

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Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

Antibody-mediated immunity is vital for human defense.
Bioengineered antibody-derived drugs show promise for cancer and autoimmune diseases.
Accurate prediction of antigen-antibody affinity is critical for antibody development.

Purpose of the Study:

To introduce DG-Affinity, a novel sequence-based method for predicting antigen-antibody affinity.
To demonstrate the efficacy of DG-Affinity without requiring structural information.

Main Methods:

Utilized deep neural networks and pre-trained language models to transform antibody and antigen sequences into embedding vectors.
Employed a ConvNeXt framework with a regression task to predict affinity from concatenated embeddings.

Main Results:

DG-Affinity accurately predicts antigen-antibody affinity from sequences alone.
Achieved a Pearson's correlation coefficient exceeding 0.65 on an independent test dataset.
Outperformed existing structure-based and sequence-based prediction methods.

Conclusions:

DG-Affinity demonstrates superior performance compared to baseline methods.
The method can significantly advance antibody design and development.
DG-Affinity is accessible via a free web server for ease of use.