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AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on

Ruofan Jin1,2, Qing Ye1, Jike Wang1

  • 1College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China.

Briefings in Bioinformatics
|July 3, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning model, AttABseq, accurately predicts antibody mutation effects on binding affinity. This AI approach accelerates therapeutic antibody optimization, outperforming traditional methods and offering insights into mutation impacts.

Keywords:
antibody optimizationantigen–antibody binding affinity changeartificial intelligencedeep learningtherapeutic antibody

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Traditional antibody optimization methods (hybridoma, phage display) are slow and costly.
  • Computational and AI methods are emerging to enhance therapeutic antibody development.

Purpose of the Study:

  • To develop an end-to-end, sequence-based deep learning model for predicting antigen-antibody binding affinity changes due to antibody mutations.
  • To create a tool that accelerates and improves the optimization of therapeutic antibodies.

Main Methods:

  • Developed AttABseq, an attention-based deep learning model using diverse antigen-antibody complex sequences as input.
  • Predicted binding affinity changes associated with antibody residue mutations.
  • Evaluated model performance on three benchmark datasets.

Main Results:

  • AttABseq demonstrated 120% higher accuracy than existing sequence-based models (Pearson correlation coefficient).
  • AttABseq performed comparably to or better than structure-based methods.
  • The model showed robust generalization across various antigen-antibody complexes and mutation scenarios, even without structural data.

Conclusions:

  • AttABseq offers a highly accurate and efficient method for predicting antibody mutation effects on binding affinity.
  • The model's interpretability aids in visualizing mutation impacts at the residue level, facilitating automated antibody sequence optimization.
  • AttABseq presents a competitive solution for advancing therapeutic antibody development.