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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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AntiFormer: graph enhanced large language model for binding affinity prediction.

Qing Wang1, Yuzhou Feng2,3, Yanfei Wang1

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, FL 32611, USA.

Briefings in Bioinformatics
|August 20, 2024
PubMed
Summary
This summary is machine-generated.

AntiFormer, a new AI tool, accurately predicts antibody binding affinity, accelerating the development of antibody-based therapeutics and diagnostics. It identifies potent antibodies against SARS-CoV-2 and reveals age-related differences in immune responses to influenza vaccines.

Keywords:
antibody binding affinityantibody maturationlarge language modelsingle-cell BCR

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Antibodies are crucial for immune defense and therapeutics.
  • Assessing antibody-antigen binding affinity is vital but challenging with current methods.
  • Affinity maturation refines antibody specificity and binding strength through somatic mutations.

Purpose of the Study:

  • To introduce AntiFormer, a graph-based large language model for predicting antibody binding affinity.
  • To evaluate AntiFormer's performance against existing methods for antibody affinity prediction.
  • To apply AntiFormer to identify neutralizing antibodies and understand immune response variations.

Main Methods:

  • Developed AntiFormer, a graph-based large language model integrating antibody sequence information.
  • Evaluated AntiFormer's predictive accuracy and computational efficiency.
  • Applied AntiFormer to analyze antibody responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients and influenza vaccine recipients.

Main Results:

  • AntiFormer demonstrated superior performance in predicting antibody binding affinity compared to existing methods.
  • The model achieved accurate predictions with reduced computational time.
  • Identified potent neutralizing antibodies against SARS-CoV-2 and elucidated age-related differences in antibody responses to influenza vaccination, particularly in young individuals.

Conclusions:

  • AntiFormer offers a powerful computational approach to accelerate antibody discovery and development.
  • The findings highlight the importance of large clonotype categories in affinity maturation and immune modulation.
  • AntiFormer provides valuable insights for therapeutic strategies, vaccine development, and understanding immune response dynamics.