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Predicting Antibody-Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV-2 Variants.

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Predicting antibody-antigen interactions for SARS-CoV-2 variants is crucial. This study introduces a novel machine learning approach combining large language models (LLMs) and structural data to accurately predict antibody binding and neutralization properties.

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Area of Science:

  • Immunology
  • Computational Biology
  • Virology

Background:

  • Antibody-antigen interactions are vital for antiviral therapeutics.
  • In vitro assays for these interactions are expensive and slow.
  • Machine learning, especially LLMs, offers a faster, data-driven alternative.

Purpose of the Study:

  • To predict both binding and neutralizing properties of SARS-CoV-2 antibodies.
  • To develop a novel computational method for antibody-antigen interaction prediction.
  • To address the gap in predicting antibody neutralization capabilities.

Main Methods:

  • Utilized large language models (LLMs) integrated with structural information.
  • Developed a new classifier for predicting antibody-antigen interactions.
  • Focused on antibodies targeting SARS-CoV-2 variants due to data availability.

Main Results:

  • The proposed method achieved high prediction accuracy, particularly for similar antigen variants.
  • The classifier outperformed existing methods in predicting antibody-antigen binding.
  • Demonstrated the potential for predicting antibody neutralization properties, a novel contribution.

Conclusions:

  • The combined LLM and structural information approach is effective for predicting antibody-antigen interactions.
  • This method offers a significant advancement in predicting antibody neutralization.
  • The findings support the use of computational methods for accelerating therapeutic development against viral infections like COVID-19.