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Researchers created a digital twin of SARS-CoV-2 to design neutralizing antibodies. AI-designed antibodies showed effectiveness against multiple strains, including Omicron, demonstrating potential for future antiviral therapies.

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Area of Science:

  • Virology
  • Computational Biology
  • Immunology

Background:

  • The emergence of SARS-CoV-2 variants necessitates rapid development of effective antiviral therapies.
  • Existing methods for antibody design are often time-consuming and may not anticipate viral evolution.

Purpose of the Study:

  • To develop a comprehensive digital twin for SARS-CoV-2.
  • To computationally design and experimentally validate neutralizing antibodies against diverse SARS-CoV-2 strains.
  • To assess the potential of AI in predicting viral evolution and guiding therapeutic design.

Main Methods:

  • Integration of diverse data types and metadata using machine learning, natural language processing, and protein modeling.
  • Development of a digital twin for SARS-CoV-2, focusing on the receptor binding domain (RBD).
  • Computational design of antibodies against over 1300 historical strains with 64 mutations.
  • Experimental validation of 70 AI-designed antibodies using binding and viral neutralization assays.

Main Results:

  • 70 AI-designed antibodies were experimentally validated against multiple SARS-CoV-2 strains, including Omicron variants.
  • 14% of designed antibodies showed strong cross-reactivity against the RBD of multiple strains.
  • 10 antibodies neutralized the Delta strain (IC50 < 10 µg/ml), and one neutralized Omicron.

Conclusions:

  • The developed digital twin approach is effective for computationally designing neutralizing antibodies.
  • AI-driven strategies can predict viral evolution and accelerate the development of broad-spectrum antiviral treatments.
  • This methodology holds significant promise for future therapeutic interventions against viral pathogens.