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Affinity and Avidity01:41

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Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
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Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an

Ranjani Gopal1, Emmett Fitzpatrick1, Niharika Pentakota1

  • 1Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA.

Viruses
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Computational antibody engineering now uses AI and machine learning. A new shuffling approach optimized antibody affinity and neutralization for SARS-CoV-2, outperforming previous methods.

Keywords:
CDRCOVID-19SARS-CoV-2affinityantibodyframework

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Area of Science:

  • Biotechnology
  • Immunology
  • Computational Biology

Background:

  • Antibody engineering has shifted from structure-guided methods to AI/ML leveraging vast next-generation sequencing (NGS) data.
  • Optimization of antibodies for clinical development requires enhanced affinity and developability.

Purpose of the Study:

  • To develop a computational shuffling approach for optimizing antibodies by combining complementarity-determining regions (CDRs) and framework regions (FWRs).
  • To engineer a SARS-CoV-2 neutralizing antibody with improved affinity and neutralization capabilities.

Main Methods:

  • Implemented a rule-based computational shuffling of CDRs and FWRs from natural antibody sequences.
  • Applied the method to optimize a known SARS-CoV-2 neutralizing antibody (H4).
  • Screened a limited set of combinations to identify an optimized antibody (CB79).

Main Results:

  • Identified fewer than 100 antibody framework-CDR combinations.
  • The engineered antibody (CB79) demonstrated a 7-fold improvement in affinity and reduced dissociation rate against the SARS-CoV-2 spike protein compared to H4.
  • Achieved over a 75-fold improvement in SARS-CoV-2 neutralization.

Conclusions:

  • The computational shuffling approach rapidly optimizes antibodies for improved affinity and neutralization.
  • This method offers a robust alternative to structure-guided optimization, with broad biotechnological applications.
  • The engineered antibody CB79 shows significant potential for therapeutic use against SARS-CoV-2.