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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
Summary
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.
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.
