High-affinity optimization potential of the virus neutralizing antibody with twin cysteine-stabilized...

Area of Science:

• Immunology
• Structural Biology
• Virology

Background:

• Neutralizing monoclonal antibodies (NMAbs) are vital for combating viral evolution.
• The heavy-chain complementarity-determining region 3 (H3 CDR) structural stability impacts antibody affinity maturation, but its role in computational optimization is not well understood.

Purpose of the Study:

• To investigate the impact of H3 CDR conformational stabilization on the AI-driven optimization of SARS-CoV-2 NMAbs.
• To evaluate the efficacy of optimized antibodies in vitro and in vivo.

Main Methods:

• An artificial intelligence (AI) model was used to optimize two categories of SARS-CoV-2 NMAbs: one with a stabilized H3 CDR (twin cysteine motif) and another with flexible H3 CDR loops.
• Optimized antibodies were assessed for binding affinity, pseudovirus and live virus neutralization, and in vivo efficacy in a murine model.
• Structural analyses elucidated interactions with the ACE2 receptor.

Main Results:

• H3 CDR stabilization via twin cysteines significantly improved AI-driven optimization, leading to enhanced binding affinity and neutralization potency against SARS-CoV-2.
• Optimized antibodies from the stabilized group showed tighter interactions with the ACE2 receptor, correlating with biological efficacy.
• In vivo studies demonstrated effective suppression of viral replication and reduced viral loads in mice treated with optimized antibodies.

Conclusions:

• Conformational stabilization of the H3 CDR is critical for successful AI-driven affinity maturation of NMAbs.
• This study provides a strategic framework for antibody development, prioritizing structurally stabilized H3 CDRs for potent therapeutics against rapidly evolving viruses.