Computationally Designed Cyclic Peptides Derived from an Antibody Loop Increase Breadth of Binding for Influenza...

Area of Science:

• Immunology
• Virology
• Structural Biology

Background:

• Influenza hemagglutinin (HA) is a key target for broadly neutralizing antibodies.
• Influenza viruses rapidly evolve resistance through mutations in HA's hypervariable regions, which overlap with antibody binding sites.
• Current antibody therapies face challenges due to viral escape mechanisms.

Purpose of the Study:

• To design peptides that mimic antibody loops for enhanced binding breadth to influenza HA variants.
• To investigate if mimicking the heavy-chain complementarity-determining region 3 (CDRH3) of an anti-influenza antibody can overcome viral escape mutations.
• To explore a novel approach for generating antibody-derived peptides against diverse biological targets.

Main Methods:

• Designed cyclic peptides to mimic the CDRH3 loop of the broadly neutralizing anti-influenza antibody C05.
• Assessed the binding affinity and breadth of these peptides against various influenza HA subtypes.
• Analyzed the structural basis for enhanced recognition by reducing clashes with hypervariable HA residues.

Main Results:

• The designed CDRH3 peptides bound to HA with high affinity (<100 nM), comparable to the parental C05 IgG antibody.
• These peptides demonstrated broader recognition of influenza H4 and H7 subtypes compared to traditional antibodies.
• The improved breadth was attributed to the peptides' ability to avoid interactions with escape-mutated HA regions.

Conclusions:

• Antibody-mimicking peptides, specifically CDRH3-based designs, can achieve high-affinity binding to influenza HA.
• This peptide design strategy enhances recognition of diverse HA antigenic variants, potentially overcoming viral escape.
• This approach offers a versatile platform for developing peptide-based therapeutics against various targets beyond influenza.