Retrieving Dissociation-Resistant Antibody Mutants: An Efficient Strategy for Developing Immunoassays with Improved...

Area of Science:

• Biotechnology
• Immunology
• Molecular Biology

Background:

• Traditional antibody discovery methods like panning are often unsuccessful for isolating high-affinity clones.
• Previous clonal array profiling (CAP) successfully identified improved single-chain Fv fragments (scFvs) but faced an affinity ceiling.
• The prototype anti-cortisol scFv (wt-scFv) had a K_a of 3.6 × 10⁸ M^-1, with improved mutants reaching 1.1 × 10¹⁰ M^-1.

Purpose of the Study:

• To develop a novel system for discovering antibody mutants that surpass the previously established affinity ceiling.
• To engineer single-chain Fv fragments (scFvs) with significantly enhanced binding affinities for cortisol.
• To improve the sensitivity of immunoassays for cortisol detection.

Main Methods:

• A dissociation-independent recovery system was devised using scFv-displaying phage (scFv-Ph) linked to antigen via a disulfide bond.
• scFv-Ph clones were screened individually, and those with ordinary affinities were eliminated using acidic and basic treatments.
• Dissociation-resistant scFv-Phs were recovered through reductive cleavage of disulfide bonds.

Main Results:

• The new system efficiently discovered scFv mutants with 33-56-fold increased K_a, reaching 1.2-2.0 × 10¹⁰ M^-1, exceeding the prior affinity limit.
• The engineered scFvs enabled a cortisol enzyme-linked immunosorbent assay (ELISA) with 18-51-fold higher sensitivity compared to the wt-scFv.
• This method demonstrates a straightforward approach to overcoming affinity limitations in antibody engineering.

Conclusions:

• The novel dissociation-independent recovery system effectively breaks the affinity ceiling for antibody fragment discovery.
• Highly affinity-matured anti-cortisol scFvs were generated, leading to significantly more sensitive immunoassays.
• This approach holds promise for developing advanced diagnostic tools and therapeutic antibodies.