PhD candidate Keck Graduate Institute Claremont, California, United States
Disclosure(s):
Sanaz Zebardast, MS: No financial relationships to disclose
Introduction/Rationale: Machine-learning–guided antibody design offers a promising approach for rapidly generating high-affinity therapeutics against emerging pathogens. Our group has developed Ab-Affinity, a large language model that predicts antibody–antigen binding and, together with genetic algorithms and simulated annealing, designs variants with markedly improved predicted stability and affinity for a SARS-CoV-2 spike epitope. Computational analyses indicate over a 160-fold affinity enhancement compared to experimentally derived sequences. This work focuses on experimentally validating these predictions through expression, purification, and functional characterization of the model-designed antibodies
Methods: Three antibodies—Ab-14-seed and the optimized variants Ab-14-SA-PSSM1 and Ab-14-SA-PSSM6—designed through Ab-Affinity were expressed in Pichia pastoris and purified using FPLC. Protein expression and purity were confirmed by SDS-PAGE and Western blot. Binding affinities are being assessed using ELISA and surface plasmon resonance to evaluate interactions with the target SARS-CoV-2 spike peptide.
Results: All three designed antibodies have been successfully expressed in Pichia pastoris and purified. Upcoming binding affinity assays will determine whether these variants demonstrate the enhanced binding affinities predicted by Ab-Affinity.
Conclusion: This study integrates large language model-guided antibody design with experimental validation to assess the real-world performance of computationally optimized antibodies. The results will clarify how effectively AI-generated sequences translate into functional high-affinity binders, informing the development of future therapeutics.
