High-Throughput Machine Learning-Aided Antibody Discovery for Cell Surface Antigens

Machine learning (ML) has the potential to revolutionize antibody design and selection, but its success depends on access to well-curated datasets of antibody-antigen interactions. We developed a synthetic Fab yeast display library optimized for seamless integration with ML processes, focusing on sequence diversity within the complementary determining region heavy chain CDRH3 loop. The library incorporates key sequence features derived from human B cell repertoires captured in a compact antigen recognition module (ARM) format. Built with the VH1-69 heavy chain and four light chains, the library was evaluated against ten human and murine cell surface antigens, including programmed cell death ligand 1 (PD-L1), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT), and roundabout guidance receptor 1 (ROBO1). This approach yielded hundreds of antibodies with robust biophysical properties, some of which were validated by flow cytometry and immunohistochemistry. Furthermore, ML analysis identified additional antibodies for ROBO2 and PD-L2 from the aggregate sequencing data. The publicly available dataset establishes an ML-compatible framework designed to accelerate and streamline antibody discovery and development. A record of this paper’s transparent peer review process is included in the supplemental information.