An expandable synthetic library of human paired antibody sequences

Abstract

The potential diversity in the global repertoire of human antibody sequences is currently not well understood due to the limited existing paired antibody heavy-light chain sequence data that has been hindered by the low throughput and high costs of current single-cell sequencing methods. Here, we report IgHuAb, a large language model for high-throughput generation of paired human antibody sequences. Using IgHuAb, we created SynAbLib, a synthetic human antibody library that mimics population-level features of naturally occurring human antibody sequences, yet is associated with significantly greater diversity in sequence space. Further, experimental validation of a diverse set of antibodies from SynAbLib showed robust expression yields. IgHuAb and SynAbLib provide a readily expandable platform for human monoclonal antibody generation that can be efficiently mined for antibody sequences with target properties.