Enhancing monoclonal antibody diversity by integrating bulk sorting and machine learning

Antibody engineering has been widely developed to improve the affinity and activity of therapeutic monoclonal antibodies (mAbs). Conventional single-cell sorting technology is a powerful method for antibody discovery, effectively producing human mAbs; however, it remains expensive and limits the number of cells available for analysis. Furthermore, sorting hundreds of B cells may not yield cells encoding antibodies with high neutralizing potency since several unexpected antibodies, such as autoantibodies and infectivity-enhancing antibodies, can be included in antigen-specific B cells in patients with infectious diseases. The preparation of antibodies for infectious diseases, such as that caused by severe acute respiratory syndrome coronavirus 2 with rapid mutations, requires discovery technology to generate and select diverse human mAb libraries. We present a synthetic chimeric antibody (SynCA) technology that facilitates <i>in vitro</i> reconstitution of diverse mAbs by integrating the bulk sorting of numerous antigen-specific B cells and machine learning. We compared our method, which cloned antibody variable regions from a single cDNA extracted from ∼5000 B cells, with a single-cell sorting method in the convalescent sera of recovered patients with coronavirus disease 2019. The SynCA method enhanced the B cell receptor repertoire diversity in both heavy and light chain genes. Additionally, the random pairing of the heavy and light chain genes reconstituted antibodies <i>in vitro</i>, providing new insights that the nucleotide sequence information from antibody gene regions (D_H and J_L regions) predicts antibody reconstitution. This method is both cost-effective and rapid, and it can be employed to produce mAbs for therapeutic, diagnostic, and research purposes.