ArticleBiochemistry and biophysics reports2025
Enhancing monoclonal antibody diversity by integrating bulk sorting and machine learning.
Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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