This premise rests on the successful use of sequence conservation in protein engineering for improving the functional properties of enzymes ( 8– 10), nanobodies ( 11), and membrane proteins ( 12). Identifying highly developable mAb sequences in silico could greatly reduce the time and costs of therapeutic mAb development.Īntibody sequences sourced from baseline human antibody repertoires could inform our ability to engineer therapeutic mAbs by ‘borrowing’ consensus mutations ( 6, 7). mAbs are engineered not only to have potent and specific binding to a given target but also to have favorable drug properties, including in vivo stability, manufacturability, immunogenicity, solubility, and polyspecificity ( 5). Monoclonal antibodies (mAbs) are now ubiquitous as therapeutics, with over $100 billion in sales worldwide in 2020 ( 1) and applications ranging from oncology ( 2) and inflammation ( 3) to infectious diseases ( 4). Our results suggest that baseline human antibody repertoires may be useful as predictive tools to guide mAb development in the future. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. In addition, high frequency mutations in baseline human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in baseline human antibody repertoires. We find that mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from baseline human antibody sequence data. Our analysis shows that human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using baseline human antibody repertoire sequences. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases.
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