Mining NGS repertoires to pick better binders with optimal developability profiles
Traditionally, hybridoma discovery workflows rely on the expression of a limited number of antibody candidates for testing with laboratory assays. Single B cell screening protocols and technologies like the Beacon can interrogate hundreds to thousands of clones. These approaches provide a small selection of well-characterized antibodies but unavoidably miss many antibodies with potentially better properties in a immune or synthetic repertoire. Antibodies not captured in immunization with similar sequences to known binders might have higher affinity, better thermostability or solubility, or less developmental liabilities. Combining these well-characterized antibody sequences with larger bulk-NGS or paired-chain sequencing approaches (e.g., 10x Genomics) enables researchers to expand and diversify the candidate pool and find better antibodies missed upon selection.
This expansion into a larger repertoire is especially relevant in in-vivo immunization or disease approaches where the animal/subject’s immune system works to generate the best antibodies against a given target. The lineages generated can be used to understand the antibody evolution and select the best candidates generated. Furthermore, mutations in this evolutionary branches can be combined to obtain synergies in binding strength yielding antibodies with superior properties than those naturally generated.