Deep learning enables therapeutic antibody optimization in mammalian cells

Therapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (10^3 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massive diversity of antibody sequence space. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (10^4) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence (~85% precision). Deep learning is then used to predict millions of antigen binders from an in silico library of ~10^8 variants. Finally, these variants are subjected to multiple developability filters, resulting in tens of thousands of optimized lead candidates, which when a small subset of 30 are expressed, all 30 are antigen-specific. With its scalability and capacity to interrogate a vast protein sequence space, deep learning offers great potential for antibody engineering and optimization.