150 words) 13 Therapeutic antibody optimization is time and resource intensive, largely because it requires 14 low-throughput screening (10 3 variants) of full-length IgG in mammalian cells, typically resulting 15 in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-16 specificity from a massively diverse sequence space to identify globally optimized antibody 17 variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, 18 rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-19 mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small 20 libraries (10 4 ) produced high quality data capable of training deep neural networks that 21 accurately predict antigen-binding based on antibody sequence. Deep learning is then used to 22 predict millions of antigen binders from an in silico library of ~10 8 variants, where experimental 23 testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set 24 of in silico predicted binders is then subjected to multiple developability filters, resulting in 25 thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate 26 high-dimensional protein sequence space, deep learning offers great potential for antibody 27 engineering and optimization. 28 29 31 hybridomas, phage or yeast display libraries typically result in a number of potential lead candidates. 32However, the time and costs associated with lead candidate optimization often take up the majority of 33 the preclinical discovery and development cycle 1 . This is largely due to the fact that lead optimization 34 of antibody molecules consists of addressing multiple parameters in parallel, including expression level, 35 viscosity, pharmacokinetics, solubility, and immunogenicity 2,3 . Once a lead candidate is discovered, 36 additional engineering is often required; phage and yeast display offer a powerful method for high-37 throughput screening of large mutagenesis libraries (>10 9 ), however they are primarily only used for 38 increasing affinity or specificity to the target antigen 4 . The fact that nearly all therapeutic antibodies 39 require expression in mammalian cells as full-length IgG means that the remaining development and 40 optimization steps must occur in this context. Since mammalian cells lack the capability to stably 41 Deep learning enables therapeutic antibody optimization in mammalian cells 1 replicate plasmids, this last stage of development is done at very low-throughput, as elaborate cloning, 42 transfection and purification strategies must be implemented to screen libraries in the max range of 10 3 , 43 meaning only minor changes (e.g., point mutations) are screened 5 . Interrogating such a small fraction 44 of protein sequence space also implies that addressing one development issue will frequently cause 45 rise of another or even diminish antigen binding altogether, making multi-parameter optimization ve...

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